SIP 2nd Phase : Automated Driving for Universal Service

SIP 2nd Phase :Automated Driving for Universal Services
Final Results Report (2018–2022)

Final Results Report (2018–2022)

Nine Years of Building Up Various Cooperations
As I finish my major role as program director (PD) for the first and second phases of the SIP-adus(Cross-ministerial Strategic Innovation Promotion Program; Automated Driving for Universal
Services), I would like to express my sincere gratitude for the support and cooperation of everyone
involved over the past nine years.
When I started working on this project nine years ago, I had no experience in automated driving
development. I had heard rumors that a project would be launched within the new framework of the
SIP, but I thought it was something that I had nothing to do with. It was then that I was assigned to it
by the previous Program Director Watanabe, who told me that automated driving is being developed
for safety. It was the first time for me to participate in a national project, and I had no idea of which
way was up, so I repeated a process of trial and error while asking for everyone's guidance. The SIP is
an industry-academia-government collaboration project, and I was able to work with a wide range of
people, including experts in academia, government ministries and agencies, and people fromindustries with which I had no previous relationship. Every day was stimulating and presented aninvaluable opportunity to learn much.
Based on the belief that both competition and cooperation are indispensable for the realization of
automated driving, I have been planning in the FOTs (Field Operational Tests) in the Tokyo waterfront
area and other R&D projects with the participation of a wide range of organizations, including theJapan Automobile Manufacturers Association, universities, venture companies, overseas OEMs, andsuppliers, under the concept of "competition and cooperation." It was a great pleasure for me to beable to be a part of the process of industry-academia-government collaboration, inter-industry
collaboration, inter-academia collaboration, and international cooperation, where the number of mycolleagues increased day by day and I interacted with them in various settings.
We have compiled the results and experiences of the SIP-adus over the past nine years in the "Final
Results Report" with the hope that the collaboration will continue to reach new heights even after thecompletion of the SIP-adus program. We hope that this report will be helpful for your future researchand development projects.
SIP-adus Program Director



Seigo Kuzumaki

Preface

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Toward Solving Social Issues Through Automated Driving,and Achieving a Mobility Society for the Future
The Cross-ministerial Strategic Innovation Promotion Program (SIP) aims to create new value, spur
economic growth, and solve social issues by realizing Society 5.0, which is, achieving a high-level fusionof cyberspace and physical space. Led by the Council for Science, Technology and Innovation, it focuseson promoting research and development technologies at a basic research level as well as practical use
and commercialization based on the technologies, by involving multiple government agencies and field.
The automated driving involves recognizing in cyberspace, massive and diverse data sets (big data)collected from roadside infrastructures and vehicle-mounted sensors, as well as processing the data andmaking decisions using AI. This data is then used to control and operate vehicles in the real world.
Through this process, the program has helped pave the way for Society 5.0. As one of 12 SIP topics, SIP
Automated Driving for Universal Services (SIP-adus) has contributed to research and development oncommon issues (in cooperative areas) concerning automated driving that require collaboration oforganizations across government, industry, and academia.
As part of these efforts, the leadership of PD (Program Director) and SPD (Sub Program Director)
have spearheaded cooperation among relevant ministries and agencies, industry organizations, andacademic institutions, among others, making steady achievements in cooperative areas in whichstakeholders across industries and disciplines should engage. These include the distribution of roadtraffic environment data necessary for automated driving, as well as data usage, safety assurance, andcybersecurity.
In addition, as part of a research and development program aimed at developing practicalapplications for and commercializing automated driving technologies, I am very pleased with theachievements that the SIP-adus has led to the social implementation of technologies, two major
examples being the commercialization of technologies related to the Dynamic Map in the first phaseof SIP-adus, and the commercialization of technologies related to a safety assurance platform in
virtual environments in the second phase of SIP-adus.
The Sixth Science, Technology, and Innovation Basic Plan (March 2021) articulates the country's
intention to solve social problems by effectively utilizing a "convergence of knowledge," which includes
both knowledge from the humanities and social sciences. From an early stage, the SIP-adus has seen
research done through an approach based on a convergence of knowledge that makes use of knowledge
from even the social sciences, which includes analyzing the effectiveness of technologies in reducing
traffic fatalities.
Automated driving initiatives that have been ongoing since the first phase of SIP-adus have led to
the formulation of a framework for Level 3 automated driving and will conclude in the second phaseof SIP-adus after having contributed in many ways to getting this technology onto the international
stage, which includes paving the way to the sale of vehicles equipped with Level 3 technology. It is myhope that the achievements of the SIP-adus will be broadly used and help to usher in, as soon as
possible, driving support and automated driving technologies that use the road traffic environment
data, which has been a central focus of the program.
The Construction of Smart mobility Platform is currently being considered as a topic for the next
phase of the SIP. In this context, I hope that the Cabinet Office will work with all stakeholders todevelop new approaches to promoting initiatives such as MaaS that will provide people with an
integrated combination of different transportation modes, with a view to utilize the achievements of
SIP-adus program.


Deputy Director General for Science, Technology and Innovation, Cabinet OfficeTakafumi Kakudo

2

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Role as Management Agency and Results
Under the Council for Science, Technology and Innovation, we at NEDO continuously performed
three activities as a management agency ((1) Operations management, (2) Creating places fordiscussions, (3) Information transmission support) from 2017 for the first phase of the SIP-adus(Cross-ministerial Strategic Innovation Promotion Program (SIP) Automated Driving for Universal
Services) that began in 2014 and from the start of the second phase of the SIP-adus that began in 2018.
Under the instruction of PD Kuzumaki and along with affiliated government agencies, including
the Cabinet Secretariat, experts on various committees, companies, universities, and organizations,
we proceeded with 57 initiatives for the social implementation of automated driving, such as
international cooperation, shaping public opinion through PR activities, and FOTs (Field Operational
Tests) for social implementation, in addition to basic technology development for practical applicationof automated driving. As we shifted activities to remote formats following the COVID-19 pandemic
and the declaration of national emergency, we continued to find a way forward through trial anderror. Despite this, we were able to complete the activities for the second phase thanks to the support
and passion of everyone related to this project. We would like to take this opportunity to express our
gratitude for these efforts.
We have taken efforts for technology development, testing, infrastructure building, andsystemization to improve convenience for the realization of automated driving in Japan. In phase twoof this project, we engaged in two activities to realize safe and secure automated driving services. One,on the development/testing side, we conducted cybersecurity technology development and built asafety assurance environment (DIVP®) for automated driving in a virtual environment and Dynamic
Map, as well as conducted FOTs. Two, in order to realize a rich society through linking surrounding
businesses and services centered on automated driving, we established the portal site MD communet®
to promote utilization and linkage of geographic data and standardization activities, implemented
services in regions, and executed PR and collaboration activities in and outside Japan. These activities
produced many results. We are happy to be able to publish these results in the form of the final report.
Aiming for the realization of automated driving technology in Society 5.0 and an automateddriving society shown in Public-Private ITS Initiative/Roadmaps, we hope that this final report andthe outcomes of the phase two will be fully utilized and lead to the next action for the realization andexpansion of safe and secure mobility-related services. We ask for the continued assistance of all
persons related to the SIP and those organizations that did not participate.
Executive Director, New Energy and Industrial Technology Development Organization (NEDO)Tomoyasu Nishimura

Preface

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For Publication of Final Results Report
Last year, SIP 2nd Phase: Automated Driving for Universal Services—Mid-Term Results Report(2018–2020) summarizing all initiatives in the second phase of SIP-adus (Cross-ministerial Strategic
Innovation Promotion Program; Automated Driving for Universal Services) was issued. Thankfully,
many people responded to this report positively. We would like to thank the readers.
This final results report summarizes the overall activities of the second phase of SIP-adus, centeredon the activities after the publishing of the mid-term results report. While including the opinions of
the readers of the mid-term results report as much as possible, we aimed for a report that would beeasy to understand and useful.
We hope that this report and the mid-term results report serve as an entrance for all readers,
including automated driving researchers in and outside Japan, to deepen their knowledge of theinitiatives of the SIP-adus program. Contact information is also available for each article. Please feel
free to reach out regarding the use of the research results from the SIP-adus.
Through the support of many affiliated persons and groups, the SIP-adus produced many resultsin the nine-year period, including the first phase, of this project. We have published this report withthe hope that it will lead to research and development and infrastructure building related to automateddriving in the future.


December 2022



SIP-adus Final Results Report Editional Committee

Editorial Committee Members(Affiliation and title is as of October 1, 2022; honorifics omitted)[Program Director (PD)/Sub-PD]
Fellow, Advanced R&D and Engineering Company, Toyota Motor Corporation
Visitng Professor, National Graduate Institute for Policy Studies /Principal Fellow, Center for Research and Development Strategy at the Japan Science andTechnology Agency
Executive Chief Engineer, Head of Computer Science Domain,
Innovative Research Excellence/Software Defined Mobility Development Supervisory Unit,
Business Development Operations, Honda R&D Co., Ltd./ Honda Motor Co., Ltd.
General Manager in Charge, Regulation and Homologation Department, Nissan Motor Co., Ltd.
[Experts, etc.]
President, ITS Japan
Prime Senior Researcher, Digital Architecture Research Center, National Institute ofAdvanced Industrial Science and Technology
Chair, Automated Driving Subcommittee, Safety Technology and Policy Committee,
Japan Automobile Manufacturers Association /Executive Chief Engineer, Software Defined Mobility Development Supervisory Unit,
Business Development Operations, Honda Motor Co., Ltd.
Director General, Institute for ACV standardization /Managing Director for Research Affairs on Automated Driving Technology Director,
Automotive Safety Research Department National Traffic Safety and Environment Laboratory,
National Agency for Automobile and Land Transport Technology
Researcher, Smart Region Division, Mitsubishi Research Institute
Professor, Graduate School of Media and Governance, Keio University[Secretariat]

Seigo Kuzumaki
Tateo Arimoto
Yoichi Sugimoto
Haruhiko Kondo
Akio Yamamoto
Shin Kato
Kunimichi
HatanoTerunao Kawai
Yurie Toyama
Manabu Omae

Hiroaki Kimura, Yuichi Araki, Kotaro Sugiyama (Cabinet Office), Takahiro Tanaka, Megumi Funahashi, Shuichi Ajiki,Michihiko Katsuragi (NEDO)

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Contents
PrefaceNine Years of Building Up Various Cooperations����������������������������������� 1
Seigo Kuzumaki (SIP-adus Program Director)

Toward Solving Social Issues Through Automated Driving, and Achieving a Mobility Society for the Future���������� 2
Takafumi Kakudo (Deputy Director General for Science, Technology and Innovation, Cabinet Office)Role as Management Agency and Results��������������������������������������� 3
Tomoyasu Nishimura (Executive Director, New Energy and Industrial Technology Development Organization (NEDO))

For Publication of Final Results Report��������������������������������������� 4
SIP-adus Final Results Report Editional Committee

The Second Phase of SIP–Automated Driving for Universal Services Final Results Report: Introduction…… 6
Section 1 The Second Phase of SIP-Automated Driving for Universal Services

11

The Second Phase of SIP-Automated Driving for Universal Services (Overview)………………………………………… 11
Kotaro Sugiyama, Hiroaki Kimura (Cabinet Office)Overview of Activities of the National Police Agency………………………………………………………………………… 16
Hisaaki Ikeuchi (National Police Agency)

Overview of Activities of the Digital Agency………………………………………………………………………………… 19
Jun Usami (Digital Agency)

Overview of Activities of the Ministry of Internal Affairs and Communications…………………………………………… 22
Takanori Mashiko (Ministry of Internal Affairs and Communications)

Overview of Activities of the Ministry of Economy, Trade and Industry…………………………………………………… 25
Shigekazu Fukunaga (Ministry of Economy, Trade and Industry)

Overview of Activities of the Road Bureau, Ministry of Land, Infrastructure, Transport and Tourism…………………… 28
Masamitsu Waga (Road Bureau, Ministry of Land, Infrastructure, Transport and Tourism)

Overview of Activities of the Road Transport Bureau, Ministry of Land, Infrastructure, Transport and Tourism……… 31
Yoshitaka Tada (Road Transport Bureau, Ministry of Land, infrastructure, Transport and Tourism)

Section 2 Establishment and Utilization of Traffic Environment Data

34(1) Technological Development Concerning the Generation of Traffic Environment Data

34

Establishment and Utilization of Traffic Environment Data and the Tokyo Waterfront Area Field Operational Tests (Overview)……… 34
Satoshi Hiyama (Honda Motor Co., Ltd.), Masato Minakata (TOYOTA MOTOR CORPORATION)

1) The Tokyo Waterfront City Area Field Operational Tests………………………………………………………………… 41
Yoshiaki Tsuda, Takahiro Yoshino (MITSUBISHI ELECTRIC CORPORATION), Shinya Muroyama,

Kentaro Isobe (AISAN TECHNOLOGY CO., LTD), Kosuke Watabe, Katsuya Akimoto (Nippon Koei Co., Ltd.)

2) Technological Development to Provide Traffic Signal Information to Automated Vehicles Connected to Infrastructures (V2N)…… 47
Yukiko Hatazaki (NIPPON SIGNAL CO., LTD.), Yuichi Takayanagi (Panasonic Connect Co., Ltd.), Toru Mabuchi (OMRON Social Solutions Co., Ltd.),
Shunichi Kawabe (UTMS Society of Japan)

3) Technological Development for Lane-level Road Traffic Information Using Probes Vehicle Data……………………… 54
Hirokazu Ichikawa, Atsushi Takenouchi, Naoki Funaoka, Naohiro Uchiyama, Akio Mori (PACIFIC CONSULTANTS CO., LTD.), Yukimasa Morisaki,
Ryuki Oshima (Mitsubishi Research Institute, Inc.)

4) Technological Development and Establishment of Simulation Environment for Lane Merging Assistance…………… 61
Nobuhiro Araki, Kenta Shintoku (KOZO KEIKAKU ENGINEERING Inc.), Koichi Miyashita, Satomi Aiko (Mitsubishi Research Institute, Inc.)

5) Improvement of Data Accuracy of Traffic Regulation Information……………………………………………………… 67
Moto Baba (JARTIC), Makoto Maeda (TOSCO CORPORATION), Ryo Sakaguchi (Dawn Corporation)6) Technological Development for Traffic Signal Control and Emergency Vehicles Information Using GNSS
(Location Information) and Other Technologies………………………………………………………………………… 73
Kenji Sumi (KOITO ELECTRIC INDUSTRIES, LTD.)(2) Technological Development Concerning the Transmission of Traffic Environment Data

80

Research for V2X Communication for Cooperative Driving Automation (Overview)…………………………………… 80
Hideaki Suganuma (TOYOTA MOTOR CORPORATION)

1) Research on Communication Methods to Realize Cooperative Automated Driving Use Cases………………………… 85
Satoshi Kimura, Takeshi Nunomoto (NEC Corporation), Masato Ogawa, Tomoaki Konishi (KYOCERA Corporation)2) Development of New Technologies, V2X and Others, for Communication……………………………………………… 94
Kinya Asano, Shoichi Nakabayashi (Oki Electric Industry Co., Ltd.), Satoshi Kimura, Masahiro Ohtsuka (NEC Corporation)3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information……… 99
Tomohiko Saito, Shota Taki (NTT Communications Corporation)I

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Section 3 Ensuring the Safety of Automated Driving

104

Technological Development and Education for Enhanced Safety (Overview)…………………………………………… 104
Osamu Hosaka, Hiroaki Kimura (Cabinet Office)1) Development of Driving Intelligence Validation Platform (DIVP®) for Automated Driving Safety Assurance……… 108
Hideo Inoue (Kanagawa Institute of Technology)

2) Research on the Recognition Technology Required for Automated Driving Technology (Levels 3 and 4)………… 121
Naoki Suganuma, Keisuke Yoneda, Ryo Yanase, Akisue Kuramoto (Kanazawa University),

Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu University), Junichi Meguro (Meijo University)3) Research of New Cyberattack Techniques and Countermeasure Technologies……………………………………… 130
Ken Okuyama, Naohide Waguri, Shinichi Kan, Yuki Imagawa (PwC Consulting LLC)

4) Research of Education Methods for Advanced Automated Driving Systems………………………………………… 135
Makoto Itoh, Huiping Zhou (University of Tsukuba), Yoshiko Goda, Masashi Toda (Kumamoto University),
Maki Arame, Junko Handa (Polytechnic University of Japan)

5) Research on Communication between Low-Speed Automated Transportation and Logistics Services Vehicles
and Surrounding Traffic Participants…………………………………………………………………………………… 139
Tatsuru Daimon, Masahiro Taima, Jieun Lee, Tomoyuki Furutani (Keio University)

6) Research of HMI for Advanced Automated Driving Systems………………………………………………………… 145
Toshihisa Sato, Kunihiro Hasegawa, Yanbin Wu, Ken Kihara (National Institute of Advanced Industrial Science and Technology (AIST)),
Kimihiko Nakano, Yang Yo (The University of Tokyo)

Section 4 A Society with Automated Driving

150(1) Automated Driving Mobility Services in Regional Communities

150

Automated Driving Transportation Services in Rural Areas (Overview)………………………………………………… 150
Yoshiyuki Kato (Highway Industry Development Organization)

1) Establishing the Environment for the Deployment of Transportation Services Relying on Automated Driving…… 154
Yoshiyuki Kato (Highway Industry Development Organization)(2) Public Acceptance of Automated Driving

162

Initiatives for Fostering Public Acceptance (Overview)…………………………………………………………………… 162
Yuichi Araki, Hiroaki Kimura (Cabinet Office)1) Research and Evaluations for Fostering Public Acceptance…………………………………………………………… 166
Yukiko Miyaki (DAI-ICHI LIFE RESEARCH INSTITUTE INC.)

2) Development of Assessment Methodology for Socioeconomic Impacts of Automated Driving Including Traffic
Accident Reduction……………………………………………………………………………………………………… 173
Yoshihiro Suda (The University of Tokyo), Hiroaki Miyoshi (Doshisha University)

3) Projects to Foster Public Acceptance…………………………………………………………………………………… 180
Tadashi Hirota (DENTSU MEITETSU COMMUNICATIONS INC.), Hiroshi Kimura (SC-ABEAM AUTOMOTIVE CONSULTING)

4) Research for Automated Driving Bus Friendly to Persons with Disabilities or Reduced Mobility andOrientation ……………………………………………………………………………………………………………… 189
Keiji Adachi, Soichiro Shibata, Ai Ikenaga, Ryo Tachikawa (NTT DATA INSTITUTE OF MANAGEMENT CONSULTING, Inc.)

Section 5 Data Connection and Use to Achieve Society 5.0

195

Building and Designing a Geographic Architecture (Overview)………………………………………………………… 195
Raita Hiraoka, Hiroaki Kimura (Cabinet Office)1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site…… 199
Naoki Iso (NTT DATA Corporation)2) Resolving Social Issues in Cities Popular with Tourists………………………………………………………………… 207
Noriyuki Hayashi, Naohisa Komiyama, Yurie Toyama, Takahiro Kashiwa, Hiroki Ozu, Wataru Uchida (Mitsubishi Research Institute, Inc.),
Kazuhiro Noguchi, Taro Morita, Natsuha Makino, Taigo Mitsuhara (MRI Research Associates, Inc.)

3) Research to Realize More Effective Logistics System with Probe Vehicle Data………………………………………… 211
Yukihiko Akao, Masaaki Kanazawa, Daijiro Sato (NX Logistics Research Institute and Consulting, Inc.)

4) Utilization and Application of Probe Data to Road Maintenance and Management………………………………… 218
Hirokazu Ichikawa, Atsushi Takenouchi, Naohiro Uchiyama, Shinichi Nedu, Kazuki Tokunaga (Pacific Consultants Co., Ltd.)

Section 6 Promoting International Cooperation

222

Overview…………………………………………………………………………………………………………………… 222
Manabu Umeda (The University of Tokyo, Collaborative Research Coordinator for SIP-adus)

1) SIP-adus Workshop……………………………………………………………………………………………………… 227
Megumi Funahashi (New Energy and Industrial Technology Development Organization (NEDO))IISIP通し.indb 2

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2) Japanese-German and Japanese-European Cooperation……………………………………………………………… 230
Manabu Umeda (The University of Tokyo, Collaborative Research Coordinator for SIP-adus)

3) Dynamic Maps…………………………………………………………………………………………………………… 234
Satoru Nakajo (The University of Tokyo)

4) Human Factors…………………………………………………………………………………………………………… 238
Satoshi Kitazaki (National Institute of Advanced Industrial Science and Technology (AIST))

5) Safety Assurance………………………………………………………………………………………………………… 242
Hideaki Sato (TOYOTA MOTOR CORPORATION)

6) Connected Vehicles……………………………………………………………………………………………………… 247
Masanori Misumi (Mazda Motor Corporation)

7) Cybersecurity……………………………………………………………………………………………………………… 251
Yasumasa Hirai (TOYOTA MOTOR CORPORATION)

8) Socioeconomic Impacts………………………………………………………………………………………………… 254
Takashi Oguchi (The University of Tokyo)

9) Service and Business Implementation…………………………………………………………………………………… 257
Yurie Toyama (Mitsubishi Research Institute, Inc.)

Section 7 Other Achievements and Activities

260

Other Achievements and Activities………………………………………………………………………………………… 260
Takahiro Tanaka (New Energy and Industrial Technology Development Organization (NEDO))

Section 8 Conclusion and Outcomes through SIP-adus to be Inherited

264

1) Looking Back upon SIP-adus History…………………………………………………………………………………… 264
Seigo Kuzumaki (SIP-adus Program Director, TOYOTA MOTOR CORPORATION)

2) SIP-adus and Mission Oriented STI Policy……………………………………………………………………………… 268
Tateo Arimoto (National Graduate Institute for Policy Studies)

3) SIP-adus Achievement as Heritage and Next Step
RoAD to the L4…………………………………………………………………………………………………………… 271
Toshio Yokoyama (National Institute of Advanced Industrial Science and Technology (AIST))

Next Phase of SIP………………………………………………………………………………………………………… 274
Kenji Ueki (Cabinet Office)4) Final Summary of SIP-adus Program –For the Next Generation of Engineers–……………………………………… 277
Seigo Kuzumaki (SIP-adus Program Director, TOYOTA MOTOR CORPORATION)

References

285

About Japanese National Laws Related to Automated Driving
The Act for Partial Amendment of the Road Traffic Act………………………………………………………………… 285
Hisaaki Ikeuchi (National Police Agency)

The Act for Partial Amendment of the Road Act………………………………………………………………………… 288
Road Bureau, Ministry of Land, Infrastructure, Transport and Tourism

The Act for Partial Amendment of the Road Transport Vehicle Act…………………………………………………… 290
Yoshitaka Tada (Road Transport Bureau, Ministry of Land, Infrastructure, Transport and Tourism)

Projects List and Publication Status of the Second Phase of SIP Automated Driving for Universal Services…………… 292

III

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The Second Phase of SIP - Automated Drivingfor Universal Services Final Results Report:

Introduction
The five-year efforts of the second phase of the SIP-adus (2018-2022) are summarized in the Final Results
Report. The structure and content of the chapters of this report are described below, and together with
the Mid-Term Results Report (SIP 2nd Phase: Automated Driving for Universal Services—Mid-Term
Results Report 2018–2020) published in 2021, we hope that this report will be of assistance to you in yourfuture research and development efforts.

Chapter

1 The Second Phase of SIP-Automated Driving for Universal Services (Overview)

Chapter 1 describes the policy positioning of SIP andpromotion framework and the main achievements in the second
phase of SIP-adus, and summarizes the efforts of each ministry
and agency.
The Japanese government has set forth the goal of the realizationof Society 5.0 (Fig.1) in the Fifth Science and Technology Basic Plan,
and the Cross-ministerial Strategic Innovation Promotion Program(SIP) is positioned as a core program for the realization of Society 5.0.
Society 5.0 is defined as "a human-centered society that
achieves both economic development and solutions to social
issues through a system that achieves a high level of integrationof cyberspace (virtual environment) and physical space (realspace)." In this context, automated driving is considered an
important theme for the realization of Society 5.0 because it isexpected to contribute to economic development through thecreation of new data industries such as those for road traffic
environment data and the expansion of the sensor market, andto solving social issues such as ensuring safe and secure meansof transportation. Following the first phase of SIP, the automateddriving was adopted as one of the subjects.
In the SIP-adus, taking advantage of its characteristics as anindustry-academia-government collaboration project, researchand development has focused on issues that should be addressedin coordinated industry efforts, such as the construction of digital
infrastructure, technological development to ensure safety,
standardization, and fostering public acceptance. (Fig.2)
We believe that we can contribute to the early realization of
an automated driving society by cooperating as an industry andcompeting in vehicle development.
Please refer to Chapter 1 "The Second Phase of SIPAutomated Driving for Universal Services" for the efforts of the
National Police Agency, the Digital Agency, the Ministry ofInternal Affairs and Communications, the Ministry of Economy,
Trade and Industry, the Road Bureau of the Ministry of Land,
Infrastructure, Transport and Tourism, and the Automobile
Bureau of the Ministry of Land, Infrastructure, Transport andTourism, which are the members of the Steering Committee of
the SIP-adus program.

Society 5.0, a new vision of society
Data linkage
Advanced integration of cyberspace(virtual environments) and physicalspace (real space)
Economic growth

Hunting
1.0

5.0

Solving social issues

2.0

Provide necessary goods and services to those
who need them when they need them,regardless of region, age, gender, language, or
ICT skills.

A people-centered society where people can lead
comfortable, vibrant, and high quality lives

4.0

Data

Agriculture

3.0

Industry

Fig.1: Society 5.0

6

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The Second Phase of SIP-Automated Driving for Universal Services Final Results Report <Introduction>

Automated driving

Realization of Society 5.0

Realization of a society in which all citizens can movearound safely and securely

Competition▷ Technological development
Building a digital infrastructure
Unification of data formats
Ensuring safety and cybersecurity
etc.
▷International cooperation and standardization▷Fostering public acceptance▷Regulatory reform and institutional
development

Cooperation

Fig.2: Technologies and issues in the cooperative area

Chapter

2 Establishment and Utilization of Traffic Environment Data

In order to realize advanced automated driving, it isnecessary to build and utilize a framework for road traffic
environment data, including high definition 3D map data for"localization" and route planning, traffic regulation information,
accident information, traffic congestion information, andtraffic signal information.
We call this database of road traffic environment data necessary
for automated driving a "Dynamic Map" and have organized it into
a concept stratified by four layers, as shown in Fig.3.
Dynamic Maps (automated driving
and driving safety support )
Link

Base

Dynamic data
ITS predictive information (e.g., vehicles in the vicinity, pedestrian
information, traffic signal information)
Semi-dynamic data
Accident information, traffic congestion information, traffic
regulation information, road construction information, narrow-area
weather information, etc.
Semi-static data
Scheduled traffic regulation information, scheduled road construction
information, wide-area weather forecast information, etc.
Static data
= High definition 3D map data
Road surface data, lane data, 3D structures, etc.

Competitive areas
Additional data
Shared (platform)
data
Cooperative areas

The first phase of SIP-adus

The second phase of SIP-adus

The ideal

Fig.3: Dynamic Maps

In the first phase of SIP-adus, we worked on the construction
and distribution of high definition 3D maps. Based on the resultsof this research, Dynamic Map Platform Co., Ltd. (DMP) wasestablished in 2017 and began distributing approximately 30,000
km of high definition 3D maps of exclusive motorway nationwide
in 2018. Subsequently, the world's first automated vehicle
equipped with these maps was launched, and the maps are also
being used by several automobile companies for their Advanced
Driving Assistant System.
In the second phase of SIP-adus, we worked on theestablishment of a mechanism for utilizing dynamic road traffic
environment data associated with these high definition 3D
maps. Based on the results of interviews with participants in
the FOTs (Field Operational Tests) in the Tokyo waterfrontarea, it was found that traffic signal information, merging lane
assistance information, lane-level traffic congestion tailinformation, emergency vehicle information, and rainfall
information were given high priority, so it was decided togenerate and distribute such information in conjunction withhigh definition 3D maps for verification purposes. (Fig.4)
The Tokyo waterfront area was selected to coincide with thePractical application ofRealization of Society 5.0(construction of CPS*)

advanced automated driving
Promoting the spread ofhigh definition 3D maps
through infrastructure
via lower costs
coordination
Visualization and social
Realization of advanced
application of road traffic
mobility and logistics
environment dataservices

Building a geospatial
information market

Establishment of a searchportal for geographic data
(MD communet)

Realization of safe andsmooth driving withpredictive information

Utilization of data held byother businesses
Private probe data collection andutilization

Development and construction of
Dynamic Map basic concept static
data

Construction of a mechanism
for dynamic data utilization(cooperative areas)

Establishment ofa static data
infrastructure
Large-scale FOTs of highdefinition 3D maps

Reduction in traffic
congestion by controlling
traffic flow

Roadside-to-vehicle
coordination, merging
support, etc.
Traffic signal
information provision

Accident reduction
using V2X
communication

Real-time distributionof regulatory data

Field operational tests (FOTs) in the Tokyo
waterfront area related to the distributionof road traffic environment data
-2018

-2022

CPS*: cyber phisical system
Fig.4: Road Traffic Environment Data Roadmap

SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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Tokyo 2020 Olympic and Paralympic Games, and SIP-adus
prepared and distributed the equipment to receive thisinformation in order to create the experiment environment andto invite a wide range of participants. As a result, a total of 29
organizations, including domestic and foreign automobile
companies, suppliers, startup companies, and universities,
participated in the experiment. They evaluated the effectiveness,accuracy, and format of the dynamic data and validated thesafety of the automated vehicles developed by each organization.
V2I (Vehicle to Infrastructure) and V2N (Vehicle to
Network) were selected as the communication methods,
depending on the data to be distributed. The experimentshowed that V2I is on track for practical application in traffic
signal information distribution, and that V2N has big potential
for road traffic environment data distribution. These results will
be applied to the Ministry of Economy, Trade and Industry and
Ministry of Land, Infrastructure and Transport project RoAD
to the L4, and the V2I facilities for SPaT (Signal Phase andTiming) information distribution in Odaiba will be used for"Construction of smart mobility platform" for the next-phase
SIP.
The details of The FOTs in the Tokyo waterfront area andthe efforts made to build a road traffic environment data
framework are summarized in Chapter 2 (1) "Technological
Development Concerning the Generation of Traffic
Environment Data". (Fig.5)
Based on the results of the FOTs in the Tokyo waterfront

ChapterTokyo Waterfront City area
 SPaT information (V2I & V2N)
 Emergency vehicle information (V2N)

Haneda Airport area
 SPaT information (V2I) Magnetic marker routes
 Temporary bus stops, dedicated lanes, etc.

Metropolitan Expressway that connects
Haneda Airport and Waterfront City area, etc.
 Merging lane assistance information (V2I) Lane-level traffic congestion tail information (V2N)
 Rainfall data (V2N)

Fig.5: The FOTs in the Tokyo waterfront area

area, the automotive and telecommunications industries jointly
discussed new communication methods for the future, andworked on the creation of a Roadmap of V2X Communication
Methods for Cooperative Driving Automation. In addition,
research was conducted to solve issues that will arise when V2N
becomes widely used in the future, such as communications
traffic.
These studies on communication methods for cooperative
automated driving are summarized in Chapter 2 (2)"Technological Development Concerning the Transmission of
Traffic Environment Data".

3 Ensuring the Safety of Automated Driving

The biggest challenge in automated driving is ensuringsafety. To mitigate anxiety and fears associated with a systembeing in charge of automated driving, and to clarify
responsibility in the event of accidents, it is necessary to provesafety. The SIP-adus regards FOTs on public roads as animportant means of verifying safety, and has thus promoted theFOTs in the Tokyo waterfront area.
On the other hand, safety verification requires evaluation
under hazardous conditions that simulate accidents, and it is
not possible to cover all of these issues with FOTs on publicroads alone. For this reason, in the second phase of SIP-adus, an
all-Japan team worked to construct a simulation environmentthat is highly consistent with the real environment and thatenables the evaluation of sensor performance, which isimportant for automated vehicles. (Fig.6)
The simulation platform developed by the DIVPⓇ(Driving Intelligence Validation Platform) Consortium has
been commercialized, and V-Drive Technologies (a wholly
owned subsidiary of BIPROGY) began sales to the public in
September 2022.
DIVP is also working to improve the accuracy and usability
as a safety assurance tool in collaboration with the AD-URBAN
project by Kanazawa University and others, as well as with thesafety assurance project SAKURA by the Ministry of Economy,
Trade and Industry to develop safety assurance methods. These
efforts of DIVP and AD-URBAN are described in Chapter 3,"Ensuring the Safety of Automated Driving."
Cyberattacks are another critical safety threat to automatedReal Vehicle Test
Public Road

Virtual Test
SILS/MILS (Software in
the Loop/Model in the Loop)

Proving Ground
Cibbect

HILS(Hardware in the Loop)
Camera

Radar

LiDAR

Cibbect

VILS(Vehicle in the Loop)

Highly Consistent Sensor Model
Fig.6: DIVP project

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vehicles, which obtain the road traffic environment data via
external communications while driving. Cyberattack methods
are becoming more sophisticated year by year, and it isnecessary to respond to their evolution.
In the second phase of SIP-adus, we worked on the researchand development of measures against new cyberattack methods
that are discovered after the vehicles are put on the market.
We worked on the "Formulation of Evaluation Guidelines
for Intrusion Detection Systems (IDS)" in collaboration with
JASPAR and on "Research on the Collection and Sharing ofThreat Information for Connected Car" in collaboration with

Chapter

4 A Society with Automated Driving

SIP-adus is a program that aims to solve social issues byengaging in everything "from basic research to practicalapplication and commercialization."
In depopulated areas in Japan, public transportation
systems are being phased out due to declining profitability, andthe elderly and other residents are facing an urgent need tosecure means of transportation. As a consequence, it isimportant to achieve mobility services with automated driving
as soon as possible.
For this reason, in the second phase of SIP-adus, wepartnered with several local promotional organizations andmunicipalities to conduct long-term FOTs using a vehicle thathas been modified from a golf cart using electromagnetic guidewires. As a result, services that meet local needs began at fourMichi-no-Eki roadside stations (Kamikoani Village, Akita
Prefecture; Higashiomi City, Shiga Prefecture; Iinan Town,
Shimane Prefecture; and Miyama City, Fukuoka Prefecture),
and are still being operated continuously. (Fig.7) Furthermore,
based on the operational know-how from these areas, animplementation manual has been prepared and efforts arebeing made to expand the service to other areas.
These efforts are introduced in Chapter 4 (1), "AutomatedDriving Mobility Services in Regional Communities"
In order to promote the widespread use of automated driving,
we believe it is necessary to promote a correct understanding of
automated driving, to present concrete advantages anddisadvantages, and to quantify various impacts (influences).

Chapter

J-Auto-ISAC. The results were transferred to the respective
organizations.
To ensure safety, it is necessary to supplement not only the
hardware but also the soft aspects, including safety education
for users and communication with traffic participants. Theseissues have been addressed through industry-academia
collaboration, in conjunction with FOTs of mobility services inrural areas.
These safety-related efforts are also described in Chapter 3,"Ensuring the Safety of Automated Driving."

For this reason, in the second phase of SIP-adus, a largescale questionnaire survey was conducted every year as a meansof gaining a fixed-point observation of public acceptance.
Efforts were made to develop methods for estimating theFig.7: Mobility services in Kamikoani Village,
Akita Prefecture

accident reduction effects and social and economic impacts ofautomated driving.
In addition, a long-term strategy was formulated to foster
public acceptance, and information was actively disseminatedto various stakeholders. These efforts are summarized in
Chapter 4 (2) "Public Acceptance of Automated Driving."

5 Data Connection and Use to Achieve Society 5.0

As shown above, the SIP-adus has been working on theconstruction and utilization of geographic data, including
Dynamic Maps. At the same time, Society 5.0 aims to realize acyber-physical space that includes not only automated driving
but also data coordination with other fields.
In the second phase of SIP-adus, "MD communetⓇ" waslaunched in the spring of 2021 to promote the coordination andutilization of various geographic data held by the governmentand companies, and to establish a searchable portal site thatgives a catalog showing the location and format of data andprovides matching opportunities. (Fig.8) The number ofcompanies that have agreed with the mission and become
members has increased, and NTT DATA plans to continue tooperate this project after the completion of the second phase of
SIP-adus. This initiative and an app contest to promote datautilization are showcased in Chapter 5, "Data Connection and
Use to Achieve Society 5.0".

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Taxis

Business
matching
Automated driving
Cataloglisting/search

MaaS

Needs/seeds
registration

Communications

Logistics/
transportation
Information surrounding the road environment

Matching
supportAggregate catalog data on portal site

Fig.8: MD communet®

Chapter

6 Promoting International Cooperation

The SIP-adus has focused on international collaboration
since its inception in the first phase. Since the second phase, wehave strengthened cooperation with standardization
organizations in Japan, and we have also actively built a network
focusing on Japanese-German and Japanese-EU cooperation
by appointing an "International Cooperation Coordinator" as acontact point for international cooperation.
In addition, Dynamic Maps, human factors, safety

Chapter

7 Other Achievements and Activities

As explained so far, the SIP-adus has tackled a wide range ofthemes, and measures not included in this Final Results Reportor themes included only in the Mid-Term Report are introducedin Chapter 7, "Other Achievements and Activities" due to space
limitations. As described Chapter 4 "Public Acceptance ofAutomated Driving", we conducted a wide range of activities

Chapter

assurance, connected vehicles, cybersecurity, socio-economic
impact, and the promotion of service implementation havebeen designated as priority themes for international
cooperation, and the theme leaders have taken the lead inpromoting international cooperation activities, including the
SIP-adus1 Workshop. These activities are summarized in
Chapter 6, "Promotion of International Cooperation."for PR, creating a website and another as SIIP-café (a web
community for thinking about an automated driving society).
How the contents of these websites will be remained after thesecond phase of SIP-adus conclusion is also mentioned in
Chapter 7 "Other Achievements and Activities".

8 Conclusion and Outcomes through SIP-adus to be Inherited

When we include the first phase, the SIP-adus has been in
operation for nine years, and in this time various collaborationsin the area of automated driving have seen progress. The
following is a review of the management innovations that havebeen developed through trial and error during this period. On
the other hand, there is still a long way to go toward therealization of automated driving, and research and development
will continue in the future. We would like to share with you
messages from representatives of the RoAD to the L4 project by
the Ministry of Economy, Trade and Industry and the Ministryof Land, Infrastructure, Transport and Tourism, which is
ongoing with the goal of Level 4 automated driving, the next
step in automated driving, while continuing to build upon theachievements of the SIP-adus program, and from the next SIP
Secretariat, which will start in FY2023. A summary by the

program director is also given in Chapter 8, "Conclusion andOutcomes through SIP-adus to be Inherited".

1 SIP-adus is an acronym for SIP Automated Driving for Universal Services

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1

The Second Phase of SIP-Automated Driving for Universal Services

The Second Phase of SIP-AutomatedDriving for Universal Services (Overview)
Kotaro Sugiyama, Hiroaki Kimura (Cabinet Office)1

Background and Policy Position

Interest in automated driving is increasing day by day,and automobile manufacturers and parts manufacturers areactively investing in research and development, and at thenational level, active efforts are underway to attract researchand development projects and Field Operational Tests(FOTs). In addition, steady progress is being made in
developing legislation and improving the environment forpractical use, mainly in Japan, the United States, andEurope.
Driving these developments, in addition to solving social
issues such as reducing traffic accidents, reducing traffic
congestion, and ensuring mobility for the elderly andvulnerable road users, there are great expectations for thesocial changes to be brought about by automated driving,such as the creation of new services and businesses related
to logistics and transportation.
Currently, the automobile industry is facing a wave ofinnovation including automated driving, electrification,connected vehicles, and vehicle sharing, so much so that it
is said that we are in a once-in-a-century era of majorchange. Winning this competition for development will not
only maintain and strengthen the competitiveness of theautomobile industry, which is currently a core industry inJapan and has a broad base, but it will also have ripplee f fe c t s on re l at e d i n du s t r i e s s u c h a s t h e d i g it a linfrastructure, sensors, and communications required forautomated driving. We can also expect the creation of new
industries and services, and there is a strong possibility that
it will contribute to the future economic development ofJapan.
Under thes e circumstances, at the C ouncil on

Investments for the Future (March 2018), the then Prime
Minister Abe said, "We will realize automated driving at theTokyo 2020 Olympic and Paralympic Games. We willtransmit traffic signal information to vehicles and develop a
testing site in the Tokyo waterfront area where vehicles canoperate more safely, thereby further accelerating our efforts
with a view to developing a variety of businesses." With thissuggestion in mind, Public-Private ITS Initiative/Roadmaps
2021 (June 2021)(1) stated that by 2030, ''We will lead theworld in realizing a safe and highly convenient digitaltransportation society that supports the prosperous lives ofthe people." Furthermore, the Integrated Innovation
Strategy 2022 (June 2022), as part of the development ofdata coordination foundations for Society 5.0, calls for thedevelopment of inter-field data coordination foundations,the development of a data coordination platform for eachfield, and the construction of an architecture.
SIP is an effort to realize "Society 5.0" set forth in theFifth Science and Technology Basic Plan (January 2016).
The Sixth Science, Technology, and Innovation Basic Plan
(March 2021) calls for research, development and social
implementation to solve various social issues and for theutilization of the convergence of knowledge to create asustainable and resilient society that ensures the safety andsecurity of the people, one in which the diverse well-beingof each and every person can be realized.

2

Overseas Trends

When we look at the world, we see that many haveawakened from the overly ambitious dream of fully
automated driving as defined by the Society of Engineers(SAE), or SAE Level 5TM, and have shifted to more practical

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1 The Second Phase of SIP-Automated Driving for Universal Services
﻿The Second Phase of SIP-Automated Driving for Universal Services (Overview)efforts. Meanwhile, discussions on safety, reliability, andethical issues are deepening. Pilot test project of automateddriving are being conducted all over the world, anddiscussions are progressing on the formulation of common
evaluation methods for sharing knowledge and common
formats for collected data. Due to the spread of infections of
the novel coronavirus, which has spread rapidly around theworld since the beginning of 2020, the flow of people andthings has been greatly affected both globally and locally.
Even in this circumstance, research and development on
automated driving has been actively pursued, although there
have been delays.
In the United States, in January 2020, the White Houseand the Department of Transportation jointly issued
Automated Vehicle 4.0 (AV4.0) with the stated purpose of
ensuring the United States' leadership in automated driving
technology. AV4.0 establishes federal guidance for thedevelopment and integration of autonomous vehicles inthree core areas: prioritizing safety and cybersecurity,
promoting innovation, and ensuring a consistent regulatory
approach. Although it was before the transition to the new
government inaugurated in January 2021, based on theprinciples of AV4.0, the Automated Vehicles Comprehensive
Plan was released. The plan defines three goals to achieve
the U.S. Department of Transportation's vision forautomated driving systems: promote cooperation andtransparency, modernize the regulatory system, and preparetransportation systems.
In Europe as well, research projects related to automateddriving are being pushed forward in various countries, suchas Germany's PEGASUS and VIVALDI, and the UK's
DRIVEN and Human Drive. Furthermore, in the EU, many
research projects related to connected and automateddriving are underway under the European Commission's
Horizon 2020. Regarding the implementation of Horizon
Europe, the successor program to Horizon 2020, anagreement was reached in December 2020, positioning
climate, energy and mobility as one of the six clusters under
the pillar of solving social issues.

3

Initiatives in the First Phase of SIPadus and Major Achievements to Date

In Japan, the first phase of SIP-adus, which started in2014, has played the central role, with the Tokyo 2020
Olympic and Paralympic Games etc. being set as amilestone. Research and development in the cooperative
area of automated driving have progressed throughindustr y-academia-government collaboration andcollaboration among ministries and government agencies.
In FY2017, large-scale FOTs were begun, progress wasmade on verifying the effectiveness of dynamic maps andformulating unified specifications, and results such as theestablishment of map platform creation systems wereachieved. Based on these results, from the end of 2018,
Dynamic Map Platform Co., Ltd. started the commercial
distribution of high precision 3D maps covering about
30,000 kilometers of motorways nationwide.
Meanwhile, on the institutional side, the "Outline on
improvement of legal system and environment for
automated driving system" (April 2018, IT Comprehensive
Strategy Headquarters) was formulated, and studies havetaken place in each relevant ministry. In May 2019, revisions
to the Road Transport Vehicle Act and Road Traffic Act
were passed, and both revisions came into force in April
2020, making it institutionally possible to drive automatedvehicles corresponding to SAE Level 3 on public roads. In
November 2020, in accordance with the Road Transport
Vehicle Act, the world's first type designation was carriedout for an automated vehicles (SAE Level 3) equipped with
an automated driving system. In March 2021, commercial
sales of that automated vehicle, which was a first in theworld. In addition, the high precision 3D maps fromDynamic Map Platform Co., Ltd. have been adopted for
multiple vehicles, including that automated vehicle.
In addition, in May 2020, a partial revision of the Road
Act was passed concerning the development of facilities onroads to assist in automated driving operations, and went
into force in November of the same year. This is an exampleof the steady progress that has been made in institutional
reforms toward the social implementation of mobility
services using automated driving technology.

4

Launch of the Second Phase of SIP-adusand Details of Research and Development

In March 2018, the Council for Science, Technology andInnovation decided on an implementation policy for thesecond phase of SIP-adus. In response to this, in July of thesame year, the Cabinet Office formulated the "Crossministerial Strategic Innovation Promotion Program (SIP)
Automated Driving for Universal Services R&D Plan". For
the social implementation of automated driving, it isnecessary to overcome three barriers: technology, the legal
system, and public acceptance. In response to these issues,
the SIP-adus has sought to promote cooperation with theDigital Agency (formerly the Cabinet Secretariat’s IT
Comprehensive Strategy Office), the National Police Agency,
the Ministry of Internal Affairs and Communications, theMinistry of Economy, Trade and Industry, and the Ministryof Land, Infrastructure and Transport, and to gain
participation from industry and universities. It advances
research, development, and FOTs through inter-ministrycollaboration and collaboration among industry, academia,
and government.
The following four pillars, namelyI) Development and verification of automated driving
systems (FOTs)

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1 The Second Phase of SIP-Automated Driving for Universal Services
﻿The Second Phase of SIP-Automated Driving for Universal Services (Overview)II) Development of platform technologies for the practicalapplication of automated driving
III) Fostering public acceptance of automated drivingIV) Enhancement of international cooperation
make up research and development.

ministry.
As shown in Fig.1, there are currently two approaches toautomated driving development: one that pursues
automated driving in a limited space and time (A), and onethat pursues application in more diverse environments (B).
In the SIP-adus, we believe that both approaches arenecessary in order to contribute to achieving these goals
more quickly by utilizing the technology of automateddriving.

The main content of research and development isorganized as follows.
I) FOTs (Field Operational Tests)(1) The FOTs in the Tokyo waterfront area(2)Social implementation of mobility and logistics services
in regional areas, etc.
II) Development of platform technologies(1) Utilization technology for road traffic environment data
(2) Technologies for safety assurance(3) Cybersecurity(4) Construction of architecture related to geographic data
III) Fostering public acceptance(1) D isseminate information to citizens and promote
understanding(2) Investigative research for solving social issues(clarification of socioeconomic impact)IV) Enhancement of international cooperation(1) Disseminating information internationally by holding
international workshops, etc.
(2) Joint research and such with overseas research institutes
Among those, the following are key themes:(1) Building and distribution of a road traffic environment
data framework(2) Building of safety assurance environments in virtualenvironments(3) Establishment of cybersecurity evaluation methods(4) Building of distribution portal for geographic data
These four themes were positioned as important themesin the second phase of SIP-adus, we made active efforts
toward practical application and commercialization. In
addition, we cooperated with the Digital Agency so thattechnological development and the development of legal
and other systems could proceed together, and coordinatedefforts related to system development pursued by each
SAE* Levels ofDriving AutomationTM

5Main Initiatives and Results of theSecond Phase of SIP-adus

5.1. The FOTs in the Tokyo waterfront area
On prefectural and municipal roads where the traffic
environment is complex, vehicles intersect and pedestrians
and bicycles come and go. Given these circumstances, it isdifficult to realize advanced automated driving with only the
information available from the sensors installed on thevehicle. As one of the solutions to this problem, we studied
the utilization of traffic signal information with automobile
manufacturers and installed ITS roadside units (RSU) thatprovide signal information through short-range wirelesscommunication (V2I) in the Tokyo waterfront area. In
addition, the development of road traffic infrastructure wasadvanced with such measures as installing ETC 2.0 roadsidewireless devices used to provide information on vehicles
traveling on the main line and information on opening andclosing of ETC gates, with the aim of supporting safe andsmooth merging from prefectural and municipal roads toexpressways and the passing through of ETC gates byautomated vehicles.
With the participation of domestic and foreign
automobile manufacturers, in the Tokyo Waterfront City
area of the Tokyo waterfront area, in the Haneda Airport
area, and along the Metropolitan Expressway that connects
Haneda Airport and Waterfront City area, etc., in October
2019, we began FOTs of vehicle-infrastructure cooperative
driving automation system under the real traf f ic

Practical application of logistics/mobility services

Level 5

Fully automateddriving

Logistics/mobility services
Level 4
Level 3
Level 2
Level 1

Address depopulation
Address driver shortages
Freedom of movement

Resolve social issues(Unmanned transport vehicles in factories)(Golf carts)(Auto-breaking)Privately owned vehicles(Pedal error control)

Expansion of operational domainfrom expressways to arterial andgeneral public roads

Traffic accident reduction
Traffic congestion reduction
Vehicle value improvement

International cooperation
Economic development(Regions, roads, environments, traffic conditions, speeds, drivers, etc.)
Restricted
Unrestricted*SAE (Society of Automotive Engineers): U.S. based standard developing organization

Fig.1: Overall initiatives of automated driving (Research and Development Plan) (2)

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﻿The Second Phase of SIP-Automated Driving for Universal Services (Overview)

environment of public roads, and conducted technicalverification as well as verification of the effectiveness ofproviding traffic signal information to automated vehicles.
Although it was temporarily suspended due to the impact of
the novel coronavirus, it was completed by the end of
FY2020. In response to requests from experimentparticipants to continue, it was decided to add a new roadtraffic environment data and extend for one year until
March 2022.
Beginning in November 2021, in addition to V2I traffic
signal information distribution, with a public long range
network (V2N) we built an experimental environment that
could distribute broader and diverse road traf f ic
environment data to vehicles. We conducted FOTs on theeffectiveness of that data and on issues for social
implementation.
In addition to the distribution of traffic signal
information, emergency vehicle information, and rainfall
information by V2N, we used real-time collection andstatistical processing of private-sector probe information
possessed by car/navigation system manufacturers, whichenabled us to generate and distribute more detailed andaccurate congestion tail information at the lane level thanconventional lane level information. We conductedverification of the effectiveness of that information andnecessary requirements.
5.2. Development of Platform Technologies
As described in Section 5.1, in the second phase of SIPadus, we worked on building and distributing dynamic roadtraffic environment data frameworks linked to the highprecision 3D maps that served as the platform for the
dynamic maps that we worked on in the first phase. We
verified these in the FOTs in the Tokyo waterfront area.
In order to realize automated driving, ensuring safety is
the most important issue. It is necessary to verify safety not
only by FOTs on public roads, but also by combining
reproduction tests with actual vehicles on test courses andevaluations by simulations. It is particularly important toevaluate the sensors that serve as the "eyes" of automatedv e h i c l e s , b u t t h e r e a r e n o s i mu l at i o n s t h at c a nsimultaneously evaluate cameras, radar, and LiDAR. We
therefore have no choice but to rely on evaluations based on
FOTs on public roads, which is time-consuming. For thisreason, in the second phase of SIP-adus, we worked on thedevelopment of a simulation platform that is highly
consistent with actual phenomena that can be evaluatedunder critical conditions in various traffic environments.
In addition, the risk of cyberattacks will increase if we
drive while using communications to obtain road traffic
environment data in the future. In the second phase of SIPadus, we worked on building guidelines for evaluating theperformance of IDS (intrusion detection systems), which
are expected to spread in the future, and building methods
for collecting and analyzing threat information on

cyberattacks.
To achieve Society 5.0, in addition to applying traffic
environment data and other data for automated driving toother fields, it is necessary to utilize data collected in otherfields, and thereby the build road traffic environment dataf r am e wor k s . In t h e SI P- a du s , we wor ke d on t h econstruction of the portal site "MD communet®" on the web
network with the aim of promoting data coordination anddistribution through the organization and structuring of
information held by businesses in various fields. This portalsite catalogs government data related to disaster prevention
and geographical spaces, as well as mobility data frommultiple private companies, and has been open to the public
since April 2021. Since going public, we have increased thevalue of the portal site by recruiting many companies andorganizations.
5.3. Fostering Public AcceptanceIn addition to trying to quantify the benefits and utility
obtained from automated driving, we have promoted theimplementation of citizen-participatory events that ensured
interactivity, and the creation and dissemination of
information content that promotes a correct understandingof automated driving. In March 2021, which has been amilestone since the beginning, we held an midterm resultspresentation event on the SIP-adus, where we exhibited thelatest research results for the practical application of
automated driving in an easy-to-understand manner. We
are planning to hold a final results presentation event inMarch 2023.
Also, in April and October 2021, we held test-ride events
for the mass media in the Tokyo Waterfront City area.
Because they were able to test-ride multiple automatedvehicles at the same time, the media praised us fordeepening their understanding of the technology andconcepts. A similar test-ride event was held in September
2022, the final year of the second phase of SIP-adus.
5.4. Promotion of International Cooperation
In order to continue to maintain the international
competitiveness of automobiles and related industries inJapan, it is necessary for Japan to take the initiative instandardization and scaling activities for automated driving,which is an important future technology, and to strive forinternational accord in the field.
As for international standardization activities, we
strengthened cooperation with standardization bodies at thestart of the second phase of SIP-adus. In addition to theJapan Automobile Manufacturers Association (JAMA) andthe Society of Automotive Engineers of Japan (JSAE), we arecooperating with the Japan Auto Parts Industries
Association, the Japan Electronics and Information
Technology Industries Association (JEITA), the UTMS
Society of Japan, the Association of Radio Industries andBusinesses (ARIB), the ITS Info-communications Forum

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﻿The Second Phase of SIP-Automated Driving for Universal Services (Overview)(ITS Forum), and others. In doing so, we have gone forwardwith standardization with both de jure standards such as
ISO, IEC, and ITU, and de facto standards via cooperation
with internationally influential industry standardization
organizations.
In addition, with the aim of cultivating human resources
who can ser ve as leaders in research topics on aninternational stage, we held an international conference
called the "SIP-adus Workshop" every year to share
information and issues related to automated driving anddiscuss efforts to solve them. In addition to being a forum
for disseminating information, it was also used as a forum
for discussion through the exchanges among experts fromJapan and overseas. International recognition increased
after the first event was held in 2014. The SIP-adusWorkshop 2021 was held online in November 2021 due to
COVID-19, and saw more than 1,000 participants fromJapan and overseas.

6

Goals and Project Achievement
Milestones

In the SIP-adus, we proceeded our initiatives according
to the stated achievement years of Public-Private ITS
Initiative/Roadmaps 2021 described below as specifictimings for the realization of the practical application andspread of automated driving.
・Mobility services: Realization of unmanned automateddriving transportation services with remote monitoring
only (SAE Level 4) in limited areas around FY2022
・Logistics services: Realize fully automated truck driving(SAE Level 4) on expressways after 2025
・Privately owned vehicles: Realize fully automated driving(SAE Level 4) on expressways by around 2025
・Privately owned vehicles: Further advancement of driving
support technology on prefectural and municipal roads(SAE Level 2 or higher)
In addition to trying to establish the technologies in thecooperative areas necessary to achieve the goals, we worked
to confirm the effectiveness of the technologies and services
through FOTs involving various local business operators,
local governments, and others. By creating multiple
examples of practical application, we aimed to haveprospects for social implementation by the end of thesecond phase of SIP-adus.
For the social implementation of research anddevelopment, it is necessary to integrate the efforts ofvarious stakeholders, which encompasses not only
technological development in cooperative areas but also
technological development in competitive areas such as
vehicle development. As a result, the Tokyo 2020 Olympic
and Paralympic Games were set as milestones. Due to theimpact of the novel coronavirus, the joint test ride eventwith the Japan Automobile Manufacturers Association,
which was part of efforts to foster public acceptance, waspostponed, but with regard to technological development,
advancements were completed on schedule.

7

System of Initiatives

With program director (hereinafter referred to as "PD")Seigo Kuzumaki as chairman, representatives from theDigital Agency, the National Police Agency, the Ministry ofInternal Affairs and Communications, the Ministry of
Economy, Trade and Industry, the Road Bureau and theRo ad Transp or t Bure au of t he Minist r y of L and,Infrastructure, Transport and Tourism, experts, relatedindustries, industry groups, etc. formed the SIP-adus
Steering Committee, which conducted discussions of basic
research and development policy and project achievement
milestones via industr y-academia-government
collaboration. Under the Steering Committee, the System
Implementation Working Group, Business Promotion
Working Group, and International Cooperation Working
Group were established. In addition, respective task forceswere set up to build road traffic environment data
frameworks, conduct FOTs in the Tokyo waterfront area,and study communication methods for cooperative
automated driving, and discussions were facilitated. The
New Energy and Industrial Technology Development
Organization (NEDO), which is the management agency,
was made responsible for the promotion of the SIP-adus.
NEDO flexibly implemented measures when necessary andperformed appropriate management and related duties.

[References]
(1) Public-Private ITS Initiative/Roadmaps 2021, https://warp.ndl.go.jp/info:
ndljp/pid/12187388/www.kantei.go.jp/jp/singi/it2/kettei/pdf/20210615/
roadmap. pdf , (Referenced 2022.08.12)(2) C ross-ministerial Strategic Innovation Promotion Program (SIP)
Aut om at e d D r i v i ng for Un i ve r s a l S e r v i c e s R & D P l an
https://en.sip-adus.go.jp/sip/file/sip_2021_plan_en_s-1.pdf as of
8.12.2022[Contacts]
Secretariat of Science, Technology and Innovation Policy Cabinet Office,1-6-1 Nagata-cho, Chiyoda-ku, Tokyo 100-8914, 03-6257-1334, Contact:
Kotaro Sugiyama, Hiroaki Kimura

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The Second Phase of SIP-Automated Driving for Universal Services

Overview of Activities of the
National Police Agency
Hisaaki Ikeuchi (National Police Agency)

1

Main Initiatives of the second phase of
SIP-adusDuring the second phase of SIP-adus (Cross-ministerial
Strategic Innnovation Promotion Program (SIP) AutomatedDriving for Universal Services), the National Police Agency
worked on "Research and Development on Advanced Traffic
Signal Information Provision Technology for AutomatedDriving," "Research and Development on Provision of Traffic
Signal Information using Cloud Technology," "Research andDevelopment on Traffic Signal Control using GNSS (location
information)," and "Research and Studies on Improvement of
Data Accuracy of Traffic Regulation Information."

1.1. Research and Development on Advanced Traffic
Signal Information Provision Technology for
Automated Driving
To achieve the practical use of automated driving systems, it
is required for vehicles to recognize traffic signals in real timeand controls themselves appropriately. Therefore, it ismeaningful to improve the recognition reliability of traffic
signals by using traffic signal information as a complement to
the on-board camera that recognizes signal light colors.
Receive traffic signal
information fromroadside unit

Roadside unit

Check signal lightcolor with
on-board camera

We therefore conducted research and development on an
ITS roadside unit that provides traffic signal information tovehicles using the 700 MHz band. (Fig.1)
The primary results of this project are as follows: thedomestic and international survey on the provision of traffic
signal information from roadside infrastructure conducted in
FY2018, the study of detailed functional and technicalrequirements, and the development of a prototype ITS roadsideunit in FY2019. In addition to these, in FY2020, we prepared
the specifications for ITS roadside units based on the results ofthe prototype test, and we made some alternations in the
prototype considering the study on providing traffic signal
information specifically to automated vehicles and thereexamination of the details of the functional and technicalrequirements for it.

1.2. 
Research and Development on Provision of
Traffic Signal Information using Cloud Technology
This project is aimed at realizing the provision of SPaT
information to automated vehicles by means other than vehicleto-infrastructure (V2I) communication using ITS roadsideunits.
To realize this, we studied a method to provide SPaT
information from traffic signal controllers to vehicles via the
Traffic Control Center of each Prefectural Police Headquarters,
the SPaT Information Aggregation System of the National
Police Agency, the SPaT Information Center, and theDistribution Centers of businesses or other entities. (Fig.2)
In FY2018, we conducted domestic and overseas case studies
on methods that enable the provision of SPaT information

Fig.1: Provision of traffic signal information by ITS Roadside unit (RSU)

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Overview of Activities of the National Police Agency

Traffic Control Center(Prefectural Police
Headquarters)

SPaT Aggregation System(National Police Agency)
LTE line
SPaT information

SPaT Information
Center(Tentative Name)Distribution Center(businesses, etc.)

SPaT
information

Fig.2: Provision of SPaT information using the cloud, etc.

other than V2I communication. In FY2019, we examined indetail functional and technical requirements, prepared draft
specifications for a model system for providing SPaT
information of Prefectural Police Headquarters, and studied
specifications for the SPaT Information Aggregation System. In
FY2020, we constructed a SPaT Information Provision Model
System in Saitama Prefecture, measured the time accuracy ofeach device, recognition errors and delay times of SPaT
information, and obtained comparative test results of three
information provision methods. In FY2021, we studied what
the SPaT Information Center should look like that receives
nationwide SPaT information from the SPaT Information
Aggregation System and transmits it to the Distribution Centersof businesses. In FY2022, we constructed the SPaT Information
Aggregation System and the SPaT Information Provision
System in Nara Prefecture as part of the Field Operational Tests(FOTs).

1.3. 
Research and Development on Traffic Signal
Control using GNSS (location information)

This project, therefore, aims to enable traffic signals to becontrolled with priority in a timely manner over a wide area bylinking the Prefectural Police Traffic Control Systems with
automated buses and other public vehicles using GNSS location
information and cellular networks. (Fig.3)
In FY2020, we studied the technical requirements, costeffectiveness, and model system specifications for traffic signal
control using GNSS and cellular networks, and in FY2021 we
conducted FOTs in Shizuoka City and the Tokyo waterfrontarea.

1.4. Research and Studies on Improvement of Data
Accuracy of Traffic Regulation Information
In this project, we conducted research and studies that
contribute to the appropriate provision and management oftraffic regulation information required by automated vehicles.
In FY2021, to reduce the workload of registering traffic
regulation information and road sign and marking information,
we studied image recognition technology to easily collect andextract location information on sign and marking from videos
and still images collected from sources such as dashboard
cameras. We also developed a model system to improve theaccuracy of data on traffic regulation information by collating itwith road sign information, and conducted FOTs in Kanagawa
Prefecture. (Fig.4)
Automatic collection ofsigns and markings

Extraction oflocation
information, etc.

Collation
Collation

The police are promoting the deployment of Public
Transportation Priority Systems (PTPS) in which Traffic
Control Centers prioritize traffic signal control, such as
extending green light timing and shortening red light timing, sothat public vehicles can pass through intersections smoothly.
Currently, infrared beacons are used to detect public vehicles toimplement priority signal control in PTPS. However, because of

its fixed-point detection, PTPS is unable to respond to changes
in traffic conditions immediately after detection, or to provide
service in locations where infrared beacons have not been
installed.

Stop [discrepancy]
Stop line
Maximum speed[discrepancy]
Pedestrian crossing(marking)
Pedestrian crossing (sign)

RevisedRegistration
Traffic
regulation
information

Prefectural Police Traffic
Regulation Information
Management System

Fig.4: Improvement to traffic regulation information precision

Controls traffic signal

Traffic Control
CenterLocation information(cellular network)

In addition, we reviewed the standard format for traffic
regulation information data developed in the first phase of SIPadus and developed an extended version of the standard format
to unitarily manage traffic regulation information and road signand marking information corresponding to it. In FY2022, wedeveloped a prototype system using the technologies we haveresearched and the extended version of the standard format,
and conducted FOTs in Chiba Prefecture.

Fig.3: System using GNSS and cellular network

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Overview of Activities of the National Police Agency

2

Conclusion

In the second phase of SIP-adus, the National Police Agency
engaged in various studies toward the realization of automateddriving. Because of the wide range of technologies required forautomated driving, many organizations, groups, and businesses
are currently collaborating to promote research anddevelopment, FOTs, and other activities. With recognition of
their potential to reduce traffic accidents and congestion, thePolice will continue to actively support the development of
automated driving technologies designed for the road traffic
environment in Japan, allowing them to be put to practical use
in an early timing.

[References]
(1) National Police Agency Website https://www.npa.go.jp/english/bureau/traffic/its/
utms.html (Reference 2022.08.15)[Contacts]
Automated Driving Planning Office, Traffic Planning Division, Traffic Bureau,
National Police Agency, 2-1-2 Kasumigaseki, Chiyoda-ku, Tokyo, 03-3581-0141

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The Second Phase of SIP-Automated Driving for Universal Services

Overview of Activities of theDigital Agency
Jun Usami (Digital Agency)

1

Introduction

In this article, we will introduce the "Establishment ofenvironment for promoting the utilization of mobilityrelated data," which National Strategy Office of IT, Cabinet
Secretariat, the predecessor of the Digital Agency, worked
on in the second phase of SIP-adus (Cross-ministerial
Strategic Innovation Promotion Program (SIP) AutomatedDriving for Universal Services), and the "Vision of Mobility
and Society 2022," built based on the "Public-Private ITS
Initiative/Roadmaps."2

Establishment of environment for promoting
the utilization of mobility-related data

Autonomous developmentby the private sector

Launched under
government initiative

In order to promote the social implementation of a datac o ord i n at i on m e ch an i s m for t h e d i s t r i but i on oftransportation-related data, it is important to develop an
environment in which various stakeholders inside andoutside the mobility field can collaborate with each other.
To this end, in service areas where use cases have not yet
been established, we need to develop rules and standardize
data specifications that are necessary for sharing functions
and data common among stakeholders, as well as data
distribution mechanisms for social implementation. In the
future, it will be necessary to proceed with further studies
among the parties concerned and to construct the systemstep by step.(1) Based on the awareness of these problems, we
conducted a survey to find out what kind of environmental
improvements are necessary to promote the utilization ofmobility-related data.
Three use cases of data utilization were defined anddiscussed, and about the extracted issues, interviews wereconducted with actually related entities, and the issues were

(1) Development of data catalogs by promotion authorities
Data provider(government)

Official
data

Service provider(private sector)(2) Trials of data utilization using government data
(Collaboration through FOTs centered on promotion authorities)
Data provider(government)

Data provider(private sector)

Official
data

Privatedata
Privatedata

Service provider(private sector)

Service provider(government)(3) Transfer data catalog management to the private sector(4) The private sector provides data to the data catalog (development of private sector data)(collaboration through FOTs centered on promotion authorities) Private sector and public
sector use private sector data (full-scale use of data)

Service provider(private sector)

Fig.1: Promotion procedure for public-private sector data coordination(2)SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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Overview of Activities of the Digital Agency

further explored.
As a result, we found that "data handling" and "publicprivate partnership for data distribution" were issues
regarding data coordination and utilization. We consideredcountermeasures, such as procedures to promote publicprivate data coordination. In the procedure shown in Fig.1,
it is not possible to foresee the business feasibility of datautilization, and data distribution does not progress.
Therefore, this shows an approach that aims to begin fromofficial data utilization and then involving private parties.

3

Vision of Mobility and Society 2022(3)This section will introduce Vision of Mobility andSociety 2022, which was decided in August 2022 at the
Director-Generals' Meeting for the Digital Society
developed based on Public-Private ITS Initiative/Roadmaps,
including initiatives related to the second phase of SIP-adus.
The "Public-Private ITS Initiative/Roadmaps", which is agovernment-wide strategy related to ITS and automateddriving, was formulated in 2014, and related ministries andagencies, private companies, and other public-private efforts
have promoted it. Initiatives that comprise the roadmap areproceeding steadily, including commercialization of theworld's first a Level 3 automated driving vehicle passenger
car and driverless automated driving transport services by
2021, and the revision of the Road Traffic Act leading to theestablishment of a system for Level 4 automated driving in2022.
On the other hand, when we look at the status of
implementation of the technologies in local areas, there aremany issues, such as the fact that there are many casesinterrupted after FOTs (Field Operational Tests), and in the
future it is necessary to aim for further development of theroadmap for full-scale social implementation.
For t h i s re a s on , t h e " Stu dy Group on D i g it a l
Transportation Society" (4) was established to study howsociety and lifestyles should be from the perspective of eachindividual citizen as the digital transportation society
progresses. Backcasting from this to identify how mobility
should be, we proceeded with the study while receiving
opinions from a wide range of perspectives.
3.1. Issue
In Japan, social problems in various fields such astransportation, medical care, and education are becoming

more serious as the population declines at an accelerated
pace. In addition, as we move from the industrial society
and information society to the new society, the economic
model is undergoing major changes.
As the population declines, there is a shift from an
economy in which demand meets supply (for example,
passengers waiting for buses at bus stops) to an economy inwhich supply meets demand (service vehicles pick up
passengers). The style of transportation services is also
being forced to shift to a model that develops services
starting from the demand side (perspective of each person's
lifestyle) while utilizing digital technology.
In addition, technologies related to transportation
services have progressed remarkably, and various means
using digital technology have emerged. It is also necessary
to have the perspective of reducing the marginal cost (theadditional cost incurred when adding one unit of service
supply) by aiming for overall optimization.
3.2. Service design from the perspective of citizen's life
Based on the recognition of the issues, we will organize
examples of initiatives that have been implemented in Japanand overseas based on the perspective of each citizen's life
and the actual methods used at that time, and the points
t hat have b e en s e en f rom t he examples of s o ci a l
implementation are presented as 11 viewpoints, including"Each region decies its own vision for the future." echnologies and systems for provision of services
3.3. T
From the perspective of realizing technologies andmechanisms that support service design from the angle oflifestyle, we will build a social implementation plan for thetechnology and promote its realization and dissemination inline with efforts toward implementation of services from theperspective of lifestyle.
The current public and private sector efforts werecategorized by means (transport mode) in Vision of
Mobility and Society 2022. The scope has been expandedfrom the framework centered on ITS/automated drivingthat has been dealt with in Public-Private ITS Initiative/
Roadmaps to "from walking to flying," and "Automateddriving and Driver Assistance," "road space," and "mobility
service and MaaS," "drones," and "flying cars," we added acategory called "mobility field cooperative Domain" as acooperative area, and organized the efforts for each.
3.4. For realization"Vision of Mobility and Society 2022" is just a snapshotof the current situation, and we will continue to update it in
the future.

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Overview of Activities of the Digital Agency[References]
(1) Public-Private ITS Initiative/Roadmaps 2020 Government CIO's portal
Japan,https://cio.go.jp/sites/default/files/uploads/documents/its_
roadmap_2020.pdf, (accessed 2022.10.24)(2) Th e second phase of Cross-ministerial Strategic Innovation Promotion
Program, Automated Driving for Universal services: "Establishing anenvironment that promotes the utilization of mobility-related data"
Mitsubishi Research Institute, Inc
https://en.sip-adus.go.jp/rd/rddata/rd04/e217s.pdf(English summary), accessed 2022.10.24(3) Vision of Mobility and Society 2022 , https://www.digital.go.jp/assets/contents/node/information/field_ref_resources/22791050-006d-48fd-914de374c240a0bd/1ae00570/20220802_news_mobility_outline_01.pdf ,(accessed 2022.10.24)(4) S tudy Group on Digital Transportation Society,https://www.digital.go.jp/councils/#mobility (accessed 2022.10.24)[Contacts]
Digital Agency, Group of Service for Citizens Mobility Team, 19th Floor,
Tokyo Garden Terrace Kioicho, 1-3 Kioicho, Chiyoda-ku, Tokyo 102-0094,
03-6771-7526

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The Second Phase of SIP-Automated Driving for Universal Services

Overview of Activities of the Ministry of Internal
Affairs and Communications
Takanori Mashiko (Ministry of Internal Affairs and Communications)

1

MIC Initiatives Regarding ITS

The Ministry of Internal Affairs and Communications(MIC) is progressing initiatives regarding ITS (Intelligent
Transport Systems) to realize V2X (vehicle to everything)communication that connects people, roads, and vehicles
using information and communications technology, withthe aim of achieving the safe and comfortable movement ofpeople and things. V2X communication is a general termthat refers to communication forms between vehicles andvarious other things, such as vehicle to infrastructure (V2I)
communication, vehicle to vehicle (V2V) communication,and vehicle to network (V2N) communication.Regarding
communication systems which use radio, such as VICS
(Vehicle Information and Communication System), ETC(Electronic Toll Collection System), in-vehicle radar system,
and ITS Connect (700 MHz Intelligent Transport Systems),
until now MIC has allocated frequencies and developed
technical standards while taking the status of radio wave use
and interference with other wireless communication
systems into consideration, and also promoted the adoptionof these systems.
As an initiative towards the realization of an automateddriving society, in recent years we have collaborated withrelated ministries and agencies to progress initiatives related
to cooperative automated driving (automated driving
realized by connecting vehicles and various other things
through communication) using V2X communication.
(Fig.1)Through the uptake of vehicles that are connected tovarious things (connected cars), more advanced automateddriving will be realized and cars will become a space where
various social and economic activities can be performed
instead of just a means of transportation, and it is expectedthat everyone will be able to enjoy safe and convenientmobility services.

2

MIC Initiatives for the Second Phaseof SIP-adus

Regarding "Automated Driving for Universal Services",
as a project of the second phase of SIP: Cross-ministerial
Strategic Innovation Promotion Program, MIC has
Advanced automated driving

Information provision and toll collection

Autonomous automated driving

Highway Radio/VICS

Vehicle-mounted sensor

Optical beacon

Radio beacon

FM multiplex

Advanced driving assistance using vehicle-mounted
sensors such as cameras and radar

Cooperative automated driving
V2X communication

Advanced driving support and automated driving
via a combination of vehicle to vehicle (V2V)
communication, high-resolution radar, etc.

Radio radar
Display VICS information (congestion, roadclosures, etc.) via car navigation systems, etc.

Collision mitigation brake

Connected car

Automatic toll collection

White line detection
Lane crossing prevention system

Automated driving system

ETC

Fig.1: Image of the evolution of the ITS system

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Overview of Activities of the Ministry of Internal Affairs and Communications

progressed research and development in cooperation with
ITS-related ministries and agencies that is aimed towards
the realization of cooperative automated driving, mainly
from the perspective of wireless communication systems.
From FY2019 to FY2021, the "Research anddevelopment related to the collection, integration, anddistribution of small-scale network and mid-scale network
information" project conducted research and development
into technology that continuously recognizes dynamic
information obtained from various information sources andcollects the necessary information from a small-scale
network area, which is about the size of a generalintersection, or a mid-scale network area which is about thesize of a municipality, integrates it in real time anddistributes it to vehicles, so that a bird's-eye view ofsurrounding traffic conditions can be ascertained in orderto realize safe and secure automated driving.The results ofthis research were partially applied to the FOTs (Field
Operational Tests) in the Tokyo waterfront area that wasalso conducted during the second phase of SIP-adus, and allof the target values were achieved within the scope of theuse cases and prerequisites defined in the research project,
and we were able to confirm the usefulness of the examined
system architecture, although it depends on the applicationand conditions.(1)In addition, From FY2019 to FY2021, we defined use
cases that use communication as a base, clarified thecommunication requirements, and studied communication
methods that satisfy the communication requirements, forthe purpose of "Research for V2X Communication for
Cooperative Driving Automation."First, in FY2019, we defined 25 use cases where wirelesscommunication systems are expected to be used incooperative automated driving (SIP use cases for
Cooperative Driving Automation) and clarified theircommunication requirements.The Japan Automobile
Manufacturers Association, experts, and related ministries
and agencies participated in the discussions to formulate theuse cases, and we also received cooperation from many
related organizations such as the ITS Info-communications
Forum during the study of the communication methods.
From FY2020 to FY2021, we conducted a technicalexamination of the requirements of communication at eachfrequency band and for communication method, related to
new communication technology in use cases that requirecommunication for automated driving, with the aim ofrealizing a safe and comfortable automated driving society
through vehicle-infrastructure cooperative driving
automation technolog y that utilizes traffic signal
information from traffic infrastructure installed on
prefectural and municipal roads and merging lane assistancei n for m at i on on e x pre s s w ay s . We t h e n c re at e d acommunication method roadmap that realizes the use cases
for cooperative driving automation using more advanced
V2X communication, based on the implementation periodof the communication and the adoption rate of automatedvehicles. (Fig.2) This roadmap shows that the introductionof a new communication method will be necessary fromaround 2030 in order for there to be a 30% adoption rate ofcooperative automated vehicles by around 2040.(1)In FY2022, we investigated and analyzed the latest
trends regarding the institutionalization and standardizationof 5.9GHz band V2X communication in areas such as
Europe, the United States and China, and examined thecommunication requirements of a 5.9GHz band V2X
system, based on the results of the examination in theprevious fiscal year and taking into consideration theconnection with the existing 700 MHz band safe driving
support system.Then, based on the evaluation results of thecommunication simulation, we designed thecommunication protocol of this system, and devised thespecifications of the wireless equipment necessary for thedevelopment and manufacturing of on-board equipment
and roadside unit.

3

MIC Study of 5.9GHz Band V2X
Communication

In order to realize automated driving, securing a new
frequency band is an urgent matter in order to introduce a
new V2X system that can handle the rapid increase incommunication traffic.MIC has conducted technicalexamination work targeting the 5.9 GHz band, which is afrequency band that is being studied and commercialized
for V2X systems internationally.
In FY2020, we started to study the technical conditions
related to frequency sharing with existing wireless systems,
which will be necessary when introducing a V2X system.In
FY2021, in addition to demonstrations aimed at examining
the sharing with existing wireless systems that use the 5.9
GHz band and adjacent frequencies (ETC, Wi-Fi, wirelesssystems of broadcasting businesses, etc.), we conducted asurvey of the actual operation of the existing wirelesssystems and considered introduction measures in response.
The results were carried over to the examination under thesecond phase of SIP-adus and accelerated the problem
resolution and examination regarding 5.9 GHz band V2X
communication.

4

Conclusion

In order to realize ITS and automated driving, it will be
ess ent ia l to de velop infor mat ion communicat ion
infrastructure since it is expected that a large amount of
real-time data transfer and exchange will be required.
Furthermore, research and operational tests are being
conducted in various countries toward the realization of
automated driving systems that utilize not only ITS but also
LTE and 5G, so the advancement of 5G and otherinformation communication infrastructure is also necessary
in order to respond to the needs of automated driving and

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Overview of Activities of the Ministry of Internal Affairs and Communications
2025-

20202022 (R4)USA

2023 (R5)

2030-

2035-

2040-

2024 (R6)

Change in allocation of 5.9 GHz frequency/technology(Disputing reduction of V2X bandwidth and transition from DSRC to C-V2X)

Transition from 5.9 GHz band DSRC
to C-V2X completed(FCC)(Reference) International trends

Development of V2X
safety evaluation criteria

Study of protocols(EuroNCAP)(EuroNCAP)

EuropeStudy of advancement of ITS-G5

Start of deployment of V2N services in 5G (DG MOVE)
V2X becomes a
subject ofassessment(EuroNCAP)R2R communication network construction using RSUs
(Czech implementation plan in C-Roads)

Start of mediation use case
deployment(DG MOVE)(C2CCC)

Fully automated driving realizedon all roads after 2040
(DG MOVE)

ITS-G5 Acceleration of RSU implementation (C-Roads)
Intelligent connected vehicle (ICV) adoption

China

PA/CA: 50% of all automobile sales
HA level automobile market introduction
Percentage of new vehicles equippedwith C-V2 terminals: 50%

Formulation of IEEE 802.11bd

International

CA: Conditional automated driving (equivalent to L3)
HA: Highly automated driving (equivalent to L4)(IEEE)
Formulation of 3GPP Release 18(3GPP)JNCAP *1 Additional new item study, survey research, study of testing and evaluation methods

Japan(China Society of Automotive Engineering)
PA/CA: 70% of all automobile sales
HA:
20% of all automobile sales
Achievement of basic adoption of vehicles
equipped with C-V2X terminals
PA: Partially automated driving (equivalent to L2)

Preliminary testing*1 Driving support technology, including V2X, becomes subjectof automobile assessment

Frequency reorganization action plan +30 MHz for next-generation mobility

AssumedCommunication
technology

700 MHz band ITS

New communication technology (5.9 GHz)A) Approach to the practical application of the use cases

Study of security andprivacy policyA-1) Formulation of
security and privacy
measures

Study of specifications forsecurity and privacy measures

Security scheme study

Issuance of specifications for security and privacy measures
Security scheme formation

Study of policies for sharing in-vehicle devices

A-2) Formulation ofpolicies for sharing invehicle devices

Study of policies for generational change ofcommunication systemsA-3) Formulation ofpolicies for generationalchange ofcommunication systems

Study on how to establish a business modelA-4) Establishing abusiness model
Study of measures to ensure communication qualityA-5) Ensuringcommunication qualityA-6) Clarification of
responsibilities

B) Approach to the practical application communication technologies(5.9 GHz band)B-1) Frequency
utilization

Organization of concept of division of responsibility

Study of channel andbandwidth allocation
Frequency utilization(if necessary)Communication protocol draft study

Communication
standard formulation

Communication
standard issuanceStudy of application specificationsB-2) Standardization,
specification

OBU draft study

Issuance of application specifications (and subsequently, issuance inorder according to the timing of application deployment)

Study of OBU common specifications

OBU connectivity confirmation scheme
formulation

Issuance of OBU common specifications
Study of RSU common specifications
Issuance of RSU common specifications
Product development
Test operation

Equipping of 5.9 GHz OBU

Adoptionrate 30%

Adoptionrate 50%

Deployment of 5.9 GHz RSU

B-3) Equipment
development andpractical application

V2I (broadcast)
V2V (broadcast: continuous)
V2V (broadcast: urgent)

V2I(mediation)

V2V(Negotiation)

Fig.2: Roadmap for V2X Communication for Cooperative Driving Automation (2)connected cars.
In the future, in addition to progressing with initiatives
aimed at the adoption and deployment of 5G, we areplanning to study specific frequency use policies for whensystems are introduced, based on the results of studies
through SIP and other activities related to the 5.9GHz band
V2X communication system, while considering the existing
wireless systems on the same frequency band.If the service
providers of this system are defined and the system is
introduced into the above mentioned frequency band, weplan to assign a frequency allocation to V2X
communication after securing the necessary frequency
bandwidth by migrating the existing wireless systems.

[References]
(1) SIP 2nd Phase :Automated Driving for Universal Services—Mid-Term
Results Report (2018–2020), 2021.
(2) SIP Roadmap of V2X Communication Methods for Cooperative Driving
Automation - Activity Report of Task Force on V2X Communication for
Cooperative Driving Automation in FY2021[Conttacts]
Ne w G enerat ion Mobi le C ommunicat ions Of f ice L and Mobi leCommunications Division Radio Department, Telecommunications Bureau
Ministry of Internal Affairs and Communications, 2-1-2 Kasumigaseki,
Chiyoda-ku, Tokyo 100-8926, 03-5253-5111

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The Second Phase of SIP-Automated Driving for Universal Services

Overview of Activities of the Ministryof Economy, Trade and Industry
Shigekazu Fukunaga (Ministry of Economy, Trade and Industry)

1

Main Activities of the second phase of
SIP-adus

1.1. R esearch on recognition technology, required
for automated driving technology (Level 3, 4)
In this research, three universities of Kanazawa
University, Chubu University, and Meiji University aimed tofind out the level of support by infrastructure needed andthe recognition determination technology level by AI in
automated driving. Therefore, researchers developed testingvehicles with level 3 and 4 automated driving technology
and conducted FOTs (Field Operational Tests) for
infrastructure-vehicle cooperative driving that used V2I and
V2N, such as installing signal devices that can receive signal
information (lights) wirelessly, on the public roads of theTokyo waterfront area.
In the five years of the second phase of SIP-adus, theearly period of 2018-2020 was spent developing signal
devices and object recognition technology and studying
basic infrastructure conditions for infrastructure-vehicle
cooperative driving that used V2I. In the later period of2021 and 2022, researchers constructed an environment toevaluate automated driving systems by simulators in order
to execute safety assurance of automated driving technology
using simulation technology and infrastructure-vehicle
cooperative driving with V2N, which provides traffic signal
information and emergency vehicle information.
In a d d it i on , re s e arch e rs prov i d e d te s t - r i d i ng
opportunities for testing vehicles in the Tokyo waterfrontarea, etc. and conducted activities to deepen understandingof automated driving technology and communicate resultsof R&D.
Based on the knowledge gained from these FOTs, wepublished the data sets of inf rastr ucture supp or t
information needed for automated driving on prefecturaland municipal roads. We are planning to utilize these resultsin the "Project on Research, Development, Demonstration
and Deployment (RDD&D) of Autonomous Driving toward
the Level 4 and its Enhanced Mobility Services" (RoAD to
the L4) promoted by the Ministry of Economy, Trade andIndustry and Ministry of Land, Infrastructure, Transport
and Tourism.

2

Major activities for automated driving by the
Ministry of Economy, Trade and Industry

2.1. RoAD to the L4
Until now, the Ministry of Economy, Trade and Industry
took efforts with the Ministry of Land, Infrastructure,
Transport and Tourism to realize driverless platooning
driving technology on expressways and driverless automateddriving services limited only to certain regions. However,these efforts were constrained to limited technology,
services, and regions. Further activities are needed for thefull development of automated driving services. In order tofully propagate driverless automated driving services, it isimportant to link services with a business based oncomprehensive initiatives to enhance technologicaldevelopment, traffic environment, and social acceptance.
Based on this background, from fiscal 2021, aiming forthe realization and propagation of driverless automateddriving services, the government started a new project

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Overview of Activities of the Ministry of Economy, Trade and Industry

Initiatives for realization of automated driving (FOTs projects)
 Aiming for the realization and spread of driverless automated driving services, we are promoting “Project on Research, Development, Demonstration and Deployment(RDD&D) of Autonomous Driving toward the Level 4 and its Enhanced Mobility Services (RoAD to the L4)” along with relevant agencies.
 Aiming to realize driverless automated driving services in 40 locations in 2025, to practical use of level 4 trucks on the expressway, we are planning on
further expanding services to spaces where pedestrians and other vehicles mix, such as cities.

Realization of Level 4 mobility services at limited areas
・We aim to realize automated driving
services (level 4) only by remote monitoring in limited areas and vehicles by fiscal
2022.

・We also aim to establish system where one person monitors
four vehicles at the same time in order to enhance businessfeasibility.

(Concept) Eiheiji-cho: Remote
automated driving system

Responses to expansion of area/vehicles
Expand area/vehicles

Promotion of testing to realize automated driving services(level 4) by only remote monitoring

Implementation of advanced logistics systems @ expressway
・We aim to realize platoon driving thatutilizes level 4 automated driving trucks on
expressways by 2025.

・Recently, we identified use cases and areas to prioritize.
Based on this, we are considering the nature of new Automated driving on expressway( Concept image)main line logistics systems, including vehicles.

・Specifically, we conduct testing to install automated driving
system in medium-sized buses, etc. and establish prototypical use
cases.

Establish services in mixed space
Mixed space support

Initiatives to implement high-performance trucks,
including platoon driving in expressways

Further initiatives to expand target area and vehicles as well as
improve business feasibility
・In order to realize driverless automated driving
services in at least 40 locations by fiscal 2025, westudy for expanding the driving environment
and improving business feasibility.
(Concept)
Automated bus

Mixed space support

Initiatives to link vehicle-to-vehicle and pedestrian-to-vehicle
and cooperated infrastructure to expand level 4 in mixed spaces

・In order to expand level 4 automated driving services in mixed
driving environments (mixed spaces) from 2025, we aim toestablish system that connects infrastructure and other vehicles.
・First, we will establish use cases that need linkage with infrastructure,and deliberate data held by vehicles and infrastructure (dynamic
surrounding situation), then develop them to tests.

Driving assistance from
infrastructure (Concept image)Fig.1: Overview of RoAD to the L4

RoAD to the L4 (Fig.1) with related agencies to takecomprehensive efforts from R&D to FOTs and social
implementation. (Refer to S ection 8 3) "SIP-adus
Achievement as Heritage and Next Step" for more details).
In the future, based on automated driving mobility
services shifting to social implementation phases, thefollowing goal was stated at the 2022 Automated Driving
Business Symposium held in March 2022: "In order torealize automated driving mobility services designed to leadresolution of social issues such as: secured mobility amid
depopulation and aging society, dissolution of traffic
accidents and congestion, and contribution to carbon
neutrality, we are going to work on primarily 4 issues,
namely technological development, improvement oftransport environment, enhancement of social acceptance,
as well as acceleration of business implementation, under
Road to the L4. The outcomes of this project will be
developed and shared nationwide then advanced toward
steady social application of the technologies always watchinginternational trend."
Under this circumstance, the Road Traffic Act wasrevised in April 2022 and we had some good prospect forrealizing level 4 driverless automated driving mobility
services in Eiheiji-cho, Fukui Prefecture. On the other hand,scenes where automated driving is actually needed are
expected to include multiple types of traffic participants,
such as pedestrians, bicycles, motorcycles, and large
vehicles. Therefore, efforts are needed in incorporating thetechnologies gained until now to realize automated driving

mobility services that fit diverse traffic environments.
This project aims to fully spread the services in aroundfiscal 2030 in promoting these activities, then implementing
such automated driving mobility services at 40 locations by2025 to build up models for cost reductions and business
implementation with know-how and outcomes which will
have been resulted from these activities and will contributeto enhance technological development, traffic environmentand social acceptance.
2.2. SAKURA ProjectTo achieve practical use of automated vehicles, we must
develop new safety assurance methods for driving byautomated driving systems, in addition to the conventionalrecognition of safety with driving by human drivers.
However, there are no such established international
methods. Each country is actively participating in thedevelopment of international regulations (WP29) andstandards (ISO) related to safety assurance.
Until now, the Ministry of Economy, Trade and Industry
has worked with the Ministry of Land, Infrastructure,
Transport and Tourism on the SAKURA (Safety Assurance
KUdos for Reliable Autonomous Vehicles) Project (Fig.2) togather traffic data required to construct the safety assurance
platform of automated vehicles and establish safetyassurance scenarios. The outcome of this project was led tothe development of the "Automated driving safety assurance
framework Ver. 2.0" by the Japan Automobile Manufacturers
Ass o c i at i on i n 2021, w hi ch w as prop o s e d to t he

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Safety assurance traffic flow scenario data preparation flow
Parameter definitions
for each scenario

Measure/data processingC) Preparation of safety standard scenario
Systemization of scenarios

High speed 24 scenario

ScenarioDBB) Real environment observation DB

Data collecting vehicle

Fixed camera

Gathering/analysis oftraffic disturbance data

Test scenario preparation
for safety assurance

Accident/near
accident data
Driving/traffic flow
observation data

Testing
Testing of traffic
environment completeness

Test results

Simulation
Test courses
Public road

Accidents/near
accidentsthat actuallyoccurred

Scenarioformation

SafetyassuranceA) Grasp safety standards
Driving behavior
data

Driver operation
behavior

Fig.2: Overview of SAKURA Projectinternational regulations (WP29) and standards (ISO).
Under the leadership of Japan, a safety assurance framework
which is based on scenarios targeting highways was issued
as an international standard, namely ISO 34502.
Succeeding the R&D results of the DIVP® (Driving
Intelligence Validation Platform), we will develop and apply
an infrastructure platform and persuasive real data that areboth fair and transparent as we aim to make the safetyassurance methods of Japan international de facto standards
in cooperating with other countries. Specifically, we willconduct analyses and research for next international
proposals, such as deploying safety assurance methods
developed for expressways on prefectural and municipal
roads, and contribute to establishing safety assurance
mechanisms shared across the globe, including traffic onprefectural and municipal roads and level 4 automateddriving.

3

driving services and advanced trunk line logistics systems.
In addition, we must organize the topics and issues that are
to be taken over from the second phase of SIP-adus to
RoAD to the L4, construct a structure that allows affiliatedagencies to work together closely, and aim for collaborationin the "Construction of smart mobility platform," for the
next phase of SIP.

[Contacts]
ITS and Automated Driving Promotion Office Automobile Division,
Manufacturing Industries Bureau Ministry of Economy, Trade and Industry,
1-3-1 Kasumigaseki, Chiyoda Ward, Tokyo 100-8901, 03-3501-1618

Conclusion

The spread of automated driving will secure means ofmobility in regions where aging society and population
decline are worsening and lead to support for labor
shortages in the logistics field. It can also be expected toreduce accidents and congestion and contribute to carbon
neutrality by improving traffic flows. On the other hand, forthe spread and popularization of the technologies, otherthan technological development by an individual company,
it is important for industry, academic, and government towork together in cooperative areas to improve traffic
environment including revisions of relevant lows, establish
standardized safety assurance methods, constructsustainable business model, and foster social acceptance.
Based on this perspective, we must proceed with theinitiatives in the RoAD to the L4 project and accelerate
efforts targeting the construction of driverless automated
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The Second Phase of SIP-Automated Driving for Universal Services

Overview of Activities of the Road
Bureau, Ministry of Land, Infrastructure,
Transport and Tourism
Masamitsu Waga
(Road Bureau, Ministry of Land, Infrastructure, Transport and Tourism)

1

services, especially in rural areas. The development andpopularization of automated driving is expected to solve
problems such as securing the flow of people and goods
while ensuring safe and smooth road traffic. With this inmind, t he Ro ad Bure au of t he Minist r y of L and,Infrastructure, Transport and Tourism is undertaking
numerous initiatives to support road infrastructure.
Automated driving technology development can bebroadly divided into two types: "privately owned vehicles"
and "logistics/mobility services." (Fig.1) The former runs on
the nationwide road network, while the latter runs in alimited area. As such, there are differences in purpose ofuse, suitable areas and speed, meaning the required
technology to support automated driving and the scenarios
for development and spread are also different. In thefollowing, we outline our undertakings for each scenario.

Introduction

The realization of automated driving requires not only
vehicle technology but also appropriate support from roadinfrastructure with consideration of status of development
and spread of automated vehicles. Looking at the situation
overseas, such efforts in the arena of road-vehicle
coordination are accelerating; in Europe C-ROADS, an
implementation project related to such developments as
automated driving, is being deployed on roads in 43 cities,
while in Las Vegas, USA, infrastructure has been establishedto provide information from cameras and sensors.
In Japan, the population is declining and increasing inage. In addition to the shortage of workers, such as drivers,
it is becoming difficult to maintain public transportation
SAE* Levels of Driving
AutomationTM

Practical application of logistics/mobility services
Fully automated driving

Level 5

Logistics/mobility services
Level 4Address depopulation
Address driver shortages
Freedom of movement

Level 3

Resolve social issues(Unmanned transport vehicles in factories)(Golf carts)

Level 2

(Auto-breaking)Level 1

(Pedal error control)

Privately owned vehicles

Expansion of operational
domain from highways togeneral public roads

Traffic accident reduction
Traffic congestion reduction
Vehicle value improvement

International cooperation
economic development(Regions, roads, environments, traffic conditions, speeds, drivers, etc.)

Restricted

Unrestricted*SAE (Society of Automotive Engineers): U.S. based standard developing organization

Fig.1: Scenario for privately owned vehicles and logistics/mobility services

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2and creating exclusive lanes, and then confirmed theseeffects too. Based on the test results, full-scale introduction
has started at four locations nationwide. (Fig.2)

Activities in logistics and mobility
services

2.1.Field Operational Tests of automated driving services
based at such locations as Michi-no-Eki (Roadside
Stations) in rural areas
There are rural areas where public transportation routes
are shrinking due to population decline and driver shortage.
In response to these issues, the introduction of automateddriving is expected to maintain and secure regional traffic.
The SIP-adus (Cross-ministerial Strategic Innnovation
Promotion Program (SIP) Automated Driving for Universal
Services) conducted short-term to long-term FOTs (Field
Operational Tests) for automated driving services at 18
locations nationwide, based at such locations as Michi-noEki in rural areas.
Among the issues identified during the FOTs, regarding
the problem of identifying the position of the automatedvehicles, we confirmed the effect of the supporting
infrastructure for automated driving such as electromagnetic
induction lines. In order to avoid collisions with othervehicles and pedestrians, we also devised various measuresaccording to the road conditions and traffic characteristicsof the region, such as clearly indicating the driving position(Short-term FOTs)
FY2017-FY2018 : 18 locations(Long-term FOTs)
from FY2018 : 9 locations(social implementation)from FY2019 : 4 locations

Michi-no-Eki "Koigakubo"
Michi-no-Eki "Akagi Kogen"

Michi-no-Eki "Kamikoani"

Michi-no-Eki "Cosmall Taiki"

Takahata Town Office

2.2. Deployment of automated driving services fromrural areas into "machinaka" (town areas)
In order to realize safe and smooth automated driving ina more complex road traffic environment, the Road Bureauof the Ministry of Land, Infrastructure, Transport andTourism is conducting FOTs of Vehicle-to-Infrastructure
cooperative systems in multiple towns with the aim ofrealizing automated driving in Machinaka, town areas. We
are studying a mechanism to generate necessary information
relying on AI analysis of road surveillance cameras andtraffic safety measures. (Fig.3)

33.1. Merging assistance
We are also researching merging assistance to realize
safe and smooth automated driving on expressways. By
providing main lane traffic information (vehicle position,
speed, inter-vehicle distance, etc.) in advance to merging
vehicles, it is possible to achieve safe and smooth merging.
(Fig.4)The Japan Automobile Manufacturers Association(JAMA) envisions four phases of merging support, in
accordance with how much implemented automatedOrataru Yamakoshi Restoration Exchange Center
Michi-no-Eki "Nishikata"

Michi-no-Eki "Taira"Michi-no-Eki "Hitachiota" andTakakura Community Center

Michi-no-Eki "Meiho"
Kusu Komorebi no Sato

Vehicle to infrastructure (V2I) communication

Michi-no-Eki "Southern Alps Mura Hase"

Miyama City Hall
Yamakawa Branch

Provision of information to merging vehicles
such as the speed of main lane vehicles

Michi-no-Eki "Donguri no Sato Inabu"
Michi-no-Eki "Ashikita Dekobon"

Undertakings for privately owned
vehiclesDetection of speed etc. of
main lane vehicles

Generates information to be provided tomerging vehicles (automated vehicles)

Michi-no-Eki "Nishiiya Kazurabashi Yumebutai"
Michi-no-Eki "Imoko no Sato"

Vehicle detection sensor

Michi-no-Eki "Okueigenji Keiryu no Sato"

Roadside processing unit

Fig.2: Main FOT locations

Fig.4: Concept for merging assistance■Characteristics of the Machinaka, town areas
Road space (difficulty)◯ Limited road space◯ Various roadside land uses, etc.

Traffic environment◯ Complicated and enormous transportation demand◯ Diverse transportation modes, etc.

Road space (advantage)◯ Many existing sensors (cameras, CCTV, etc.)
are installed, etc.

■Countermeasures
Traffic safety measures◯ Package countermeasures against risks that are difficult forvehicles to deal with based on regional characteristics◯ Cooperate with "Zone 30" policy, the curve side
management, and Shared-space

Measures for bicycle lanes andon-street parking

Clarification of area to passthrough in intersection

Vehicle development and popularization◯ Get involved in plans related toregional public transportation andcommunity development◯ Establish long-term strategy with perspectiveof rising level of driving automation

Vehicle-to-Infrastructure cooperative systems◯ Build a highly versatile system that utilizes
existing systems to flexibly respond to thedevelopment and spread of vehicles

Transfer of analysis data

Immediate analysis of camera images

Fig.3: Investigation of traffic safety measures and information provision from the roadside

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No Information

Autonomous (non-cooperative automated) driving relying on information In case of sudden deceleration of leading vehicles, the following cannot avoid to
only from front view of the vehicle detected through its on-board sensor.
do likewise or might not be able to smoothly change lane.
Lane regulation

Automated vehicle

By obtaining lane-level information beforehand, thevehicle can change lanes with well prepared mannerWith Information provision

Vehicles need to know:-- Which lane is better to drive?
-- When is the better timing to change lane?
→Lane-level road traffic information is needed tomake determination for the above.

Solutions forthe needs

Efficient information generation with combined different information sources:○ Probe vehicle information Area-covering and detailed information○ Information provided by Road and traffic administrators Information
related to incidents impossible to detect by vehicle-itself.
○ Other information (emergency call and others) Urgent information
grasped with high immediacy

Fig.6: Conceptual Fig.for acquisition of lane-level road traffic information

vehicles are. We mainly studied on 2 systems: Day 1 system,
assuming as early phase of implementation, combining spotmeasurement of main lane traffic and provision of mainlane inter-vehicle gap forecast information, and Day 2 as
i mp l e m e nt at i on p h a s e c ombi n i n g are a - c ove r i ng
measurement of main lane traffic and continuous provisionof inter-vehicle gap information on main lane. Specifically,
FOTs for the Day 1 system were conducted in the Tokyo
waterfront area, while the Day 2 system was tested throughsimulation.
As a result of these studies, we confirmed that safer andmore efficient merging could be realized in free-flow toslightly dense traffic flows by distributing the movement of
main lane vehicles to merging automated vehicles andhaving them adjust their speed accordingly.
3.2. Traffic prediction support information provision
Automated vehicles can be driven safely even undervarious conditions when using on-board sensors to detectand analyze such data as vehicle position, road conditions,and obstacles. On the other hand, especially during highspeed movement, data on more distant traffic is also
required, but is difficult to detect with just on-board sensors.
(Fig.5)

Merging information

To provide main lane traffic data (lane-level vehicle
position, speed, etc.) to merging vehicles
Vehicle to infrastructure (V2I) communication
ProvideGenerate
Roadside processing unit

Construction regulation information

To provide location of construction
regulations, blocked lanes, etc.

and lane changes can be made with well prepared manner,enabling safer and smoother driving. In order to deal withthese issues, we conducted tests in the Tokyo waterfront
area.
The FOTs involved the creation of a system to aggregate
probe vehicle data collected by the private-sector, and thegeneration of lane-level congestion tail positions from thisaggregated data. The tests revealed the effectiveness of thesystem, but issues were also identified, such as the age andvolume of available probe data having a large impact on theerror in the generated congestion tail position (the deviationfrom the actual congestion tail position). In order toimprove this accuracy, it is necessar y to secure an
appropriate volume of recent probe data, improve theprocessing logic, and supplement the data. (Fig.6)
In addition, the Road Bureau of the Ministry of Land,
Infrastructure, Transport and Tourism launched their " Joint
Research for Collaboration with Roads to promotewidespread use of automated driving" in 2021, in order toproceed with the study of safe and smooth automateddriving on expressways. We plan to continue to study forsupport from the road toward the realization of automateddriving, including research on predictive information.

Falling objects information

To provide information on falling objects
on the road as obtained by on-board
sensors and road administrators
Position and lane of falling object[Contacts]
ITS Policy and Program Office, Road Bureau, Ministr y of Land,
Infrastructure, Transport and Tourism, 2-1-3 Kasumigaseki, Chiyoda-ku,
Tokyo 100-8918, 03-5253-8111

Collect Vehicle detection sensor

Fig.5: Conceptual Fig.for traffic prediction support information provision

If the traffic conditions further ahead in each lane can beobtained in advance, early deceleration can be performed
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The Second Phase of SIP-Automated Driving for Universal Services

Overview of Activities of the Road Transport Bureau,
Ministry of Land, Infrastructure, Transport and Tourism
Yoshitaka Tada
(Road Transport Bureau, Ministry of Land, infrastructure, Transport and Tourism)

1

Overview of the Activities by Ministry ofLand, Infrastructure, Transport and Tourism

The public and private sectors are working together todevelop and disseminate automated driving technology. In
particular, we recognize that the framework of SIP-adus(Cross-ministerial Strategic Innovation Promotion Program(SIP) Automated Driving for Universal Services) has further
strengthened the cooperation system among related
ministries and agencies, and has accelerated technological2017 Feb

Establishment of certification system for relaxingof standards for automated driving FOTs.

By relaxing standards, on the condition that safe alternative
measures are taken, it becomes possible for remote-controlled automated vehicles to run on public roads.
2018 Sep Formulation of safety technology guidelines for automated vehicles.
The safety requirements that level 3 and 4 automatedvehicles should meet are summarized as guidelines,
allowing for continued development and practicalapplication of automated driving vehicles prior to thethorough establishment of safety standards.
2019 May Revision of the Road Transport Vehicle Act
Added level 3 and 4 automated driving systems with thefunction of substituting all of the abilities related torecognition, prediction, judgment, and operation normallyassociated with a driver, to the list of equipment subject tosafety standards.
2020 Mar Establishment of safety standards for automated vehicles
ahead of the rest of the world.
2020 Jun Establishment of international standards equivalent todomestic standards<Summary of safety standards for automated driving on expressways, etc.>● Operates in traffic jams of 60 km/h or less on expressways, etc.
● Limited to lane keeping
2020 Nov World's first Level 3 automated vehicle type designation

Outline of Honda Legend automated driving system *Provided by Honda Motor Co., Ltd.

Fig.1: Undertakings by the Ministry of Land, Infrastructure,
Transport and Tourism

development.
The Road Transport Bureau of the Ministry of Land,
Infrastructure, Transport and Tourism is promoting theestablishment of systems, such as the formulation of safety
standards, in accordance with the progress of automateddriving technology. (Fig.1)

2

Basic approach to vehicle safetymeasures

Vehicle safety measures are being implemented step bystep in accordance with the status of technological progress
and technological spread. (Table 1) For example, during thetechnology development stage, the direction of development
is discussed with the participation of industry, academia,
and government, with the aim of creating an environment
in which it is easy for automobile manufacturers and others
to introduce new technologies into the market, and we arepromoting efforts such as formulating technical guidelines.
The international harmonization of standards is also
absolutely vital because automobiles are internationally
Table 1: Approach to safety measures
Phase

Purpose of measures

Measures(1) Technology
development

Developing an environment thatfacilitates the introduction of
new technologies into the market

Formulation of ASV promotion
plan and technical guidelines(2)
Technologicalcompetition

Promoting technologicalcompetition in the market

Vehicle assessment (comparisonand publication of performance
between vehicle models)Expansion of installation
through incentives

ASV subsidy, ASV tax system

National certification of
performance

Performance certification system

Installed in all vehicles, ensuring
at least minimum performance

Formulation of (compulsory)
safety standards(3)
Popularization(4) Standard
installation

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distributed products. For this reason, the World Forum for
Harmonization of Vehicle Regulations (WP29) was
organized under the United Nations Economic Commission
for Europe (UNECE) to discuss international standards.
From the viewpoint of ensuring the international
competitiveness of Japanese manufacturers, it is importantfor Japan to lead this international discussion.
Based on these basic approaches, we are also proceedingwith the establishment of systems related to automateddriving technology.

3

Formulation of safety technology
guidelines for automated vehicles

The year 2018 was a milestone year for the realization of
automated driving. The "Outline on improvement of legal
system and environment for automated driving system"(April 2018) was formulated, and the direction of the entire
government regarding the review of the legal system related
to automated driving was summarized. As a result, Japan
has accelerated its efforts toward the earlycommercialization of Level 3 and Level 4 automatedvehicles.
For the practical application of automated vehicles, it isnecessary to establish new safety standards and evaluation
methods that correspond to the operation of the vehicle by
the system. International discussion on this topic is
proceeding with the WP29, in which the Ministry of Land,
Infrastructure, Transport and Tourism is participating.
The Ministry of Land, Infrastructure, Transport andTourism, taking into account such developments as such as
the formulation of a Japanese system development outline,
seeks to promote the practical application of automateddriving even before the international standards areformulated. Guidelines have therefore been created inregard to the safety requirements that Level 3 and 4
automated vehicles should satisfy. We believe that this hascreated an environment that facilitates development byautomobile manufacturers and others, and that we haveestablished a framework that allows us to reflect Japanese
ideas in international discussions.

4

Revision of the Road Transport
Vehicle Act

At the Ministry of Land, Infrastructure, Transport andTourism, and based on the Road Transport Vehicle Act,
safety is ensured in an integrated manner from the design

and manufacturing stages of vehicles through to the stage ofuse on public roads. Specifically, we are working on
formulating safety standards and measures for typecertification, service and maintenance, inspections, andrecalls and others.
On the other hand, this law was created in 1951, and atthat time it was not considered that some non-humansystem would also operate vehicles.
For this reason, the law was revised in May 2019 toaccommodate automated vehicles (Level 3 and Level 4). The
main revisions are the following four points.
(1)When used under the conditions (driving environment
condit ions) att ache d by t he Minister of L and,Infrastructure, Transport and Tourism, automateddriving systems are defined as having the function ofsubstituting all of the abilities related to recognition,
prediction, judgment, and operation related to theoperation of the driver, and said systems are now also
made subject to safety standards.
(2)Approval from the Minister of Land, Infrastructure,
Transport and Tourism is compulsory when performing
wireless software updates for vehicles that are alreadyrunning on public roads.
(3) Maintenance and remodeling of automated driving
systems etc. will be designated as "specific maintenance."
In order to carry out it, certification from the director of
the District Transport Bureau will be required, andautomobile manufacturers will be duty-bound to provide
the technical information necessary for maintenance.
(4)Establishment of an environment in which inspection
agencies nationwide can utilize the information necessaryfor electronic inspection of vehicles (OBD inspection).

5

Formulation of safety standards

The revisions to the Road Transport Vehicle Act madeautomated driving systems subject to safety standards.
Based on this change, in March 2020, safety standards forautomated vehicles were formulated for the first time in theworld, based on the safety requirements of the safety
technology guidelines for automated vehicles. In June of thesame year, international standards were established by the
WP29.
In order to realize more advanced automated driving
technology, subsequent discussions on international
standards were promoted by the WP29, and in November
2021, a revision was made to expand the applicable vehicle
types to all passenger cars, buses, and trucks. Furthermore,

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in June 2022, it was agreed to raise the upper speed limit to130km/h and to implement a lane change function only forpassenger cars. (Fig.2)
The Ministry of Land, Infrastructure, Transport andTourism will continue to lead discussions on international
standards.
[United Nations Regulations enacted in June 2020]
Lane keeping at 60km/h or less on expressways (level 3, passenger cars only)
Automated driving (level 3)
starts in congestion

Following the vehicle
ahead in the same lane

Automated driving ends when congestion
is cleared or exit is approacheddriving manners along the route taken.
The Ministry of Land, Infrastructure, Transport andTourism will continue studies toward the realization ofadvanced automated driving.

[Contacts]
Ministry of Land, Infrastructure, Transport and Tourism Road Transport
Bureau Engineering and Environmental Polic y Division, 2-1-3
Kasumigaseki, Chiyoda-ku, Tokyo 100-8918, 03-5253-8111[Revised in November 2021]
Expansion of applicable vehicle types: Passenger cars only All passenger cars, buses and trucks[Overview of revisions agreed in June 2022](1) Raised upper limit speed
60km/h or less

130km/h or less

(2) Addition of lane change function
Only lane maintenance in the same lane
→ Lane change is also possible (limited to passenger cars, etc.)
Fig.2: Study on international standards

6

Issues for future activities

In the future, in order to realize automated driving in amore complex driving environment, it is important to takecomprehensive measures to further develop vehicle
technology, improve the driving environment, and improve
public acceptance. (Fig.3) Specifically, it is the responsibilityof the operator to appropriately build, maintain, and manage
the driving environment, and to review it as necessary.
Therefore, it is important for the public and private sectors
to work together to study measures to reduce the risk of all
dangerous events that can occur on roads, with theautomated vehicles strictly adhering to all traffic rules and(1) Vehicle technology development
Technological developmentof software, sensors, etc.

(2) Improving social acceptability(3) Maintenance and management of driving environment

Local understanding and cooperation
Clarification of responsibilities of stakeholders

Infrastructure support
Pedestrian separation, etc.

Fig.3 Toward the realization of automated driving

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2

Establishment and Utilization of Traffic Environment Data

(1) Technological Development Concerning the Generation of Traffic Environment Data

Establishment and Utilization of Traffic Environment Data andthe Tokyo Waterfront Area Field Operational Tests (Overview)
Satoshi Hiyama (Honda Motor Co., Ltd.),
Masato Minakata (TOYOTA MOTOR CORPORATION)

1

Phase and Timing) information that changes every second.
In the SIP-adus (Cross-ministerial Strategic Innovation
Promotion Program (SIP) Automated Driving for Universal
Services), linking rules are established according to thelocation, time, and unique ID of each piece of information,
which is organized based on the concept of a dynamic map.
(Fig.2)

Need for road traffic environment
data

Automated vehicles use information collected bymultiple types of on-board sensors in an integrated manner
to recognize, judge, and operate the vehicle on behalf of thedriver. However, the information collected by such sensors
is limited to a range of approximately 200 meters around thevehicle, and only real objects can be recognized as physicalphenomena. Therefore, if the road traffic environment data
provided by infrastructure through wirelesscommunication, etc. can be utilized, it is possible to realize
safe and smooth operation in more complex road traffic
environments. (Fig.1)
Road traffic environment data includes various factors,
ranging from static information such as high precision 3D
map data to dynamic information such as SPaT (Signal

Dynamic data

Link

Semi-dynamic data
Accident information, traffic congestion information,
narrow-area weather information, etc.
Semi-static data
Scheduled traffic control data, road construction
schedule data, wide-area weather forecast data, etc.
Static data =High precision 3D map data

Base

Laser scanner

Radar

Information around thevehicle (other vehicles,
pedestrians, road signs,
lane markers, etc.)

Road surface data, lane data, 3D
structures, etc.

Fig.2: Concept of dynamic map

Mechanism of automated driving
On-board sensor data

ITS traffic prediction support information

Road traffic environment data High-precision 3D map data / Traffic signal SPat
information / traffic regulation information /Obstacle information / traffic congestion
information etc.

CameraIntegrated utilization on top of
high-precision 3D map data

Road sign
Lane link
Road marking
Edge marking✓Vehicle position estimation✓Travel route planning✓Advanced driver assistanceFig.1: Utilization of road traffic environment data in automated driving

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(1) Technological Development Concerning the Generation of Traffic Environment Data
Establishment and Utilization of Traffic Environment Data and the Tokyo Waterfront Area Field Operational Tests (Overview)

2

system that enables safer and smoother automatic driving
control by providing various road traffic environment data
prov i d e d by t he i n f r a st r u c tu re t h rou g h w i rel e ss
communication, etc. and information owned by the vehicle,
as well as the road-to-vehicle and vehicle-to-vehiclecommunication, it is necessary to synchronize vehicle
development with infrastructure development and tointegrate various technologies. For this reason, the plan wascentered on FOTs under actual traffic conditions to establish
a system for utilizing road traffic environment information.
(1) Determination of technical specifications and promotionof standardization in the cooperative area(2) Accelerated development of automated vehicles andinfrastructure(3) F ostering public acceptance of automated driving
through information dissemination and use in events
I n c o l l a b o r at i o n w i t h t h e Jap a n Au t o m o b i l e
Manufacturers Association (JAMA), we worked on thedevelopment of an internationally open FOTs environment
for the purpose of the items noted below:
In selecting the location, the following factors weretaken into account:(1) Moderate traffic volume and road traffic environment
suitable for public road experiments(2) C onvenience of transportation for domestic andinternational participants in the FOTs
(3) Appeal for the technologies using the opportunity of theTokyo 2020 Olympic and Paralympic Games.
The Tokyo Waterfront City area was selected from theviewpoint of the above reasons. Specifically, we conducted
FOTs in three distinctive areas: the Tokyo Waterfront City
area for automated driving on prefectural and municipal

Activities to utilize road traffic
environment data

In t he f irst phas e of SIP-adus, we worked on t heestablishment of a static information platform , and in 2019,
based on the results of the R&D, a project to provide highprecision 3D map information for approximately 30,000 km of
expressways and motorways nationwide was launched, and theinformation was adopted by advanced driving support systems
and the world's first vehicles equipped with automatic driving
level 3 systems that have acquired certification. In the second
phase of SIP-adus, the "Road Traffic Environment Data
Roadmap" (Fig.3) was formulated to promote research anddevelopment for the establishment of a system for utilizing
dynamic road traffic environment data that is linked to highprecision 3D map information, and standardization and practicaluse were tackled through FOTs (Field Operational Tests) in theTokyo waterfront area.
In addition, we worked on the National Police Agency's
policy: Improvement of Data Accuracy of Traffic Regulation
Information [See Section 2 (1) 5) for more details], and also theconstruction of a geographic data search portal (MD commune®)[See Section 5 (1) 1) for more detarils] to create new services byutilizing this road traffic environment data in other fields.

3

Overview of the FOTs in the Tokyo
waterfront area

3.1. Positioning of the FOTs

The first phase of SIP-adus

The Second phase of SIP-adus

What we aim to be

In the development of a cooperative automatic driving
Realization of Society 5.0(Construction of Cyber
Physical System)

Promoting the spread of high-precision
3D maps at a lower cost

Building a geospatial information market
Visualization and social application ofroad traffic environment data

Realization of advanced mobility
and logistics services

Practical application of advanced

Establishment of a searchportal for geographic data
Utilization of information held by other businesses(MD communet®)
Collection and utilization of prove information collectedby private sectors

automated driving throughinfrastructure coordination
Realization of safe and smooth drivingwith traffic prediction support

Construction of a mechanism for dynamic
data utilization (cooperative area)

Dynamic map Basic Initiatives
Establishment and Building-up of Static data

Establishment of a static
data platform

Reduce traffic congestion bycontrolling traffic flow

Road-to-vehicle cooperation, merging support, etc.
Traffic signal information
provision

FOTs in the Tokyo waterfront area related to roadtraffic environment information provision

Large-scale FOTs of highprecision 3D maps
〜2018

Accident reduction using
V2X communication

Real-time distribution of
traffic regulation information〜2022

Fig.3: Road Traffic Environment Data Roadmap

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Establishment and Utilization of Traffic Environment Data and the Tokyo Waterfront Area Field Operational Tests (Overview)

Chuo Ward
Koto Ward

Tokyo Waterfront City area

Minato Ward

Metropolitan Expressway
Ota Ward

Kawasaki Ward

Haneda Airport area

Fig.5: FOTs participating organizations (FY2019-2020)

Fig.4: FOTs area (Digital Topographic Map, GSI Map)

roads, the Metropolitan Expressway for automated drivingon expressways, and the Haneda Airport area for automateddriving services on public transportation (buses and ART)on prefectural and municipal roads. (Fig.4)
3.2. 2019-2020 FOTs
In consultation with JAMA, we decided to set a
milestone of a test drive event at the Tokyo 2020 Olympic
and Paralympic Games to clarify the effectiveness andtechnical requirements of V2I (short-range wirelesscommunic at ion: Vehicle to Inf rast r uc ture) SPaT
information and merging lane assistance information on
expressways, etc. The FOTs were conducted using thematching fund method, which aims to stimulate privatesector investment in automated driving and research anddevelopment. The SIP-adus set up and operated the roadtraffic environment data provision equipment and prepared
the on-board equipment to receive such information, while
the participants in the FOTs paid for the preparation of theirtest vehicles and the costs associated with the test driving,
including the personnel. Participants were called globallyfrom organizations promoting automated driving R&D. As
a result, a total of 29 organizations, including domestic andforeign automakers, suppliers, venture companies, anduniversity research institutes, participated in the FOTs.
(Fig.5)
As an FOTs environment using V2I, ITS wirelessroadside units were installed at 33 signalized intersectionsin the Odaiba area and 7 on roads around Haneda Airport,
and signal data was transmitted using V2I (760 MHz band).
(Fig.6)

Source: Geospatial Information Authority of Japan map (GSI Web)

Fig.6: Intersection with ITS wireless roadside units (Odaiba)

At the Airport West IC of the Metropolitan Expressway,an ETC gate/merging lane assistance information system
consisting of main line vehicle detection sensors, merging
lane assistance information generation servers, and V2I (5.8
GHz band) wireless systems were installed to collect andprovide information on the operation of the ETC gate andvehicles traveling on the main line. (Fig.7)
Antenna before entrance(ETC gate passage assistance information)

Antenna after entrance(merging assistance information)

Airport West Entrance
Test vehicle

Test vehicle

Passage closed
Main line vehicle information
Gate
information

Gate information
provision server

Passage of test vehicle +radio wave command

Vehicle sensor
processing unit

Mainline
vehiclesFig.7: ETC gate/merging lane assistance information system

As previously reported in "SIP 2nd Phase: AutomatedDriving for Universal Services—Mid-Term Results Report(2018–2020),"(1)(2)(3) the SPaT information provision FOTs
using V2I demonstrated that signal light color information
can be provided stably under various environments, and the36

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Establishment and Utilization of Traffic Environment Data and the Tokyo Waterfront Area Field Operational Tests (Overview)

participants in the tests confirmed and agreed theeffectiveness and practicality of its use in automated vehicles
with specifications compliant with ISO/TS 19091 while
maintaining compatibility with DSSS (Driving Safety
Support System), a service that is already in practical use.
The FOTs have provided a technical prospect for thepractical use of SPaT information in automated driving, butt he p ar t i c ip ant s c om me nte d t hat t he pr i or it y ofinfrastructure installation was not determined by thelocational conditions of the intersection, such as
backlighting due to the westering sun, and that "providing
area-covering" was essential. These results will be developed
to the RoAD to the L4 project for the realization of Level 4
automated driving services and will continue to be studied
for implementation. In addition, there were many requests
from FOTs participants to continue to utilize the Odaiba
area equipped with the whole area covering network as test
environment as a development base for automated driving
FOTs. In response to these requests, it was decided to utilize
the area for research and development for the next step in"Construction of smart mobility platform " as the next phaseof SIP.
On the other hand, as previously reported in the "SIP
2nd Phase: Automated Driving for Universal Services—
Mid-Term Results Report (2018–2020),"(1) as a result of the
FOTs of the merging lane assistance information provisionusing V2I, it was found that spot information provision is
not sufficient to provide accurate merging lane assistanceinformation even when the traffic flow changes, and a moreadvanced information provision system using continuous
main line vehicle detection and continuous mainline vehicle
detection and continuous communication technology isneeded. Therefore, we conducted a simulation based on datacollected from actual traffic flow observations to confirm
the feasibility of a merging lane assistance information
provision’ system that can respond to various traffic flow
fluctuations and to derive the system requirements. [SeeSection 2 (1) 4) for more details]demand for the provision of road traffic environment datawith network covering the whole area. That is why that, in
the FY2021 FOTs, in addition to the provision of SPaT
information using V2I, we also worked on the provision ofroad traffic environment data using V2N (public long range
network (V2N)). (Fig.8)
V2I

V2N

760 MHz band

LTE/4G
Cloud

Within 200-300 m

Public long range network services
provided by carriersFig.8: V2I/V2N communication system (for traffic signal information)

In addition to the need for utilization in automateddriving revealed through interviews, we also considered theeffectiveness of providing information to drivers, anddecided to work on the generation and provision of thefollowing four types of data as highly prioritized in roadtraffic environment data.
[Prefectural and municipal roads] SPaT information[Expressways] Lane-level traffic flow information[Common] Emergency vehicle information, rainfall
information
As wit h t he 2019-2020 FOTs, a bro ad ca l l forparticipants resulted in the participation of 22 institutions.
(Fig.9)3.3. FOTs in FY2021
Due to the spread of COVID-19 starting in 2020, theTokyo 2020 Olympic and Paralympic Games were
postponed to FY2021, and the large-scale automated vehicle
test-ride event that was originally planned in collaboration
with the Japan Automobile Manufacturers Association was
also cancelled. There was also no small impact on vehicle
development for each manufacturer, who requested toextend the FOTs schedule in the Tokyo waterfront area,
hence the FOTs were continued for FY2021. And reviewing
the results of the FY2019-2020 FOTs, there was a highFig.9: FOTs participating institutions (FY2021)

For the V2N FOTs, an information generation andprovision network environment was constructed with anarchitecture for assumed social implementation, andinformation was provided to test vehicles via public longrange network communications (4G/LTE). (Fig.10)
For SPaT information, a V2N information provisionenvironment was prepared for 33 signal intersections in
Odaiba where ITS Roadside Units (RSU) were installed,

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enabling comparative verification of V2I/V2N.
Data generation and aggregation

Predicted SPaT
information
simulated emergency
vehicle information
Lane level traffic
flow information
RainfallinformationData
aggregation
serverData management and distribution
Linkeddistributionfunction

Short andmedium-range
serversHigh
precision
map

LTE

Data conversion and vehicle control output
V2I terminal

GNSS
receiverMobilerouterOn-board
equipment for
FOTs

Automated driving
and driverassistance systems

Dynamic Map
viewer

Fig.10: V2N information provision network architecture

In the utilization of V2N information, transmission
delay via networks is expected to be an issue. For SPaT
information, which requires particularly high temporal
information accuracy, we developed technologies forgenerating signal cycle time schedule information for eachsignal control system method, studied the provision system,
and verified it in the Tokyo waterfront area FOTs after
confirming the functionality with a model system. (Fig.11)[See Section 2 (1) 2) for more details]
As a result of the FOTs, it was confirmed that amechanism for efficiently distributing information onscheduled signal cycles at necessary intersections along aroute f rom t he network to e ach vehicle could b e
implemented for use in automated driving vehicles.
Participants in the FOTs also commented that V2N can be
expected to contribute to CO2 reduction through optimalroute planning, green wave, etc. by utilizing traffic signal
information that is unique to V2N.
On the other hand, there are issues in the accuracy of
the generated signal cycle time schedule information andthe realization of a fail-safe feature, hence further researchand development are required for practical application.
Stakeholders will continue to discuss these issues in thetechnical committee established by the UTMS Association.
For lane-by-lane traffic flow information, we statisticallyprocessed private-sector probe data with road-way levelaccuracy that is now possible to be collected from moving
vehicles as the spread of connected cars, and generated lane-

Fig.11: SPaT information provision through V2N

Fig.12: Lane-level Traffic flow information provision using probe informationlevel traffic congestion tail information. Although there is adelay in the provision of information depending on theprocessing interval due to the statistical processing after acertain amount of probe vehicle data is collected andaccumulated, we confirmed that the system can generate
and provision information that is superior to the roadwaylevel traffic congestion information of existing services interms of its precision. (Fig.12) The accuracy of the generated
information is expected the further spread of connectedcars and the evolution of information collected fromvehicles equipped with advanced driver assistance systems
and automated vehicles. [See Section 2 (1) 3) for moredetails]
As for the utilization of information on emergency
vehicles, a V2V alert service has been put to practical use,
but it has not yet been widely adopted because it requires
dedicated radio equipment to be installed in both
emergency vehicles and vehicles that use the information.
In the FOTs in the Tokyo waterfront area, information
on emergency vehicles was generated and provided on asimulated basis. Although there was a transmission delay of
two to three seconds, participants in the FOTs noted thatthe system was effective in detecting the relative position
and relative speed of the vehicle and in determining whether
to avoid the vehicle when an emergency vehicle approached.
Some participants also suggested that the system would beparticularly effective for remote monitoring type automateddriving. (Fig.13)
Although not the FOTs in the Tokyo waterfront area,
one of the use cases of information utilization with V2N: tocollect information on the approach of public transportation(buses, etc.) and emergency vehicles to traffic signal
intersections, then to use this information for signal priority
control, the possibility of this case was also verified. As aresult of the FOTs in rural area, it was confirmed thatvehicles that should be given priority can smoothly passthrough signalized intersections. [See Section 2 (1) 6) for
more details]
For rainfall information, the Hig h-Res olution

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(1) Technological Development Concerning the Generation of Traffic Environment Data
Establishment and Utilization of Traffic Environment Data and the Tokyo Waterfront Area Field Operational Tests (Overview)Fig.13: Emergency vehicle information provision through V2NPrecipitation Nowcast service provided by the Japan
Meteorological Business Support Center was linked to ahigh precision 3D map and provided to the vehicles in the
FOTs. The ability to select an area and utilize theinformation, including forecast information, wasdemonstrated, showing that V2N enabled to utilize a wide
variety of road traffic environment data. (Fig.14)
Please switch to manual
operation.
Automated driving will be turned
off due to heavy rain.

Fig.14: Provision of rainfall information via V2N

3.4. Activities for information utilization through V2N
As described above, the provision of road traffic
environment data via V2N has potential for utilization ofwider variety of information and is promising. On the other
hand, there are many issues that need to be resolved before
practical application.
In the FOTs in the Tokyo waterfront area, we specifically
discussed how to use the road traffic environment data to
be provided in the context of increasing communication
traffic, and verified the information provision method withthe participants.
Information provision methods can be broadly classified
into the PULL method, which is suitable for high-volume,
low-frequency updates, and the PUSH method, which issuitable for low-volume, high-frequency updates. (Fig.15)
Through the provision of road traffic environment data in
the FOTs, it was found that communication traffic can be
reduced by properly using both methods. In addition,
network and server load measurements and simulations forsocial implementation were conducted, and the following
was identified as issues that should be taken into
consideration by business entities for a large-scale social

implementation before practical use.
・Realization of a system for efficient selection and use ofnecessary information
・Minimize information processing/transmission delays via
network and check impact
・Server capacity forecast and reduction of communication
data volume commensurate with the implementation of
the target service scale[See Section 2 (2) 3) for more details]
PULL system

PUSH system

ServerSpecify distribution
area from vehicle

Receive information on thespecified area from the server(PULL)

ServerPeriodic uplink of own
vehicle positionEventoccurrence
Distribute data for that area ascalculated by the server(PUSH)

Fig.15: Information provision method via V2N

In addition to these issues, the Task Force (TF) on V2X
communication for Cooperative Driving Automation has
discussed to find other issues in cooperation with academic
experts, relevant ministries and agencies, and members ofindustry organizations (automobile, electronics, etc.), andhas clarified issues to be addressed, such as the formulationof cybersecurity and privacy measures. [See Section 2 (2) 1)
for more details] We sincerely hope that industry, academia,
and government will share the roles of research anddevelopment, and deepen cross-industry discussion andcollaboration to realize the utilization of road traffic
environment data via V2N in the future.
3.5. S tudy of the system of information utilization
through V2N
The establishment of a system is also an important issue
for the utilization of road traffic environment data throughV2N. The European Commission has mandated theestablishment of National Access Points (NAPs) in eachcountry to collect and utilize mobility-related data throughnetworks, as well as a system to provide data using NAPs.
In Japan, it is necessary to continuously study particularcases of mobility data utilization as well as its advantages
and disadvantages related to automated driving andAdvanced Driving Assistance System of which the practicaluse will be accelerated. In the second phase of SIP-adus, westudied the ideal system for utilizing road traffic
environment data via V2N in Japan through R&D and
FOTs.
In the process of studies conducted through the second
phase of SIP-adus, V2N SPaT information needs to be as
accurate as highly required for provided information andalso to be implemented with a system allowing to give

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nationwide service under unified interface conditions, we
received such opinions and others through questionnaires
and interviews with experts, related companies, and others,.
We plan to examine these opinions and propose thenecessity of establishing a centralized SPaT information
center that will serve as the implementation entity, as well as
the requirements and conditions that the organization
should have. [See Section 2 (1) 2) for more details]
On the other hand, weather information, such as rainfall
data, is considered as an area where the information
provision business is expected to evolve and expand as acompetitive area based on the needs and use cases ofi n for m at i on ut i l i z at i on , i n clu d i ng n on - m o bi l it yapplications, and it is desirable to develop it withoutestablishing a unified information distribution system.
Based on the results of these studies, we plan to propose asystem for utilizing road traffic environment data throughV2N. [See Section 2 (1) 3) for more details]
3.6. Activities in fostering public acceptance
Information on the activities of industry, academia, andgovernment toward the realization of automated driving in
the FOTs in the Tokyo waterfront area was actively
disseminated through SIP-café, the Status Report Meeting,and other opportunities for public relations activities. In
addition, at a test-ride event aimed at fostering public
acceptance, a public road demonstration and test-ride ofLevel 4-equivalent automated driving test vehicles was
conducted as a result of the research and development ofeach company through the FOTs in the Tokyo waterfront
area , and publicized the current status of the research anddevelopment progress of automated driving technology andsome of the results of the SIP-adus program R&D. (Fig.16)[See Section 4 (2) 3) for more details]

development on the utilization of road traffic environment
data and the FOTs in the Tokyo waterfront area, we worked
to clarify the requirements for road traffic environment datanecessary for the realization of automated driving and toestablish a system for information generation and provision.
We have now the technical prospect of practical applicationof SPaT information provision through V2I, which isnecessary for the introduction of automated driving on
prefectural and municipal roads, and have developed thisprospect to the RoAD to the L4 project aiming for therealization of Level 4 automated driving services. We willcontinue to study for the implementation of the system inthe future. In addition, the FOTs environment in the Odaiba
area will be passed on to and used for the next step ofresearch in the next phase of SIP as the legacy we leave . In
order to meet future needs for the transmission of
information from area-covering network , we also
conducted research, development, and FOTs on the use ofvarious types of traffic environment information using V2N.
We demonstrated the effectiveness of the system and thepotential for utilizing the information provided, identified
the requirements and issues for realizing the system, andmade proposals for the construction of the system. We hopethat the results of the efforts in the second phase of SIP-adus
will be utilized to continue to promote efforts toward thepractical application of the system for utilizing road traffic
environment data.

[References]
(1) Masafumi Kobayashi, et al.: Traffic Signal Information Provision
Technology for Infrastructure-Based Cooperative Automated Driving,
SIP 2nd Phase: Automated Driving for Universal Services—Mid-Term
Results Report (2018–2020), pp. 16-23, 2021.
(2) Yoshiaki Tsuda et al.: Data Analysis of the FOTs in Tokyo Waterfront
City Area, SIP 2nd Phase: Automated Driving for Universal Services—
Mid-Term Results Report (2018–2020), pp. 54-62, 2021.
(3) Yoshiaki Tsuda et al.: Data Analysis of the FOTs on the Metropolitan
Expressway, SIP 2nd Phase: Automated Driving for Universal Services—
Mid-Term Results Report (2018–2020), pp. 68-72, 2021.

[Contacts]
R & D a n d E n g i n e e r i n g M a n a g e m e n t D i v. T OYO TA M O T O R
CORPORATION, 1 Toyota-cho, Toyota-shi, Aichi 471-8572, Contact:
Masato Minakata (masato _minakata@mail.toyota.co.jp)

Fig.16: Demonstration of prototype automated vehicles

4

Conclusion

In the second phase of SIP-adus, through research and40

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2

Establishment and Utilization of Traffic Environment Data

(1) Technological Development Concerning the Generation of Traffic Environment Data

1) The Tokyo Waterfront City Area Field
Operational Tests
Yoshiaki Tsuda, Takahiro Yoshino (MITSUBISHI ELECTRIC CORPORATION), Shinya Muroyama, Kentaro Isobe (AISAN TECHNOLOGY CO., LTD),
Kosuke Watabe, Katsuya Akimoto (Nippon Koei Co., Ltd.)(Abstract) From FY2021 to FY2022, as part of the Field Operation Tests (FOTs) in the Tokyo Waterfront area, an informationdelivery system that uses a public long range network (V2N) was set up and used to perform verification testing for fourtypes of traffic environment information: rainfall information, lane-specific road traffic information, mock emergency
vehicle information for vehicles on emergency calls, and predictive SPaT (Signal Phase and Timing) information. A totalof 22 organizations from Japan and abroad participated in these FOTs. These included motor vehicle manufacturers,
suppliers, venture companies, and universities. The participants evaluated the effectiveness of delivering traffic
environment information via V2N and identified the issues that were involved. PUSH and PULL methods of informationdelivery were set up, to be used as appropriate given the characteristics of the information to be delivered. Participantsevaluated the impact of increased communications traffic volume and the impact of delays caused by the network,
looking towards future practical implementation. The results of the tests were evaluated and verified by test participantsin FOTs working group meetings, and information regarding the effectiveness of traffic environment information and theissues involved were summarized.
Keywords: V2N, field operation tests, rainfall information, lane-specific road traffic information, mock emergency vehicle information for vehicles on emergency calls, predictive SPaT information

1

Overview of the FOTs

1.1. Objectives of the FOTs
As part of the FOTs in the Tokyo Waterfront area, aninformation delivery system that uses V2N communication
was set up and used to perform verification testing of rainfall
information, lane-specific road traffic information, mockemergency vehicle information for vehicles on emergency calls,
and predictive SPaT information with the aim of promoting
greater use of traffic environment information.

1.2. FOTs area
Fig.1 shows the testing area used in the FOTs. Tests of mockemergency
vehicle information for vehicles on emergency calls
Fig. 1
Source:Geospatial Information Authority ofJapan map (acquired on September
28, 2021)(2) Metropolitan Expressway(Expressway routes connecting Haneda Airport andthe Waterfront City area, etc.)

https://maps.gsi.go.jp/#12/35.632884/139.810982/&base=pale&ls=pale&disp=1&vs=c0j0h0k0l0u0t0z0r0s0m0f0(3) Joban Expressway (YatabeInterchange) to Shin-Tomei Expressway(Shimizu Ihara Interchange)(1) Waterfront City area
* Created using Geospatial Information Authority of Japan
light color map

Fig.1: FOTs Area

SIP通し.indb 41

and predictive SPaT information were performed in the
Waterfront City area (1). Lane-specific road traffic information
testing was performed in the Metropolitan Expressway area (2).
In order to evaluate rainfall information as long-range
information, testing was performed in areas (1) and (2) andalso in the Joban Expressway to Shin-Tomei Expressway area(3).

1.3. Test system
The FOTs system was created while envisioning thearchitecture that might be used in future real-world deployment.
In conjunction with participants to the second phase of SIPadus (Cross-ministerial Strategic Innovation Promotion
Program (SIP) Automated Driving for Universal Services)
project, a test system was created, consisting of a data generation
and aggregation section, a data management and delivery
section, and a data conversion and vehicle control output
section, as shown in Fig.2.
Each type of traffic environment information was aggregatedin a data aggregation server and linked with a high-accuracy 3D
map before being delivered to test vehicles via a short-range
and medium-range area server and LTE communications
network. On the vehicle side, the traffic environment
information that was received and the vehicle location
information was processed by on-board test equipment andoutput via a LAN or CAN interface to automated driving/
driving assistant system (prepared by the test participants).
Analysis software was also prepared, as was a dynamic map
viewer for displaying received information in real time.

SIP 2nd Phase: Automated0 Driving for Universal Services - Final Results Report (2018-2022)41

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data

Fig. 2

1) The Tokyo Waterfront City Area Field Operational Tests

Data management anddelivery section*3

Data generation andaggregation section*2
Rainfall information
Lane-specific roadtraffic informationData
aggregation
serverMock emergency
vehicle location
information

Highaccuracy Linkage andmap conversion of highaccuracy maps
Linkage
Short/mediumdelivery
range areafunction*1
serverPUSH
PULL

Traffic signal
prediction informationLTE

V2N device(Mobile router)
GNSS
receiver(V2I device)

Test vehicle onboard
equipment(BOX-PC)

Fig. 4 Fig. 4

information involved. This is why the information delivery
system was designed to use two delivery methods, PUSH and
PULL, as shown in Fig.4. PUSH delivery is well-suited to low
volume information that is frequently updated, while PULL
delivery is well-sui ted to high volume information that isinfrequently updated. In the FOTs, the method used to deliver
information was selected based on the characteristics of eachtype of traffic environmental information.

Dynamic map viewer(real-time display and log
data playback)

Event
Event

Automated driving anddrive assistance system(prepared by test
participant)

PUSH
PUSH

PUSH

Data conversion and output to vehicle control section (test vehicle-side)*1*1:Handled by “Implementation of FOTs in the Tokyo Waterfront Area” contractors (author of this report)*2:Handled by "Examination and Evaluation of Automated Driving Control Technologies that Use Lane-specific
Probes, etc." contractors*3:Handled by "Research and Development on the Collection, Integration, and Delivery of Short-range andMedium-range Information" contractors

PUSH method
PUSH method
MethodShort/medium
Short/medium-rangearea-rangearea
serverserver

Vehiclelocation
Vehiclelocationis isregularly
uploaded
regularly
uploaded1

Provided
when
event
Provided when
anan
event
occurs,basedonon
occurs, based
thethevehicle'slocationvehicle's location(upper-levelserver-side)(upper-level server-side)

Fig.2: Test system structure

1.4. Testing schedule
The FOT period extends from November 15, 2021, toDecember 23, 2022. (Fig.3) Rainfall information and predictive
SPaT information testing are being performed over the entire
PULL
PULL method Method
period. Due to information delivery period limitations,
PULL method
multiple, short tests are being performed for lane-specific roadtraffic information tests and tests of mock emergency vehicle
information for vehicles on emergency calls. Test participants
are holding FOTs working group meetings each month to verify
test results and discuss them with other participants.
Fig. 3
Item

2021
Nov.

Dec.

2022
Jan.

Feb.

Mar.

Apr.

May

June

July

Aug. Sept. Oct.

Nov.

Dec.

Rainfallinformation
Lane-specificroad traffic
information

FOTs

Mock
emergency
vehicleinformation forvehicles on
emergency
calls

Traffic signal
prediction
information
Field Operational
Test WG

Held every month/every other monthFig.3: FOTs schedule

Information is stored in
Short/medium
Information is stored in-range area
server in chronological order Short/medium
area
server in chronological order -rangeserver
server

VehicleVehicleindicates
area indicates
numberarea number

PULL
PULL

Acquired periodically,
Acquiredperiodically,
based onthebased
on thevehicle'slocationvehicle'slocation(vehicle-side)(vehicle-side)3

3

Fig.4: PUSH method and PULL method

When using the PUSH method, vehicles periodicallyuploaded their location to a short-range and medium-range
area server in the data management and delivery section. Whenan event occurred, information was delivered from the shortrange and medium-range area server to the vehicle based on thevehicle's location.
When using the PULL method, on the other hand, vehicles
indicated the areas they wanted data for, and they periodicallyacquired the information from the short-range and mediumrange area server in the data management and delivery section.
22

Creation and evaluation of the V2N
information delivery system

Delivering traffic environment information via V2N enables
the use of more diverse information over a wider area, so
methods of efficiently extracting information from the cloudand delivering that information were created with an eye toward
future practical implementation. The amount of communication
traffic and transmission delays due to network factors are being
evaluated through testing.

2.1. PUSH/PULL-based information delivery system
When delivering information via V2N, it is important to use
delivery mechanisms that take into consideration data volume
and transmission delay based on the characteristics of the

2.2. Traffic Traffic environmental information reception characteristics
2.2.1 Characteristics of rainfall information
Table 1 shows an overview of the rainfall informationdelivered during the FOTs. The High-resolution Precipitation
Nowcast information provided by the Japan MeteorologicalBusiness Support Center is updated every 5 minutes, making it
low frequency data, so the PULL method was used. Fig.5 shows
what is displayed in the dynamic map viewer when rainfall
information is received by the vehicle. Fig.6 shows a rainfall
information output example (including forecast information).
Rainfall information contains precipitation information for thefollowing 30 minutes from the current time, so it can be used
for handover between automated driving and manual driving
and to provide caution information to the driver.

42

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
1) The Tokyo Waterfront City Area Field Operational Tests

In Fig.7, a test vehicle encounters the tail end of traffic
congestion affecting all lanes. As the Fig. shows, it slows down
and continues driving, and then when the traffic congestion
dissipates it speeds back up

Table 1: Overview of rainfall information
Information source
Provided
information

Provision scope
DeliveryFig.
5 methodJapan Meteorological Business Support Center"High-resolution Precipitation Nowcasts" or "High-resolution
Precipitation Nowcasts" (5-minute precipitation amount)(cumulative rainfall over a 5-minute period in a grid squaremeasuring 250 m x 250 m, current condition analysis and 30
minute forecasts issued every 5 minutes, binary data)
SIP Phase 1 and Phase 2 high-accuracy 3D map range (Waterfront
City + Metropolitan Expressway, Joban Expressway, Tomei,
Shin-Tomei)
PULL methodofction tD i r eo v e m e nmRainfallamountCurrent

Impediment location
4.1 km (lanes 1 and 2)

Tail end of traffic congestion:
3.2 km (all lanes)
Driving route: Bayshore Rote eastbound (Airport Central ⇒ Oi Junction)

1.5

140

2.0

Fig. 65５分後予測値
min. forecast
15
min. forecast
15分後予測値

60

4.0

40

4.5

20
0

10:02

10:01

10:00

Traffic congestion
dissipated at 4.4km point

09:59

09:57

2.00

09:56

Entered traffic
4 congestion area
at 3.3 km point

09:58

5.0

3.00

Fig.7: Reception characteristics of lane-specific road traffic information

1.00
0.00

80

3.5

09:55

Precipitation (mm/h)

降水量（mm/h）

10min. forecast
10分後予測値

100

Driving Speed

3.0

Speed（km/h）

Distance（km）

Current
現在値value

4.00

120

2.5

Fig.5: Dynamic map viewer screen when rainfall information is received
07:10,
October 19,07:10
2021 Precipitation(mm/h)
2021/10/19
降水量(mm/h)

Driving Trajectory

Ariake有明JCT
Junction

Tatsumi辰巳JCT
Junction

Kasai

葛西JCT
Junction

2.2.3. Characteristics of mock emergency vehicle
information for vehicles on emergency calls

Komatsugawa Kosuge
小松川JCT
小菅JCT
Junction
JunctionFig.6: Rainfall information output example (including forecast information)
5

2.2.2. Characteristics of lane-specific road traffic
information
Table 2 shows an overview of lane-specific road traffic
information. Lane-specific road traffic information includes
information such as coordinate and lane information regarding
traffic congestion tail ends, impediments, and the like. This
information is generated from probe information provided by
vehicles driving on expressways and is sent to other vehicles.
When this information is provided, it must be linked to thehigh-accuracy map data in the vehicle's own system, so thelinkage delivery function, shown in Fig.2, is used to provide thevehicle with the location of the information, converted intoeither the distance between the information and a common
reference point (CRP) on the high-accuracy map, or thecoordinates of the location (in accordance with ISO17572-4).
Lane-specific road traffic information is updated infrequently,
once per minute, so it is delivered using the PULL method. The
characteristics of received lane-specific road traffic informationis shown in Fig.7.

Fig. 8

Table 3 shows an overview of mock emergency vehicle
information for vehicles on emergency calls and Fig.8 shows
the reception characteristics of this information. The mockemergency vehicle information for vehicles on emergency calls
is frequently updated, with vehicle location history information
Table 3: Overview of mock emergency vehicle information for vehicles on
emergency calls
Information source Mock emergency vehicle (rental vehicle)
Provided
information
Provision scope

Location information for emergency vehicle updated every 100 ms,
distributed every 2 seconds (1 location per 100 ms x 2 s = 20 items of
GNSS information sent at a time)
General roads in the Waterfront City area

Delivery method PUSH method

Mock emergency vehicle

Viewer screen

Table 2: Overview of lane-specific road information
Information source Probe information (OEM/car navigation equipment manufacturer)
Traffic congestion tail end location information, impediment location
Provided
information (1 minute intervals, (1) start time, (2) alleviation time, (3)information
occurrence location, (4) route name, (5) lane number, (6) occurrence
location accuracy, (7) traffic congestion accuracy)
Provision scope

Metropolitan Expressway Haneda Route and Bayshore Route

Delivery method

PULL method

Test vehicle
Mock emergency
vehicleFig.8: Front-facing image and dynamic map viewer screen when mockemergency vehicle information for vehicles on emergency calls was received

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2.Establishment and Utilization of Traffic Environment Data

Viewer screen(1) Technological Development Concerning the Generation of Traffic Environment Data

Daiba1) The Tokyo Waterfront City Area Field Operational Tests

Central Odaiba No. 1 (North)

Drive
route

in 100 ms increments being updated every 2 seconds, so the
PUSH method was used.
Fig.8 shows an image taken from the front of the vehicle
when the mock emergency vehicle passed and the dynamic
map viewer screen showing the relationship between thelocations of the test vehicle and the mock emergency vehicle.
Fig.9 shows the distance between the test vehicle and the mockemergency vehicle on an emergency call that passed each other,
together with their speed characteristics. Through these tests,
we were able to deliberate regarding use cases for emergency
vehicle information for vehicles on emergency calls and vehicle
behavior in preparation for future real-world deployment.

Central Odaiba No. 2 (South)
Viewer screen
Daiba

Drive
route

Teleport EkimaeViewer screen
Daiba
Central Odaiba No. 1 (North)Viewer screen
Central Odaiba
Odaiba No.
2 (South)
DaibaCentral
No.
1 (North)

DriverouteDrive

Central Odaiba No. 1 (North)Central Odaiba No. 2 (South)route

Teleport
EkimaeCentralOdaibaNo. 2 (South)

Start receiving infomation →Teleport
EkimaeTeleport
EkimaeDaiba
Start receiving
DR 3 infomation →CentralDaiba↓
Odaiba Start receiving
infomation →No. 1(North)
Daiba DR 2
Start receiving
infomation →DR
3
CentralDaiba↓4 3
OdaibaDRDR
CentralCentralNo.
1(North) DR↓ 2
OdaibaOdaiba↓Fig. 9No. 1(North)

Vehicle speed

Mock emergency
vehicle speed

No. 2(South) DRDR
32
CentralCentral DR
DR24
↓DR 4
OdaibaOdaiba
CentralDR ↓3
OdaibaNo.Teleport1(North)No.
2(South)DRDR2↓ 2

Distance between
vehiclesDistance between vehicles (m)Speed (km/h)

No. 2(South) DR 2↓

Ekimae

DR
DR
DR
133
CentralTeleport
TeleportDR ↓4↓
OdaibaEkimae
Ekimae ↓DR 11 （1）ルート１の場合
No. 2(South) DRDR
2(1) If Route 1（1）ルート１の場合（1）ルート１の場合
DR 3
Teleport
Viewer screen↓
Daiba
Viewer screen
EkimaeDaiba
DriveDrive DR 1 Daiba
Viewer screen
Drive
route
route
CentralOdaiba1 (North)Central Odaiba
No.No.
1 (North)
routeCentral Odaiba No. 1 (North)（1）ルート１の場合
CentralNo. No.
2 (South)CentralOdaiba
Odaiba2 (South)

Mock emergency vehicle
driving route
Test vehicle driving route

Fig.9: Speeds and location relationships of test vehicle and mock emergency vehicle

Central Odaiba No. 2 (South)

2.2.4. Characteristics of traffic signal prediction
information
Traffic color information for each traffic signal was generated
by the predictive SPaT generation server 3 seconds before thelight turned green and provided to test vehicles via the data
generation and aggregation section and the data managementand delivery section. Table 4 shows an overview of predictive
SPaT information.
Table 4: Overview of predictive SPaT information
Information source Metropolitan Police Department
Traffic signal color prediction information when traffic signal cycle isconfirmed
In these tests, predictive SPaT information was generated and provided
3 seconds before the start of the cycle (the time when the major road's
traffic signal turned green), and the provided predictive SPaT
information contained the cycle start time and light color information
for two cycles for each exit direction of each entry route

Provided
information

Provision scope

Traffic signals in Waterfront City area

Delivery method

PUSH method, PULL method, and specified intersection PUSH
method.

predictive SPaT information could be used in various ways
in future real-world deployment, so we used three delivery
methods, as shown in Table 5.
Table 5: predictive SPaT information delivery methods
MethodCharacteristic
The test vehicle provided its location information to the data management

delivery section short-range and medium-range area server in 1 second
PUSH method andintervals and received traffic signal information for int º¡ ersections in thedesignated radius around the vehicle's position in 1 second intervals

SPaT information for intersections in the designated rectangular area in the

PULL method direction of travel of the test vehicle was requested by and provided to thevehicle in 1 minute intervals

Specifiedintersection
PUSH method

Traffic signal ID information requested for the intersection or intersections
along driving routes was provided by the short-range and medium-range
area server in the data management and delivery section in predictive SPaT
information update intervals.

Teleport Ekimae
8

Drive
route

Daiba

Teleport
Ekimae Viewer screen
Teleport
EkimaeCentral Odaiba No. 1 (North)Start receiving infomation →Central Odaiba No. 2 (South)
DR 3
Start receiving
infomation →StartreceivingDaiba↓ infomation →
DR
DR 32

DR ↓3
CentralDaiba↓DR 24DR
Odaiba↓No. 1(North)DR 2
DR 2
Daiba

Teleport Ekimae

DR 4
CentralOdaiba↓4 4DR
DR
CentralCentralNo. 1(North)Start
receivingDR↓ 2infomation →OdaibaOdaiba↓No. 2(South)32
No. 1(North) DRDR
DR24
DRCentralDaibaOdaiba↓DR↓ 3
Teleport↓4No.
2(South)
DR
DR
DR2
2
EkimaeCentral
DR 1↓
OdaibaDRDR4 3CentralNo. 2(South)
Teleport DR 2↓
OdaibaEkimae ↓DR 1
No. 1(North)（2）ルート２の場合

DR
DR 23
Teleport↓(2) If Route 2（2）ルート２の場合
Ekimae DR 4
CentralDRcharacteristics
1
Fig.10:shows
theof predictive SPaT information. This↓
Odaibaexample shows the specified intersection PUSH method.
No. 2(South)
DR 2 （2）ルート２の場合
DR 3 the characteristics of predictive SPaTFig.10shows
Teleport↓ example shows the specified intersection
information.
This
EkimaePUSH method.DR 1
Fig.10 shows (1) predictive
SPaT information requested for（2）ルート２の場合
the three intersections in front of the Central Odaiba No. 1(North) intersection on route 1 and (2) predictive SPaT
information requested for the four intersections in front of the
Daiba intersection on route 2. This shows that SPaT information
for multiple intersections along the driving routes could bereceived at the same time, which would make it possible to
avoid stopping at intersections and would be effective forselecting the optimal driving route.

Output method In the case of the PUSH method and the PULL method, among the received
predictive SPaT information, the predictive SPaT information forto vehicle
intersections in the vehicle traveling direction is output in 100 msec intervals.
control output In the case of the specified intersection PUSH method, the predictive SPaT
information for specified intersection is output in 100 msec intervals.
section44

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
1) The Tokyo Waterfront City Area Field Operational Tests

2.3. Delivery system evaluation(2) Delivery delay characteristics of lane-specific road traffic
information
As with rainfall information, the PULL method was used todeliver lane-specific road traffic information, so for lanespecific road traffic information there was also a delay time of
up to 66 seconds when outputting the information to the test
vehicle on-board equipment (BOX-PC) in the data conversion
and output to vehicle control section (test-vehicle side). This isroughly equivalent to the information source delivery cycle
length, so this does not present a problem. Lane-specific roadtraffic information is updated infrequently, once per minute, so
it would be preferable to keep the transmission settings as they
currently are.

2.3.1. Time synchronization characteristics
In order to evaluate the impact of delays resulting fromnetwork transmission, we synchronized the times of the data
generation and aggregation section, the data management anddelivery section, and the data conversion and vehicle control
output section, as shown in Fig.2, and then evaluated thedelivery system's delay times. We found that the maximum
time deviation between each device could be kept to ±20 ms,
and we built a test system that enabled us to evaluate network
delays in the transmission of information.

2.3.2. Delivery delay characteristics
In the FOTs, information delivery methods varied dependingon the information, so Fig.11(1) shows the transmission
transactions and delay times for each method.
(1) Delivery delay characteristics of rainfall information
For rainfall information, we used the PULL method shownin Fig.11 There was a rainfall information delivery delay time of
between 10 and 130 seconds when outputting the informationfrom the data generation and aggregation section to the test
vehicle on-board equipment (BOX-PC) in the data conversion
and output to vehicle control section (test-vehicle side) as
shown in Fig.2. Japan Meteorological Business Support Center
data is delivered in 5 minute intervals, so this does not appear
to present a problem. However, if data requests were sent inintervals of 1 minute or less, this would cause an increase intransmitted data, so it would be preferable to keep thetransmission settings as they currently are.
Fig. 11
PUSH method
Short/medium-range
area server

Data aggregation
serverTest vehicle on-board
equipment

Data aggregation
server(3) Delivery delay characteristics of mock emergency vehicle
information for vehicles on emergency calls
For mock emergency vehicle information for vehicles on
emergency calls, we used the PUSH method shown in Fig.11
There was a delivery delay time of up to 1.3 seconds when
outputting the information from the data generation andaggregation section to the test vehicle on-board equipment(BOX-PC) in the data conversion and output to vehicle control
section (test-vehicle side) as shown in Fig.2. In these tests, mockemergency vehicle information for vehicles on emergency calls
was supplied in 2 second cycles, so delivery times had no
impact. If the cycle length of emergency vehicle information forvehicles on emergency calls is reduced 1 second in the future, itwill be important to deliberate about how to reduce the delay
time to 1 second or below. However, if vehicles learn in advancethat an emergency vehicle is approaching, this delivery delay is
not expected to have an impact. It will be important to deliberate
more deeply regarding the methods of using emergency vehicle
information, including these issues, and regarding potential use
PULL method
cases, etc.
Short/medium-range
area server

Test vehicle on-board

equipment(4) Delivery
delay characteristics of predictive SPaT information
Delay time
One
PUSH
For
predictive
SPaT information, we measured the delay
second1 minute
methodinterval
Delay time
request
time
for
the
three
methods shown in Fig.11 when outputting
：
cycle
：
Delay time
Maximum delay time of slightly
theinformationfromthe short-range and medium-range area
over 1 second
serverinthedatamanagementand delivery section to the test
The most recent information is sent in response torequests issued once per minuteFig. 11
The maximum delay time is slightly over 1 minuteSpecified intersection PUSH method
vehicle on-board equipment (BOX-PC) in the data conversion
PUSH method
PULL method
Short/medium-range
Test vehicle on-board
and output to vehicle control section (test-vehicle side) as
Data aggregation
area server
equipment
serverShort/medium-range Test vehicle on-board
Data aggregation
shown in Fig.2. With the PUSH method, the information wasSpecify intersection for
Test vehicle on-board
Data aggregation
area server
Delay time
serverequipment
Short/medium-range
which information is desired
equipment
serverarea server
Delay timeFig. 11
sent from the short-range and medium-range area server to theTraffic signal
Delay timeOne
PUSH method
PULL method
Delay timecycle length
test vehicle on-board equipment in 1 second intervals, so there
second1 minute
interval PULL
Delay time
Information is thenrequestWhen the first request is：
was a delay of roughly 1 second. With the PULL method, thedelivered whenever therecycle received, the most recent
method：are updates. The average
information as ofDelay
that time
time
Maximum delay time of slightly
delay time is roughly 100
is sent
test vehicle requested information from the short-range andover 1 secondms.
Delay time
The most recent information is sent in response to
medium-range area13server in 1 minute intervals, so there was arequests issued once per minute
The maximum delay time ：is slightly over 1 minuteSpecified intersection PUSH method
delay of Delay
60timeseconds or longer. For the specified intersection：
Short/medium-range
Test vehicle on-board
Data aggregation
PUSH
method,on the other hand, the vehicle side issued
area server
equipment
serverSpecify intersection forrequestsforinformation
for intersections for which it wished to
Delay time
which information is desired
Specified intersection PUSH method
receivedata(usingthe
MQTT
protocol). The latest predictive
Delay time
SPaT
informationfortheintersections
was provided in response
Information is then
When the first request isSpecifieddelivered whenever there
received,
the most recent
totheserequests.
This
made
it
possibleto keep the transmission
are updates. The average
information as of that timeintersection
delay time is roughly 100
is sent
ms.
PUSH
load down and to keep delay times to roughly 100 ms, the13methodshortest delay time of any of the three methods. Predictive
information was finalized 3 seconds in advance, and the delay
13
time was roughly 100 ms, so vehicles could use SPaT information
sufficiently far in advance of the starts of traffic signal cycles.
Fig.11: Transmission transactions and delay times for each method (specified
For predictive SPaT information, it would be best to use thedistance PUSH method, PULL method, and specified intersection PUSH method)
specified intersection PUSH method.
Delay time

Traffic signal
cycle length

Data aggregation
serverShort/medium-range
area server

Test vehicle on-board
equipment

Delay time

Traffic signal
cycle lengthOnesecondinterval

Data aggregation
serverDelay time

Short/medium-range
area server

Test vehicle on-board
equipment

1 minute
requestcycle

Maximum delay time of slightly
over 1 second

The most recent information is sent in response torequests issued once per minute
The maximum delay time is slightly over 1 minute

Data aggregation
serverShort/medium-range
area server

Test vehicle on-board
equipment

Specify intersection forwhich information is desired

Delay time

Delay time

Information is thendelivered whenever there
are updates. The average
delay time is roughly 100
ms.

When the first request is
received, the most recent
information as of that timeis sent

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
1) The Tokyo Waterfront City Area Field Operational Tests

3

Effectiveness of traffic environment
information

Through the FOTs and deliberations in Tokyo Waterfront
City FOTs working group meetings, which involved theparticipation of all test participants, test environment providers,the Cabinet Office, related government agencies, NEDO, andthe like, we confirmed the effectiveness of the four types oftraffic environment information

3.1. Effectiveness of rainfall information
The rainfall information delivered during the FOTs isenvisioned for use in a wide range of applications, including
switching from automated driving to manual driving, providing
drivers with caution information, predicting travel times,
adjusting vehicle speeds, and adjusting following distances.
Also, we confirmed that when rainfall information is used bydrive assist or automated driving systems, the systems would be
able to respond to sudden weather changes if the amount oftime taken from generating to delivering information wereshortened. Rainfall information use cases should be clarified
and fleshed out in preparation for practical implementation,and ongoing deliberation should be conducted regarding
systems for delivering information based on these use cases.

3.2. Effectiveness of lane-specific road traffic information
Lane-specific road traffic information appears to havetremendous potential when there is traffic congestion (including
traffic congestion extending from exits to side strips), when
making vehicle control decisions (following/lane changes), andwhen providing drivers with caution information
recommending making lane changes or driving route changes
far in advance in the event of driving impediments (such asaccidents or fallen objects). In situations such as this, theinformation is envisioned as being used in the issuing ofrequests to handover from automated driving to manual
driving, in preliminary deceleration, in making lane changes, inmaking route changes, and more. Furthermore, with regard to
the content of the messages sent in the FOTs, there wererequests to add information regarding information reliability,
traffic congestion likelihood based on statistical information,
information regarding whether traffic congestion areas are
growing longer or shorter, the average vehicle speeds at the tailends of traffic congestion, and the linking of tail end informationand start point information. We strove to share this information
with the FOTs participants responsible for generating lanespecific road traffic information.

3.3. Effectiveness of mock emergency vehicle information for vehicles on emergency calls
Mock emergency vehicle location information would be
effective for driving assistance and vehicle control because it
would enable vehicles to learn in advance about approaching
emergency vehicles. Use cases for this information would
include providing sufficient advance notice to take necessary
action when an emergency vehicle is approaching (such as
decelerating, stopping, pulling off to the side of the road,switching from automated driving to manual driving, alerting

the driver, etc.), using the information together with highaccuracy map data to confirm if an approaching emergency
vehicle would directly affect one's own vehicle, and determining
if an approaching emergency vehicle detected by one's own
vehicle's cameras is on an emergency call or not. In preparation
for real-world deployment, additional discussion is needed
within the industry regarding use cases and information
utilization with respect to how autonomous vehicles are to
behave when an emergency vehicle is approaching.

3.4. Effectiveness of predictive SPaT information
The FOTs confirmed that traffic signal prediction
information (V2N) could be used in use cases such as situations
involving impediments to traffic signal color identification ordeviations in traversal decision-making,(2) for which shortrange communication (V2I) was also effective. Information
can be received not only for the area immediately surrounding
a vehicle, but also for a large area, so it could be applied todriving route plans to reduce the number of vehicle stops andshorten the amount of time required, thereby contributing to
the achievement of carbon neutrality. Even in suburban andrural areas where traffic signals are far apart, the information
could assist in setting cruising speeds and providing driving
assistance, so laterally expanding the area for which V2N
predictive SPaT information is provided has the potential toassist with the expansion of areas where automated driving ispossible. Ongoing deliberation is recommended in preparation
for real-world deployment.

4

Conclusion

An information transmission system that used a public longrange network (V2N) was set up in the Waterfront City area.
Information delivery testing was performed for rainfall
information, lane-specific road traffic information, mockemergency vehicle information for vehicles on emergency calls
vehicle information, predictive SPaT information, and the like.
The results of these tests were discussed in FOTs working groupmeetings, and we evaluated the delivery system and how the
information could be used. We organized information regarding
the issues involved in this utilization, and verified theeffectiveness of the information. Based on the issues identified
through these FOTs and the results of the effectivenessevaluation, use cases should be clarified, discussions should beheld regarding situations where delivered traffic environmental
information can be utilized, and progress should be made in thereal-world deployment of this information.
[Reference] (1) FOTs in the Tokyo Waterfront Area Consortium: FY2021"Cross-ministerial Strategic Innovation Promotion Program (SIP) Phase Two- Automated Driving (Expansion of Systems and Services)/Implementation of
FOTs in the Tokyo Waterfront Area" Results Report, March 2022, pp.42-73,(2021)(2) Yoshiaki Tsuda, et al: "Data Analysis of the FOTs in Tokyo Waterfront City Area,
SIP 2nd Phase : Automated Driving for Universal Services —Mid-Term Results
Report (2018–2020), pp.54-62, (2021)[Contacts]
Spatial Information Systems Engineering Section, Information Technology Systems
Department, Kamakura Works, Mitsubishi Electric Corporation, 325 Kamimachiya,
Kamakura City, Kanagawa, 0467-41-6228, Contact: Yoshiaki Tsuda

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2

Establishment and Utilization of Traffic Environment Data

(1) Technological Development Concerning the Generation of Traffic Environment Data

2) Technological Development to Provide
Traffic Signal Information to Automated
Vehicles Connected to Infrastructures (V2N)
Yukiko Hatazaki (NIPPON SIGNAL CO., LTD.), Yuichi Takayanagi (Panasonic Connect Co., Ltd.),
Toru Mabuchi (OMRON Social Solutions Co., Ltd.), Shunichi Kawabe (UTMS Society of Japan)(Abstract) From the viewpoint of improving reliability and availability, three requirements for SPaT (Signal Phase and Timing)
information for automated driving were identified: (1) the error between SPaT information and actual light color should be lessthan ±300ms, (2) errors in SPaT information should be detected and the vehicle should be notified of the errors, and (3) the
provision of SPaT information in various signal control systems should be achieved. First, for the V2I system, the technicalspecifications for SPaT information provision infrastructure using V2I were decided on by FY2020. Next, from the viewpoint of
reducing maintenance costs, etc., we started studying the V2N system using mobile lines in FY2019, and by FY2021, throughFOTs (Field Operational Tests) in Saitama Prefecture, we worked on the establishment of SPaT information provision technology
using the V2N method. We also studied the social functional requirements of the SPaT Information Center, which constitutes
the core of the social implementation model envisioned in the study of the V2N method. Based on these studies, in FY2022,
we conducted FOTs integrating a series of components from SPaTs at 17 locations to simulated on-board devices, andverified the effectiveness of the three SPaT information provision methods and of the overall system configuration.
Keywords: V2N, SPaT information provision, SPaT Information Center, MQTT

1

Purpose of the Study

Automated vehicles recognize SPaT light colors using onboard cameras, but there are some issues, such as the inability torecognize signal light colors with a single camera, and reduced
recognition accuracy due to backlighting. As a measure to addressthese, there is a need to provide SPaT information from theinfrastructure. V2I systems have already been developed, but there
Table 1: Overall research and development plan
Case study and examination of issues with highly feasible methods
FY ・Case study of methods other than V2I communication to provide SPaT information2018 ・Sorting out of SPaT information provision methods other than V2I communication
・Study of measures to address issues with a view to realization of methods with high feasibility
Verification using a simulated system and preparation of draft specifications for a model system
FY ・Detailed functional and technical requirements for traffic signal information provision methods
2019 ・Verification of three SPaT provision method proposals with a simulated system
・Draft specifications for the model system to be built in the FY2020 project
Model system test in a single prefecture and specification study of a SPaT information aggregation system
FY ・Development and verification of a model system for provision of SPaT information by individual prefectural police
2020 forces・Study of specifications for the Police Agency's SPaT information aggregation system
Study of the SPaT Information Center for social implementation
FY ・Study of SPaT Information Center requirements
2021 ・Study on coordination and integration with other information
・Verification to improve the accuracy of SPaT information
Construction and verification of the prefectural police system and the National Police Agency SPaT information
FY aggregation system
Establishment and effectiveness verification of a SPaT information provision system at prefectural police departments
・
2022 Establishment of a SPaT information aggregation system for the National Police Agency
・・Verification of effectivenesshave been issues in terms of maintenance costs. Therefore, researchand development was begun in FY2018 to establish a new SPaT
information provision method that utilizes cloud computing andother means with the aim of reducing costs. (Table 1)
The method of providing SPaT information is based on asystem configuration in which SPaT information generated attraffic control centers and SPaT controllers (hereinafter referred
to as "signal controllers") is first aggregated in the National Police
Agency's SPaT information aggregation system via LTE andother lines, and then sent to a distribution center. We determinedthat the control center-based method, the centralized controlbased method, and the controller-based method would be themost applicable methods. Fig.1 shows the three methods.

2

Study of the socio-functional requirementsof the SPaT Information Center

The social implementation model studied in this project isshown in Fig.2. In this model, in addition to the direct users of
SPaT information, companies promoting automated driving,
local governments operating automated driving buses, andothers, they bear the cost in course of the service operation.
We asked the opinions of experts, related companies, andothers through interviews and questionnaires about the SPaTSIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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(1) Technological Development Concerning the Generation of Traffic Environment Data
2) Technological Development to Provide Traffic Signal Information to Automated Vehicles Connected to Infrastructures (V2N)

Note: “Distribution Center” is a tentative name.

Predicted SPaT informationTraﬃc Control Center
Dedicated line

Metropolitan Police
Department SPaT
information aggregation
systemSPaT Information
CenterThe control center-based methodTraﬃc Control Center
Dedicated line

Predicted SPaT
information

Metropolitan Police
Department SPaT
information aggregation
systemSPaT Information
CenterThe centralized control-based methodTraﬃc Control Center
Predicted SPaT
information

LTE line

Metropolitan Police
Department SPaT
information aggregation
systemSPaT Information
CenterThe controller-based method

Distribution Center(e.g., private business servers)

Method where predicted SPaT information iscreated at the Traﬃc Control Center

Distribution Center
Method in which predicted SPaT information is created at
SPaT controllers★This method applies to SPaT control that is remotely
controlled from the Traﬃc Control Center.

Distribution Center
Method to create current light color information andpredicted SPaT information at SPaT controllers at eachintersection★This method is applicable to signal controllers operating
independently.

Fig.1: SPaT information provision methods

Prefectural policeiiNational Police
Agencyi

Cost burden•Companies involved inpromoting automateddriving, etc.
•Local governments, etc.

i

Traﬃc signal information, etc.
Traﬃc signal information distributor￥
Economic burden￥
Traﬃc signal information aggregation
system
Traﬃc signal information center
facilitiesii

Non-competitive area

Competitive area

￥￥

i

ii

i

￥￥

SPaT Information
Center￥i i

Cost burden￥
Beneficiary

￥
￥
i i

Distribution ￥￥Center

Fig.2: Schematic of a social implementation model

Information Center, which exists between the non-competitive
areas (police area + cooperative area) and the competitive
areas on the route from the prefectural police that manages
SPaTs to vehicles, because it plays a decisive role in the successor failure of social implementation.
Table 2: Necessary social functional requirements
Item

Necessary social requirements

IssuesOperation oforganizational
bodies・Management planning functions
・Functions to secure financing

・Securing human resources (financeexperience, including experience securing
beneficiary contributions)・Facilitation of communication with relevant
agencies

Relaying SPaT
information

・Relay-related operational functions ・Securing human resources (operations
experience)・Recovery functions in case of an
event
・Experience in similar work

・SPaT Information Center Facility
planning, design and related
Development,
ordering functions
maintenance and・Human resources (field engineeringfunctions)management of SPaT ・Cybersecurity management
Information Center functions
・Experience in similar work
facilities
・Routine fault handling・Customer support

The results of the study included a variety of issues, suchas the need to accurately identify needs, consideration of
local needs, the utilization of the human resources andexpertise of agencies that have been providing transportation
information, and the need for careful consideration of theinitial and maintenance cost burden. Table 2 shows thenecessary social functional requirements. There were several
comments on the need for various types of human resources.
There were also opinions that it is necessary to provide thesame level of service under nationally standardized interface
conditions and to ensure a nationwide system that canoperate 24 hours a day.
The conditions for the implementing entities are shownin Fig.3.
Relationshipwith the Road
Traffic Act

The traffic signal information must satisfy at least Article 38-7, Paragraph 2 of
the Enforcement Regulations of the Road Traffic Act from the viewpoint that it
is more important than conventional traffic information.

Fulfillment ofsocialrequirements

Necessary social requirements (Slide 7) must be fulfilled• Of the functional requirements, items for which it is appropriate to seek
technical solutions, such as facility construction, technical support, etc.,
can depend on other organizations.

Factors toconsiderThe purpose of this article is to contribute to the safety and smoothness of traffic on roads by providing
information on road traffic. [...] There are the necessary and appropriate organization, facilities andcapacity to conduct the affairs pertaining to the provision of traffic information prescribed in Paragraph 1of said article" (Article 38-7, Paragraph 2 of the Enforcement Regulations of the Road Traffic Act).

• Limits to number• Utilization of existingorganizations• Participation ofexperienced
organizations• Because of the nature of operations, which entail relaying between the police and competitive
areas, in consideration of the burden on the police and the necessity to maintain cybersecurity,
it is important for the organizations responsible for the Traffic Signal Information Center to be
as few in number as possible (if no particular problem exists, then just one organization).
• Due to the nature of supporting automated driving, which is expected to develop permanently,
it is important to have certainty of management to ensure sustainability, and one way toachieve this is by expanding the operations of an existing organization that has a certain trackrecord.
• Due to the necessity of securing the human resources necessary for planning, management
decisions, etc., the participation of experienced organizations is necessary.

Fig.3: Conditions for Implementing Entities

Technical
connections tocustomers

・Installation of equipment forconnections, etc. (including・Securing human resources (technicalconstruction work on SPaT
management abilities)
Information Center facilities) and ・Experience in similar work
other implementation functions

3

Proceduralconnections tocustomers

・Receipt (confirmation of
conditions) functions
・Cost (beneficiary's portion)collection functions

・Securing human resources (customer service
experience)・Experience in similar work

3.1. Overview of the Control Center-based Method

Technical support to ・Technical support to confirm
customers
specifications, standards, etc.

∙ Securing human resources (knowledge of
newly developed SPaT information provision
technology)* Red font shows personnel who must be secured by the SPaT Information Center. Operations
and facilities can be externally dependent.

Methods of providing SPaT Information

The control center-based method is a method in which
SPaT information is generated by a SPaT information
distributor installed in the Traffic Control Center for centrally
controlled SPaT controllers.
Centralized control is control in which the Traffic Control

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(1) Technological Development Concerning the Generation of Traffic Environment Data
2) Technological Development to Provide Traffic Signal Information to Automated Vehicles Connected to Infrastructures (V2N)

Center determines signal control parameters, such as cycle
length, split, and offset, using sensor information, etc., anddictates them to the signal controller before the signal cycle
begins. SPaT information may differ from cycle to cycle.
With the control center-based method, SPaT information
is predicted using the control history of the previous cycle ofthe signal controller and the signal control parameters
dictated to the signal controller. With this method, theaccuracy of the time stamp added to the control history isimportant.
In the FOTs in Saitama Prefecture (FY2020), the target SPaT
information accuracy was not achieved due to the low time
accuracy of the signal controller at that time. Therefore, in
FY2021, we tested the latest specification of the signal controller
with the addition of time synchronization by GPS, and verified
that the target performance could be achieved through infactory verification.
In the course of the study in FY2021, it was recognized that,as a new issue, although signal controllers have a function todetermine the split when tracking offsets, the method ofprocessing fractions of seconds of less than 100 milliseconds
differs from manufacturer to manufacturer, resulting in a
maximum error of about 200 milliseconds. Among the companies
contracted for this project, we verified that no error occurredwith the SPaT controller of one company as it generates SPaT
information in accordance with the method for processing
fractions. (Fig.4) We are investigating possible countermeasures
for signal controllers manufactured by other companies.
The main functions of the SPaT information distributor are
shown in Table 3. In addition to SPaT information generation,
distribution, and data storage functions, the system has
JAMA
Requests-

300ms

0 error

300ms

㸩＋

㸫

The control
center-based method
Theoreticalerror value

380ms

380ms
150ms

The control
center-based method Legend
prototype
Error verification result

150ms: Traﬃc signal controller time error (within 100ms): Lamp color error (within 50ms): Oﬀset tracking error (assumed to be within 200ms): On-board device time error (within 30ms)

Fig.4: Error model and validation results of the control center-based method
Table 3: Main functions of SPaT information distributors
Function name
Information gathering

Function overview
Information used for SPaT information generation is collected from the signal
controller via the terminal control block.

SPaT information generation and SPaT information of the control system is generated and distributed via MQTT
distribution
brokers.
SPaT information conversion and Converts the SPaT information from the Centralized method and distributes it via
distribution
MQTT brokers.
Data accumulation

Various information updated by the MQTT broker is stored in the database.

SPaT information monitoring

Monitors whether the distribution of SPaT information has been stopped.

SPaT information verification

Verifies the accuracy of the SPaT information from the control method.

MQTT broker

Receives connections from SPaT information aggregation systems, SPaT information
roadside units, etc., and distributes information.

functions for intersection management information for
managing SPaT information, a predicted SPaT information
verification function for constantly monitoring the accuracy of
SPaT information, and other functions. The device also has an
MQTT broker function and uses the MQTT protocol tocommunicate with external devices such as SPaT information
aggregation systems and SPaT information roadside units.
The MQTT protocol is used because it is advantageous in
distributing SPaT information with low latency since new
information can be sent in a push fashion. Communication
messages are created based on a common draft standard thatis referenced by each device that generates or uses signaling
information.
3.2. Overview of the Centralized Control-based Method
The centralized control-based method, like the control
center-based method, is applicable to centralized controllers.
Unlike the control center-based method, SPaT information is
generated on the signal controller side, enabling immediate
processing, such as sensitivity control, which cannot be achieved
with the control center-based method.
In the SIP project, formally titled "Research and Developmentof Advanced SPaT Information Provision Technologies for
Automated Driving" (completed in FY2020), a SPaT information
generation method for SPaT controllers had already been
established, so in realizing the centralized control-based method,
compliance with that method was a prerequisite.
3.3. Outline of the Controller-based Method
The controller-based method is for non-centrally controlled
SPaT controllers that do not have communication functions. Two
methods were considered: one was to add SPaT information
roadside units to the currently operated signal controllers (roadsidedevice method), and the other was to add signal controllers capableof generating SPaT information and wireless devices.
In the roadside device method, SPaT information is generated
by detecting the on/off of light signals with a CT sensor. Since
SPaTs are controlled according to the day of the week and time of
day in non-centrally controlled SPaTs, the SPaT information canbe easily predicted. However, because offset tracking is performed
when the signal pattern changes, it is difficult to predict with anerror of 300 milliseconds or less, and during a FOT in Saitama
Prefecture in FY2020, a decline in accuracy was observed. As aresult, in FY2021, measures were taken to address this by learning
the offset tracking behavior in 100 millisecond increments.

4

Verification by Prefectural Police Model System

4.1. Outline of the Prefectural Police Model System
Fig.5 shows an overview of the prefectural police model
system configuration.

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(1) Technological Development Concerning the Generation of Traffic Environment Data
2) Technological Development to Provide Traffic Signal Information to Automated Vehicles Connected to Infrastructures (V2N)

Traffic Control Center
Traﬃc signal control block

Systems managementblock

Terminal control blockTraﬃc signal information
distributor
To SPaT information
aggregator

Terminal-compliant device
Analog or wide area
Ethernet line
Traﬃc Signal Controller(Version 5 compliant)

LTE line
SPaTl information
roadside unit

The Controller-based
methodThe Control
center-based methodTraﬃc signal controller(for centralized system)

4.4. Verification MethodTraﬃc signal controller

Centralized control

Non-centralized control (point control)

LegendThe centralized
control-based method

Fig.6: Capturing of video

Indicates new or an update.
Indicates a modification.

Fig.5: Overview of the system configuration of the prefectural police model system

4.2. Purpose of Verification
The purpose of this verification is to demonstrate that SPaT
information at a wide variety of intersections can be provided
accurately and in a cost-effective manner using equipment in
operation. To this end, the verification was conducted at theintersections shown in Table 4.
Table 4: Verified intersections and signal control status by method
Number ofintersections

Number ofrecall controls

Number ofright-turn
sensitive
controls

SystemNumber ofcontrols

Nighttime
Number of
flashes

The control
center-based
method9 Note60

90Centralized
method

6 Note33

40Controller-based
method

5

1

0

4

4

Methods

Item

Note: Both the Controller-based method and Centralized method are implemented at threeintersections.

4.3. Verification items
Table 5 shows the main verification items using theprefectural police model system.
Table 5: Main verification items using the prefectural police model system
Perspective

Verification items

Improvement of accuracy of Controller-based method and Controller-based
Verification of SPaT
methodinformation
Confirmation of compliance with each signal control such as recalls andgenerationright turn sensitivities
Verification of communication interface standardization
Verification of SPaT
Verification of intersection definition information
information
Verification of communication delay time
distribution
Verification of Δt acquisition in a simulated on-board unit
Verification of connection to SPaT information aggregation system
Verification focusing
Verification of the effects of actual line performance, GPS reception
on actualenvironment, etc. on the accuracy of SPaT information
environment andVerification of the long-term operation of SPaT information provision
operations
Verification of monitoring of SPaT information provision status

As in the Saitama FOTs in FY2020, the accuracy of the
SPaT information was verified by taking video of the signal
lights and the simulated in-vehicle devices and by calculating
the error in the number of remaining seconds of the SPaT
information when the signal light color changes. Fig.6 shows
the verification from the Saitama FOTs in FY2020. In addition
to capturing video, the information stored in each device wascompared to calculate the delay time and verify the consistency
between the data.

5

SPaT Information Aggregation Systems
and SPaT Information Centers5.1. Overview of Functions
A schematic diagram of the SPaT information provision
service is shown in Fig.7.
The SPaT information aggregation system collects andconsolidates SPaT information from SPaT information distributors
installed in the 47 prefectures. The collected SPaT information is in
the same format and can be handled in the same way regardless ofthe method used. At the same time, the SPaT Information Center
distributes the received SPaT information to each private SPaT
Information Distribution Center. In order to realize social
functions, the SPaT Information Center also manages the
distribution history, aggregates operational results, and provides
operational support functions such as format verification of SPaT
information.
Traffic control systems in prefectural police departments generatepredicted SPaT information using either the controller-based method,
the centralized control-based method, or the controller-based method,and distribute it via SPaT information distributors. Next, the SPaT
information aggregation system installed in the National Police
Agency aggregates the predicted SPaT information distributed by the
SPaT information distributors nationwide and distributes it to the
SPaT Information Center. Finally, the SPaT Information Center
distributes the predicted SPaT information to distribution centers
operated by private operators. Ultimately, the predicted SPaT
information is distributed to automated vehicles via the distribution

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
2) Technological Development to Provide Traffic Signal Information to Automated Vehicles Connected to Infrastructures (V2N)
ਦ̕
Prefectural Police Traﬃc
Control Center

National Police Agency
aggregation system

Predicted SPaT information
Intersection management
information

SPaT Information Center
operatorPrimary role: Information
generation

Primary role: Information
aggregation

Primary role: Information
distribution

Generation of predicted SPaT
information

Receiving and transmitting of
SPaT information

Managing the distribution ofpredicted SPaT information

Registration of intersection
management information

Communication
history inquiry

Guarantee of accuracy, etc.

SPaT information
aggregator

SPaT Information
Distribution Center

Prefecture A

Traﬃc signal
information
distributor

Prefecture Z

Traﬃc signal
information
distributor

Private SPaT Information
Distribution Center

SPaT Information
Distribution Center
Operator A
SPaT Information
Distribution Center
Operator B

0477
The control
The centralized
center-based method control-based method

The controllerbased method

L3
/
L2
/C2
&
/
L1
C1
&

Obtaining of information on the direction of vehicle travel

Fig.7: Overview of functions of SPaT information aggregation systems

centers.
Considering the social requirements of the SPaT Information
Center, the functions of each system were examined from thestandpoint of the SPaT information aggregation system and the
SPaT Information Center, and the functions were organized sothat the SPaT Information Center would ensure the consistencyof SPaT information rules and manage the information provided,
while the aggregation system would only amalgamate routes andmonitor communications.
5.2. Evaluation of Communication Time Up to SPaT
Information Reception in an Integrated Environment
Prefectural
policeTraﬃc signal
information
distributor
A

National Police
AgencyPrivate companies
etc.

SPaT information
aggregator

SPaT Information
CenterSPaT Information
Distribution Center

DatabaseD
C
MVNO environment

B

Z

On-board device(Android device)E

Simulated on-board terminal
Fig.8: Streamlined test environment1

measured.
A low bandwidth (12.5 Mbps) environment was establishedbetween the SPaT information aggregator and the SPaT Information
Distribution Center by designing on paper the required bandwidthat 2,048 intersections (four-prefecture connection). (The bandwidth
between A and B in Fig.8) In addition, between the SPaT
Information Distribution Center and the simulated on-board
device a permanent communication call was established via onesecond interval MQTT-PING communication.
Fig.9 shows the average daily results of delay time including
communication time for each section in Fig.8.
The predicted SPaT information was measured using 585
bytes of data, assuming a complex intersection.
ms

Measured performance of each section

1100
1000
900
800
700
600
500
400

The SPaT Information Center distributes SPaT information
for more than several thousand intersections nationwide and isalso considering a data storage function, but there is concernthat the processing delay time will increase. The system
configuration shown in Fig.8 was constructed by utilizing the
MQTT protocol as a common platform to create acommunication load verification environment where SPaT
information is streamlined from simulated signal controllers tosimulated on-board devices.
The performance of the SPaT Information Center system
was measured and examinations were conducted for faster
SPaT information transmission. However, the area from the
SPaT Information Center to the on-board device is acompetitive area, and this is only a reference example of
implementation.
SPaT information must reach the on-board device Δtseconds before the light color turns yellow (Δt is about 5
seconds). Therefore, a simple simulation system wasconstructed and the communication delay time was

300
200
100
0

A to BsectionB to C
sectionC to DsectionD to E
sectionIndication of E
reachedFig.9: Communication processing time for each section

We were able to demonstrate that data transmission froma roadside SPaT to a simulated on-board device is possible inabout one second. Although it was under limited conditions in
a simulated environment, it is very significant that we wereable to demonstrate that this service can be established via
devices that are equivalent to those in the real environment.
5.3. FY2022 Verification Environment
In FY2022, the prefectural governments and the National
Police Agency prepared a verification environment consisting ofa SPaT information distributor and a SPaT information

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
2) Technological Development to Provide Traffic Signal Information to Automated Vehicles Connected to Infrastructures (V2N)

ਦ̏̎೧ౕ‫ڧ؂ৄݗ‬Competitive areas

Traffic Control Center(Prefectural police
headquarters)

National Police Agency SPaT
Information Aggregation System

In the Traffic Control Center
Traffic
signalinformation
distributor

Distribution Center(businesses, etc.)

Predicted SPaT information

SPaT Information Center(NPA)

Building a new NW
environment

SPaT information/aggregator

RouterRouterFW

FW

SPaT information
aggregator

LAN for verification
RouterFW
Simulated SPaT Information Center
Simulated SPaT
Information
L2SW
Center Device

Pseudo-SPaT Information Distribution Center
Simulated SPaT Information
Router
M2M Router
Distribution Center DeviceFW

For verification

L2SW

Verification
deviceLTE

Simulated on-board device:
Laptop PC or Android device

GNSS

Fig.10: Testing environment for FY2022

aggregation system developed based on the specifications, as wellas simulated devices set up from the SPaT Information Center tothe on-board devices, and conducted verification. (Fig.10)
As for the circuit network, the evaluation was conducted
on a closed network in line with other SIP-adus FOTs
environments.
The purpose of the verification environment using
simulated devices from the SPaT Information Center to theon-board devices is to provide an environment that isassumed to be more similar to that of social implementation.

6

Examination of the Linkage Between SPaT
Information and High Precision 3D Maps

In the FOTs in the Tokyo waterfront area conducted from
FY2019 to FY2020 as part of the second phase of SIP-adus
project, SPaT information linked with a high precision 3D map
is provided, utilizing the results of the "Research and Developmentof Advanced SPaT Information Provision Technologies for
Automated Driving". We conducted a survey on the method andsummarized the information necessary for linking SPaT
information and high precision 3D maps as follows.
· Intersection information: Intersection ID andintersection location· Information to identify which intersection is pointed
to on the map· Signal light information: Light ID and location
information· Travel route: Combination of in-flow and out-flowroutes· Reference light information: Reference lights for eachtravel route· Stop line information: Location information

2

Table 6: Proposed layout of functions linked to high precision 3D map
Prefectural and Metropolitan Police(including National Police Agency)

Competitive area
Cooperative areas in providing SPaT
(automakers, automatedinformation (SPaT information
driving businesscenters, map vendors, etc.)
operators, etc.)

・Management of SPaT information
Provides the following information
correspondence table to link SPaT・Intersection ID to identify information information (intersection ID,
and location information to identify theDistribution of SPaT
direction) with high precision 3D
location on the map
information to vehicles
maps (stop line [direction], lights)
・Direction ID and information to・Linking high precision 3D maps andidentify the location on the map
SPaT information· Information that links lights on the map with thedirection of SPaT information
As for information necessary to link with high precision 3D
maps, Table 6 shows a proposed functional arrangement forproviding SPaT information by V2N.
In the FOTs in the Tokyo waterfront area, the SPaT information
correspondence table was managed by the simulated on-board
devices, but for social implementation, this information should not
be managed by each operator but by the cooperative area.

7

Conclusion

7.1. Outcomes of the Project
The introduction of absolute time and new specifications for
SPaTs (e.g., synchronization of time by GPS and a change fromcontrol in 1-second increments to control in 0.1-second
increments) have enabled the V2N SPaT information technology
to meet the technical requirements for providing SPaT
information required for automated driving, including an error
margin of ±300ms or less between SPaT information and actual
light colors. This is a significant step forward in terms of satisfying

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
2) Technological Development to Provide Traffic Signal Information to Automated Vehicles Connected to Infrastructures (V2N)

the technical requirements for the provision of SPaT information
required for automated driving.
At the same time, regarding the technical functional
requirements for the SPaT Information Center, which occupies
the core position of the social implementation model assumed
in this research and development, we proposed 58 functions, 14
screens and 5 forms. Based on the functional requirements ofthe proposed SPaT Information Center, we also studied thefunctions that can be reduced from the proposed specificationsof the SPaT information aggregation system studied in FY2020.
For cybersecurity, we proposed 15 countermeasurerequirements from the perspective of availability, continuity,and scalability.

driving at Level 4 or higher.
[Contacts]
UTMS Society of Japan, Airman’s Building Ichigaya, 7th Floor,2-6
IchigayaTamachi Shinjuku-ku, Tokyo 162-0843, 03-3235-6520, Contact:
Shunichi Kawabe (Managing director, 090-1185-5373, kawabe@utms.or.jp)

7.2. Future Technical Issues and Responses
Technical issues remain, such as fail-safe operations, offset
tracking, and the delay in the notification of anomalies due tothe communication time. Therefore, it will be necessary tocontinue technical examinations.
In FY2022, we implemented a SPaT information distributor
for prefectural police and a SPaT information aggregation system
for the National Police Agency. We also constructed a verification
environment consisting of a simulated SPaT information centerand distribution center, and conducted various verifications
using the MQTT protocol. This enabled us to identify phenomenathat could not be experienced in the simulated verification
environment available until FY2021, and which would occur
only in a real environment.
The remaining issues mentioned above and those identified
in FY2022 will continue to be studied in FY2023 and beyond
as part of independent research.
7.3. Differentiating Methods
For the V2N method, we studied the control center-based
method, the centralized control-based method, and controllerbased method, but the control center-based method, which weparticularly focused on, is an especially promising technology topromote the process of social implementation because it canutilize existing traffic safety facilities. However, it is undeniable
that the V2N method is not as good as the V2I method in termsof delay, fail-safe operations, and other technical aspects. In
addition, methods other than the control center-based method
studied for the V2N method have their own technical merits.
Therefore, it is necessary to differentiate among the methods
according to the traffic conditions at each intersection, thecharacteristics of signal control, the details of use cases, andother factors.
7.4. Efforts at Social Implementation
In addition, we will continue to study issues other thantechnical aspects that are necessary for social implementation,such as commercialization, and will continue our activities
toward the realization of social implementation of automated
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2

Establishment and Utilization of Traffic Environment Data

(1) Technological Development Concerning the Generation of Traffic Environment Data

3) Technological Development for Lane-level
Road Traffic Information Using Probes
Vehicle Data
Hirokazu Ichikawa, Atsushi Takenouchi, Naoki Funaoka, Naohiro Uchiyama, Akio Mori (PACIFIC CONSULTANTS CO., LTD.),
Yukimasa Morisaki, Ryuki Oshima (Mitsubishi Research Institute, Inc.)(Abstract) Autonomous automated vehicles operate based on a limited range of information. If there is an accident orother traffic congestion ahead then they may need to decelerate suddenly, and if there is heavy traffic then they may
be unable to change lanes smoothly. At such times, allowing the traffic situation ahead to be known in advance foreach lane (anticipatory information) would make it possible for automated vehicles to drive more safely and smoothly;for example, by decelerating earlier or changing lanes in a more prepared and relaxed way. With the spread ofconnected vehicles, a data acquisition environment for generating such anticipatory information is becoming available,
but not in a format that enables direct lane-by-lane understanding of traffic conditions. This project aims to develop
technology for lane-level traffic information, which would be considered highly useful for the safe and smooth drivingof automated vehicles. As an element of this goal, the project established an FOT (Field Operational Tests) environment
to confirm the level of lane-by-land data that can be generated from the probe information from vehicles currently on
the market and then offer centralized aggregation and distribution of the generated data together with other roadtraffic environment data.
Keywords: p
 robe vehicle data, Lane level road traffic information, traffic congestion tail, traffic congestion tail, turn signal information, FOTs

1

Purpose and Outline

In order to realize completely automated driving and ahigher level of safe driving support, various types of road traffic
environment data, including lane-level road traffic data, areexpected to be utilized to anticipate conditions that lie beyond
the range of on-board sensors. As roadside sensors can only
make fixed-point observations, it is therefore necessary todevelop a system to generate and provide road traffic data usingprobe vehicle data, allowing for monitoring of road traffic
conditions of surrounding areas.
The creation of just such a system is the purpose of thisproject. We formed a study group of public and private
stakeholders to develop a system for providing road traffic datafrom probe vehicle data. During the FOTs in the Tokyo
waterfront area, probe data obtained from cars on the market
was collected from probe operators and used in FOTs to createand distribute road traffic data, such as lane-level traffic
congestion tail information for the Tokyo Metropolitan
Expressway, proving the effectiveness of this data while also
helping to highlight further issues.

2

Our Future Visions

2.1. The requirement for lane-level road traffic information
As shown in Fig.1, the on-board sensors in an automatedvehicle can only collect information in a limited frontal area. If
obstructions such as traffic congestion at the lane-level could be
anticipated in advance, safer and smoother automated driving
would become possible. Such information would also bebeneficial for human drivers.
One possible source of such data is probe vehicle dataobtained from increasingly popular connected vehicles. In
No Information
provision

Autonomous (non-cooperative automated) driving
relying on information only from front view of thevehicle detected through its on-board sensor.

In case of sudden deceleration of leading vehicles, the following cannot
avoid to do likewise or might not be able to smoothly change lane.
Laneregulation

Automated vehicle

With Information
provision

By obtaining lane-level information beforehand, thevehicle can change lanes with well prepared manner

Vehicles need to know:• Which lane is better to drive?
• When is the better timing tochange lane?
→Lane-level road traffic
information is needed to makedetermination for the above.

Solutions forthe needs

Efficient information generation with combineddifferent information sources:○Probe vehicle data
Area-covering and detailed data
○Information provided by Road and traffic administrators
Information related to incidents impossible to detect
by vehicle-itself.
○Other information (emergency call and others)
Urgent information grasped with high immediacy

Fig.1: Requirement for lane-level road traffic information and its sources

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
3) Technological Development for Lane-level Road Traffic Information Using Probes Vehicle Data

addition, vehicles with emergency notification services in theevent of an accident are becoming increasingly widespread;such information can also be expected to help immediately
determine the location of obstructions caused by accidents.

(2) integration of data from multiple information sources, (3)generation of lane-level road traffic information, (4) conversion
to data capable of representing a location, and (5) data
distribution.

2.2. Scope of project3.1. Study of data sharing (aggregation) functions

As shown in Fig.2, the decision-making and operations
performed by an automated vehicle in response to a road traffic
event in front of it vary in accordance with the distance fromthe event. This project targets the use of this information inpath planning, focusing on lane changes. The immediacy of therequired information can be as short as a few minutes, meaning
there is potential for generating the required information evenfrom currently available probe data.

Here, we examined the data sharing specifications between
centers when aggregating probe data from probe operator
servers. The probe data obtained from probe operators is shownin Table 1. The aggregation time unit of the probe data was set
to 5 minutes, based on such considerations as the current stateof data collection.

Required immediacy

Path change, etc.
Lane change, etc.
Path planning

Event detection, etc.

Several hundred m upstream

Several km upstreamHigh

A few tens ofseconds

Emergency braking, etc.

A few
seconds

In the future, more immediate andadvanced utilization will also be
considered.
Immediate scope of this projectTechnical study using probe vehicle data that has already been put to practical use for early social implementation

Fig.2: Scope of project3

Note 1) Image of data
format for number
Speed by direction at Speed by direction at linkof vehicles by speed
DRM link unit
junction (5 min)prior to junction
classification

Study of each component technology

The overall flow of the provision of lane-level road traffic
information is assumed to be the collection of data from probe
operators, followed by the provision of the generated
information to individual vehicles via the same probe operators.
(Fig.3)
Therefore, the component technologies that need to beconsidered for the cooperative area of lane-level road traffic
information generation can be broadly classified into thefollowing five areas: (1) data aggregation from probe operators,

Data item

Average speed

Link speed(5 min)Event occurrence point

A few
minutes

Low

Table 1: Used probe data
Aggregate link
unit

Number of vehicles by
speed classification Note 1)

DRM link
100m division
unit
Number of vehicle
events(5 min)Number of braking
occurrences

Speed Classification

Number ofvehicles0＜V≦10km/h
10＜V≦20km/h

Number of turn signal
occurrences
Number of steering
occurrences:
110＜V≦120km/h
120 < VIn addition, it is assumed that a certain percentage of probe
data is uplinked from the vehicle to the probe data provider
center's server with a delay, and that the number of samples
may not be sufficient by taking only the most recent 5 minutesof data. Therefore, in the data sharing (aggregation) phase, we
considered the following points (1) and (2), and made it possible
to use up to 30 minutes old data retroactively. (Fig.4)
Exit time of 100m link

Generation time 18:30of delivered
18:35
information

18:3518:40

18:4018:45

18:4518:50

18:5018:55

18:5519:00

19:00

Layer 5Layer 4Layer 310

Layer 25

Layer 14

Layer 6

19: 05Layer 6

19: 10

19: 20Integrated from Layer 1 to 3 to

19:05- 19:10- 19:15ensure therequired
19:10
19:15
19:20sample size ofLayer 4Layer 3

10 for the generation of
distribution information at 19:00.
As of 19:05,
integrated from
Layer 1 and 2
Layer 1
As of 19:05, only
10
Layer 1 isLayer 2 Layer 1
available

Layer 5Layer 4Layer 3Layer 5Layer 4Layer 310

Layer 28

Layer 16

Layer 6Layer 5Layer 4Layer 312

Layer 2
10

At the next generation
time, uplink delay data Layer 5
due to passage of timeis also added
Layer 6

19: 1519:0019:05

Layer 2

Layer 1

Fig.4: Data collection method giving consideration to uplink delay
Overall goals and scope of project's undertakings
Competitive area

Probe operator

Public institution

OEM, etc.

vehiclesProbe operator
serverIntegration andgeneration servers

OEM and otherservers

VehiclesProbe operator
server

FOT system
serverFOTs in the Tokyo
waterfront area
Consortium server

Test vehicle

Functionality thatshould be present

Basic format

Cooperative area

This test

Competitive area(2) Integration of data from・Removal ofDatamultiple sources
personal data
(1) sharing(5) Data sharing(3) Generation of lane-level・Statistical(aggregation)(distribution)road traffic information
valuation
specifications
specifications (4) Conversion to・Tying to a sharedmap (if necessary)
location-representable data
(CRP, conversion to
dynamic map, etc.)

Conversion to eachcompany's
specifications(individual company
support)Data sharing(distribution)formatInput to routegeneration
function

Technical specifications and division of functions and roles to beconsidered in this projectFig.3: Scope of this study

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
3) Technological Development for Lane-level Road Traffic Information Using Probes Vehicle Data

<Pattern 1>

DRM Link(Lane Link)● Speed by direction of thelink before the junction

Left direction
speed
V1

V2

Linear speed

Speed by direction of the link before the junction
V4
V
3

Possible congestion in left lane if left
direction speed is low...

Number of vehicles by speed
classification (speed distribution)

DRM link(100m equidivision)

V1 V2 -1 V2 -2 V2 -3● Number of vehicles by speed
classification● Number of vehicle events(indicators etc.)

V4

Frequency<Pattern 2>

V3

With
congestion...

Possible lane-specific congestion if speed
variation occurs

Number of vehicle events
Indicator
Left indicator
Right indicator

NormalLeft steeringBrake

Low speed range

High speed zone

Speed

:

Number ofcases

15 cases
0 cases
15 cases
9 cases

If there are many vehicles
using left turning signal, it ispresumed that obstruction
has occurred in the right lanein front of the vehicle
If many vehicles areusing their left turning
signal, it is presumed that
obstruction has occurredin front of the vehicle

Fig.5: Basic concept of generating lane-level information from probe information tied to road links(1) The data is collected in 5-minute increments (called "layers"),
based on the travel time of the probe vehicle and including
uplinked data that comes in late, with data collecting goingback a maximum of 30 minutes in the past.
(2) Each segment also aggregates data from the previous
information generation time collected up to that point.
Layer 1 aggregates the most recent 5-minute data, while
Layer 6 aggregates all levels of 5-minute data from theprevious 30 minutes.
When estimating average speed with a certain accuracy
from sample data, an error of ±10 km/h (95% confidence level)
is considered to be acceptable when 5 or more vehicles are
acquired, and so the base level for minimum number of samples
was set at 6.
We also studied data items and data definitions collectedfrom probe operators and subsequently defined a unified data
format (JSON format). Using this format, we collected probe
information from probe operators and conducted the FOTs
(field operational tests) as reported in Section 4.

3.2. Study of functions to integrate data from multiple sources
Here, we examined the specifications for processing the
integration of probe data collected from multiple providers.
When integrating probe statistics from multiple probe providers
into single link unit data, the travel speed and travel speed bydirection took the form of averages weighted by the number ofsamples from each probe provider. In addition, quantitative
data such as speed tier data and other vehicle information(number of activation of turn indicator, etc.) used the sum of
the values counted by each probe operator.

3.3. Study of function for generating lane-level roadtraffic information
Here, we examined the technical specifications forgenerating lane-level road traffic information from the lanespecific probe data as integrated in the previous section. First,
the location seeing a speed reduction in the direction of travel
are identified based on the number of vehicles by speed

classification (speed distribution) in 100-meter link units. If the
distribution of the number of vehicles by speed layer straddles
the line between low speed and high speed, it is assumed thatsome lanes are congested. In this case, at the junction, the lanecorresponding to the direction of the junction is determined tobe congested based on the speed by direction (in DRM linkunits); in other locations, the direction of the obstructed lane
(left/right) is determined based on turn signal information (in100m link units). (Fig.5)

3.4. Study of function for conversion to representable data
Here, we examined the specifications for converting lanelevel traffic information generated on the Metropolitan
Expressway into a location-representable data format forsuperimposition onto a high precision 3D map. In this project,
the probe data is collected from the probe operators in linkformat, with the DRM links divided into 100-meter segments toimprove the resolution of the direction of travel, and during thecreation of lane-level information lane numbers were generated
to display alert information at the upstream end of the link. For
the lane representable data format we adopted an expanded
DRM-DB specification(1), which is compatible with the datacollected from sources such as OEMs. Use of a high precision
3D map then allows for the creation of a node-link map
expressing the number of lanes in each 100-meter section, andmakes it possible to represent warning information for eachlane.

3.5. Study of data sharing (distribution) functions
Here, we examined the required data sharing specifications
between centers when distributing the generated information
to servers that relay the information to participants in thetesting. Specifically, the representation of the lane-by-lane
information for each 100-meter link segment by sending thelatitude and longitude information of the node in question andthe lane number in which the alert information is displayed.
The distribution specifications were based on the Vehicle
Information Shared API Specifications from(2) Japan Automotive

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
3) Technological Development for Lane-level Road Traffic Information Using Probes Vehicle Data

Software Platform and Architecture (JASPAR), with alert
information content used to distribute the information.

4

Outline of Lane-Level Road Traffic Information Distribution Field Operational Tests

In order to evaluate each component technology, weconstructed a FOT system for two routes on the Metropolitan
Expressway. The system was connected online with probe
information providers and capable of delivering information
processed in real time.

4.1. Overall schedule
The first FOT was conducted in the winter of FY2020. A
total of four FOTs were ultimately conducted, seeking to
improve information accuracy while implementing various
enhancements based on the PDCA cycle. (Fig.6)

4.2. O verall configuration of the field operational tests
The test system constructed for the FOTs was as shown in
FY2019, 20

Fig.7. The system was connected online to probe providers andthe own system of the Consortium for the FOTs in the Tokyo
waterfront area, enabling the distribution of information tovehicles participating in the tests. In addition, starting with
FOTs in FY2021, various types of road traffic environment data
(rainfall information, V2N traffic signal information, etc.) that
could be used for appropriate decision-making and control of
automated vehicles was collated with lane-level road traffic
information and distributed to vehicles participating in thetests.
Here, collection of road traffic environment data was
performed according to the distribution specifications of theinformation source. On the other hand, the information was
distributed to the OEM data center or equivalent in accordancewith specifications based on the characteristics of theinformation. The data format for distribution was based on
JASPAR (Dynamic vehicle information sharing specification),
but when high real time performance was required, the data
format of the information source was used without modification,
taking into consideration the data volume after conversion with
JASPAR specification (Table 2).

FY2021

FY2022

Additional information generation function using vehicle event information (turn signal etc.)

3) Study and evaluation of data integration technology4) Study and evaluation of distribution technology5) Study and evaluation of technologies for collecting and distributing various types ofroad traﬃc environment data (rainfall information, V2N traﬃc signal information,
etc.)

Field operational test planning

Test systemdevelopment

Test system
improvements/expansion

Field
operational tests

Dec. 2021 Jan. 2022

Probe data online
connection
destination added

Field operational tests

Feb.
2023

Systemimprovements

Field operational
tests

Field
operational tests

・Additional data

・Improvedfunctionality

Spring (April, May)

Fall (September, October)(On road testing)(Spring)・More data, indicators
etc.

(Theoretical testing/on road testing)Technical evaluation &overview

2) Study and evaluation of probe processing technology

Field operational
tests

Technologicalstudy1) Study of technology for generating lane-level information(Fall)

Fig.6: Overall schedule for FOTs

This project (data generation and aggregation)
Probe operator 1
Probe operator 2
Probe operator n
Navigation systemmanufacturer

Lane-level roadtraffic information
serverField Operational Test ProjectData
aggregation
serverDistribution of information
to small- and mid-scale
network projects and FOT
projects

Lane-level roadtraffic
information

FY2020 FOT system construction
The Japan Meteorological BusinessSupportRainfallCenter (JMBSC)
information
Distributor A **"Research and Development on
Traﬃc Signal Control using GNSS
(location information) and othertechnologies."

Simulated emergency
vehicle location
information

Distributor B **"Constructing the Field Operation
Tests environment to provide signal
information via the public network."

V2N SPaT (Signal
Phase and Timing)
information

Linked distributionfunctionHigh
precision
Map

High precision map
Tied/conversion

Small- and mid-scale network projects *Aggregate information fromvarious sources* (Data conversion/vehicle output)(Data management &distribution)

Data server

Distribution server

OEM centerequivalentDistribution of various road traﬃcenvironment data to automatedvehicles via small- and mid-scale
network projects*"Research and development related to the collection, integration, anddistribution of narrow-range and medium-range information"

This project(data generation and aggregation)
Utilize distributed information forsafe and smooth driving
Lane-level road traﬃc
information

Rainfallinformation

Simulated emergency
vehicle location information

V2N traﬃc signal control
information

Mobile terminal(Mobile router)
BOX-type
PC

Vehiclecontrol

Viewer display
terminal
Correspondence and output conversion withhigh precision maps

Fig.7: Overall structure of the FOT environment

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(1) Technological Development Concerning the Generation of Traffic Environment Data
3) Technological Development for Lane-level Road Traffic Information Using Probes Vehicle Data

Table 2: Collection and distribution methods for various types of road traffic environment data
Gathering information from sources
By information type

Information distribution to OEM center

Information source

Data representation format

Communication
system(1) Lane-level road traffic information

Lane-level road traffic information
serverJASPAR: JSON format
Content：attention

HTTP

1 minute cycle

JASPAR: JSON format
Content：attention

HTTP

1 minute cycle(2) Rainfall information

The Japan Meteorological BusinessSupport Center (JMBSC)GRIB2 *1SFTP

5-minute cycle

JASPAR: JSON format
Content: Environment

HTTP

1 minute cycle(3) Simulated emergency vehicle location
Distributor A
information

Proprietary binary format

UDP

As required

Proprietary binary format

Web Socket

As required(4) Traffic signal schedule information

Proprietary binary format

UDP

As required

Proprietary binary format

Web Socket

As required

Distributor B

Collection cycle

Data representation format

Communication
systemDistribution
cycle*1 GRIB2 format: WMO Maual on Code I.2 FM92 GRIB General regularly distributed information in binary form

Haneda Line inbound toward branch at Hamasakibashi JCT junction
Ginza area

Sections of road used for information generation during
FOTs and primary testing fields for each use caseShinjuku area

Occurrence of lane-level
congestion on the Haneda
Line side

FOT section (1): Route 1 Haneda Line (Kuko West IC to Shiodome IC) L = approx. 14 km (2 lanes in each direction)
HeiwajimaPA*

Suzugamori IC

Congestion tail (junction-entering
support) test case (A-1)

Katsushima IC

Shibaura IC
ShibauraJCT

Oi PA
Kuko-Nishi IC

Tokai
JCT

Congestion tail (junction-entering support)
test case (A-2)

Shiodome ICTokyo Bay TN

Oi JCT
Oi-MinamiIC

From
Hanedaarea

Hamasakibashi
JCT

From Rainbow Bridge area

Shio
dom

Oi IC

Tokyo PortTN area

Rinkai Fukutoshin IC

LegendLane-level congestion on the
Bayshore Route mainline

Kuko-Chuo IC

Metropolitan Expressway
Main line merging point

Ariake JCT

Main line road-split point

Test section (2): Expressway Bayshore Route (Kuko-Chuo IC to Rinkai Fukutoshin IC) L =approx. 10 km (3 lanes in each direction)

IC merging
IC merging

C NTT InfraNet

Shinonome JCT

PA

AriakeHighway Bayshore Route eastbound Tokai JCT merging point*From CCTV footage

Merging from the Bay shore branch routeFig.8: Sections of road used for information generation during FOTs
A-1 Congestion tail (junction-entering support)
Primarily Hamasakibashi JCT junction
Actualphenomenon

Congestion tail*Junction

Generated
information

Primarily Tokai JCT merging point！
！

Within all testing areas

Lane regulation

Informati Traveling direction: Large speed distribution variation
on source Lane direction: frequent right turning sign

Direction of travel resolution(100m units)
0*

Merging

Congestion tailInformati Traveling direction: Large speed distribution variation
on source Lane direction: low speed in the left direction at the junction

B Lane regulation

A-2 Congestion tail (passing support)

Informati Traveling direction: Large speed distribution variation
on source Lane direction: frequent right turning sign
*Displayed when

100

Congestion tail(by lane)

200

300

400

Congestion tail！ (by lane)！ Location of obstruction(By lane)

500

！
！

800
600 congestion occurs700
Location of obstruction
Congestion tail(By lane)(All lanes)

Uses the same
logic

！
！
！

！
！

*The road structure as shown in the figure diﬀers from the actual Hamasakibashi JCT and Tokai JCT connecting points.

Fig.9: Overview of information generation in the target use cases

4.3. Sections of road used for information generation and primary testing points

4.4. Overview of information generation in the target use cases

During the FOTs, information distribution was conducted
for two Metropolitan Expressway routes. (Fig.8) On theinbound Haneda Line, traffic congestion is a regular occurrence
during mornings and evenings on weekdays, leading from theHamasakibashi JCT. With congestion in the direction of
Shinjuku overflowing into the left lane of the Haneda Line,causing lane-specific congestion depending on the time of day,we positioned this as the primary testing point for evaluatinglane-level information. The eastbound Bayshore Route also hasa high probability of encountering congestion on weekday
mornings and evenings, starting from the Tokai JCT and caused
by vehicles merging from the Bayshore Branch Route, and so
this was also positioned as a primary testing site.

[A-1: Congestion tail (junction-entering support)] isintended to be provided at a branch. In the case of a lane-bylane congestion, the system estimates the congested lanes basedon the speed information in each direction at the junction andprovides information on the congestion tail. [A-2: Congestion
tail (passing support)] is intended to be provided at a merging
point. In the case of lane-by-lane congestions, the systemestimates the congested lane based on turn signal informationand provides information on the congestion tail. Furthermore,at the head of the congestion, the system uses turn signal
information to estimate the lane of the obstruction and provides
information on the location of the obstruction. [B: Accidents,
falling objects, etc.] is intended to be provided for all segments.

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(1) Technological Development Concerning the Generation of Traffic Environment Data
3) Technological Development for Lane-level Road Traffic Information Using Probes Vehicle Data

The information generation and provision logic is the same asthat of [A-1: Congestion tail (passing support)], but when there
is little information and congestion does not occur, the system
uses turn signal information to estimate the lane of the
obstruction and provides information as the location of the
obstruction. Furthermore, the position of the congestion tail(obstruction) is distributed in 100-meter section and fiveminute cycles as alert information. If the congestion is estimatedto be in the left lane only, the alert information is displayed in
all lanes except the rightmost lane, and if all lanes are congested(cross-sectional congestion), the alert information is displayedin all lanes. (Fig.9)

4.5. Status of acquisition of probe information
The number of sample vehicles collected online from thefour probe service providers during the FOT period (spring
FY2022) averaged 5.2 vehicles per 5 minutes in Layer 1 (themost recent 5 minutes) across the Metropolitan Expressway
Haneda Line (Kuko West IC to Shiodome IC) FOT information
generation section. (Fig.10) In addition, looking at the actual
distribution of the number of vehicles sampled (Fig.11), only
40% of the sections and time periods in Layer 1 were able toobtain six or more vehicles, and for the remaining 60% of thesections and time periods, information was generated using
samples from Layer 2 (last 5 to 10 minutes) or other levels.
Average number of samples per 5 minutes

Average number of units sampled (units/5 min)16

Bayshore Route 10-15 minutes
Eastbound
beforeHaneda Line
Inbound14

12 Last 10 minutes
10 Last 5 minutes
8

5.2

6

8.4

6.9

6.5

8.0

15-20 minutes
before9.5

8.6

20-25 minutes
before25-30 minutes
before

10.4

10.2

10.5

8.8

8.9

Layer 5Layer 64
20
Layer 1

Layer 2

Layer 3Layer 4

Survey Date: Monday, April 4 to Friday, April 8, 2022 (5 weekdays) 9:00 a.m. to 5:00 p.m.

Fig.10: Average number of probe vehicle samples per 5 minutes

40%

30%

Survey Date: Monday, April 4, 2022 - Friday, April 8, 2022, 9:00 a.m. - 5:00 p.m.

Haneda Line Inbound100%

Average: 5.2 units/5 min.

80%

Information
freshness 4 or less

60%

Information freshness 5
20％

40％
10％

10％

13％

14％ 13％

11％

9％

6％

7％

2％

5％

0％
0

1

2

3

4

5

6

7

8

9

20％
3％ 2％
1％ 1％ 1％0％
10Number of samples

11

12

13

14

Fig.11: Probe vehicle sample volume distribution(Level 1: most recent 5 min)

15

0％

Cumulative composition

Number of samples ratio

50%

Composition ratio of information by number of cases
Cumulative ratio of information by number of cases

N=100 Note 1)

Deviation distance from observed congestion tailNote 2)

t
por
e re
Fals

【Direction-of-travel accuracy【 Lane direction accuracy

Note 3)Note 4)

】 51%

】 63%

Note 1) Period for the spring testing (May 2022): 2022/5/10 - 5/31
Note 2) False report: When congestion tail information was displayed but the corresponding congestion tail was notobserved, such as the test vehicle failing to encounter congestion.
Note 3) Direction-of-travel accuracy: Percentage of cases in which congestion actually occurred after the provision of
information on a congestion tail.
Note 4) Lane direction accuracy: Percentage of the information on the congestion tail that is correct for the direction of travel
and correct for the direction of the lane in which the congestion occurs.

Fig.12: Distribution of deviation distances from the observed congestion
tail position

4.6. Verification of accuracy of generated information
Fig.12 shows the distribution of the number of information
items by distance for the deviation distance between thecongestion tail information provided during the FOT (spring
FY2022) and the actual congestion tail position observed by thetest vehicles. According to the results, 24% of the information
items were provided within 200m of the actual congestion taillocation, and 36% were provided within 400m of the actual
congestion tail location. The directional accuracy rate--thepercentage of cases where congestion was actually observed-topped out at only 51% of the total number of provided
information on congestion. In those cases in which the direction
was accurate, the lane directional accuracy rate--the percentage
of cases where the direction of the congested lane was correct-was 63%. Some of these factors require medium-term
improvement, such as shortening the current 5-minute cycle
and increasing the amount of acquired turn signal information.
On the other hand, FOTs will be conducted in the fall of FY2022
to confirm the improvements that can be made using current
methods of determination, with those that prove effective beingreflected in the technical specifications.

5

Future Direction and Issues

In the second phase of SIP-adus, we have been working toclarify the business structure and establish a business model inparallel with technical studies for the generation and provisionof lane-level road traffic information through technical testing.
Issues to be considered for practical application include
clarification of the division of functions and roles, establishment

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
3) Technological Development for Lane-level Road Traffic Information Using Probes Vehicle Dataof a sustainable operating system, establishment of a data
infrastructure development and maintenance system, andclarification of a roadmap for service expansion.

6

Conclusion

We actually collected real probe information from multiple
probe operators via cars on the market, and examined andtested the generation of lane-level road traffic information andmethods for its representation. Although the amount of probe
data was limited, we conducted FOTs on two Metropolitan
Expressway routes to investigate the usefulness of theinformation and to identify any further issues. As a result, wewere able to obtain results regarding the amount of informationthat can be collected and the possibility of generating lane-levelroad traffic information using probe data, with a view towardpractical application.

[References]
(1) Japan Digital Road Map Association, "Draft Data Standard for Expanded
DRMDB (Outline and UML Class Diagram)" and "Data Standard for Expanded
DRM-DB (Record Format)" (Version 12/11/2019)(2) J ASPAR, ST-VI-1 Dynamic Vehicle Information Sharing API Specifications
Ver.1.0, Dynamic Vehicle Information Sharing Study WG, 2020.1.17[Contacts]
Pacific Consultants K.K., Digital Services Division, 3-22 Kanda Nishiki-cho,
Chiyoda-ku, Tokyo 101-8462, 03-6777-4476, Contact: Hirokazu ICHIKAWA
Hirokazu, Atsushi TAKENOUCHI
Mitsubishi Research Institute, Inc., Smart Region Division, 10-3, Nagatacho
2-chome, Chiyoda-ku, Tokyo 100-8141, 03-6858-0241, Contact: Yukimasa
MORISAKI60

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2

Establishment and Utilization of Traffic Environment Data

(1) Technological Development Concerning the Generation of Traffic Environment Data

4) Technological Development andEstablishment of Simulation Environment
for Lane Merging Assistance
Nobuhiro Araki, Kenta Shintoku (KOZO KEIKAKU ENGINEERING Inc.) ,Koichi Miyashita, Satomi Aiko (Mitsubishi Research Institute, Inc.)(Abstract) Merging on expressways is one particular use case in which automated vehicle controls are difficult toachieve, as the vehicle must merge by selecting and moving into a space between other vehicles that are being driven
at high speeds on the main lane. In particular, expressways in urban areas, including the Tokyo Metropolitan
Expressway, provide little distance to merge. As such, vehicles must merge into gaps between other vehicles on themain lane after accelerating to their speed in just a short merging area. We believed that smooth merging in this area
could be realized by using a vehicle to infrastructure (V2I) communication system to share vehicle location information
with automated vehicles to assist with their automated driving. In this study, we built concepts and evaluated system
feasibility by using a simulator to test a "lane merging assistance system," assisting merging using a V2I communication
system. Specifically, we acquired tracking data for actual traffic patterns based on the outbound Higashi Ikebukuro
Entrance of the Metropolitan Expressway Route No. 5 Ikebukuro Line, and then developed a traffic flow simulator thatrecreated it. Using this simulator, we compared and analyzed the impact of changing merging assistance system
factors such as communication and sensing area on the main lane traffic flow and merging vehicle behavior, both withand without assistance.
Keywords: Merging assistance, service feasibility, merging assistance system to select and enter inter-vehicle gap on the main lane(Day 2 system), cooperative assistance system for main lane vehicles (Day 3 system)

1

Background and Purpose of the study

To realize automated driving on expressways, merging
points are one location that require sophisticated driving
maneuvers. The merging vehicle must merge into a gap
between other vehicles on the main lane after accelerating to
the target speed. On the expressways in urban arears, manyroads with shorter merging lanes, where it is also hard toscan the main lane for merging beforehand, make driving
maneuvers more difficult.
We believe smooth merging of vehicles would be possible
by providing information on vehicles in the main lane to themerging vehicle via a merging assistance system andproviding navigation for cooperative action to the main lanevehicles on the expressway.
1.1. Concept of Merging Assistance System
From the perspective of technological feasibility, the Japan
Automobile Manufacturers Association, Inc (hereinafter called"JAMA") defines four steps as Day 1 to Day 4 systems for

merging assistance systems. (Fig.1)
Day 1 and Day 2 systems presume scenarios from free traffic
flow to slightly tight traffic. The Day 1 system delivers a traffic
forecast based on sensing from a main lane traffic cross-section,(merging assistance to adjust speed on merging lane). Then thesystem assists in merging at the same speed as the main lane
traffic, in the case that the traffic is less busy, after assessing thenecessity for handover based on main lane conditions. The Day
2 system performs entire-area sensing and communicates
information continuously (merging assistance to select andenter inter-vehicle gap on the main lane). Knowing the traffic
flow on the main lane beforehand from continuously delivered
information allows a vehicle to smoothly enter the flow withspeed being suitably adjusted beforehand when targeting aninter-vehicle gap on the main lane.
In the Day 3 system, based in a scenario in which more than
30% of traffic is automated and equipped with on-board
equipment, and when additionally the traffic is tightly packed,
merging assistance navigation to main lane vehicles provided byroadside infrastructure is added to the Day 2 system (cooperative
assistance for main lane vehicles with roadside infrastructure).
This assistance seeks to control the speed of merging vehicles by

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data

Ad opt i on r ate of au tom ate d d r i v i n g w i t h on - b o a rd e qu i pm e nt

4) Technological Development and Establishment of Simulation Environment for Lane Merging Assistance

Day 1

Day 2

Day 3

Day 4

Initial implementation

Implementation

Adoption rate 30%+

Adoption rate 50%+

Merging assistance to adjust speed
on merging lane

Merging assistance to select and enter inter-vehicle
gap on the main lane

Measure main lane traffic(speed/concentration) and deliver thedata before merging

In addition to Day 1 system,
dynamically deliver movement of
main lane vehicles near the merging
area

Cooperative assistance for mainlane vehicles with roadside
infrastructure

Merging assistance in heavy trafficwith negotiation by inter-vehiclecommunication

In addition to Day 2 system, add
merging assistance navigation to mainlane vehicles from roadside
infrastructure

In addition to Day 3 system,
negotiation via V2V
communication for heavy
traffic

Merging assistance navigation via
roadside infrastructure

Negotiation via V2V
communication*Not possible to forecast accurate vehicle
behavior in merging area

Cross-section data sensing + forecast
information delivery

Area covering measurement + successive
information delivery

Deliver main lane traffic
information (ETC2.0, etc.)
Cross-section
sensing of mainlane traffic

Acceleration lane
starting point

Successive
sensing of mainlane traffic

Successively deliver mainlane traffic information
Acceleration lane
starting point

Navigate each vehicle to merge
alternately (during heavy traffic)

Request to adjust
inter-vehicle gap
or to change lane

[Assistance details] Merging vehicles todetermine necessity of hand-overbased on main lane traffic and toaccelerate to match the main lane
speed in case of less traffic[Assistance details] Merging
vehicles to adjust speed not tocompete with main lane vehicles
based on main lane traffic

From uncongested traffic to relatively tight traffic flow
S u i t a b l e[Assistance details] Merging vehicles toadjust speed not to compete with mainlane vehicles based on main lane traffic,
main lane vehicles to adjust inter-vehicle
gap or to change lane for easier merging[Assistance details] Vehicles
to merge with automatedtechnologies when congested

Up to tight traffic flow

Traffic flows including
heavy traffic environment

e n v i r o n m e n t*Necessary to define system requirements with consideration of truck platooning

Fig.1: Merging Assistance Step-by-Step Development Process(1)1.2. Purpose of the Study
The SIP-adus (Cross-ministerial Strategic Innovation
Promotion Program (SIP) Automated Driving for Universal
Services) aims to achieve early realization of a lane merging
assistance system. As the first step, a system which can bepromptly implemented, the Day 1 system, was considered by
combining existing technologies. For the second step, we are
now studying a system which will be realized in the future,
the Day 2 system.
During the SIP-adus, we applied the Day 1 system at the
Airport West IC on the Haneda Line of the Metropolitan
Expressway during FY 2019 and 2020 as part of the FOTs in
the Tokyo Waterfront area, in order to conduct test driving
and evaluate the system. As a result, despite comments fromdrivers of the Human Machine Interface evaluation test thatthe system has potential, the Day 1 system using the method
to estimate merging timing by cross-section sensing was
unable to provide sufficient accuracy in heavy traffic.
Therefore, we concluded that the Day 2 system using entirearea sensing and providing the data for the main lane is moresuitable. Expecting the prompt realization of the merging
assistance system, in order to clarify issues for service

availability and social implementation, we decided to test the
Day 2 and Day 3 systems via simulation during our FY 2021
and 2022 activities.
We therefore carried out during this study simulation
tests for the Day 2 system, based on which proceeded thedetailed study for Day 2 and discussed concepts for the Day
3 system while studying its feasibility.

2

Selection of Area for the Simulation

In order to select an area for the simulation, weinvestigated alignment of roadways around merging areas of
the interchanges on the Metropolitan Expressway, thenclarified the length of the merging lanes as well as traffic
volume of each area.
Ikebukuro Line (outbound)
Higashi Ikebukuro entrance
Merging lane length 68.17 meters
Main lane traffic volume 32,840/day

Traffic volume on main lanes
per day (10,000 vehicles)

watching main lane traffic so as not to compete with main lanevehicles, and also to adjust the inter-vehicle gap of main lanevehicles or to change lanes to allow for easier merging.
In the Day 4 system, assistance scenarios consider heavy
traffic patterns in which 50% or more of vehicles are automateddriving with on-board equipment. Based on the Day 3 system,
the further system aims to provide automated merging
assistance by negotiations via vehicle to vehicle (V2V)
communication during heavy traffic (merging assistance inheavy traffic with negotiation by inter-vehicle communication).

"Distance from hard nose to taper edge" and "main lane traffic volume"
New ICOld IC
Other IC

5
4
3

Haneda Line (inbound)
Airport West entrance

2

Merging lane length 91.5 meters
Main lane traffic volume 41,440/day

1
0
0

50

100

150

200

250

300

Distance from hard nose to taper edge (m)

350

400

Fig.2: "Distance between hard nose and taper edge" and "main lane traffic volume"in the Metropolitan Expressway merging area

Fig.2 shows that the outbound Higashi Ikebukuro
entrance on the Ikebukuro Line has a merging area withextremely short merging lane length. We also found that
there were two peaks in the distribution of the Metropolitan
Expressway merging lane lengths, one for ICs using the old62

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
4) Technological Development and Establishment of Simulation Environment for Lane Merging Assistance"Distance from hard nose to taper edge" ratio (only IC n=85)
Ikebukuro Line (outbound)
Higashi Ikebukuro entrance
Merging lane length 68.17 meters

Haneda Line (inbound) Airport
West entrance
Merging lane length 91.5 meters8％6％Tomei (inbound) Yokohama Machida entrance(Reference)
Merging lane length 298 meters

4％

New ICOld IC

〜390

〜370

〜350

〜330

〜310

〜290

〜270

〜250

〜230

〜210

〜190

〜170

〜150

〜130

〜90

〜110

〜70

〜50

〜30

〜10

0％

Distance from hard nose to taper edge (m)400 meters or
more

Other IC2％

Fig.3: Percentage of "Hard nose-Taper edge length" in whole merging area

standard and the other for those using the new. (Fig.3)
Areas with short merging lane lengths (old standard),
including the Higashi Ikebukuro entrance, provide little time
between starting and completing the merge. This means there
are many potential cases in which merging assistance could be
helpful. Areas with comparatively long merging lane lengths(new standard) make it possible to merge via autonomous
driving: that is automated driving without human assistance.
On the other hand, the Ministry of Economy, Trade andIndustry’s SAKURA Project (Safety Assurance KUdos for
Reliable Autonomous vehicles) prepared vehicle tracking
data for the Higashi Ikebukuro entrance. JAMA also has asimulation that recreates merging behavior at the Higashi
Ikebukuro entrance. Therefore, for the FY 2021 and 2022
initiatives, we decided to carry out a simulation using theoutbound Higashi Ikebukuro entrance on the Metropolitan
Expressway Route No.5 Ikebukuro Line.

3

Study of Day 2 System Feasibility

3.1. Definition of Merging Quality
In order to test the feasibility of merging assistance systems, itis necessary to evaluate the degree of improvement in merging
behavior via the service by first defining what is better or worse
regarding merging.
As an indicator to determine the quality of merging
behavior, we defined and introduced evaluation scorescalculated using inter-vehicle time and relative speed. The
vehicle subject to calculation of the score is the vehicle in theInter-vehicle
time

100 points

main lane nearest to the merging vehicle while merging.
The relationship between the score used in this analysis andinter-vehicle time/relative speed is shown in Fig.4. Smaller
relative speed and larger inter-vehicle time gaps showed safer
and more efficient merging with higher scores. Considering thedanger of collisions, areas where easier merging is possible were
evaluated with 100 points. The father away from that area, thelower the evaluation score, and area with less than 0 points were
defined as those with no margin and unsuitable for merging.
3.2. Simulation Environment Construction
As the basis for the simulation environment, we used asimulation model that recreated the merging behavior at theHigashi Ikebukuro entrance to Metropolitan Expressway No
5. (hereafter called the "Higashi Ikebukuro Model"). The
simulation model is owned by Kozo Keikaku Engineering.
The traffic flow data applied in the Higashi Ikebukuro Modelis the real traffic data acquired by the SAKURA Project
excluding data during traffic congestion.
In order to conduct effectiveness verification simulations of
the Day 2 system, we added a function to simulate the Day 2
system, as well as behavior rules for merging vehicles assisted by
the Day 2 system to the Higashi Ikebukuro Model. The former
is to consecutively deliver data of main lane vehicles acquired
from main lanes sensing areas toward merging vehicles in thecommunication area. The latter is merging vehicles to accelerateand decelerate so as improve the merging evaluation scores.
The following parameters were introduced into thesimulation as physical conditions of this system in order totest requirements of the Day 2 system. (Fig.5)•Length of sensing area
•Length of communication area
•Information delivery delay by vehicle detection sensor•Positioning error for delivered information by vehicle
detection sensors•Velocity error for delivered information by vehicle detection
sensors
Communication
area start point x =-145 meters

Communication area
Sensing area

Sensing area start point
x = -200 meters~
100 points

Merging in sufficientgap is possible

Upstream side of Downstream side
Merging behavior
decision-making starting point merging area edge of merging area
edge x = 50 metersx = 0 meters
x = -25 meters

Fig.5: Road alignment for the simulation

99 points~

3.3. Verification of the Day 2 System's Effectiveness0 points-1 points~

1.65 sIn narrow gap, but
merging ispossible-100 points~-200 points~

1.15 sMerging without
enough gap
7.0 km/h

Merging area

14.0 km/h

Relative speed

Fig.4: Merging evaluation score map (tentative)

We conducted simulations for scenarios with and without
the Day 2 system's assistance and compared the scores foreach. The ideal physical conditions for the Day 2 system werethat the sensing area and communication area were
sufficiently long and that there was no information delivery
delay or transmission information errors. Specifically, theparameters were set as follows: the length of sensing area at a
maximum of 200 meters, starting from the point where the

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
4) Technological Development and Establishment of Simulation Environment for Lane Merging Assistancemerging is possible toward direction of upper flow of traffic;
the communication area is 120 meters from the point wherea driver makes a merging decision toward the direction of
the upper flow.
The results of comparing the scores in scenarios with andwithout assistance with 20% of automated vehicles in traffic
are shown in Fig.6. Considering the decrease of the number of
merging vehicles with less than 0 points, applying the Day 2
system could increase the number of merging with sufficient
gaps or decrease the number of merging without enough gaps,
thereby improving the overall evaluation score. Overall, thesystem improved the scores. As a result, under ideal physicalconditions in the Day 2 system, we concluded that the Day 2
system is effective in achieving better and safer merging.

Table 1: Day 2 system physical conditions parameters verification scope andimpact on assistance effectivenessPhysical conditions
parameters
Length ofsensing area

Verification scope of parameters

Impact on assistance effectMaximum 200m-Minimum 140m to the upper flow direction
Mostly no impact
from the point where the merging is possible

Length ofcommunication area

Maximum 120m-minimum 40m from the start point ofdecision-making for merging behavior toward the upper flow
direction

Information delivery
delay

Mean value of 0-1.3 seconds, normal distribution of 0.2 seconds Larger the latency, the more thestandard deviation
negative impact

Delivered information
Uniform distribution of -1m to +1m
positioning error

The shorter the length, thegreater the negative impact

Mostly no impact-6km/h to 6km/h,
Negative impact only when
Delivered information-12km/h to +12km/h,
speed error distribution is overly
velocity error
Normal distribution with -12km/h to 0km/h in 95% percentile negative

4

Detailed Verification of the Day 2 System

Merging evaluation score (automated vehicles only)
Less than 0 points
No assistance 20%
automated vehicles

0-99 points

53

With assistance 20%
automated vehicles

21

29

43

25

0

100 points

20

40

35

60

80

100

Number of vehicles
Fig.6: Distribution of the scores for each scenario

For the vehicles that scored less than 0 points, meaning
they were not in a situation suitable for merging even in the
scenario with assistance, we checked the simulation videos toclarify the cause. This verification revealed that when themain lane is busy the inter-vehicle gap on the lane cannot be
sufficiently secured for effective assistance by the Day 2
system. Under such circumstances, we believe it necessary toconsider assistance via the Day 3 system or beyond, including
assistance vehicles on the main lane, to improve merging
maneuvers.
Furthermore, we tested the impact upon surrounding
traffic flow by merging assistance, but no impact was
confirmed.
3.4. Verification of the Day 2 System's Physical Conditions
We conducted a simulation changing the Day 2 system's
physical conditions as a parameter and verified the impact ofcondition changes on assistance effectiveness. The impact ofeach physical condition is shown in Table 1. The verification
scope for the sensing and communication areas was calculated
from the driving distance necessary to adjust speed of thevehicle receiving the Day 2 system's assistance. The verification
scope of information delivery delay, error (location) of vehicle
detection sensor and error (speed) were decided by referencing
existing research.(2)(3)

In section 3, we verified the effectiveness of the Day 2
system. In this section, we further developed this, estimating
the effectiveness of the Day 2 system over combinations ofvarious physical condition parameters in order to verify theDay 2 system requirements.
Changes in traffic volume are also expected to greatly impact
assistance effectiveness, especially during congestion on themain lane. Therefore, in this section, we will evaluate the impactof traffic volume on the Day 2 system assistance effectiveness,allowing us to further test the Day 2 system.
4.1. Improvement of Simulation Environment
We identified three issues in the simulation environment
used in Section 3 and earlier, and therefore revamped thesimulation environment prior to this detailed verification toaddress each of them. The issues and their solutions are asshown below.
(1) Revision of the evaluation score map
When using the evaluation score map for analysis in
Section 3, we found a non-negligible amount of cases inwhich scores were not directly linked to the behavior ofvehicles in the simulation. For the detailed study in thissection, we revised the map to realize an evaluation moreclosely based in reality. With the applied formula to calculate
boundaries, it is possible to evaluate based on the intervehicle gap while accounting for the response delay oftrailing vehicle maneuvers. Table 2 shows the formula forboundaries calculation at scores of 0 and 100 in the evaluation
score map.
A merging and a main lane vehicle getting closer together,and the two same vehicles moving apart create two situations
that are almost completely different in terms of the difficultof the merging maneuver. As such, in order to correctly
distinguish these situations to evaluate, the scores for the
relative velocity in negative field are also revised. The final
version of the relative score map is shown in Fig.7.

64

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
4) Technological Development and Establishment of Simulation Environment for Lane Merging AssistanceItem

0 points

100 points(1) Inter-vehicle gap after decelerating
Time margin [m]1525(2) Reaction delay
Time [seconds]

1.0

1.5

(3) Deceleration speed [G]

0.3

0.2

A (x: Relative speed)x2──＋1.0x＋15
0.6Gx2
──＋1.5x＋25
0.4G

Inter-vehicle distance [m]100

100 points or higher:
Merging in enough gap ispossible800-100 points: In narrow gap, but
merging is possible

60

40

0 points or lower: Merging without
enough gap

20

0

-50

-25

0

25

50

Relative speed [km/h]

Fig.7: Final version of the evaluation score map(2) Construction of main lane vehicle behavior model prior
to entering the merging area
As we did not have the real trajectory data of main lanevehicles prior to entering the merging area during testing,uniform linear velocity was applied to the vehicles. Therefore,lane changes, speed changes, and others maneuvers were notcorrectly reflected in the model. We recognized this as aremaining issue.
Vehicle behavior, such as lane changes and speed changes
in the sensing area, is considered to be a factor that changes
the main lane situation and is expected to greatly impact on
the effectiveness of the Day 2 system assistance. Therefore, forfurther evaluation of the Day 2 system, we plan to construct
the behavior model of main lane vehicles in the upper stream
traffic flow, improving the simulation in the sensing area. We
intend to acquire additional data of real trajectory of vehicles
in the main lane upper stream for these purposes.
(3) Revision of automated vehicle behavior model
In the Higashi Ikebukuro Model, the non-automatedvehicles (hereafter called "standard vehicles") are programmed
to keep the desired gap according to the distribution of the intervehicle gap captured from real trajectories. The standard vehicles
keep the desired gap as programmed for traffic flow, and there
are not a few vehicles that have a time between vehicles of lessthan 1 sec. On the other hand, it is more common to recognizethat an automated vehicle keeps the gap longer. Hence theautomated vehicles are programmed to basically keep the intervehicle time gap at 2 seconds.
4.2. Verification of the Day 2 System Requirements
The simulation is carried out changing the Day 2 system
physical conditions as parameters to evaluate the impact on

the assistance effectiveness by this parameter change. It is
expected that assistance effectiveness will be enhanced when
the simulation area is larger and the delay and errors are
smaller. The system requirements will be verified byinvestigating physical conditions that make assistance more
effective compared with circumstances without said
assistance.
4.3. Evaluation of Impact of Changing Traffic Volume on
Day 2 System Assistance EffectivenessIf there is light traffic volume in the main lane, it ispossible to merge smoothly even without merging assistance.
On the other hand, if there is heavy traffic volume, it isdifficult to merge smoothly even using Day 2 system
assistance because the gap between vehicles in the main lane
is narrow. The assistance effectiveness of Day 2 systems
differs based on the changes in main lane traffic volume. The
system is most effective under the conditions when the mainlane traffic volume is at optimal levels, and below maximum
traffic capacity of the road.
Time

October 16, 2021 (Saturday) - October 22, 2021 (Friday)

Saturation level: Low

90
80

Average driving speed [km/h]

Table 2: Formula for 0 and 100 point threshold in the evaluation score map

0:00

23:55

Saturation level: High

70
60
50
40
30
20
10

Heavy traffic is outside scope of Day 2 and Day 3 assistance

0

50

100

150

200

250

Number of passing vehicles [vehicles/5 minutes]

Fig.8: Categories of traffic volume and QV chart based on traffic counter
data measured in simulated area

In order to evaluate the impact of main lane traffic
volume, we estimated the effectiveness of Day 2 system
assistance in the four traffic volume areas shown in Fig.8,
using the simulation. The traffic counter data in the simulatedarea was provided by Metropolitan Expressway Company
Limited. By comparing the assistance effectiveness in thefour traffic volume areas, we investigated the traffic volume
that is more effective for Day 2 system assistance and less for
Day 3.
In the case of congestion, V2V communication andcooperation in the merging area are necessary. As long as thesituation is out of scope of Day 2 and Day 3 systems, we
excluded the case from this study.

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
4) Technological Development and Establishment of Simulation Environment for Lane Merging Assistance5

Concept Planning and Feasibility
Verification of the Day 3 System

5.1. Concept Planning of the Day 3 System
The Day 3 system gives merging assistance for both themerging vehicle and the main lane vehicle. However, if themain lane vehicle is not an automated vehicle but rather astandard vehicle, the system cannot provide it with merging
assistance. Furthermore, if the gap between the trailing car
and the leading car in the main lane is sufficiently wide, thenthe merging vehicle can merge smoothly without assistancefor the vehicles in the main lane. Hence what is provided by
Day 3 system assistance must differ depending on types ofsituation of vehicles around the merging point during the
maneuver. This program was therefore designed to determine
whether to assist the merging vehicle and main lane vehicles
based on two factors: whether the three main lane vehicles (avehicle considered as leading by the merging vehicle, itstrailing vehicle, and another following vehicle) are automatedvehicles or not, and as second, the inter-vehicle time between
each main lane vehicle.
5.2. Verification of Day 3 System's Effectiveness
It can be expected that assistance effectiveness would be
improved by implementing the Day 3 system in situations
when the effectiveness of Day 2 system assistance fell due tohigh traffic volume. In planning the concept for the Day 3
system and verify effectiveness, we apply traffic volume inwhich assistance effectiveness was lower during the detailed
verification of the Day 2 system. The traffic flow volume forthe verification will also be estimated by simulation. The
physical conditions for the Day 3 system are equivalent to theideal physical conditions and the Day 2 system requirements.

6

Expectations of Technological
Outcomes in Fiscal 2022

In FY 2022, we plan to construct the vehicle behavior
model for the main lane traffic stream and revise the automatedvehicle behavior model. Further, we will analyze the effects ofDay 2 system assistance, systems requirements, and Day 3
system effectiveness. The following are expected as results.
•D ay 2 system effectiveness and system requirements using
simulations where vehicle behavior reproducibility isimproved in the sensing area
•Evaluation results on impact of traffic volume on Day 2 system•D ay 3 system assistance effectiveness in traffic flow where
Day 2 system assistance effectiveness is reduced•E stimated one-day assistance effectiveness of Day 2 systemand Day 3

7Conclusion

7.1. Considerations for this Study
The study of this project was carried out in a limited
model, applied to the Higashi Ikebukuro entrance and using
standardized vehicles (non-large-sized) alone.
7.2. Recommendations for Future Development andAnalysis
In this project, we built a simulation environment toevaluate the assistance effectiveness of Day 2 and Day 3
systems, and evaluated these around the Higashi Ikebukuro
entrance. Based on the results of this project, we areconducting discussions with related parties participating in
the meetings about merging assistance systems simulations
to continue discussions after the conclusion of the second
phase of SIP-adus, hopefully leading to the realization of
merging assistance systems.
As noted in 7.1, this study is limited in terms of road alignment
and type of vehicles. The assistance effectiveness and system
requirements for Day 2 and Day 3 systems are thought to differ
based on the road alignment in the merging area. Therefore,
using the simulation built for this project, it would be preferable
to also evaluate the assistance effectiveness of Day2 and Day 3
systems in merging areas besides the Higashi Ikebukuro entrance.
In addition, it is possible to consider further developmentof the previously mentioned simulation environment forlarge-size vehicles as a future expectation. When doing so, itwill be necessary to take into account for the accelerationand deceleration range of the vehicle, which is smaller thanstandard-sized cars.

[References]
(1)

(2)

(3)

NEDO: FY2021 Briefing materials for open call to the second phase of SIP-adus/
Construction and analysis of the Simulation Environment fot the Merging
Assistance Systems (targeting Main lane inter-vehicle gap) Verification, https://www.nedo.go.jp/content/100936118.pdf (Reference 09/01/2022)
Toshimasa Nakagawa, et. all: "Verification of Merging Support Information
Provision System (DAY 1 system) through Field Operational Test in theTokyo Waterfront Area" JSTE Journal of Traffic Engineering, Vol. 8, No. 1
pp. 39-48, 01/2021.
Toshimasa Nakagawa, et. all: "Basic Study on Measurement Accuracy ofVehicle Detection Sensor of Merging Support Information Provision System(DAY 2 System)" JSTE Journal of Traffic Engineering, Vol. 8, No. 1 pp. 49-58,
01/2021.

[Contacts]
KOZO KEIKAKU ENGINEERING Inc., Innovative Information Technology
Department, Data Science Section, 4-5-3 Chuo, Nakano-ku, Tokyo 164-0011, 035342-1100, Contact: Nobuhiro Araki
Mitsubishi Research Institute, Inc., Smart Region Division, Mobility Strategy
Group, 10-3, Nagatacho 2-chome, Chiyoda-ku, Tokyo 100-8141, 03-68580241, Contact: Koichi Miyashita

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2

Establishment and Utilization of Traffic Environment Data

(1) Technological Development Concerning the Generation of Traffic Environment Data

5) Improvement of Data Accuracy of Traffic
Regulation Information
Moto Baba (JARTIC), Makoto Maeda (TOSCO CORPORATION), Ryo Sakaguchi (Dawn Corporation)(Abstract) Held over three years from fiscal year 2020, this research aims to improve the precision of traffic
regulation information data managed by the police department that is required to establish a traffic environment
in which automated vehicles are able to drive safely and securely. In order to enhance traffic regulation
information precision, we deliberated issues and improvement plans for the standard format of existing traffic
regulation information and prepared a new expanded standard format and its manual. Through the FOTs (Field
Operational Tests), based on the model system requirements definition (proposal) prepared in fiscal 2020, wedeveloped and tested a model system to confirm consistency between the traffic regulation information and theinformation conveyed with traffic signs and marks and to link these information, and also the image recognition
technology was evaluated, In fiscal 2022, we used the technology and expanded standard format developed theprevious year to construct a prototype system, conduct FOTs related to improving traffic regulation information
data precision and effects verification, and organized a requirement definition (proposal) for the system
implementation.
Keywords: Traffic regulation information, traffic signs and road markings information, image recognition technology,
data precision improvement, expanded standard format, high-precision 3D map

1

Purpose and Overview of Research

A vehicle can capture high-precise data of road signs andmarkings on the road with on-board camera and mobile
mapping system (MMS). However, it is difficult to produce
traffic regulation information from this data. Therefore, we
conducted research related to improving traffic regulation
information data precision for the utilization of safe driving
support information in order for automated vehicles to drive
safely and in accordance with traffic regulations by improving
precision of data derived from traffic regulation informationthat is held and released by the police department.
1.1. T echniques to Improve Traffic Regulation
Information Data Precision
We aimed to improve traffic regulation information data
precision by comparing road signs and markings on the roadagainst the traffic regulation information held by the police
department. We used two methods for collecting road signs/
markings data. As Method-1, we extracted and collected roadsigns/ markings data using image recognition technology
from imagery data of an onboard dashboard cameras. As
Method-2, we collected road signs/ marking data using
smartphone web applications (the field-research application).
The collected road signs and markings data were registeredinto the road signs/marking position prediction system usedin FOT, in which the data was automatically compared withthe traffic regulation information, then this was followed by aconsistency check in which it was categorized as matching or
not matching, aiming for improving the precision of the traffic
regulation information. For signs and markings recognized as
unmatched without consistency, a field research was conducted
as Method-2 to verify again the consistency of data, then weverified improvement of the data precision, as well as worked
to find an optimal method which can improve the traffic
regulation information precision.
As for the image recognition system, we studied how to
support production of traffic regulation information fromcollected road signs and markings.
Additionally, the standard format of the traffic regulation
information we had used contained structural issues. Therefore we
prepared a new version as the expanded standard format (proposal)that can unify the management of traffic regulation information forthe purpose of solving the issues and improving the data precisionof traffic regulation information, along with a user manual.
1.2. Outline of the FOTs
In fiscal 2020, we prepared a requirements definition(proposal) for model system development. In fiscal 2021, weconstructed a model system (the field-research applicationand the road sign position prediction system) and conducted
FOTs in the target area of Kanagawa Prefecture.

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
5) Improvement of Data Accuracy of Traffic Regulation Information

In order to improve the precision of data effectively, we
conducted a study to automatically recognize road signs andmarkings data recorded by a dashboard cameras using image
recognition technology. In fiscal 2022, we constructed a
prototype system using technology developed in fiscal 2021
and the expanded standard format, conducted FOTs and effectverification, and prepared a systems requirement definition(proposal) to add functions into the traffic regulation
information management systems of the National Police
Agency and prefectural police.

2

Construction of Model System

2.1. The field-research application
The field-research app supports field research of roadsigns and marking with smartphones. The application has
the function to collect road signs and markings data and to
display traffic regulation information.
The field-research application is used in a web browser on asmartphone device. It compares traffic regulation data displayedon the smartphone with road signs placed on the road and canrecord or revise data if there are any insufficiencies or errors.
Information that can be recorded include road sign administrative
information, road sign board types, state of deterioration, andphoto images. It is possible to link pieces of information manually
as with the road sign position prediction system.
By using this, it is possible to tentatively link signs that
could not be automatically linked to traffic regulation
information with road sign position prediction system.
2.2. Road Sign Position Prediction System
The road sign position prediction system is a system that
supports linking of data to verify consistency of traffic
regulation data and road sign data, as well as identifying
erroneously recorded data in the system. First, importing
traffic regulation data controlled by the prefectural polices,
the system predicts position of road sign corresponding toeach regulation data, then check if targeted road sign exists
inside predicted area to automatically make tentative linkageof the regulation data with the sign data. The results of theautomatic tentative linking were confirmed on the road sign
position prediction system map screen and list screen. For
data that could not be linked, the operator can manually link
data on the prediction system. For the position prediction,
we applied a best candidate method for each traffic regulation
type out of multiple prediction methods. Also, we created asystem that can adjust parameters because registration of
coordinate data and linking of traffic regulation data withtraffic signs data, these are conducted with various ways ineach prefectural police.

2.3. FOTs with the Model System
The fiscal 2021 FOTs were conducted for regional traffic
regulation information covering district held by some police
departments in Kanagawa prefecture.
The procedures for these tests were as follows. (Fig.1)·The standard format data and road sign data of the target
area are registered in the road sign position prediction
system, then are made tentative linkage.
Following verification of the tentative linkage result, field·
research is carried out for areas of low precision using the fieldresearch app, then pieces of the data are linked again as tentative.
·The tentative linkage is compared with the traffic regulation
linkage data which has been made in the prefectural police
system, then verified eventual linkage.
·The position prediction methods are reconsidered based on
the result of linkage to find more optimal one, which istested then used to make another tentative linkage.
Scope of FOTs (Field Operational Tests)
Standard format data

Standard data (held by prefectural police)
Data creation

Road signs position prediction systemtentatively kinked(1) Tentative linkage XX%

not tentatively linked

Field-research application etc. (Field research)tentatively kinked

not tentatively linked(2) Tentative linkage XX%

Road sign prediction system modification

tentatively
kinked(3) Tentative linkage XX%

not tentatively
linkedIs there a diﬀerence? Reason?
= Reconduct research?

(4) Tentative linkage: failed X XX% investigation of causes
Is the sign wrong? The regulation wrong? Other?

Compare with current linage data

Fig.1: FOTs flow chart

As for verification of effectiveness, we calculated"tentative linkage rate" and "precision" of the collected traffic
regulation data in accordance with the flow chart in Fig.2. As
a result of 5 times of revising position prediction method, thetentative linkage rate was 91.5% and the precision was 93.5%.
The 27 items of traffic regulation data that could not be linked
were all able to be linked through the field research with theapplication.
[Tentative linkage rate] The percentage of the number oftentatively linked traffic regulation data against the numberof traffic regulation data subject to the testing[Precision] The percentage of the number of correctly linked
regulation data against the number of data of tentatively linked
regulations which are also made linkage in the prefectural
police system tudy and Evaluation of Image Recognition Technology
2.4. S
As for the image recognition technology, we studied
method to collect and extract with easier way informationsuch as road signs and marking location from image datacollected through the prefectural polices’ duties including
installation and inspection of such road signage, in the flow as

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
5) Improvement of Data Accuracy of Traffic Regulation Information

Tentative linkage
evaluation(1) All data
Regulation subject
to evaluation*Regulation with position
information

Automatic
tentative
linkage
Success(2) Tentatively
linkedRegulation having
linkage in thepolice system

Precision evaluation
Precision
evaluation
Correct

Regulation having
no linkage in thepolice system(3) Not tentatively
linkedFailureRegulation having
linkage in the police
system(4) Correct linkage
Regulation of which tentative
linkage data has correspondence
to the police's(5) Incorrect linkage

Incorrect

Regulation having nolinkage in the police
systemRegulation of which linkage
data doesn't havecorrespondence to the police's.

Fig.2: Evaluation flow in FOTs

shown Fig.3.
2.4.1. Research and Evaluation of the Technology
We performed studies from perspectives shown in Table
1 for the technologies of extraction, recognition, and position
estimation required to construct the systems.
For the technological evaluation, methods for extraction
and recognition are categorized into two types, one is the
template matching based and another is the machine learning
based. The template matching based can be realized bycreating specialized matching algorithms, but requires massive
amount of development to catch irregular cases. In contrast,
the machine learning based does not require all patterns todevelop. Collecting necessary data to additionally learn thensecuring time to learn this data, it is possible to handle

irregular cases.
Therefore, we developed a prototype of evaluation
software using machine learning method and performed
evaluations.
(a) Extraction
As for evaluation of extraction method, we used 200
images including images of poor conditions such as rainy
weather and deteriorated sign boards (containing 297 ofroad signs, 131 of supplementary signs as target), and 34
minutes of driving video (containing 219 road signs and 112
supplementary signs as target). As a result shown in Table 2,each of the outputs indicated more than 95% extraction.
We also conducted evaluation of stop lines, crossing
sidewalks, direction arrows, and speed limits on road marks
and confirmed results in 95% on average.
Table 2: Extraction results
Number of
signs

Road sign type, etc.

Number of
extractions

Extraction
rate

Still images of principal signs

297

281

95％

Still images of supplementary
signs

131

129

98％

Video of principal signs

219

214

98％

Video of supplementary signs

112

111

99％

Road markings

261

248

95％

(b) Recognition
As for evaluation of the recognition method, we used 297
images of extracted road signs.

Flow from data collection to extraction
Scope of study of image recognition technology

Image recognition system

Image data collection
Collect still images
and videos frominstallation andinspection work forsigns/markings

Operating PC

Road signs/markings
position prediction system

Signs/Markings data extraction
Signs/Markings data recognition

Still image

Add signs/markings position information

Signs/
Markings
CSV

Video

Fig.3: Flow from data collection to extraction
Table 1: Planned process
Process

Extraction

Recognition

Explanation
Process to detect area
containing sign or marking instill image or video, then to
extract image from the area

Process to determine whether
the detected signs or markings
is correct or not in theextracted area image, then to
identify the type of regulation.

Illustration of Process

Identify areas and extract images
By TOSCO CORPORATION

Recognize by regulation type407:
Crosswalk203:
Stop line
By TOSCO CORPORATION

Estimate distance and angle
Position
estimates

Process to calculate latitude andlongitude using distance andangle estimated from data of
position where objects (signs ormarkings) contained in video
and still image are recorded

Distance 3 m
Angle -30 degrees
Distance 3 m
Angle 0 degrees●Distance
3m●AngleLeft 30 degrees

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
5) Improvement of Data Accuracy of Traffic Regulation Information

As a result, there were cases in which the designateddirection only signs lane usage signs (Fig.4) were falsely
recognized as the one-way street signs. (Fig.5) Though, therecognition rate could be by relearning the learning data ofthe designated direction only signs. Recognition results are
as in Table 3.

regulations. This expanded standard format aims to reduce theinput work by the prefectural police by covering insufficientcodes needed to accurately display regulations and difference
updates, eliminating similar items, and clarifying input
definitions. This format can centrally manage three types of
information: traffic regulation information, corresponding
traffic signs information, and road markings information.
The overall structure of the expanded standard format isshown in Fig.6.
What is the expanded standard format?
Interim open data target
Traffic regulation information

Fig.4: Designateddirection only sign

Fig.5: One-way street sign

*Revied version of current standard format

Number ofsigns

Number of
extractions

Extraction
rate

Parking prohibited

106

106

100％

Maximum speed 40km

15

15

100％

Crosswalk/bicycle crossing

14

14

100％

Designated direction only

13

13

100％

Direction specific lane usage*Result after relearning shown in ( )

11

3
(11)

27％
(100％)

Maximum speed 50km

11

11

100％

297

274

92.3％

(Omitted)
Total(c) Position Estimation
As for evaluation of the position estimation, we used 1,600
images with distance data between 2m to 10m measured
beforehand, and applied the depth estimation algorithm and theregression model.
As a result, among 1,600 images, approximately 96% canbe used for position estimation with error of within 2 meters.
(Table 4)
Table 4: Distance measurement results(error between actual distance and estimated distance)
Error range
2 meters or lessRoad markings

Fig.6: Structure of expanded standard format

Table 3: Recognition results
Class

Road signs information*New information to verify linkages for the purpose of improving traﬃc regulation
information precision (the linkage with traﬃc regulation information is controlled in thesigns/markings information side.)

Number of images(1,600 images)

Percentage
1,539

96.2％

More than 2 meters - within 4 meters

59

3.7％

More than 4 meters

2

0.1％

2.5. Expanded standard format
The current standard format was a data format about thetraffic regulation information, which had been organizedthrough research study related to a project "advancement ofdriving support by utilizing traffic regulation information", aspart of SIP, the Cross-ministerial Strategic Innovation
Promotion Program. But in order to address issues clarified inthis research study, we deliberated and created the expanded
standard format.
2.5.1. Expanded standard format
In order to enhance traffic regulation information data
precision, we prepared an expanded standard format
complying with traffic regulation standards, which arestandards that prefectural police departments apply to traffic

3

Prototype System

3.1. The road signs/marks/display position prediction
system and the field research application
In order to confirm the relationship between road signs
actually on the ground and traffic regulations determined by theprefectural police, we built up a new prototype system (Fig.7)
with additional functions as below, based on the system that had
been established and used for the FOTs in 2021 for making
linkage between the traffic regulation data and the road signsdata.
- Input/output of the expanded standard format (traffic
regulation data, Road signs/marks data, linking)- Prediction of road marking position from traffic
regulation data
- Prediction of traffic regulation position from roadsigns
- Registration of supplementary signs- Loading of image files
Further, we added the function as below to the fieldresearch application (Fig.8) prepared for supporting theroad signs and markings position prediction system andlinking the traffic regulation data and road signs/ marksdata.
- Input/output of the expanded standard format (traffic

Fig.7: Road signs/markings position prediction system sample screen

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
5) Improvement of Data Accuracy of Traffic Regulation Information

regulation data, Road signs/marks data, linking)- Registration of supplementary signs- Registration of road signs/ marking board direction

Expanded standard format data

Is there a diﬀerence? Reason? =Reconduct research?

Compare with current linkage data inthe police system(2) Data registration(3) Photo registration

Image recognition system

Road signs/markings position prediction system

not tentatively linked

tentatively
kinkedField-research
application(Field-research)(1) Tentative linkage XX%

Drive
video

Collected
image

not tentatively
linked(3) Tentative linkage: failed X XX% investigation of
causes(2) Tentative linkage XX%

tentatively kinked

Is the sign wrong? The regulation wrong?
Other?

Fig.10: FOTs flow chart of prototype system(1) Acquire position by GPS

Fig.8: Image of registering road signs/markings on smartphone device

3.2. Image recognition system
As for the data recognition system which aimed forcomprehensive collection of road signs/markings information
on wide area and effective data organization, we established a
prototype system. (Fig.9) This prototype system has thefunctions, extracting the road signs/markings informationsuch as regulation type, position information and direction,transporting the extraction output to the prediction system.
3.3. FOTs and evaluation of effectiveness using theprototype system
In fiscal 2022 FOTs, we used regional traffic regulation data
and road signs/markings data held by some police departments
in Chiba prefecture. We improved the precision of traffic
regulation data by loading road signs/markings data acquiredby using the field research application and the image recognition
system into the road signs/markings position prediction system.
As for the image recognition system, we studied toacquire information such as signs/marking position,
regulation type, direction of photographing the sign, using
image data acquired by the field research app as well as by
Prefectural police
Still image

dashboard cameras and others. This was associated with thestudy to acquire data of supplementary road signs.
For the effectiveness evaluation of the prototype system,
we sought the tentative linkage rate and the precision like infiscal 2021. In fiscal 2021, we used the standard format data
and the road sign data for the evaluation. In fiscal 2022, it isthe expanded standard format including traffic regulation
data and road signs/markings data in accordance with theevaluation flow shown in Fig.11 to calculate the tentative
linkage rate and the precision.
Automatic tentative
linkage(1) All data
Regulation subject
to evaluation*Regulation withposition information

Success

FailureTentative linkage
evaluation(2) Tentatively
linkedRegulation having
linkage in the police
systemRegulation having
no linkage in thepolice system(3) Not tentatively
linkedRegulation having
linkage in the police
systemRegulation having
no linkage in thepolice system

Precision
evaluation(4) Correct linkage

Correct

Regulation of which tentative
linkage data hascorrespondence to the police's

Incorrect

Regulation of which linkage data
doesn't have correspondence tothe police's.

(5) Incorrect linkage

Fig.11: Evaluation flow of the prototype system

The detailed procedures are as follows. First, we carry out
prediction of road signs/markings position with the position
prediction system for the expanded standard format data,
checking position of traffic regulation data and categories ofregulation, then tentatively link the road sighs/markings data
which is inside the predicted position area. This is followed by

National Police Agency

Image recognition system
Image recognition
system (Client)Road signs/
markings
positionprediction
systemImage recognition system (Server)
Image recognition function

Video
UploadVerify and revise
extraction output

Precision evaluation

Extraction
Recognition

Signs/Markings
extraction output

Outputdata

Roadsigns.
csv

Position prediction© OpenStreetMap.org

Fig.9: Image recognition system configuration

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2.Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
5) Improvement of Data Accuracy of Traffic Regulation Information

adjustment of the predicted position area based on the tentative
linkage output to find out optimal position as prediction. The
regulation data unable to be linked is recognized as unmatcheddata at the end of the process. For this unmatched data, wecollect the road signs/markings data actually on the road using
the field research app and the image recognition system. The
collected data is subjected to the prediction again through theprediction system for another tentative linkage to seek the final
tentative linkage rate and precision. If there is traffic regulation
data of which the linkage is impossible or false, causes areanalyzed to improve the data precision.
3.4. Preparation of expanded standard format manual
Based on results of hearings of prefectural police, FOTs
carried out with the expanded standard format, and expert
meetings, we narrowed down the regulation categories to 74
items for the expanded standard format (proposal) studiedin fiscal 2021. We also reviewed 224 traffic regulation
information categories in the proposal format. In particular,
the traffic regulation information was defined basically withcategories consisting of "regulation type", "regulation
location", "regulation target", and "date related to theregulation" based on the traffic regulation standards, thenverified data to eliminate similar categories. Further, in orderfor registrants to register the traffic regulation data correctlyin the expanded format, also for users to understand theformat, we prepared a manual.
The manual is comprised of the main manual andsupplementary documents. We included data overviews andfile specifications in the main manual, and format, code list,
as well as input definition and registration examples for eachregulation category in the supplementary documents.

4

The traffic regulation information generated through thisstudy and the road signs/markings information made fromthe high precision 3D measurement with the mobile
matching system (MMS), both are utilized to verify
technology to incorporate such data into the high precision
3D map.

Traffic regulation
information

ProvideFig.12: Image of high precision 3D map(Source) Dynamic Map Platform Co., Ltd.

5

Conclusion

5.1. Concept of future operation
We are considering implementing the expanded standard
format into traffic regulation information management
systems of the prefectural police in accordance with Fig.13.
The National Police Agency must be able to receive data inboth the standard format and the expanded standard format
until the prefectural police can support the expanded standard
format. Operations related to improving traffic regulation
Fiscal year

FY2021

Scope ot this research

FY2022Standard F

Prepare configuration plan

Add data items for annual statistical report
Expanded F
FOTs

FY2023 andlater

Expanded FStandard F

FOTs

Data format

Reflect FOTs (Field
Operational Tests)
results

Expanded F[Fixed]Expanded F
Transition

Simultaneous operation

3.5. System operation manual
In order to improve data precision of traffic regulation
information, we prepared a system operation manual for the
prototype system constructed taking into account the futuresocial implementation. The system operation manual iscomprised of the main volume describing the purpose andoverall structure, the operating manual for each tool (roadsigns/markings position prediction system, the field research
application, the image recognition system), and the operating
volume that includes operation techniques.

- Study of traffic regulation information provision methods-O rganizing issues related to traffic regulation information
provision

Fig.13: Concept of future operation

Cooperation with high precision 3Dmap

As utilization of the high-precision 3D map (Fig.12) is
expected for automated driving on public road, we carriedout as below:- Research on High precision 3D map data

information data must be handled in the prefectural police.
Therefore, it is preferred that operations are conducted withlesser workload.
[Contacts]
The Japan Road Traffic Information Center, Research Department, 102-0072,
Iidabashi, Chiyoda Ward, Tokyo 1-5-10, 03-3261-7672, Contact: Moto Baba

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2

Establishment and Utilization of Traffic Environment Data

(1) Technological Development Concerning the Generation of Traffic Environment Data

6) Technological Development for Traffic Signal Control
and Emergency Vehicles Information Using GNSS
(Location Information) and Other Technologies
Kenji Sumi (KOITO ELECTRIC INDUSTRIES, LTD.)(Abstract) Currently, traffic signal priority control for public transit buses and emergency vehicles is mainly provided bya system using optical beacons installed on the roadside, however we have developed a system using GNSS (location
information) and mobile phone communication networks. The GNSS enables the service to be provided even inlocations where optical beacons are not installed. In FOTs (Field Operational Tests) of traffic signal priority control, it
was demonstrated that the non-passage rate of buses during extended green signal periods was reduced by 88.9%
compared to that of optical beacons by continuously obtaining location information and travel speed using GNSS, andthat finer traffic signal priority control was possible. In addition, FOTs were conducted in the Tokyo waterfront area
using the mechanism developed in this technology development to distribute information on emergency vehicles(simulated) to automated vehicles. By providing the location information of emergency vehicles such as ambulances tothe automated vehicle, it is possible to recognize their presence from a greater distance. We expect that the results ofthis technological development will be implemented in society as soon as possible with the goal of realizing "practicalapplication of advanced automated driving with infrastructure cooperative system" as indicated in the SIP Roadmap of
V2X Communication Methods for Cooperative Driving Automation.
Keywords: GNSS, signal control, location information, emergency vehicles, public transportation1

vehicle operation.(1)These systems use near-infrared light to communicate with
optical beacons installed on the road to detect approachingvehicles and determine whether or not they are operating in an
emergency, and then implement traffic signal priority control(extended green signal time, shortened red signal time) fornecessary vehicles.
As shown in Fig.1, the conventional traffic signal priority
control system uses optical beacons installed in front ofintersections to detect the approach of public vehicles (buses)and emergency vehicles (fire trucks, police cars, etc.) in order

Overview of Research and Development

1.1. Background
Among the new traffic management systems (UTMS:
Universal Traffic Management Systems) currently in wide usage,
there are two subsystems that implement traffic signal priority
control for road traffic signals: Public Transportation Priority
Systems (PTPS) (since 1995) for route buses, and the Field
Ambulance Support System (FAST) (since 2003) for emergency

Detects public vehicles, etc., passing directly under the optical beacon and implements traﬃc signal priority control
Conventional
traﬃc signal
priority control

Optical beacon
Detects only when passing directly
under the optical beacon

Cable
network

Vehicle passage Control center
information

Cable network
SPaT switching
information

Priority passage

Issue: Because the system uses fixed-point sensing, the service cannot respond to changes in traﬃc conditions(e.g., traﬃc congestion), and service is only available at the location where the optical beacon is installed.
･ Real-time signal control by acquiring location information of public vehicles by GNSS, etc.

Traﬃc signal ･ Possible to provide services even where optical beacons are not installedpriority control
Mobile telephone network
Signal switching
Vehicleusing GNSS,
information
Cable
location
etc., aimed for information
network
Mobile baseControlin this R&D
stationcenter

Fig.1: Comparison of conventional and GNSS traffic signal priority control

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(1) Technological Development Concerning the Generation of Traffic Environment Data
6) Technological Development for Traffic Signal Control and Emergency Vehicles Information Using GNSS (Location Information) and Other Technologies

to determine their locations, and the cost of installing opticalbeacons has been an issue.
In addition, because the optical beacon is a fixed-point
sensor, it cannot respond to changes in the traffic environment(e.g., traffic congestion before an intersection) after the opticalbeacon has been passed, causing stopping at red lights, which
would result in wasted green signal time for traffic in the crossing
direction.
The location information that is being considered for use
in this technology development to determine the location ofvehicles is based on GPS (Global Positioning System: a satellite
positioning system operated by the United States of America),a type of GNSS (Global Navigation Satellite System) which has
been widely used as a satellite-based positioning system inJapan, but generally has a position error of 10 to 20 meters.
To solve this problem, Japan's own GNSS, QZSS (QuasiZenith Satellite System (MICHIBIKI)), began operating four
satellites in November 2018 to complement GPS. RTK and
DGPS positioning methods have improved positional errors
and are expected to provide more accurate positioning.
1.2. Purpose
The purpose of traffic signal priority control in thetechnology development for traffic signal control and emergency
vehicle information using GNSS (location information), etc.
(hereinafter referred to as "this technology development") is toenable service deployment even where optical beacons are notinstalled by changing the means of determining the location of
buses and other vehicles from optical beacons to the morecommon GNSS. The purpose is to reduce the error in theestimated arrival time at intersections by continuously obtaining
the location information of buses and other vehicles closer tointersections compared to optical beacons, in other words, toimprove the effectiveness of priority signaling control.
1.3. Overview
This technology development was carried out in cooperation
with UTMS Society of Japan after deliberations by the National
Police Agency, prefectural police, vehicle manufacturers, traffic
infrastructure manufacturers, and experts with GNSS knowledge.
In addition, the system for collecting location information
on emergency vehicle operation was used to generate andprovide simulated emergency vehicle information in the
FOTs in the Tokyo waterfront area.
In the specification development phase, the draft
specifications (1) through (6) shown in Fig.2 were developed.
In the effectiveness verification phase, traffic signal priority
control FOTs were conducted in Shizuoka City, Shizuoka
Prefecture, based on the draft specifications and verification
plan.

FY2020Specification phase
Detailing functional andtechnical requirements(1) Preparation of TechnicalRequirements for GNSS Traﬃc
Signal Priority Control(2) Preparation of Guidelines for
Location Information Collection
Methods(4) Preparation of operation
monitoring unit application
standard (draft)(5) Preparation of on-board
communication unit
application standard (draft)

FY2021
Effectiveness verification phase

FOTs
specifications(3) Preparationof Prefectural
Police Model
SystemSpecifications(Draft)System construction based onmodel system specifications(6) Preparationof Prefectural
Police Model
SystemVerification Plan

Shizuoka City, Shizuoka
Prefecture FOTs
The FOTs in the Tokyo
waterfront area

Fig.2: Technological development overall process2

Traffic signal priority control in thistechnology development

2.1. Current traffic signal priority control
As shown in Fig.3, the current traffic signal priority control
system uses an optical beacon (fixed point) installed in front of
the intersection to detect an approaching bus and extend thegreen light time or shorten the red light time for the number of
seconds it takes the bus to reach the intersection at apredetermined, fixed, calculated speed, and is effective when
the calculated speed and actual speed are almost the same.
Distance (m)Calculation
speed (opticalbeacon)Green light(2) With control

Intersection 0

Optical beacon
position -150

Red light

Actualspeed(1) No control

Optical beacon use
fixed point only
Time (seconds)-300

Fig.3: Comparison of conventional method with and without traffic signal
priority control

2.2. Current issues
When there is a change in traffic conditions, such as traffic
congestion before an intersection, the difference between
calculated and actual speeds increases, as shown in Fig.4, andthere are cases where drivers may not be able to pass through anintersection with a green light.
Distance (m)Intersection 0

Optical beacon
position -150

-300

Calculation
speed (opticalbeacon)Actual speed

Red light
Green light(2) With control

Optical beacon use
fixed point only
Time (seconds)

Fig.4: Conventional traffic signal priority control issues

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(1) Technological Development Concerning the Generation of Traffic Environment Data
6) Technological Development for Traffic Signal Control and Emergency Vehicles Information Using GNSS (Location Information) and Other Technologies

2.3. Traffic signal priority control in this technologicaldevelopment
In this technological development, the final locationdetermination point is set closer to the intersection than the opticalbeacon using GNSS, as shown in Fig.5. By determining the arrival
time based on the actual speed at the final location-determination
point, the error in the intersection arrival time can be minimized.
Distance (m)Calculation speed(optical beacon)

Calculated
speed (GNSS)

Red light
Green light

Actualspeed

Pass through the intersection on green
light extension (GNSS)
Intersection stop on green light
extension (optical beacon)0

GNSS (final determination position)

Optical beacon (final determination position)-150

3.4. Position correction method
The position correction method when GNSS positioning
accuracy degrades was optional in consideration of theintroduction of new technology in the future.

4

Traffic signal priority control FOTs

4.1. FOTs outline
Verification of the operation and control effectiveness of
GNSS traffic signal priority control was conducted based on
the Prefectural Police Model System Verification Plan. An
outline of the FOTs is shown in Table 1.
Table 1: FOTs outline

Time (seconds)-300

Fig.5: Traffic signal priority control based on this technology development

3

the same system.

Detailing functional and technicalrequirements

Using location information, etc., we detailed functional
and technical requirements to improve the accuracy of
estimated arrival times at intersections for buses and othervehicles.

Item

Contents

Verification area

Shizuoka City, Shizuoka Prefecture

Implementation
policyVerify the operation and control effects of GNSS traffic signal priority
control in a model system.
The verification route shall be an existing route in order to compare with theexisting traffic signal priority control (PTPS and FAST) using opticalbeacons. Because verification is conducted by connecting to existing routes(public roads) and existing control systems, sufficient consideration should
be given to ensuring safety, and the configuration should be designed tominimize the impact on existing systems. From the Prefectural Police Model
System Verification Plan.

Target intersection

3.1. Position error target performance
The position error was assumed for a driving speed of 50
km/h. Because the time setting of the signal controller
shortening/extending time is in units of 1 second, it isdesirable to set the position error so that the expected arrival
time error at the intersection is less than 1 second. Therefore,
the performance requirement for the position error was set
to 10 m or less, which results in an arrival time error of 0.8
seconds at a travel speed of 50 km/h.
3.2. Measurement cycle target performance
The measurement cycle was set to 2 seconds, taking into
account the expected communication delay of several hundred
milliseconds to 1 second, including fluctuations caused by thecellular phone network, the processing load of the operation
monitoring unit, and the volume of data communication.
3.3. Positioning method
For the positioning method, RTK, which uses a reference
station and its correction information via a cellular phone
network, or DGPS, which increases positioning accuracy byreceiving reinforcement signals delivered from a satellite with a
GNSS receiver, shall be adopted from among widely usedmethods, depending on the external environment of theoperation route. One or more of the two methods can coexist in

Verification period

PTPS

FAST

2 directions, 4 intersections(including 1 new intersection)
Inbound: Kagoue, Zaimokucho,
Abecho, Nakacho (new)Outbound: Abecho

11 intersections (including 2 new
intersections)
Yuzuki, Gokoku Jinja Minami, Gokoku
Jinja Iriguchi, Tokiwacho 2-chome,
Kuroganecho Nishi, Aikawa Koen Kita,
Aikawa Koen Minami, Ikama Jinja Kita,
Ikama Jinja Nishi, Egawacho (new),
Nakacho (new)Optical beacon:
November 29 - December 3,
Optical beacon:
January 18 - 21: 9 days
November 27 - December 3, January 17(The period is adjusted to the- 21: 12 days
number of days for GNSS control
verification (9 days))
GNSS:
GNSS:
December 15 - 21, January 11 - 14: December 15 - 21, January 10 - 14: 12
9 daysdays(Not including weekends or(including weekends and national
national holidays)
holidays)

Monday - Friday (weekdays) 9:00
a.m. - 4:00 p.m.
Verification day time
(The project is conducted duringslot
daytime hours when there is less
impact on area traffic.)

24 hours including weekends andnational holidays

Number of vehicles
Buses: 3 + FOTs vehicle: 1
verified

Ambulances: 3

The FOTs routes were selected from the existing routes
shown in Fig.6 to compare with the existing optical beaconbased traffic signal priority control (PTPS and FAST) in
Shizuoka City, Shizuoka Prefecture.
Because verification was conducted by connecting to theexisting route (public road) and existing control system, it was
conducted in a configuration that minimized impact on theexisting control system.
Specifically, as shown in Fig.7, the system configurationconsists of a control system with operation monitoring unit,
etc., and buses and ambulances with newly installed

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(1) Technological Development Concerning the Generation of Traffic Environment Data
6) Technological Development for Traffic Signal Control and Emergency Vehicles Information Using GNSS (Location Information) and Other Technologies

Shizuoka City
Shizuoka
Hospital

Shizuoka City
Emergency
CenterJapan Red Cross Hospital
JR

Saiseikai
GeneralHospital

tion
a Sta
uok
Shiz

Legend: PTPS controlled intersections and routes (current status): FAST controlled intersections and routes (current status): PTPS model system Traffic signal priority control system controlled Intersections (3 existing intersections): Intersections under PTPS model system Traffic signal priority control (1 new intersection): Intersections under FAST model system Traffic signal priority control (9 existing intersections): Intersections under FAST model system Traffic signal priority control (2 new intersections)Fig.6 FOTs routes and intersections
Additional scope:Traﬃc Control
System (Traﬃc
Control Center)
PTPS/FAST traﬃcsignal priority control(processing andcommand)Control center
High-level unit

CentralsystemLow-level
unit/terminal
support unit

Display
deviceTrunknetwork

PTPS/FAST traﬃc signal
priority control(command)Operation
monitoring unit

MobilerouterLocation
information, etc.

Mobilephone
network(LTE)

Terminal system

Signalcontroller

Signalcontroller

Signalcontroller

On-board
unit

Public vehicle (bus) Emergency
vehicle (ambulance)

Fig.7: FOTs system configuration(additional) on-board GNSS-compatible instrumentation.
The existing control system was notified at the timing
determined by the operation monitoring unit, and traffic signal
priority control of traffic signals was executed (operated) byexisting equipment. For comparison, the number of seconds forsetting the PTPS and FAST devices using optical beacons andthe position of the optical beacons were implemented under thecondition that the existing system was not changed.
PTPS evaluation was conducted from 9:00 a.m. to 4:00
p.m. outside morning and evening rush hour times while thesystem was in operation so as not to affect current traffic. Thiswas done to confirm that the results of PTPS and FAST using
optical beacons are equivalent to or better than the standard.
The validation period was one month long, and the opticalbeacon traffic signal priority control and GNSS traffic signal
priority control were conducted alternately twice each week inorder to minimize the difference in traffic volume during thevalidation period and to average the effects of each day of theweek.
For the PTPS, only weekdays were used for the FOTs inaccordance with the current operational status.
4.2. FOTs results
The basic operation, position error, delay time, etc. wereconfirmed, and the control effect was confirmed to be equivalentor superior to that of optical beacon control, as shown in Table 2.
The following table shows each verification item, its purpose,
evaluation values, and results. For details, please refer to the report(2).

GNSS accuracy was confirmed to meet the target position
error of 10 m or less within the PTPS verification route as
shown in Table 3.
Table 2: Verification items and results
No.

Verification items

1 Priority control operation

2 Control effects

Objective/evaluation

Results

Confirmation of basic operations: green lighttime extension and red light time shortening

Priority control (green light timeextension and red light timeshortening)
Reduction of differences in passing
through stop lines
Average -74.7 sec.
Reduction of non-passage rateduring green light time extension -88.9%

Basic operation: Equivalent to or better than
optical beacon control

Confirmation of travel time reduction effect inpriority control implementation
Impact on cross-directional traffic
Confirmation
Control effectiveness: Equivalent to or better
than optical beacon control

3 GNSS accuracy

Confirmation of position error for selectedroutesPosition error: 10 m or lessIntersection vehicle passenger time
Average 2.1 sec.
reduction 
Reduced route travel time  Average
8.1 sec.
Reduction of wasteful green light
extension time
Average 2.1 seconds
Shizuoka City: 10 m or lessTokyo waterfront area: 10 m or
more in multipath propagation
environment in some cases

Validation against the measurement cycle
4 Extension time

Communication and processing delay time
281 ms (max. 960 ms)
from on-board unit to traffic signals: less than 1
secondBus priority control
5 operation when multiple
buses pass

Operation confirmation on continuous bus
approach
Deceleration at stop: 1.96 m/s2 (0.2 G) or lessMaximum deceleration at bus stop
0.83 m/s2 (0.09 G)

Operation confirmation at time of crossing
direction congestion extension to a bus route

Priority control release is executed
when the set value is exceeded
6 Priority control conditions Priority control is released when the congestion (Release is confirmed at 100 m or
length in the crossing direction exceeds the set more compared to the set value of
100 m)
value

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6) Technological Development for Traffic Signal Control and Emergency Vehicles Information Using GNSS (Location Information) and Other Technologies

Table 3: Position error verification results within the verification routeEvaluation site

AbechoKagoueZaimokucho

NakachoMean value

1.58

2.74

2.39

2.49

Maximum value

4.95

5.12

4.84

4.89

Minimum value

0.16

0.99

0.63

0.63

Table 4: Position error verification results outside the verification routeindeterminate after it passes the optical beacon.
For example, if there is a bus stop after passing the opticalbeacon and the bus stops, it may not be possible to pass even
if the green light time is extended.
On the other hand, the GNSS-based traffic signal priority
control system can continuously monitor the bus position sothat even when a bus stops at a bus stop triggering a red light,
the system can implement additional traffic signal priority
control to shorten the red light time. Fig.8 shows an exampleof how this function allows a bus to pass through anintersection quickly.
(2) Traffic signal priority control release operation
functionWith the current optical beacon system, the position of
the emergency vehicle after it passes the beacon isindeterminate, as is the case with buses. The GNSS-based
traffic signal priority control system can continuously
determine the position of the emergency vehicle, allowing
the traffic signal priority control system to be released
immediately after it passes through an intersection.
Fig.9 shows a case in which traffic signal priority control
using GNSS reduced the amount of wasted green light time,
whereas a conventional optical beacon would have continued
to extend the green light time even after an emergency

Unit: mUnit: mEvaluation site

In front of the Japan Red Cross
Hospital

From Japan Red Cross Hospital to Egawacho

Mean value

2.59

3.19

3.23

Maximum value

9.71

11.57

20.00

Minimum value

0.42

1.44

0.50

Egawacho

The area near Shizuoka Station was excluded from thevalidation route because of multipath propagation (reflectedradio waves from buildings, etc.). As a reference, Table 4 shows
the results of position error verification outside the validationroute.
The results of function verification specific to thistechnology development are shown below.
(1) A dditional operating functions for traffic signal priority
control
The current optical beacon system detects the location of abus only when it passes directly under an optical beacon
installed on the roadside, so the location of the bus isTime

09:08:56(4) Red light time shortening
eﬀect reduces transit timeby 3 seconds
09:08:13

Red light time
reduced by 3 seconds(3) 9:07:07 Send sensing
notification Extend red
light time

09:07:29(1) 9:06:44 Send sensing
notification Speed: 18km/h Extend green
light time

09:06:46(2) Extension of bus passage
prediction green lighttime when sensing
notification is sent

Bus travelroute
09:06:03
500

Intersection lightcolor status(execution history)
Distance from intersection

400

300

200

100

0

-100

-200

-300

Bus stop (85m) intersection
Optical beacon (80 m)

Fig.8: Example of a function unique to this technological development (addition of priority control)Time

22:34:39

22:34:30

Reduce green
light time by
10 seconds

22:34:22(2) 22:34:06 Send sensing notification
Emergency vehicle detection
notification (reset)

22:34:13

22:34:05

22:33:56

22:33:47(3) Reduction of replacement time for extended
step (17 seconds) to 7 seconds by sensing
notification Wasted green light time
reduced by 10 seconds(1) 22:33:46 Send sensing notification
Emergency vehicle detection
notification (set)Ambulance travel
route

Intersection lightcolor status(execution history)

Kuroganecho Nishi
intersection stop
line location

22:33:39

Kuroganecho Nishi Intersection passing location

Optical beacon (set)22:33:30
500

400

300

Optical beacon (set)

200

100

0

45 m

-100

-200

Distance from intersection-300

Fig.9: Example of a function unique to this technological development (cancellation of priority control)

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6) Technological Development for Traffic Signal Control and Emergency Vehicles Information Using GNSS (Location Information) and Other Technologies

vehicle passed through the intersection.
By reducing wasted green light time, the impact on theflow of traffic in the crossing direction can be reduced.

5

Simulated emergency vehicle
information transmission FOTs

5.1. FOTs outline
We conducted emergency vehicle information (simulation)
distribution and GNSS position error FOTs using an on-board
device with simple and minimum performance requirements,
which was developed for GNSS traffic signal priority control
in this technology development, for 10 days from January 10
to 14 and 17 to 21, 2022, from 10:00 a.m. to 4:00 p.m.
A mechanism has been developed in which location
information from an on-board unit is controlled by theoperation monitoring system and utilized for traffic signal
priority control. Functionality has been added for sending
location information to the operation management system inthe form of an information collection and distribution server.
Location information was collected from the on-board
unit installed in the simulated emergency vehicle via thecellular phone network LTE (closed network) and was
distributed through other servers via the business Ethernet(closed network) to the automated vehicles participating in
the FOTs in the Tokyo waterfront area.
Fig.10 shows the FOTs configuration and Table 5 shows
the distributed information specifications.
Data aggregation server

Scope of maintenance in this R&D

Information collection anddistribution server

On-board unit

Information collection and distribution server(in Koito Electric Industries)
Information collection anddistribution server

Router

LTE router

On-board unit: Generation of location information(in a simulated emergency vehicle)

Simulated emergency vehicle

On-board unit

LTE router
GNSS
satellite

GNSS module

Connection diagram

Fig.11: Simulated emergency vehicle

Fig.12: Travel route5.2. FOTs results
The fact that the location information was distributed to andutilized on the participants' automated vehicles was checked ona confirmation screen (Dynamic Map viewer) associated with ahigh precision 3D map installed in the automated vehicles.
Position error was calculated by installing 2 cameras on
the simulated emergency vehicle, obtaining the evaluationsite passing time from 19 locations in images from the front
rear, and above, then comparing the true value with theposition information (latitude and longitude) obtained fromthe GNSS receiver at the same time.
The evaluation sites in the Tokyo waterfront area areshown in Fig.13.
E

System configuration chart

Fig.10: Test configuration in the FOTs in the Tokyo waterfront area
Table 5 Distribution information specifications
Item

Standard

Communication control procedure

UDP

Transmission cycle

Every 2 seconds

Number of GNSS information units stored

Max. 20 (100 ms)

GSI Map

Route A
Route B
Route C
Route D
Route E

K J

I H G

L

F
Q

R

D

S

P
A

M

B

C
GSI Map

In the FOTs, two passenger cars simulating emergency
vehicles were prepared, and stickers were attached to the cars
bodies to serve as identification markers so that the participants
could check them with drive recorders, etc. A colored box was
mounted on the roof rack.
Fig.11 shows the appearance of the simulated emergency
vehicle that was actually driven in the Tokyo waterfront areafor location information distribution testing, and Fig.12
shows the five different routes it traveled.

NO
Fig.13: Evaluation sites

The results of the position error evaluation are shown in
Table 6.
The PTPS FOTs verification route measured during thetraffic signal priority control FOTs was in an open sky
environment, and as expected, the maximum position error

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6) Technological Development for Traffic Signal Control and Emergency Vehicles Information Using GNSS (Location Information) and Other Technologies

was less than 10 m at 5.12 m.
On the other hand, in the Tokyo waterfront area, there
are many environments where radio waves from satellites areblocked, such as at points where multipath propagation is aconcern due to adjacent buildings and under the Yurikamome
Line elevated railway tracks.
Although the average value was less than 10 m, themaximum value exceeded 10 m in some cases, and there
were many locations where there was a significant difference
between the minimum and maximum values.
Table 6: Evaluation results (Part 1)
Evaluation site

A

B

C

Unit: mD

E

Mean value

2.41

2.07

2.49

2.90

1.98

Maximum value

4.73

3.51

3.87

5.30

4.67

Minimum value

0.59

0.57

0.38

0.97

0.16

Evaluation results (Part 2)
Evaluation siteF

G

Unit: mH

I

J

Mean value

2.14

3.22

5.29

8.37

5.09

Maximum value

4.60

5.27

9.92

17.67

14.10

Minimum value

0.16

0.19

1.41

1.12

0.92

Evaluation results (Part 3)
Evaluation site

K

L

Unit: mM

NO

Mean value

2.35

1.27

2.18

3.20

2.41

Maximum value

5.23

2.19

3.62

20.81

5.36

Minimum value

0.82

0.34

0.50

0.45

0.70

Evaluation results (Part 4)
Evaluation siteP

Q

Unit: mR

S

Mean value

2.55

2.23

2.56

1.92

Maximum value

8.28

3.47

3.79

2.19

Minimum value

0.25

0.39

0.13

1.56

In addition, because two simulated emergency vehicles
were used to distribute location information, the error
evaluation of the on-board unit was conducted in the L-S
section where the two vehicles traveled the same route.
The evaluation results for Vehicle 1 and Vehicle 2 areshown in Tables 7 and 8, respectively.
A significant difference occurred only at evaluation siteN, but the analysis revealed that it was due to the effect ofposition error fluctuations during vehicle stops.
No significant errors were found in the results.

Table 7: Evaluation results by vehicle: Vehicle 1 (Part 1)

Evaluation site

L

M

N

Unit: mO

Mean value

1.13

2.32

1.90

2.75

Maximum value

1.96

3.56

2.97

5.36

Minimum value

0.63

1.56

0.85

0.70

Evaluation results by vehicle: Vehicle 1 (Part 2)

Evaluation site

P

Q

R

Unit: mS

Mean value

2.39

3.47

3.79

1.91

Maximum value

6.97

3.47

3.79

1.91

Minimum value

0.28

3.47

3.79

1.91

Table 8: Evaluation results by vehicle: Vehicle 2 (Part 1)
Evaluation site

L

M

N

Unit: mO

Mean value

1.41

2.04

4.47

1.99

Maximum value

2.19

3.62

20.81

4.89

Minimum value

0.34

0.50

0.45

0.77

Evaluation results by vehicle: Vehicle 2 (Part 2)

Evaluation site

P

Q

R

Unit: mS

Mean value

2.79

0.99

1.33

1.94

Maximum value

8.28

2.08

2.19

2.19

Minimum value

0.25

0.39

0.13

1.56

6

Conclusion

In this technology development, the impact on thecurrent control system was minimized and a configuration
was used that can be implemented in society at an early stage.
The results of the two-year project are as follows.
In FY2020, the functional and technical requirements
were detailed, and the following 6 specifications (drafts) andother documents were developed.
(1) GNSS Traffic signal priority control TechnicalRequirements Document(2) Location Information Collection Method Guidelines(3) Operation Monitoring unit Communication
Application Standard (Draft)(4) O n-board unit Communication Application Standard(Draft)(5) Prefectural Police Model System Specifications (Draft)(6) Prefectural Police Model System Verification PlanIn FY2021, traffic signal priority control FOTs wereconducted in Shizuoka City. We confirmed that theeffectiveness of traffic signal priority control has been
improved compared with conventional optical beacons.
In addition, with the realization of practical applicationof advanced automated driving with infrastructure
cooperative system in mind, we conducted FOTs of simulated
emergency vehicle information distribution in the Tokyo
waterfront area, and confirmed that the information
distributed from the infrastructure was effective.
We will promote standardization as social infrastructure
equipment for early social implementation.
[References]
(1) UTMS Society of Japan History of Traffic Signals in Japan: The Next 40
Years, p.54
(2) New Energy and Industrial Technology Development Organization(NEDO): FY2020-2021 Progress Report: The second phase of SIPAutomated Driving for Universal Services/Research and Development
on Traffic Signal Control using GNSS (location information) and othertechnologies, (2022)[Contacts]
Human Resources and General Affairs Department KOITO ELECTRIC
INDUSTRIES, LTD. 720 Minami Isshiki, Nagaizumicho, Sunto-gun,Shizuoka 411-0932, 055-955-5688, Contact: Koji Inage, Kazuko Narushima

SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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2

Establishment and Utilization of
Traffic Environment Data

(2) Technological Development Concerning the Transmission of Traffic Environment Data

Research for V2X
Communication for Cooperative
Driving Automation (Overview)
Hideaki Suganuma (TOYOTA MOTOR CORPORATION)

1Introduction

As various efforts are being made by industry,
government, and academia to develop the technology andlegislation to realize an automated driving society,expectation is being placed upon "cooperative automateddriving," in which vehicles and road traffic infrastructure
cooperate with each other in order to realize safer and more
comfortable automated driving. However, there is yet to be aunified view on the communication method required for the
adoption of cooperative automated driving.
In Japan, ARIB-STD T109(1) (hereinafter referred to as"700 MHz Band ITS"), which utilizes radio waves in the
700 MHz band, has been specified as an ITS radio to support
safe driving, and the service is already in actual use. Therefore,
it is thought that the expansion of the 700 MHz Band ITS willcontribute most to the early adoption of communications forcooperative automated driving. On the other hand, there areconcerns that the bandwidth will not be sufficient with only
the frequencies that are currently allocated, and that Japan,
which uses the 700 MHz band, will fall behind the global
standard when compared to Europe and the United States,
where the 5.9 GHz band has been allocated.
A conclusion has not come into sight because there arealso opinions that, although from a global perspective many
regions and countries have allocated the 5.9 GHz band as afrequency for ITS, the radio waves in the 5 GHz band,
including the 5.9 GHz band, have strong linearity and may
not be suitable for ITS, which needs to detect conditions
beyond the visible range. There are also opinions that theapplications and locations may be limited due to it causing
interference with ETC and other systems and sufferinginterference from wireless LAN frequency extension, andmost of all, that it might not be appropriate for the situationof Japan’s frequency use since the 5.9 GHz band is still being
used by broadcasters as FPUs (Field Pickup Units), andfrequency migration work across industries will be necessary.
As industry, academia, and the government haveestablished a system to work together toward the adoption of
automated driving in SIP, in light of this background the "Task
Force (TF) on V2X communication for Cooperative Driving
Automation" (hereinafter the "TF") was established in FY2019
to study future communication methods under a three-year
plan. The following sections describe the efforts of the TF.

2Outline of Activities

2.1. Definition, Purpose, and Goals of Cooperative
Automated Driving
The activities of the TF started with defining thecooperative automated driving system that the TF would
handle and setting its goals. Clarifying this is very importantto facilitate the TF’s discussions.
The TF defined cooperative automated driving as "asystem that is based on an autonomous automated driving
system and which enables safer and smoother automateddriving control by obtaining information from outside thedetection range of on-board sensors, providing information
which the vehicle possesses, and vehicle-to-vehicle andvehicle-to-infrastructure mutual communication."
The purpose of establishing the TF was "to draw aroadmap towards the adoption of the ideal cooperative
automated driving, and to establish a policy for the optimalcommunication system for ‘all-Japan’, while taking into

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
Research for V2X Communication for Cooperative Driving Automation (Overview)account international standards." In order to achieve thispurpose, the goals were to (1) propose an appropriate
communication system for cooperative automated driving,and (2) establish a roadmap that specifies the communication
system required for cooperative automated driving and thetiming when that communication system will be required.
2.2. Phases of the Study
In order to achieve the goals stated in section 2.1, the TF
conducted the study by dividing the tasks to be performed
into three phases.
Phase I: S orting the use cases to be used as the basis of the
study
Phase II: Clarify the communication requirements for eachuse case
Phase III: S tudy communication schemes that meet thecommunication requirements and develop theroadmap
In addition, the structure of the study was enhanced by theparticipation of ITS-related ministries and agencies in thecapacity of committee members who proactively promoted
the study, and by the broad participation in the study by people
with knowledge of communications from Phase II onwards.
These activities are described in Section 3. The membersof the TF and the overall schedule are shown in Fig.1.
Schedule

FY2019Q3Q4FY2020Q
1 Q2

FY2020Q
3 Q4

FY2021Q
1 Q2

FY2021Q
3 Q4

Study Members
National Police Agency, Ministry of Internal Affairs
and Communications, Ministry of Economy, Tradeand Industry, Ministry of Land, Infrastructure,
Transport and Tourism, Cabinet Office (SIP), JAMA,
Data communication experts from universities

Phase I
Selection of Use Cases

Phase IIClarification of communication requirements

Phase III
Study of communication methods

Phase I members plus UTMS
Society of Japan, National
Institute for Land andInfrastructure Management,
ITS-Forum, JEITA

Development of roadmap

Fig.1: TF Study Members and Overall Schedule3

Study of communication methods

3.1. Selection of Use Cases
Many use cases have been proposed for the utilization ofcommunications for cooperative automated driving, both inJapan and overseas. However, there are many use cases inwhich communication is clearly the objective, such as using
communication instead of on-board sensors, or that appear
difficult to realize only through the application ofcommunication due to significant issues with the elemental
technologies other than communication being visible at thispoint in time. The TF considered it unrealistic to implement
all of these use cases, so it decided to define use cases that are
expected to be realized in the future. However, the TF did
not consider all of the use cases anew, but adopted a methodof collating and sorting use cases that have been proposed
around the world and selecting the use cases that should be
dealt with the TF. For the actual use cases, proposals from theJapan Automobile Manufacturers Association (JAMA) andthe results of the "Study of utilization of new communication
technologies including V2X technology to automated driving
system",(2) a research study conducted in FY2018 as SIP-adus
project. JAMA’s proposals were use cases that are consideredessential for automated vehicles to be able to drive, such as
the distribution of SPaT information, merging assistance,and emergency vehicle notification. In addition, the FY 2018
research study collected the use cases used in cooperative
automated driving and driving safety support projects in
Europe, the U.S., and Asia, including Japan.
The TF selected use cases based on the above, with focus
placed upon the following three points during the selection
process.
(1) All traffic participants basically comply with the law(2)It is not feasible by autonomous automated driving system.
(3) Compatibility with the use cases defined in thedefinition of a cooperative automated driving system forthe purposes of (1) obtaining information beyond thedetection of on-board sensors, (2) providing
information possessed by the vehicle, and (3) vehicle-tovehicle/ vehicle-to-infrastructure communication
Through this, the use cases that have been proposed
globally were sorted and 25 use cases to be handled by the TF(hereafter referred to as "SIP-UC") were selected. The details
of SIP-UC were published in September 2020.(3) Fig.2 shows,as an example of a use case, a use case of cooperative merging
assistance for mainline vehicles via roadside infrastructure
network.
3.2. Clarification of Communication Requirements
In Phase II, with the cooperation of the ITS Infocommunications Forum (hereinafter referred to as the "ITS
Forum"), which has abundant knowledge and experience in thestandards and standardization of ITS wireless communications,the communication requirements were sorted under the lead by
the ITS Forum. In addition, because it is necessary to specify thedetails of each service in order to define the communication
requirements, interviews were also conducted with researchgroups that are conducting technical studies and field
operational tests) similar to those for SIP-UC. Table 1 shows thecommunication requirements for the cooperative merging
assistance for mainline vehicles via roadside infrastructure
network, as an example of the communication requirementsthat were defined by the ITS Forum based on the informationcollected from various organizations.(4)SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
Research for V2X Communication for Cooperative Driving Automation (Overview)

Functional classification

a. Merging/lane change assistanceName of the use casea-1-3. Cooperative merging assistance for mainline vehicles with roadside infrastructure network

Target areas

Expressways + prefectural and municipal roads

Overview

Providing merging vehicles with area-covering information including mainline vehicles location and speeds through infrastructure, thesystem navigates mainline vehicles to adjust inter-vehicle gap as merging assistance.

Target vehicles

Privately owned vehicles

Image of the use caseContinuous information provision of mainline traﬃc

Speed adjustment

Conduct continuous sensing (area covering
measurement) of main lane traﬃc and navigate
mainline vehicles for merging assistance from
infrastructure

Infrastructure

Request for inter-vehicle gap
keep/expansion or for lane change

Arrival time at the merge point (main line vehicle),request for adjustment of inter-vehicle gap

Sensor data

Speed, position

Rich content-

V2I

Topology

One-to-many

Control Usage

Speed adjustment, vehicle distance adjustment

Data category/ content of
information

Remarks(communication
requirements,
etc.)

MessageCommunication

Responsiveness

Required

Data amount

Small

Fig.2: Use case example: merging assistance
Table 1: Example of communication requirements
Functionalclassification
Use cases
No.
Message namea. Merging/lane change assistance
Cooperative merging assistance for mainline vehicles with roadside infrastructure
networka-1-3
Negotiation request Negotiation response
LocationControl request
Update request
Update response
information

Communication
V2I (I to V)
V2I (V to I)
methodRoadside
MessageNon-specific vehicles
infrastructure
destination
From 6 seconds
Target area
before the start ofWithin the control(minimummerging until therequest range
range)
merging point
Number oftransmission
1 vehicle
1 vehicle
vehicles per area
Connecting road:
Required
66.7 - 116.7 m
66.7 - 116.7 m
communication
Main line: 111.1 distance266.7 mConnecting road: 20 Connecting road: 20
Maximum - 70 km/h
- 70 km/h
relative velocity Main line: 20 - 120 Main line: 20 - 120
km/h
km/h
5,236 bytes (4,986 +Maximum data
250)
287 bytes
size
Estimated number of(2)vehicles: 184
Periodic/
Periodic
Aperiodic
Aperiodic
Transmissioninterval

PAR per packet
Maximum
acceptable delay
in radio area

100ms

V2I (I to V)

V2I (V to I)

Specific vehicle

Specific vehicle

Within the control
request range

Within the control
request range

1 vehicle (x numberof controlled
vehicles)Connecting road:
66.7 - 116.7 m
Main line: 111.1 266.7 m
Connecting road: 20- 70 km/h
Main line: 20 - 120km/h

48 vehicles (numberof control vehicles,
during congestion)
Connecting road:
66.7 - 116.7 m
Main line: 111.1 266.7 m
Connecting road: 20- 70 km/h
Main line: 20 - 120km/h

273 bytes(2)287 bytes(2)

Aperiodic

Aperiodic

not specified

PAR ≥ 99%
(tentative)

PAR ≥ 99%
(tentative)

PAR ≥ 99%
(tentative)

PAR ≥ 99%
(tentative)

Not specified

Assumed 100ms

Assumed 100ms

Assumed 100ms

3.3. Technical Testing of Communication Systems
The feasibility of the use cases were evaluated from theperspective of the feasibility of communication by applying thecommunication requirements defined in Section 3.2 to the SIPUC selected in Section 3.1. In the evaluation, the applicability of
the following two communication methods to twenty of the
SIP-UC use cases was studied, excluding the five use cases forwhich information distribution over a wide area by V2N(Vehicle to Network) communication is clearly suitable.
(1) Expansion of 700 MHz Band ITS(2) Application of C-V2X(*) as an example of a new \communication method*Adopted as an example of utilization of the 5.9 GHz

band frequency
C-V2X (Cellular V2X: X stands for ‘everything’)
The actual evaluation was conducted by Kyocera
Corporation (hereinafter referred to as "Kyocera") and NEC
Corporation (hereinafter referred to as "NEC"), who were
entrusted with this task. Of these, Kyocera was responsible
for the evaluation of the 700 MHz Band ITS, while NEC was
responsible for the evaluation, roadmap development, andoverall summary of the new method based on C-V2X.
Regarding these evaluations conducted by Kyocera andNEC, please refer to the details in Section 2 (2) 1) "Researchon Communication Methods to Realize Cooperative
Automated Driving Use Cases" that follows this section. Thissection briefly describes the results of the testing.
In the evaluation of 700 MHz Band ITS, Kyocera
evaluated the feasibility of adding all twenty SIP-UC to theexisting ITS services that support safe driving. As a result,among the V2I (Vehicle to Infrastructure) communications
between vehicles and the roadside infrastructure, information
distribution from the roadside infrastructure to vehicles was
feasible in all cases. On the other hand, some of therequirements for information distribution from vehicles tothe roadside infrastructure could not be met. As for V2V(Vehicle to Vehicle) communication, eight of the eleven use
cases in SIP-UC that assumed V2V communication could
not meet the requirements.
The use cases that failed to meet the requirements werecharacterized by the need for a function (called "negotiation")in which the vehicle that is the source of information
identifies the vehicle to be communicated with and exchanges
individual information with it. The negotiation function is amechanism that does not exist in the 700 MHz Band ITS,
which is based on broadcasting, and this evaluation
confirmed that a new communication method, as well as a
new communication bandwidth in some instances, arerequired to realize such use cases.
On the other hand, NEC’s evaluation using C-V2X

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
Research for V2X Communication for Cooperative Driving Automation (Overview)

focused on the evaluation of packet arrival rate (PAR),
reception level, and the impact of shadowing, based on
merging assistance through negotiation and services aroundintersections, which could not be realized in the evaluationof the 700 MHz Band ITS. As a result, it was confirmed that
there were cases in which the requirements could not be met,such as sudden deterioration of communication conditions
due to shadowing and a drop in packet arrival rate due toroad reflection and other effects.
The results of the technical testing are summarized below.
·F or infrastructure-to-vehicle communications under V2I, alluse cases can be met by expanding the 700 MHz Band ITS.
· Some of the vehicle-to-infrastructure communication use
cases under V2I and all of the V2V communication use
cases did not meet the requirements· A review of the detailed conditions for each individual use
case scenario and the application of new communication
methods are necessary· For use cases that require advanced communication, suchas negotiation between vehicles, the requirements were notmet even in the C-V2X evaluation.
From these results, it was found that it will be difficult torealize all of the SIP-UC simply by adding new methods, andit was concluded that "further study is needed".

4

Development of roadmap

The SIP-UC defined by the TF are classified into two
categories: those that are essential for driving automatedvehicles and those that support safer and more comfortable
driving. The timing of their realization varies, including thosethat can be realized through the extension of safe driving
support, those that require advanced mechanisms, and thosethat have already been positioned as national policies by ITSrelated ministries and the national government. Therefore, inPhase III, the deployment timeframe for each of the SIP-UC
use cases was arranged and defined using a timespan ofapproximately five years, based on information from thevarious related parties. In addition, because the realization of
the use cases requires a wide range of issues to be solved, notonly technological but also including the legal system, businessmodels, and clarification of the roles and responsibilities
among service providers, at the same time the TF identified
the necessary issues to realize the SIP-UC and included themin the deployment plan.
In the deployment plan, the use cases that can be extended
from safe driving support to automated driving and the use
cases that are essential for the driving of automated vehicles
are expected to be realized around 2025, and the 700 MHz
Band ITS can be utilized. The use cases for safer and more

comfortable automated driving, which are expected to berealized from 2030 onwards, will require advanced
communications such as the negotiation that is describedabove, and will require the introduction of new communication
methods and the securing of frequency bandwidth. Based onthese considerations, the TF developed the roadmap shown in
Fig.3 and reached the following conclusions.
· It is assumed that new communication methods fornegotiation, etc. will be required around 2040
· If we anticipate, for example, 30% adoption of cooperative
automated vehicles around 2040, introduction of the newcommunication methods will be required from around 2030
· The 700 MHz Band ITS can be utilized for use cases thatwill commence earlier, before 2030
Further, in order to realize each use case, it is very
important to have timelines to address the many other issues,
not only the results of the TF’s study of communication
methods.
Regarding the deployment plan and roadmap, pleaserefer to the details in Section 2 (2) 1) "Research on
Communication Methods to Realize Cooperative AutomatedDriving Use Cases" that follows this section.

5

Conclusion

The TF defined 25 use cases to realize cooperative
automated driving that utilizes communication. It thenconducted a study of the communication methods thatshould be applied to these use cases and compiled them
together with the timeframe for realization of each use caseand issues that need to be solved in a roadmap.
In order for the early adoption of automated driving, it isnecessary to continuously study the application of use casesthat utilize the 700 MHz Band ITS and the newcommunication methods needed to realize the use cases that
require advanced communication in the future.
We hope that the study will be progressed steadily in the
future according to the roadmap that has been defined on
this occasion.
[References]
(1)

ARIB-STD T109 700 MHz Band Intelligent Transport Systems, https://www.arib.or.jp/english/std_tr/telecommunications/desc/std-t109.html,(accessed 25th October 2022)(2)

Study of utilization of new communication technologies including
V2X technology to automated driving system, 2019, https://en.
sip-adus.go.jp/rd/rddata/rd02/204s.pdf, (accessed on 25th October
2022)

(3)

SIP Use Cases for Cooperative Driving Automation-Activity Report of
Task Force on V2X Communication for Cooperative Driving
Automation in FY2019-, First edition, 2020, https://www.sip-adus.go.

SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
Research for V2X Communication for Cooperative Driving Automation (Overview)

2025-

20202022 (R4)USA

2023 (R5)

2030-

2035-

2040-

2024 (R6)

Change in allocation of 5.9 GHz frequency/technology(Disputing reduction of V2X bandwidth and transition from DSRC to C-V2X)

Transition from 5.9 GHz band DSRC
to C-V2X completed(FCC)
Start of deployment of V2N services in 5G (DG MOVE)(Reference) International trends

Development of V2X
safety evaluation criteria

Study of protocols

V2X becomes a(EuroNCAP)
subject ofassessment(EuroNCAP)
Study of advancement of ITS-G5(EuroNCAP)

EuropeR2R communication network construction using RSUs
(Czech implementation plan in C-Roads)

Start of mediation use case
deployment(DG MOVE)(C2CCC)

Fully automated driving realizedon all roads after 2040
(DG MOVE)

ITS-G5 Acceleration of RSU implementation (C-Roads)
Intelligent connected vehicle (ICV) adoption

China

PA/CA: 50% of all automobile sales
HA level automobile market introduction
Percentage of new vehicles equippedwith C-V2 terminals: 50%

Formulation of IEEE 802.11bd

International(China Society of Automotive Engineering)
PA/CA: 70% of all automobile sales
HA:
20% of all automobile sales
Achievement of basic adoption of vehicles
equipped with C-V2X terminals
PA: Partially automated driving (equivalent to L2)

CA: Conditional automated driving (equivalent to L3)
HA: Highly automated driving (equivalent to L4)(IEEE)
Formulation of 3GPP Release 18(3GPP)JNCAP *1 Additional new item study, survey research, study of testing and evaluation methods

Preliminary testing*1 Driving support technology, including V2X, becomes subjectof automobile assessment

Japan

Frequency reorganization action plan +30 MHz for next-generation mobility

AssumedCommunication
technology

700 MHz band ITS

New communication technology (5.9 GHz)A) Approach to the practical application of the use cases

Study of security andprivacy policyA-1) Formulation of
security and privacy
measures

Study of specifications forsecurity and privacy measures

Security scheme study

Issuance of specifications for security and privacy measures
Security scheme formation

Study of policies for sharing in-vehicle devices

A-2) Formulation ofpolicies for sharing invehicle devices

Study of policies for generational change ofcommunication systemsA-3) Formulation ofpolicies for generationalchange ofcommunication systems

Study on how to establish a business modelA-4) Establishing abusiness model
Study of measures to ensure communication qualityA-5) Ensuringcommunication qualityA-6) Clarification of
responsibilities

B) Approach to the practical application communication technologies(5.9 GHz band)B-1) Frequency
utilization

Organization of concept of division of responsibility

Study of channel andbandwidth allocation
Frequency utilization(if necessary)Communication protocol draft study

Communication
standard formulation

Communication
standard issuanceStudy of application specificationsB-2) Standardization,
specification

OBU draft study

Issuance of application specifications (and subsequently, issuance inorder according to the timing of application deployment)

Study of OBU common specifications

OBU connectivity confirmation scheme
formulation

Issuance of OBU common specifications
Study of RSU common specifications
Issuance of RSU common specifications
Product development
Test operation

Equipping of 5.9 GHz OBU

Adoptionrate 30%

Adoptionrate 50%

Deployment of 5.9 GHz RSU

B-3) Equipment
development andpractical application

V2I (broadcast)
V2V (broadcast: continuous)
V2V (broadcast: urgent)

V2I(mediation)

V2V(Negotiation)

Fig.3: Roadmap for V2X Communication for Cooperative Driving Automationjp/rd/rddata/usecase.pdf, (accessed on 25th October 2022)(4)

ITS Forum RC-017: Study report on communication scenarios andrequirements for "SIP Use Cases for Cooperative Driving Automation",
2022, https://itsforum.gr.jp/Public/E3Schedule/p40/ITS_FORUM_RC017eng_v10s.pdf, (accessed on 25th October 2022)[Contacts]
ITS Promotion Office, Connected Supervisory Dept., Connected Company,
Toyota Motor Corporation, 1-4-18 Koraku, Bunkyo-ku, Tokyo 112-8701,
050-3165-1695, Contact: Hideaki Suganuma

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Establishment and Utilization of Traffic Environment Data

(2) Technological Development Concerning the Transmission of Traffic Environment Data

1) Research on Communication Methods toRealize Cooperative Automated Driving
Use-cases
Satoshi Kimura, Takeshi Nunomoto (NEC Corporation), Masato Ogawa, Tomoaki Konishi (KYOCERA Corporation)(Abstract) This project was conducted to formulate the roadmap for the timing of social implementation of communication
technologies necessary for the realization of an automated driving society, based on the study, verification, and field testsof the technical feasibility of wireless communication, including specific requirements for wireless communication
technologies, for the use-cases for cooperative driving automation utilizing the V2X as formulated by the Task Force on
V2X communication for cooperative driving automation, system implementation working group. Specifically, we examinedthe communication requirements for use-cases; verified whether the wireless communication cellular V2X system could
be used, formulated measures to solve any problems that arose, and then verified their effectiveness; verified whether
the existing wireless 700 MHz band Intelligent Transport Systems could be used, formulated measures to solve any
problems that arose, and verified their effectiveness; and developed the roadmap of communication requirements forcooperative automated driving as necessary for the realization of an automated driving society.
Keywords: cooperative automated driving, V2X, cellular V2X (C-V2X), 700 MHz band ITS

1A Study on V2X communication forachieving use-cases of cooperative driving automation (cellular V2X system)

1.1. Overview
Based on the SIP use-cases for cooperative driving
automation (hereafter "SIP-UC")(1) formulated by the Task Forceon V2X communication for cooperative driving automation(hereafter "TF"), the technical and communication requirements
for SIP-UC were studied jointly with Radio System Technology
Task Group of advanced ITS info-communication systemcommittee in ITS Info-communication Forum (hereafter "TG").
We verified the suitability of the cellular V2X system, one of theavailable wireless communication systems, to meet with thesecommunication requirements, formulated measures to solve
any problems that arose, and verified their effectiveness. (V2X:
Vehicle to X, V2I: Vehicle to Infrastructure, V2V: Vehicle to
Vehicle, V2N: Vehicle to Network)

1.2. Study of communication requirements
Through discussions with the TG and organizations andcompanies involved in each SIP-UC, the technical requirements(function sharing between road and vehicles, communication
functions,
etc.)
and
communication
requirements(communication sequences, message content, communication
performance, etc.) required to realize the use case were defined forTable 1: Examples of communication requirements
Functional classification

a. Merging/lane change assistanceUse-cases

Merging assistance by preliminary accelerationand deceleration

Merging assistance by targeting
the gap on the main lane

No.

a-1-1

a-1-2

Message nameLocation information

Location information

Location information

Control request

Negotiation request/update request

Communication method

V2I (I → V)

V2I (I → V)

V2I (I → V)

V2I (V → I)

V2I (I → V)

V2I (V → I)

Message destination

Non-specific vehicles

Non-specific vehicles

Non-specific vehicles

Roadsideinfrastructure

Specific vehicle

Specific vehicle

Target area (minimum range)

From 6 seconds before the merging point to thecenter between the 6 seconds before the merging
point and the merging point

From 6 seconds before the start ofmerging until the merging point

Within the control request range

Within the control request range

Number of transmission
vehicles per area

1 vehicle

1 vehicle

1 vehicle

1 vehicle

1 vehicle (xnumber of controls)

48 vehicles (number of control
vehicles, during congestion)

Required communication
distance

33.9 to 59.3 m(National Research Institute Specifications: 95 m)

67.8 to 118.6m

Connecting road: 67.8 to118.6 mMain lane: 112.5 to
270.0 m67.8 to 118.6m

Connecting road: 67.8 to 118.6 m
Main lane: 112.5 to 270.0 m

Connecting road: 67.8 to 118.6 m
Main lane: 112.5 to 270.0 m

Maximum relative velocity

Connecting road: 20 to 70 km/h

Connecting road: 20 to 70 km/h

Connecting road: 20 to
70 km/hMain lane: 20 to
120 km/hConnecting road:
20 to 70 km/h
Main lane: 20 to
120 km/hConnecting road: 20 to 70 km/h
Main lane: 20 to 120 km/h

Connecting road: 20 to 70 km/h
Main lane: 20 to 120 km/h

Maximum data size

1510byte estimate: 46 vehicles

2752byte estimate: 92 vehicles

5236byte estimate: 184
vehicles287byte369bytes

287bytePeriodic/Aperiodic

Periodic

Periodic

Periodic

Aperiodic

Aperiodic

Aperiodic

Cooperative merging assistance with vehicles on the main lane by roadside control
a-1-3

From 6 seconds before
Within the control
the start of merging until
request range
the merging point

Negotiation response/update response

Transmission interval

100ms

100ms

100ms

PAR per packet

PAR ≥ 99% (tentative)

PAR ≥ 99% (tentative)

PAR ≥ 99% (tentative)

PAR ≥ 99%
(tentative)

PAR ≥ 99% (tentative)

not specified
PAR ≥ 99% (tentative)

Maximum acceptable delay inradio area

Not specified

Not specified

Not specified

Assumed 100ms

Assumed 100ms

Assumed 100ms

PAR: Packet Arrival Rate

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

Control server

VehicleCommunication
delay

Control

Processing delay

Collect

Base station DU/CU

Communication delay

Base station radio unit

Core network

Processing delay

Transmission network

Communication delay

Transmission

Judgment

Collect

Processing
delay

VehicleMNO telecom
infrastructures*Core network

Base station DU/CU

Communication delay

Transmission network

Base station radio unit

Processing delay

Issue 4: Feasibility of transmission
methods according to use cases,
acceptable number of vehicles
according to transmission methods,
and communication frequency
feasibility

Issue 1: Communication delay
during teleoperation

MNO telecom
infrastructures*Transmission

Detection

Cause ofdelay

Locationmanagement

Issue 3: Frequency of communicationof location information to determine
the vehicle sending the information

Collect

Issue 2: Communication volume
during remote monitoring andoperation(Video, etc.)

Processing delay

Series of processes from detection to control*MNO network (wide area network) is basically composed as multi-carrier.
1

Fig.1: Issues in realizing communication requirements© NEC Corporation 2021

Use Case a-1-3 (cooperative merging assistance)
Overview

V2I communication area
(1) Main lane vehicle location information

Merging vehicle

Roadside unit

(2) Control request(4) Negotiation request/(7) Update request(9) Update response

Main lanevehicleInfrastructure(3) Negotiation request(6) Update request(5) Negotiation response(8) Update response

Req. to keep/expand intervehicle gap or to change lane

V2I Communication Area

Fig.2: Communication model for merging assistance

SIP-UC in cooperation with the TG. Examples of communication
requirements for use-cases related to merging and lane change
support are shown in Table 1.
The results of the study were developed by the TG as a
Study report on communication scenarios and requirementsfor "SIP Use-cases for Cooperative Driving Automation".(2)1.3. Consideration of wide area network
For each SIP-UC using a wide-area network (d. Lookahead
information: trajectory change, f. information collection/
distribution by infrastructure, etc.), we organized the issues
facing the realization of communication requirements andtheoretically studied measures to deal with these issues. Fig. 1
Table 2: Proposed measures to address each issue
IssuesIssue 1: Communication delay
during teleoperation

Item to study—Reducedcommunicationdelay(V ⇔ N)

Issue 2: Communication volume
during remote monitoring andoperation (Peripheral Images, etc.)—Reducedcommunicationdelay(V to N)

Issue 3: Frequency ofcommunication of location
information to determine thevehicle sending the information
Issue 4: Feasibility of transmission
schemes according to use-cases,
acceptable number of vehicles
according to transmission
schemes, and communication
frequency feasibility—Reduction ofcommunication
frequency(V to N)

V: Vehicle, N: Network—Reduction ofcommunication
volume(N to V)

Policy Outline—Priority control of wirelesscommunications—Securing of dedicated frequency
bandwidth—Securing of end-to-end dedicatedbandwidth (e.g. slicing)—Priority control of wirelesscommunications—Securing uplink (V to N) communication
capacity—Securing of dedicated frequency
bandwidth—Securing of end-to-end dedicatedbandwidth (e.g. slicing)shows the issues in realizing the communication requirements,
and Table 2 shows proposed countermeasures for each issue.

1.4. Study of Short range communication
For each use case that utilizes short range (road-to-vehicle
and vehicle-to-vehicle) communication, we identified issues inrealizing communication requirements, verified communication
performance through cellular V2X communication simulations(5.9GHz band and 10 MHz bandwidths), and proposed
theoretical measures to address these issues. Two major usecases (1) cooperative merging assistance and (2) intersection
area were taken up for testing. Fig.2 shows an example of thecommunication model used in the simulation of cooperative
merging assistance communication.
Through communication simulations, issues in achieving
the communication requirements were clarified, and areassessment including countermeasures was then conducted.
[PAR %]
100

After taking
countermeasures

90
80
70
60

—Feasibility study for each transmission
method(Unicast, multicast, broadcast)

Before taking
countermeasures

50
40

Line-of-sight0

Non-line-of-sight
47[Distance m]116.7

Fig.3: Example of communication simulation results

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases[Reception level dBm]

[PAR %]

-60

100
90

PAR

-70

80
70

-80

60
50

-90

40
30

-100
-110
140

Reception Level
120

Measuringvehicle

100

80

60

Blocking
vehicle40

20

Transmitting
vehicle

20
10

RSSI
PAR

0
0 [Distance toblocking
vehicle m]

Fig.4: Example of test results on shadowing impact by a vehicle

Fig.3 shows, as an example, the results of the initial simulation
Corporation 2022
(packet arrival1 rate© NEC(PAR)
depending on the distance) and thatof the simulation after taking countermeasures (to aggregate
messages to be transmitted, to reduce impact of shadowing loss
due to vehicles) against assumed factors causing declining of
packet arrival rate, such as reception error on roadside unit byconflict of transmission/reception timing, as well as packet
conflict at timing of data transmission to vehicle due to hidden
node.
In addition, we conducted a communication performance
evaluation test using vehicles and cellular V2X communicators
on a test course to verify the effects of elements such as
shadowing and communication congestion, which require
verification in the field for wireless communication. From theperspective of communication performance, we evaluated (1)the impact of shadowing by vehicles and (2) the impact ofcommunication congestion depending on the communication
volume. Fig.4 shows the result the field operational tests (FOTs)of the impact of shadowing by a vehicle, as an example.
As a study of short range communication, the following
were confirmed from the results of communication simulations
and FOTs.
1) In order to realize a single use case, communications withvarious requirements are mixed and mutual influence occurs.
2) In order to realize multiple use-cases in the same location,communications with various requirements are mixed
and mutual influence occurs.
In actual operation, the wireless communication
environment (shadowing, multipath, etc.) and the driving
environment (number of vehicles, inter-vehicle gap, etc.)
cannot be uniquely specified or limited. Communication
taking place in an unexpected environment can lead tocommunication congestion and subsequent communication
delay or failure. It is therefore necessary to consider measures
to prevent such problems. Specifically, the following
measures should be considered: 1) reducing the impact ofcommunication congestion by controlling communication
congestion at not only the communication layer but also theapplication level, in accordance with the wirelesscommunication environment and the driving environment;2) reducing the impact of communication congestion by
allocating communication channels according to thecommunication area (by lanes, by direction of traffic, etc.)
and the content of communication.

2

A Study on V2X communication forachieving use-cases of cooperative driving automation (700MHz band ITS)

2.1. Overview
As in Section 1, among the SIP-UC formulated by the TF, 20
use-cases (see Table 3, with specific use-cases hereafter referred
to by those designations), targeting V2I and V2V wereconsidered. Based on study report on communication scenarios
and requirements for "SIP Use-cases for Cooperative Driving
Automation",(2) we verified whether the use-cases can besupported by 700 MHz band Intelligent Transport Systems(hereafter "700 MHz band ITS") and developed countermeasures
to solve relevant issues through desk study and simulations.

2.2. Desk study
Desk study evaluated communication quality from theperspective of linked budgets, transmission time constraints,
roadside unit installation constraints, and message sets.
In the evaluation of communication quality andtransmission time constraints, SIP-UC a-1-3, a-1-4, a-2, anda-3, in which interaction is required, did not meet thetransmission time constraint due to the number of transmitting
vehicles per area. In the use case of unmanned platooning offollowing vehicles by electronic towbar (g-1), thecommunication requirement specifies a transmission cycle of
20 ms in the case of emergency, but the 700 MHz band ITS
standard ARIB STD-T109 specifies a communication cycle of
100 ms, so the communication requirement was not met. In
other use-cases, the transmission requirement was met.
In the roadside unit installation constraint evaluation, we
examined the allocation of wireless transmission slots(hereafter "slot") for roadside units, and found possible toallocate the four slots required for SIP-UC. We therefore
confirmed that the slot allocation for roadside-vehiclecommunication and inter-roadside communication is
feasible even if SIP-UC is added to the 700 MHz band ITS
service.

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

In the message set evaluation, we examined the possibilityof adding SIP-UC message sets to 700 MHz band ITS
messages, and confirmed that SIP-UC onboard unit message
sets can be added by utilizing the free field (optional area) of
ITS Connect TD-001.(3)system for 700 MHz band ITS, when onboard unit A and B are
located outside the carrier sense range and do not know eachother’s transmission timing, if both transmit at the same timing,
their messages will collide at the position where each transmission
arrives (hidden terminal problem).

2.3. Simulations

2.4. Issues and future actions

To confirm whether SIP-UC is compatible with the 700 MHz
band ITS, we constructed a simulation environment in whichradio waves transmitted from multiple roadside units and onboard
units interfere with each other. Fig.5 shows the SIP-UC study
model. The road model is a 300m-plane layout, which has thehighest density of roadside units in the existing model simulation(4),and includes the main lane and connecting routes of the expressway.
Table 3 shows the simulation results.
SIP-UC infrastructure-vehicle communication (a-1-1, a-1-2,
a-1-3, b-1-1, c-2-2, d-1 to d-5) and some SIP-UC vehicleinfrastructure communication (d-1 to d-4, f-2) were met thecommunication requirements of the TG. SIP-UC inter vehiclecommunication (a-1-4, a-2, a-3, c-1, c-2-1, c-3, e-1, g-1, g-2) andsome SIP-UC vehicle-infrastructure communication (a-1-3) could
not be met the communication requirements of the TG. The
reason for this is considered that under CSMA/CA, the radio

Based on the results of Sections 2.2 and 2.3, we identified
issues and proposed future actions for the use-cases that did notmeet the communication requirements of the TG.
The communication requirements of the TG are more
stringent than the service requirements because the details of theservice requirements have not yet been finalized. This shows thatin confirming these communication requirements in detail, it will
be necessary to further organize the service requirements forautomated driving, such as the applicable scenarios and means ofcollecting information for each SIP-UC. One future action is towork with related organizations that have studied each use case tofurther specify the service requirements and define practical andoptimal communication requirements.
For SIP-UC a-1-3, a-1-4, a-2, a-3 that require interaction,
since the current 700 MHz band ITS specification is a broadcast
protocol, supporting interaction is difficult. We therefore
propose the following possible two countermeasures.
Proposal 1: New communication method only
A new communication method will be used that includes
recognition of the surrounding situation (e.g., position andspeed information). In such cases, it is necessary to study
bandwidth and transmission (propagation). In addition,
relationships with existing safe driving support services need
to be considered (in the case of coexistence, the impact of costand installation is significant. Deployment and disseminationof vehicles and quality assurance are issues when transitioning
to a new system).
Proposal 2: 700 MHz band ITS + new communication method
This proposal is for the concept of "Basic Message (BM) +Advanced Message," in which basic recognition of thesurrounding situation (position, speed information, etc.) is
performed by the 700 MHz band ITS, and the subsequentinteractive sequence is performed by new communication
methods.
The above requires further discussion in the future.
The challenges for practical application of the system
include standardization of message sets, slot allocation
1
placement rules, and cybersecurity requirements. Future
actions to address this issue include formulating guidelines
and promoting standardization in consultation with relatedorganizations.
In the use case g-1, the issue is the 20 ms cycle transmission
during emergency braking. As a future action, the ARIB
STD-T109 standard should be revised, or the requirements should
be modified by further digging down into use case requirements.

SIP-UC (Expressway)
Roadside unit

Expressway: Roadside unit at key intersection: Roadside unit at intersection: SIP-UC (Expressway) Roadside unit

Fig.5: SIP-UC study model
Table 3: Simulation results
SIP-UC Namea-1-1. Merging assistance for accelerating/decelerating on merging lane
a-1-2. Merging assistance for aiming inter-vehicle gap on main laneb-1-1. Driving assistance by providing SPaT information (V2I)
c-1. Collision avoidance assistance for sudden stop or sudden deceleration in front
c-2-1. Driving assistance by providing intersection information (V2V)
c-2-2. Driving assistance by providing intersection information (V2I)
c-3. CollisionKYOCERA
avoidance assistanceby providing hazard warning information
Corporation
d-1. Driving assistance by providing notification of abnormal vehicles
d-2. Driving assistance by providing notification of wrong-way driving
d-3. Driving assistance by providing congestion information
d-4. Assistance for congestion at highway junction and exit pointd-5. Driving assistance by providing hazard warning information
e-1. Driving assistance by providing information on emergency vehicles
f-2. Information collection for traffic flow optimization
a-1-3. Cooperative merging assistance for mainline vehicles with roadside
infrastructure network
a-1-4. Merging assistance with inter-vehicle negotiation
a-2. Lane change assistance during congestion
a-3. Assistance for entering to priority road from non-priority roads during congestion
g-1. Electrified platoon without drivers
g-2. Adaptive cruise control (ACC) driving and platoon with driver using vehicles
equipped with ACC

Compatible or not with
700 MHz band ITS
✓
✓
✓
–
–
✓
–
✓
✓
✓
✓
✓
–
✓
–
(V2I is ✓)
–
–
–
–
–

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

2.5. Future prospects
2.5.1. Promote verification testing and social implementation of 700 MHz band ITS, which can beutilized over the long term
In this R&D project, we verified the applicability of SIP-UC
to the 700 MHz band ITS through theoretical studies andsimulations. Excepting SIP-UC a-1-3, a-1-4, a-2, and a-3, which
require interaction, most of the use-cases handle the same basic
information—such as vehicle position, speed, and direction—as
the 700 MHz band ITS services currently in use. In addition, the700 MHz band wave ability to reach far and travel aroundobstacles is said to be very advantageous for Japanese roadconditions, which face issues such as poor visibility, and forvehicle antenna design. It is therefore important to maximize
the use of the 700 MHz band ITS for cooperative automateddriving, while taking into account international trends. For thispurpose, it is important to define more detailed service
requirements and communication requirements for each SIPUC, taking into account various conditions such as roadconditions; to prioritize essential services for driving automatedvehicles according to the use case roadmap; and to steadily
implement these services in society. In addition, more advanced
communication is also required.

2.5.2. Consideration of a new communication method, assuming the period for realization of SIPUC that requires mutual communication
SIP-UC a-1-3, a-1-4, a-2, and a-3, which require mutualcommunication, are difficult to support using a broadcast systemdesigned for information distribution such as the 700 MHz band
ITS. A new communication method that enables bidirectional
communication will be necessary for such communication.
However, further study is needed to investigate thecommunication requirements and new communication
methods. As we continue to study this issue, it is important todiscuss new communication methods, including a proposal thatincorporates the concept of "basic message + advanced message."

3

Establish a roadmap for the timing of social
implementation of communication technologies necessary for the realization of an
automated driving society

3.1. Overview
In developing the roadmap, based on the plans alreadydeveloped by the relevant ministries, agencies, and organizations, aswell as case studies of their efforts, we established a timeline for therealization of each SIP-UC, and organized a use case deployment
plan that lists the realization timeline in chronological order. Then,considering the issues in realizing the communication requirementsof the use-cases described in Sections 1 and 2, we organized theitems necessary for the realization of the deployment plan. Based on
the above, we have compiled a roadmap in chronological order ofthe items to be implemented to realize the deployment plan.

3.2. Considerations of use case deployment plans
In considering the use case deployment plan, thecommunication methods (V2I, V2V, and V2N) applicable toeach use case were assumed and studied through the TF
discussions. Fundamentally, the use-cases are to be realized ascooperative automated driving, but since some of them may beinitially used first for driving safety support, they are divided
into cooperative automated driving and driving safety support
in the deployment plan. Based on the above, we have establisheda timeline for the realization of each use case and organized ause case deployment plan that lists the realization timeline inchronological order, based on plans already established byrelated government agencies and organizations, as well as
examples of initiatives being taken by various organizations.
Fig.6-8 show the use case deployment plans for each
communication method.

3.3. Implementation items for realization of use case
deployment plans
Based on the issues in realizing the communication
requirements of the use-cases described in Sections 1 and 2, wehave organized the items necessary to realize the deployment
plans. In realizing the use case deployment plans, we haveorganized the items to be implemented into the following two
categories.
(A) Clarification of the direction of various issues throughexamination and discussion among the entities
concerned (details shown in Fig.9).
(B) Practical application of new communication methods (e.g.,
application of 5.9GHz band) (details shown in Fig.10)
For the compilation of A), Issue Survey Report ofAdvanced ITS and Automated Driving Using Cellular
Communications Technologies by ITS Info-communications
Forum Cellular System the TG(5) were referred to.

3.4. Development of the roadmap
Based upon study results up to this point, we can conclude
that in order to realize all 25 SIP-UC: new communication
methods for V2I and V2V are needed around 2040; newcommunication methods need to be in place around 2030
(when the automated driving begin to be adopted) so as to beinstalled in 30% of cooperative automated vehicles by theassumed plan target of 2040; and the 700 MHz band ITS, an
existing ITS wireless communication system, is expected to be
utilized for the use-cases that are starting relatively sooner.
Taking these points into account, the items required for therealization of the plan to expand the use-cases were broken
down and a roadmap of communication requirements forcooperative automated driving necessary for the realization of
an automated driving society was developed. The roadmap isshown in Fig.11.

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

Deployment of each use case and expected communication requirements foreach communication method: V2I
2025-

2030-

2035-

2040-

▼: Assumptions regarding use case start date●: Assumption of the development plan for related infrastructure, etc.
(Bold = assumed from the descriptions in the roadmap by the relevant ministries andagencies, etc.; normal = assumed by project participants)

b-1-1. Driving assistance by using traffic signal information (V2I)
Some of the use cases are already in service through ITS connect (red light alert, guidance for preparing to start waiting at a traffic light).
▼ Assumed from existing service offerings (assumed by project participants)

c-2-2. Driving assistance based on intersection information (V2I)
Some of the use cases are already in service through ITS connect (turn right alert)▼ Public-Private ITS Initiative/Roadmaps: "Deployment of Driverless Automated Driving Mobility Services in Limited Areas to More Than 40 Locations by
FY2025"▼ Assuming that b-1-1 and c-2-2 are necessary to realize automated driving mobility services in limited areas, and that the service will start around 2025 (assumed
by project participants)

b-1-1. Driving assistance by using traffic signal information (V2I)
c-2-2. Driving assistance based on intersection information (V2I)

Cooperative automated driving

Driving Safety Support and Cooperative Automated Driving

Driving Safety Support▼ Assumed from existing service offerings (assumed by project participants)● Locations where mobility services are
deployed
40 locations (Public-Private ITS
Initiative/Roadmaps)● 100 locations where mobility
services are deployed assumed byproject participants)● Further expansion of locations where
mobility services are deployed(contractor assumption)▼ Public-Private ITS Initiative/Roadmap "Realization of Level 4 Automated Trucks on Expressways Around FY2025"▼ Assumed to aim to realize the service of main line merging assistance to realize the goals of public-private ITS Initiative/Roadmaps (contractor assumption).

Merging/lane change assistance(V2I)
a-1-1, a-1-2▼ Merging support Day 3 system
Automated driving adaptation
rate of 30% or higher (FromJAMA materials)
a-1-3. Cooperative merging
assistance with vehicles on themain lane by roadside control● Adaptation of automated vehicles(L3 and above) reaches about 30%(Assumed by project participants)

Fig.6: Use case deployment plan (V2I)

Deployment of each use case and expected communication requirements foreach communication method: V2V
2025-

2030-

2035-

2040-

▼: Assumptions regarding use case start date●: Assumption of the development plan for related infrastructure, etc.
(Bold = assumed from the descriptions in the roadmap by the relevant ministries andagencies, etc.; normal = assumed by project participants)▼ Assumed from existing service offerings (assumed
by project participants)

Driving Safety Support

c-2-1. Driving assistance based on intersection information (V2V)
Some of the use cases are already in service through ITS connect (turn right alert)▼ Assumed from existing service offerings (assumed by project participants)

e-1(1). Driving assistance based on emergency vehicle information
Some of the use cases are already in service through ITS connect (emergency vehicle notification)c-1. Collision avoidance assistance when a vehicle ahead stops or decelerates suddenly
Collision avoidance assistance by using hazard
information▼ Assumed from existing service offerings for driving safety (assumed by project participants)

c-2-1. Driving assistance based on intersection information (V2V)1

e-1(1). Driving assistance based on emergency vehicle information▼ Public-Private ITS Initiative/Roadmaps: "Goal for 2030: Realize a safe and convenient digital transport society that supports
enriched quality of life of citizens, getting ahead of the rest of the world."▼ Assumed that c-1 services will be realized to achieve the goals of the public-private ITS roadmap (assumed by project participants)

c-1. Collision avoidance assistance when a vehicle ahead stops or decelerates suddenly

Cooperative automated driving

Driving Safety Support and Cooperative Automated Driving

c-3.

▼ Assumed to be realized later than c-1 because lane change assistance is also
included in the plan (assumed by project participants)

c-3. Collision avoidance assistance by using hazard
information▼ Merging support Day 4 system
Automated driving adaptation rate ofaround 50% (JAMA document based)▼ Commercialization of platooning technology (envisioned in METI’s "RoAD to the L4")
▼ Public-Private Roadmap: "Realization of Level 4 Automated Trucks on Expressways Around
FY2025"▼ Similar services have already been tested in FOTs Assumed that the service is implemented as soon as
possible basically after deployment of FOTs output or verification of technology (assumed by project
participants)

Merging/lane change
assistance(V2V)
a-1-4, a-2, a-3● Adaptation of automatedvehicles (L3 and above)
reaches about 50%(Assumed by project
participants)

Platooning/adaptive cruise control (V2V)
g-1. g-2● Addition of priority lane on
certain areas of
Osaka-Tokyo major expressways(Assumed by project participants)● Addition of priority lanes on the
Osaka-Tokyo major expressways(Assumed by project participants)● Expansion of priority lanes on
major expressways in Honshu(Assumed by project participants)

Fig.7: Use case deployment plan (V2V)

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

Deployment of each use case and expected communication requirements foreach communication method: V2N
2025-

2030-

2035-

2040-

▼: Assumptions regarding use case start date●: Assumption of the development plan for related infrastructure, etc.
(Bold = assumed from the descriptions in the roadmap by the relevant ministries and agencies,
etc.; normal = assumed by project participants)▼ Assumed that automated vehicles willtake time to become widely available
and that services will be provided to support driving safety for the time being
Assumed to start around 2025 based on SIP R&D trends (assumed by project participants)

b-1-2. Driving assistance by using traffic signal information (V2N)▼ Assumption that services will begin in 2025, as rapid and widespread deployment can be expected, and early start-up is expected tobe effective (assumed by project participants).

Lookahead information: trajectory change (V2N)
d-1, d-2, d-3, d-4, d-5e-1(2). Driving assistance based on emergency vehicle information▼ Assumed from existing service offerings (assumed by project participants)

f-1. Request for rescue (e-Call)
Some of the use cases have already in service through Help Net▼ Start of service through OEM telematics service (collection of vehicle and driving information)▼ Assumed from existing service offerings (assumed by project participants)

f-2. Collection of information to optimize the traffic flow
f-4. Distribution of dynamic map information▼ Need to verify technology
through field operational
tests for f-3.

▼ Assumed to be realized later than other use cases due to the need for technical verification forrealization (assumed by project participants)

f-3. Update and automatic generation of maps

▼ On-going study under SIP for providing SPaT information to automated vehicles via V2N▼ Assumed by project participants (discussion needed on when to start use cases for automated driving)b-1-2. Driving assistance by using traffic signal information (V2N)
Lookahead information: trajectory change(V2N) d-1, d-2, d-3, d-4, d-5

Cooperative automated driving

Driving Safety Support and Cooperative Automated Driving

Driving Safety Support▼ Assumed to start around 2025 based on SIP R&D trends (assumed by project participants)▼FOTs conducted under SIP▼ Assumed to be realized as early as possibly based on SIP R&D trend (assumed by project participants)

e-1(2). Driving assistance based on emergency vehicle information
f-1. Request for rescue (e-Call)
f-2. Collection of information to optimize the traffic flow
f-4. Distribution of dynamic map information
f-3. Update and automatic generation of maps
▼ Practical application of mobility services vehicles using remote monitoring (envisioned in METI’s "RoAD to the L4")
▼ Public-Private ITS Initiative/Roadmaps: "Deployment of Automated Driving Mobility Services in Limited Areas to More Than 40 Locations by FY2025"▼ Field operational tests of similar services▼ Assumed to implement the services as early as possible basically after deployment of FOTs output or verification of the services (assumed by project
participants)

h-1. Operation and management of mobility service cars
● 40 locations where mobility services ● 100 locations where mobility services areare deployed
deployed assumed by project participants)

Fig.8: Use case deployment plan (V2N)

A) Activities
practicalof use casesof use casesA)toward
Activities
towardapplication
practical application•••

Negotiation use cases are expected to begin after 2040, when automated• Negotiation use cases are expected to begin after 2040, when automatedvehicles will be in full use.
vehicles will be in full use.
It is difficult to realize negotiation use cases with the current 700 MHz• It is difficult to realize negotiation use cases with the current 700 MHz
band ITS.
band ITS.
On the other hand, the 5.9 GHz band is mainstream internationally.
• On the other hand, the 5.9 GHz band is mainstream internationally.
New communication methods need to be put to practical use.
New communication methods need to be put to practical use.

Approach

Implementation
Item

Fig.9: Undertakings toward practical application of use-cases

Implementation
Item

Implementation
Item

Implementation
ItemA-1) Formulation of security and privacy measuresA-1) Formulation of security and privacy measuresA-2) Establishment of policy on shared use of onboard unitA-2) Establishment of policy on shared use of onboard unitA-3) Formulation of policy on generational changes inA-3) Formulation of policy on generational changes incommunication methods
communication methodsA-4) Establishment of business modelA-4) Establishment of business modelA-5) Secure communication qualityA-5) Secure communication qualityA-6) Clarification of division of responsibilitiesA-6) Clarification of division of responsibilities

Approach•

In regard to practical use cases for V2I/V2V/V2N,• In regard to practical use cases for V2I/V2V/V2N,
the ITS Information and Telecommunication Systems Promotion Council
the ITS Information and Telecommunication Systems Promotion Council
and the study the TF have pointed out various issues (discussion points)
and the study the TF have pointed out various issues (discussion points)
via the study the TF
via the study the TF
These stakeholders need to cover a wide range of issues (discussion points)• These stakeholders need to cover a wide range of issues (discussion points)
through examination and discussion among the concerned entities,
through examination and discussion among the concerned entities,
making clear the direction that should be taken.
making clear the direction that should be taken.
3

Approach

Approach•B) Activities
thetoward
practicalof new of newB)toward
Activities
theapplication
practical application
communication
methods methods
communicationB-1) Frequency utilization studyB-1) Frequency utilization studyB-2) Standardization and specificationsB-2) Standardization and specificationsB-3) Equipment development and commercializationB-3) Equipment development and commercialization

Fig.10: Activities toward practical application of new communication
methods

1© NEC Corporation 2022
© NEC Corporation 2022
1

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2025 -

2020 2022 (R4)USA

2023 (R5)

Start of deployment of V2N services in 5G (DG MOVE)(R e fe re nc e ) Inte rna tio na l tre nds

Development of V2X
safety evaluation criteria(EuroNCAP)(EuroNCAP)

2040 -

Transition from 5.9 GHz band DSRC
to C-V2X completed

-V2X)

(FCC)

Europe2035 -

2024 (R6)

Change in allocation of 5.9 GHz frequency/technology(Disputing reduction of V2X bandwidth and transition from DSRC to C

Study of protocols

2030 -

Study of advancement of ITS-G5

V2X becomes a
subject ofassessment(EuroNCAP)R2R communication network construction using RSUs
(Czech implementation plan in C

Start of mediation use case
deployment(DG MOVE)-Roads)Fully automated driving realizedon all roads after 2040
(DG MOVE)(C2CCC)ITS -G5 Acceleration of RSU implementation (C-Roads)Intelligent connected vehicle

China

PA/CA: 50 % of all automobile sales
HA level automobile market introduction
Percentage of new vehicles equippedwith C-V2 terminals : 50 %Formulation of IEEE 802.11bd

International

CA: Conditional automated driving (equivalent to L3)
HA: Highly automated driving (equivalent to L4)(IEEE)
Formulation of 3GPP Release 18(3GPP)JNCAP *1 Additional new item study, survey research, study of testing and evaluation methods

Japan(ICV) adoption(China Society of Automotive Engineering)
PA/CA: 70% of all automobile sales
HA:
20% of all automobile sales
Achievement of basic adoption of vehicles
equipped with C -V2X terminals
PA: Partially automated driving (equivalent to L2)

Preliminary testing*1 Driving support technology, including V2X, becomes subjectof automobile assessment

Frequency reorganization action plan +30

MHz for next -generation mobility

AssumedCommunication
technology

700 MHz band ITS

New communication technology (application of 5.9 GHz, for example)A) Appro a c h to the pra c tic a l a pplic a tio n o f the us e c a s e sStudy of specifications forsecurity and privacy measures

Study of security andprivacy policyA-1) Formulation of
security and privacy
measures

Security scheme study

Study of policies for sharing inA-2) Formulation ofpolicies for sharing in vehicle devices

Issuance of specifications for security and privacy measures
Security scheme formation-vehicle devices

Study of policies for generational change ofcommunication systemsA-3) Formulation ofpolicies for generationalchange ofcommunication systems

Study on how to establish a business modelA-4) Establishing abusiness model
Study of measures to ensure communication qualityA-5) Ensuringcommunication qualityA-6) Clarification of
responsibilities

B ) Appro a c h to the pra c tic a l a pplic a tio n c o m m unic a tio n te c hno lo g ie s
(5.9 GHz band)B-1) Frequency
utilization

Organization of concept of division of responsibility

Study of channel andbandwidth allocation
Frequency utilization(if necessary)Communication protocol draft study

Communication
standard formulation

Communication
standard issuanceStudy of application specificationsB-2) Standardization,
specification

OBU draft study

Issuance of application specifications (and subsequently, issuance inorder according to the timing of application deployment)

Study of OBU common specifications

OBU connectivity confirmation scheme
formulation

Issuance of OBU common specifications
Study of RSU common specifications
Issuance of RSU common specifications
Product development
Test operation

Equipping of new 5.9 GHz OBU

Adoptionrate 30%

Adoptionrate 50%

Deployment of new 5.9 GHz RSU

B-3) Equipment
development andpractical application

V2I (broadcast)
V2V (broadcast: continuous)
V2V (broadcast: urgent)

V2I(mediation)

V2V(Negotiation)

Fig.11: Proposed Roadmap

4Conclusion

Through the verification of the compatibility of theexisting wireless communication cellular V2X system, andthe planning and verification of measures to solve any issues
that arose, a direction has been determined for the realizationof the issues facing SIP-UC.
Through the verification of the compatibility of theexisting wireless communication 700MHz band ITS, and theplanning and verification of measures to solve any issues that
arose, it has been shown that the long-term utilization of the
700 MHz band ITS is an effective means to realize SIP-UC.
The roadmap formulated based on the above was compiled
based on discussions among academic experts, relevant ministries
and agencies, and members of industry associations (automotive
industry, electronics industry) in the TF, as well as technologicaldevelopment trends and practical application trends incommunications as of October 2021 to March 2022. The practicalapplication of new communication methods (e.g., application of
5.9GHz band) necessary to realize cooperative automated driving
is assumed to take place around 2030, and efforts for frequency

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utilization, standardization and specifications, and equipment
development and practical application should be made toward
the practical application of these new methods. In addition, forthe practical application of the use-cases themselves, it isnecessary to discuss and clarify the direction of various related
issues, such as cybersecurity and privacy measures for theprovided services, policies for the shared use of onboard unit,
policies for the generational changes of equipment, businessmodels, ensuring communication quality, and sharing of
responsibility.
It is expected that, starting from the roadmap and usecases, each of the parties concerned, including vehicles andinfrastructure as well as telecommunication, will play theirrespective roles in the study, and that deepening discussion
and cooperation across industries will promote efforts
toward the realization of cooperative automated driving.
[References]
(1) Collaborative Automated Driving Communication Method Consideration The
TF: SIP Use-cases for Cooperative Driving Automation-Activity Report of Task
Force on V2X Communication for Cooperative Driving Automation in FY2019-,
https://en.sip-adus.go.jp/rd/rddata/usecase.pdf, 2022.
(2) ITS Info-communications Forum: Study report on communication scenarios andrequirements for "SIP Use-cases for Cooperative Driving Automation" ITS FORUM
RC-017 Version 1.0, ITS Info-communications Forum, https://itsforum.gr.jp/
Public/E3Schedule/p40/ITS_FORUM_RC-017eng_v10s.pdf，2022.
(3) Inter-vehicle Communication Message Specifications ITS Connect TD-001 ver.
1.1, 2018, https://www.itsconnect-pc.org/en/_img/about/TD-001_210310.pdf.
(4) Land Mobile Communications Committee, Information and Communications
Technology Subcommittee, Information and Communications Council Report
No. 2029, July 28, 2009, on "Technical Conditions for ITS Radio System,"referring specifically "Technical Conditions for Advanced 700 MHz Band
Intelligent Transport Systems."(5) ITS Info-communications Forum Cellular System The TG: Issue Survey Reportof Advanced ITS and Automated Driving Using Cellular Communications
Technologies, Supplementary Material: Issue Survey for SIP Use-cases for
Cooperative Driving Automation, ITS Information and Telecommunication
Systems Promotion Council, https://itsforum.gr.jp/Public/J7Database/p69/
Cellular_system_202205.pdf, 2022.

[Contacts]
NEC Corporation, Cross Industry Business Initiative Division 5-7-1 Shiba, Minato
City, Tokyo 108-8001, Tel: 03-3454-1111 (main line), Contact: Satoshi KimuraIntelligent Transport Systems R&D Division, Systems R&D Group, Corporate R&D
Group, Kyocera Corporation, 3-7-1 Minatomirai, Nishi-ku, Yokohama-shi,
Kanagawa 220-0012, 045-605-7189, Contact: Masato Ogawa

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2

Establishment and Utilization of Traffic Environment Data

(2) Technological Development Concerning the Transmission of Traffic Environment Data

2) Development of New Technologies, V2X
and Others, for Communication
Kinya Asano, Shoichi Nakabayashi (Oki Electric Industry Co., Ltd.), Satoshi Kimura, Masahiro Ohtsuka (NEC Corporation)(Abstract) In this study, based on the road map , which describes specific requirements for wireless communication
technologies applied to the cooperative automated driving use cases developed through R&Ds in the 2nd phase of
SIP-adus, we proposed a specification of wireless devices including communication protocol requirements for
implementation of the V2X system with radio wave in 5.9GHz band, in order to accelerate to solve relevant issues andto proceed study for the realization of cooperative automated driving.
Keywords: cooperative automated driving , V2X, cellular V2X (C-V2X), DSRC, communication protocol

1

Scope of Study

1.1. General Concept
We have studied feasibility of communication technologies
such as requirements for wireless communication technology
applied to the cooperative automated driving use cases(1)(hereafter the Use cases) through the R&D project named as"Research on Communication Methods to Realize Cooperative
Automated Driving Use Cases" as part of the 2nd phase of SIPadus. As a result of this study, including expectation tocommunication technologies to be developed for the future,
the project proposed the use cases and the road map (hereafter
the Roadmap) defining requirements for each wirelesscommunication technology.
In this study, based on output from the above mentioned
feasibility study and the Roadmap development, the wirelessdevice specification was prepared mentioning communication
protocols required for the implementation of V2X system withradio wave in 5.9 GHz band (hereafter V2X system in 5.9 GHzband) in order to advance solving related issues and study. (V2X:
Vehicle to X, V2I: Vehicle to Infrastructure, V2V: Vehicle to
Vehicle)Summary of communication specifications
International
standardization
trends research

Research aimed atdeveloping
communication
protocol proposals

Analysis and research of standardization and institutionalization trends incountries and regions

Organization of operation methods

Preliminary research of communication requirements
Examination of communication requirements via simulation

Developing a communication protocol proposal
Designingcommunication
protocol proposals

Developing a message set proposal

Stakeholder
meeting

1.2. Actions and Objectives
The activities shown in Figure 1 were carried out withmedium and long term perspectives in mind.
a) International standardization trends research
We researched and analyzed standardization andinstitutionalization trends related to the V2X system in 5.9
GHz band communication protocol, message sets, as well as
relevant communication specifications in countries andregions including the U.S., Europe and China.
b) Research aimed at developing communication protocol
proposals
The V2X system with 5.9 GHz band communication
requirements for the technological research ofcommunication protocols was studied based on thecommunication requirements and message sets discussed
through ITS Info-communication Forum, as well as on the
result of the Roadmap development.(2)c) Designing communication protocol proposals
Based on the evaluation results of the communication
simulation, we designed the communication protocol for theV2X system in the 5.9 GHz band, which was concluded ascommunication protocol proposal mentioning communication
procedures and protocol stack, along with message sets.
d) Proposing wireless device specifications
Taking into account functions required for practicalimplementation and cooperation with existing wireless systems,
we developed a specification for on-board unit and roadside

Simulation-based communication performance assessment

Examination of wireless device specifications
Proposing wirelessdevice specifications

FOTs on test courses, etc.
Clarification of issues and its solutions for implementation

Fig.1: Activities for study

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2) Development of New Technologies, V2X and Others, for Communication

unit using V2X system in 5.9 GHz band then clarified issues for
implementation and counter measures to solve the issues.
Further, we carried out verification and discussions
concerning evaluation results and issues at the meeting
structure as shown in Fig.2 with stakeholders when necessary
to appropriately apply these results to the study.
Observers(Cabinet Oﬃce, Ministry ofInternal Aﬀairs andCommunications, NEDO)

Stakeholder meeting(Organizers: OKI, NEC)Japan Automobile
Manufacturers Association
Electronics subcommittee(Mobile Communication
Expert Group)

ITS Info-communications Forum
Advanced ITS Info-communication
System Committee(Radio System TG)(Related organizations)Fig.2: Stakeholder meeting structure

2International standardization trends
research

For the Use cases,(1) we prepared the Roadmap defining
specific action items related to the wireless communication
technologies. The result of this process was utilized to research
and analyze the latest and consistent trend of standardizationand institutionalization in terms of V2X system in 5.9 GHz
band communication protocol (communication procedures
and stacks etc.) adapted to traffic circumstances in foreigncountries and regions including the US, Europe, and China,also message sets and relevant communication specifications
are involved. The output of the range of these efforts wasapplied to the studies described in Chapter 3, 4, and 5. Please
see Fig.3 for specifications of standardization organizations inregions subject to the study.

3

Research aimed at developing
communication protocol proposals

The communication requirements for the V2X system inthe 5.9 GHz band required for technological study ofcommunication protocol were studied as shown below, taking
into account cooperation with the driving safety support system
in 700 MHz band. This was conducted based on the results of
the International standardization trends research, the study
report on communication scenarios and requirements
developed by the Radio System Technology Task Group of
Advanced ITS Info-communication System Committee in ITS
info-communication Forum (hereafter Radio System TG (2)),
and the result of the Roadmap development. The results of thisresearch was used for studies described in Chapter 4 and 5.
(1) Communication channel allocation
The Use cases were broken up to groups based oncommunication medias (V2I, V2V) applied in the Use casesand communication contents (broadcasting, negotiation),then the channel allocation proposal for each group wasdeveloped following the estimation of communication
volumes based on case studies under specific circumstances.
The grouping criteria for the Use cases is in Fig.4.
(2) Communication congestion control methods at upper
layersBased on the results of study on specifications in SAE,ETSI and other organizations and the communication
channel allocation proposal, the study of communication
congestion control methods at upper layers was conducted.
Overview of communication congestion control methods
at upper layers are shown in Fig.5.
(3) Traffic flow simulation-based effectiveness evaluation
Based on the results of research for (1) and (2), we ran
ETSI (Europe)

SAE (U.S.)CCSA (China)

Application・Facility[BSM, etc.]
SAE J2735[CAM/DENM]ETSI EN302 637[BSM, etc.]
YD/T 3709[WSMP]IEEE 1609.3[GeoNetwork]ETSI EN302 636-5[DSRC] ETSIEN302 636-4-1[DSMP]
YD/T 3707[C-V2X] ETSITS102 636-4-3[DSRC]
EN302 663

[C-V2X]
EN303 613

[C-V2X]
YD/T 3340[C-V2X]
TS103 574

[C-V2X]

ProfileTotal[DSRC]SAE J2945

Transport
network

Access[C-V2X]SAE J3161[DSRC]IEEE 1609.4[C-V2X]
3GPP TS23.285

IEEE 802.11p

Operation
methodCongestion
control

Cybersecurity[DSRC]ETSI TS302 665[C-V2X]ETSI TS103 723[C-V2X]
YD/T 3400

IEEE 802.11p[DSRC]SAE J2945/1[C-V2X]SAE J3161/1[Security]
IEEE 1609.2
IEEE 1609.2.1
SAE SS V2X 001[DSRC]
TS102 687

[Security]
TS102 940, 941, 942, 943
TS103 097

Refer to (3GPP C-V2X)[Security]
GB/T 40856

Fig.3: Standardization organization specifications, etc. in regions subject to study
SAE: Society of Automotive Engineers
ETSI: European Telecommunication Standards Institute
CCSA: China Communication Standards Association

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V2I

V2V

a-1-1. Merging assistance by preliminary acceleration and deceleration
a-1-2. Merging assistance by targeting the gap on the main laneb-1-1. Driving assistance by using traffic signal information(V2I)
c-2-2. Driving assistance based on intersection information (V2I)

Negotiation

Vehicle broadcasts every time position, speed, etc.
c-2-1. Driving assistance based on intersection information (V2V)
e-1. Driving assistance based on emergency vehicle information

Vehicle broadcasts alert warning when taking sudden hard breaking

Emergency

BroadcastContinuous

Vehicle receives broadcast from roadside units

c-1.
c-3.

Automated vehicle negotiates for merging withusing V2I communication
a-1-3. Cooperative merging assistance with vehicles on the main laneby roadside control

Collision avoidance assistance when a vehicle ahead stops or
decelerates suddenly
Collision avoidance assistance by using hazard information

Automated vehicle negotiates for merging using V2V
communication

a-1-4. Merging assistance based on negotiations between vehicles
a-2. Lane change assistance when the traffic is heavy
a-3. Entry assistance from non-priority roads to priority roads during traffic congestion

Fig.4: Use case grouping criteria

Radio communication environment
management(Radio resource occupancy, etc.)

Driving environment management(Vehicle speed, traffic density, etc.)

Transmission timing control andpriority control for communication
congestion reduction

Fig.5: Communication congestion control methods for upper layers

traffic flow simulation to evaluate the effectiveness of theV2X system in 5.9 GHz for the Use cases, including bothsituations where the intelligent transportation systems in 700
MHz band was and was not being used.
This traffic flow simulation re-creates the Use cases based oncase studies under specific conditions and evaluates differencesin vehicle behavior (deceleration, etc.) due to differences amongcommunication specifications (communication timing, etc.). A
model showing vehicle behaviors during traffic flow simulationis shown in Fig. 6.

4

Event
occurrence

Precedingvehicles(Event detection
and message
transmission
under conditions)

Designing communication protocol
proposals

In response to the results of the research described in
Chapters 2 and 3, we developed a communication protocol
for the V2X system in the 5.9 GHz band shown below basedon status of institutionalization in Japan related to traffic
environment and frequency allocation, etc.
(1) Developing communication protocol proposals
Based on the results of research described in Chapters 2
and 3, and on the results of simulation-based communication
performance evaluations described below, we researchedcommunication content, procedures, layer configuration,requirements, etc. for each Use case and developed
communication protocol proposals. Table 1 shows examples
of communication protocols for reference standards.

Following vehicles(receive messages)

Transmit message

Time
Sensing
distance○
Direction of travel

Application requirements (transmission
timing, priority, communications quality,
etc.)

×

×

○
×
Dottedline
GrayData received
Data not received
In case of data received at
the second transmission of data
General vehicle trajectory

Fig.6: Model of vehicle behaviors during traffic flow simulation(2) Developing a message set proposal
A message set proposal was prepared considering status ofstandardization and its studies in and outside Japan,(2) thecommunication requirements and the communication channel
allocation mentioned in Chapter 3 etc. For this, possibilities ofreuse/ communalization of messages were also included in theconsideration as another perspective.
(3) Simulation-based communication performance evaluation
Through a system-level communication performance
evaluation conducted using a computer simulation, we
studied the impact of deploying V2X systems when thecommunication protocol proposal and message set proposal
we developed as described in (1), (2) above were implemented.
Based on the expectations and assumptions used for thecommunication channel allocation and others in Chapter 3,
we defined an evaluation scenario, such as combination ofuse cases, traffic road models (road scale, installation ofintersections etc.) and traffic flow models (driving speed,inter-vehicle gap etc.) to study radio propagation models andcongestion/interference models, then built up an evaluation
model. Fig.7 shows examples of radio propagation models.

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Table 1: Examples of communication protocols for reference standards
Communication control flow
Layer

U.S.
DSRC

Protocol stack

Europe
C-V2X

DSRC

U.S.

C-V2X

Europe

DSRC

C-V2X

DSRC

C-V2X

Application

Request message generation upon event detection

EEBL, FCW, etc. (DSRC: SAE J2945/1, etc., C-V2X: SAE
J3161/1, etc.)

Co-operative Awareness, Road Hazard Warning, etc. (ETSI
TS 102-637-1, etc.)

Corresponds toupper layers(layers 5-7)

・Collect required data, generate message
・ Congestion control (bandwidth control (*1))

Communication services: BSM Exchange, etc. (DSRC: SAE
J2945/1, etc., C-V2X: SAE J3161/1, etc.), Message: BSM, etc.
(SAE J2735, etc.)

Communication services: CA Basic Service, DEN Basic
Service, etc. (ETSI TS 102-894-1, etc.), Message: CAM,
DENM ,etc. (ETSI TS 102-894-2, etc.)

Transport layer(layer 4)

Adds data that specifies upper layer services

WSMP Transport Protocols (IEEE 1609.3)

Basic Transport Protocol (ETSI EN 302 636-5)

Network layer(Layer 3)

No substantialfunctions

WSMP Networking Protocols (IEEE 1609.3)

GeoNetworking (ETSI EN 302 636-4)

Adds data that specifies
network addresses

Adds data that specifies upper layer protocols

LLC (IEEE 1609.3)

PDCP (ETSI TS 136 323)

LLC (ISO/IEC 8802-2)

PDCP (ETSI TS 136 323)-

RLC (ETSI TS 136 322)-

RLC (ETSI TS 136 322)

Performs switching when multiple channels exist

Channel Coordination(IEEE 1609.4)

-

-

-

・ Adds data that specifies sender link
・ Send timing control
・ Congestion control (priority control (*2))

MAC (IEEE 802.11)

MAC (ETSI TS 136 321)

MAC (IEEE 802.11)

MAC (ETSI TS 136 321)

Physical layer(layer 1)

Sends after encoding and modulating

PHY (IEEE 802.11)

PHY (ETSI TS 136 201)

PHY (IEEE 802.11)

PHY (ETSI TS 136 201)

Cybersecurity
layer

Adds data (signature) to guarantee senderauthenticity and message integrity

Splits packets
Data link layer(layer 2)

Electronic signature(IEEE 1609.2)(*1) Output control available through U.S. DSRC (*2) Bandwidth control and output control available through Europe DSRC

Category
Metric property
ShadowingMultipath fading
Shadowing loss

Overview

Study model
ITU-R P.1411 (assuming urban environment)
Fading due to surrounding
Log-normal distribution
environment changes
Standard deviation at 5.9 GHz: 3.68 dB
Fading by symbol
3GPP Extended Vehicular A model (EVA)
Attenuation when there are ・ 3GPP TR 38.901 Blockage model B(Model using knife-edge diffraction)
large vehicles between
・ Attenuation is a fixed value based on blockage by one
transmitter and receiveradjacent large vehicle(One second of space between vehicles at 40 km/h and 5.9
GHz: 10 dB)
Attenuation due to communications range

Roadside unit (transmitter)

Blocking vehicle

Vehicle (receiver)*Occurs in situations where the number of adjacent vehicles,
buildings, etc. changes

Fig.7: Examples of radio propagation models for communication performance evaluation systems

5

Proposing wireless device specifications

We examined wireless device (on-board devices androadside units) specifications for the V2X system in the 5.9
GHz and developed a proposal for wireless device
specifications. Based on the results of studies described in
Chapters 2, 3, and 4 concerning specifications and functions
for each communication layer, we conducted the study
taking into account the possible cooperation with existing
wireless systems. Examples of items to study for wirelessdevice specifications are shown in Table 2.
Further, we carried out the FOTs (Field Operational
Tests) on test courses under the condition that multiple use
cases co-exist, then analyzed the result of this communication
performance evaluation with multiple channels in comparing
with the result of evaluation with simulation in Chapter 4.

6Conclusion

For the realization of cooperative automated driving, wedefined a wireless devices specification including communication
protocols to be required for implementation of the V2X systemin the 5.9 GHz band, as we need to accelerate to study and solve
issues for this implementation.
Studies aimed at developing communication protocols
involved examining communication requirements for the V2X
system in the 5.9 GHz band (communication content,
communication channels, communication congestion control,etc.) based on international trends (related institutions andstandards). In designing a communication protocol proposal
and wireless device specifications proposal, based oncommunication simulation evaluation results that took into
account radio propagation characteristics and the impact ofinterference between communication channels, among otherfactors, we studied communication protocol proposal including
communication procedures and protocol stacks, and also

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Table 2: Examples of items to study for wireless device specifications
V2X communication function, characteristics, andinterface details

Transmitter characteristics

Antenna power

Overview

Coding Rate:

Antenna power permissible deviation
Leakage power during carrier off
Transmitter spurious

Transmission timing accuracy
Adjacent channel rejection
Non-adjacent channel rejection
Secondary emitted radio wave strength
Receiving maximum input power
CCA requirements

Antenna

Antenna structure
Antenna gain
Antenna polarization
Antenna installation

Communication control section functions, characteristics, interface

Modulation accuracy

Layer 7 functions

Layer 7 communication control

Data format

Transmit/receive procedure:

Layer 7
Service interface

Parameters

Protocol data unit

Overview

Transmit spectrum mask

Functions

Service primitive

Layer 1 communication control

Modulation method

Frequency permissible deviation-

Layer 1Service interface

Layer 1 functions

ChannelReceiver sensitivity

Receivercharacteristics

General requirements and technical requirements for wireless device

Generalrequirements

Data rate

V2X communication function, characteristics, and interface details

-

:

:

-

Functions

Service primitive

Parameters

Security primitive
Protocol data unit

FunctionsParameters
Data format

Transmit/receive procedure

Functions

Layer 1
Layer management
Entity functions

Layer management
Service interface

Management information base
(MIB)
:

:
Functions

Layer 7

Management information base
(MIB)

Security layer functions
Communication sequence
Use cases
a-1-1Interface ofservice in use
Transmission parameters

Other functions:

Transmission requirements:
Communication sequence

Use cases
g-2

Interface ofservice in use
Transmission parameters

Congestion control-

Between antenna section andtransmission/receiving section

Physical interface--

Between transmission/receiving section
and communication control section

Physical interface--

Physical interface--

Logical interface--

Between application and layer 7

message set proposal such as communalization and expandability
when coexisting multiple use cases.
With the wireless device specification including
communication protocol developed in this study, it is expectedthat all relevant research institutes and companies align andwork together for the realization of cooperative automateddriving.

[References]
(1) SIP Use Cases for Cooperative Driving Automation-Activity Report of Task
Force on V2X Communication for Cooperative Driving Automation in FY2019,2020.
(2) Study report on communication scenarios and requirements for "SIP Use Cases
for Cooperative Driving Automation" ITS FORUM RC-017 Version 1.0, ITS
Info-communication Forum, 2022.

[Contacts]
Oki Electric Industry Co., Ltd., DX Business Center, 1-16-8, Chuou, Warabi, Saitama
335-8510, 048-431-7211 (main line), Contact: Kinya Asano
NEC Corporation, Cross Industry Business Initiative Division 5-7-1 Shiba, Minato
City, Tokyo 108-8001, 03-3454-1111 (main line), Contact: Satoshi Kimura

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2

Establishment and Utilization of Traffic Environment Data

(2) Technological Development Concerning the Transmission of Traffic Environment Data

3) Research and Development Concerning the Collection
and Transmission of Mid-Scale Network Information
Tomohiko Saito, Shota Taki (NTT Communications Corporation)(Abstract) There are a number of issues that will need to be addressed in order to make automated driving ubiquitousin the future. One of these is the fact that automated vehicles cannot themselves perform adequate sensing incomplex environments such as intersections, forcing these vehicles to stop or slow down in front of intersections inorder to safely proceed through intersections, potentially interrupting the flow of traffic. As part of research anddevelopment conducted in fiscal 2019 and fiscal 2020, preliminary research and field tests were conducted involving
core technologies concerning the collection of data on objects such as vehicles and pedestrians from sensors, etc.
in the vicinity of intersections, as well as data distribution control technologies for automated vehicles. In fiscal 2021,
steps were taken to support a broader range of data such as emergency vehicle data and traffic signal schedule
information, as well as steps to support information distribution in mid-scale network for a variety of use cases.
Meanwhile, tests were run to gauge server load in mid-scale network area and data transmission efficiency, in addition
to assessing the usefulness of information distribution system in mid-scale network, with the goal of practicalapplication of these technologies. The results of this research and tests led to the creation of the Mid-Scale Network
Server Implementation Guidelines for business operators considering V2N information distribution in mid-scale
network.
Keywords: V2N, V2X, Information distribution system in mid-scale network, emergency vehicle information, SPaT information,
PULL/PUSH data delivery

1

Overview of this Research andDevelopment

There are a number of issues that will need to be addressedin order to put automated driving into practical use in the
future. One of these is the fact that automated vehicles cannot
themselves perform adequate sensing in complex environments
such as intersections, forcing these vehicles to stop or slow
down in front of intersections in order to safely proceed throughintersections, potentially interrupting the flow of traffic.

Safely enter and passthrough intersection

Avoid vehicles parked onroads (lane changing)

Bypass upcoming intersections(route changing)Fig.1: Vision and goals for this R&D

In this study, as shown in Fig.1, to resolve these issues, wehave established use cases for assisting easier entrance tointersections, avoiding cars parked on streets, and assisting tochange route to avoid upcoming intersections. Based on the use
cases, the target information in blind spots from multiple
information sources was collected and integrated, anddistributed to the small-scale network area surrounding anintersection around which the information was collected and to
mid-scale network area which covers multiple intersections
blocks or municipal(s). These are the research and development
we have carried out since fiscal 2019 to contribute to passage
assistance at intersections and lane-change assistance before
entering to intersections. In fiscal 2019, preliminary research
was done for core technologies to efficiently collect, integrate,and distribute multiple object data from roadside infrastructure,and others. Then the Field Operational Tests (FOTs) wereconducted in fiscal 2020. Based on these results, specifications
for a common interface, as well as data distribution methods,
integration methods, and edge server installation standards
were established in the interest of building a "system to assist
automated vehicles."These efforts shed light on the fact that practical applicationof these technologies would require addressing a broad range ofdata encompassing more than just object data to support
automated driving, as well as identifying issues for different use
cases.(1)SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information

2

Fiscal 2021 R&D activities andresults

As part of fiscal 2021 R&D activities aimed at solving
problems with a view to implement the above technologies insocieties, research was conducted with respect to the expansion
and application of technologies concerning the distribution ofdata to automated vehicle at mid-scale network area. In
addition, through FOTs with user participation conducted incoordination with the large scale FOTs carried out in the Tokyo
waterfront area, a more detailed research was performed withthe goal of practical use of the technologies, while examining
the adaptability of the whole system of mid-scale network
information distribution system.

2.1. Research into the expansion and application of
distribution-related technologies
With respect to information distribution in mid-scale
network, it is necessary to examine the need for immediacy andthe effective distribution area for each kind of information
based on the use cases, and then to formulate distribution
methods. In light of this and given the characteristics of
distribution systems (PULL/PUSH), these FOTs have centeredon distribution systems, communications protocols,
distribution areas, and distribution frequencies for each
information to be distributed.
(1) Research concerning distribution methods for use cases
With regard to information to be distributed as part of theseR&D efforts, the use cases we assumed and the immediacy thatwill be required for each of the use cases are presented in Table
1. Also the adequate distribution method in light of the
distribution system characteristics was examined as shown in
Table 2.
Table 1: Use cases and other conditions for distributed information
Information

Needs for real-time
distribution

Assumed UC

Rainfallinformation

Handover to manual driving & underpass
avoidance (route changing)

Low

Lane-level roadtraffic
information

Avoid medium and wide range congestion/
restrictions (route/path changing)/detect jam
endpoints and slow down in advance

Low

Emergency
vehicleinformation

Detect emergency vehicles approaching andslow down/stop

High

SPaT
information

Dilemma avoidance/assistance when traffic
lights are at blind angles/ adjust to best route/speed

For lane-level road data and rainfall information, because of
the low update frequency, the PULL system was deemed to be
the most suitable as a vehicle can acquire information when
they need it. The PUSH system, on the other hand, was deemed
to be the most suitable for emergency vehicle information and
SPaT (Signal Phasing and Timing) information as theinformation can be distributed from the server to the vehicle,
satisfying the need for immediate information processing.
Due to the likelihood of data volume increasing with the
PUSH system as information is sent to individual vehicles at ahigher frequency, this R&D focused on studying emergency
vehicle data and SPaT information, which was distributed by
two methods: distance-based and intersection-based.
(2) Communications protocols used for information distribution in mid-scale network
MQTT was selected as the communications protocol used
for distribution in mid-scale network. The MQTT protocol,
which uses a Publish/Subscribe model and is simple, lightweight,
and fast, with an extensive library, was made to send and receive
short messages frequently among numerous devices. It is alsoeasy to sort transmissions differently depending on the Topic,
and has seen extensive use in connection with IoT devices.
These and other factors give MQTT more advantages than otherprotocols. MQTT was selected as it was deemed the best fit forthese FOTs. Its QoS controls the assurance of message delivery
between client and broker depending on its level setting. Fig.2
shows a system configuration diagram for mid-scale network
servers using MQTT, while Fig.3 shows a software chart.
Cloud server
DB server
Distributed Information DB

Externallylinked server
Externally linked
serverLegend: Uplink

Table 2: Characteristics of data suited to distribution via each system
Distribution system

Best information use caseBest information
type

Needs to be distributed
in real-time

PULL system
Distribute information
based on user request

When the receiver chooses
and uses needed data frombroad ranging and profuse
data

Static data
Semi-static data
Semi-dynamic
data

Low

PUSH system
Distribute information
to target vehicle from
distribution source

When wanting to
distribute information
upon event occurrence orin short intervals

DynamicdataHighMid-scale network
application server
Mid-scaleMid-scaleMid-scaleMid-scaleMid-scaleMid-scaleMid-scale
network
network
network
network
network
network
network
applicationapplication
application
application
application
application
Mid-scaleMid-scale…× Vehicle countnetwork
network
application
application: Downlink

Distance-based
distribution only
Vehicle

MQTT serverDatasourceserver

MQTT broker

Fig.2: Mid-scale network server system configuration diagram
V2X system application
Mid-scale network server application
Spring Framework

Medium/highVehicleinformation DB〜

Mid-scale network server application

Externally linked application

Spring Framework

Spring Framework

MQTT client:
Eclipse Paho 1.0.2
VM:
Amazon Corretto 8 8.262.10.1
OS: Ubuntu 18.04.2 LTS

DB:MemSQL
6.8.17

MQTT
broker:
EMQX
4.1.2

Fig.3: Mid-scale network server software configuration diagram

Mid-scale network server application configuration(3) examination
For PUSH distribution system, based on the location

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information

information of a vehicle, we implemented a server in mid-scale
network to distribute information narrowed down to that ofvicinity of the vehicle. As required and searched informationdiffers for each vehicle, we had an application launch for eachvehicle. Using this configuration, in order to reduce the delay
between information update and distribution, information was
distributed from the server every second (parameters can bechanged) and MQTT Topics were given unique values for eachvehicle. Application installation was performed using Java’s
Spring Framework.
(a) Emergency vehicle information
Emergency vehicles require responding in real time when
they approach from behind or from out of view, but do notaffect vehicle travel when they are far away. We consequently
studied using PUSH distributions for emergency vehicle
information within a certain radius around the vehicle, as
shown in Fig.4.
➢ Distribution frequency : 1-second interval➢ Distribution radius: 300m
[Emergency vehicle information]
300m
Informationreceiving vehicles

Transmissionarea

Fig.4: Emergency vehicle information distribution frequency anddistribution area(b) SPaT information (distance-based method)
Drivers sometimes cannot make out a traffic light color due
to sunlight or obstruction, for example. For such situations, weconsidered having vehicles receive SPaT information in advanceas a means to support dilemma avoidance at intersections, planroutes efficiently, and achieve green waves. To reduce
communications traffic using the distance-based system, SPaT
information is transmitted within a rectangle around the
direction of the distributing vehicle’s travel. This is shown in
Fig.5.
➢ Distribution frequency: 1-second interval➢ Distribution radius: 1 square kilometer (parameters can be changed)[SPaT information]

Informationreceiving vehicles

1km

1km

intersection. As shown in Fig.6, vehicles designate intersections
on their own travel routes and divergent routes and continuously
receive SPaT information.
When a vehicle designates a new intersection, the updated
version of the SPaT information it has received. It also stops
receiving unneeded data about previous intersections.
➢ Distribution frequency: Whenever SPaT information is updated[SPaT information]

Informationreceiving vehicles

Fig.6: SPaT information distribution method (intersection-based system)

This system was achieved as follows. For SPaT information
distributed to a server in mid-scale network from a data source
server, an intersection ID that identifies each intersection is
published as a Topic. When a vehicle first subscribes, it
immediately distributes up-to-date information through
MQTT’s retain function. As long as a vehicle is subscribed, it
receives data every time data is updated.
Under this system, while vehicles need to designate theintersections for which they require data, server load can be
reduced as working servers do not have to process informationfor every vehicle. Moreover, as data is only transmitted upon
subscription and SPaT information update, distribution
frequency and communications traffic can be decreased.

2.2. Examination of system configuration feasibility
at the time of social implementation
As a step toward practical application of an information
distribution system in mid-scale network, we measured server
load in mid-scale network area and communications traffic,
and assessed the effectiveness of an information distribution
system in mid-scale network.
(1) Measuring server load for PUSH distributions
Using the configuration in Table 3, we measured the load
imposed on a server in mid-scale network when simulated
emergency vehicle information and SPaT information weresent to vehicles, based on the number of information-receiving
vehicles.
Table 3: Test server specifications

Transmission area

Fig.5: SPaT information distribution method (distance-based method)(c) SPaT information (intersection-based system)
The intersection-based system sends out PUSH
transmissions every time SPaT information is updated at anServer name

vCPU core countMemory (GB)DB server

4

16

Externally linked server

28Mid-scale network application server

856

MQTT server

4

16

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information

As described in Section 2.1 (3), when using the distancebased method for emergency vehicle information and the SPaT
information, one server application was launched for eachvehicle. However, under intense resource usage, it was notpossible to launch more than 70 server applications due to alack of enough CPU resources. Likely reasons for this are that
one application was launched for each vehicle, the Spring
Framework was running for each application, and transmissions
were processed every second for each application.
By contrast, the intersection-based method for SPaT
information has none of these issues, with a server load lower
than that of the distance-based method. There was also no
increased MQTT server load with the intersection-based
method. Table 4 shows CPU and memory usage when
distributing information to 70 vehicles.

(3) Examination of distribution system feasibility
As a step toward practical use of an information distribution
system in mid-scale network, we calculated costs of server in
mid-scale network that would be incurred to transmit a larger
amount of dada then examined feasibility of the system.
Based on calculations performed in Section 2.2.(1), we
calculated what the cost would be if they were to hypothetically
distribute information to vehicles using a Microsoft Azure
server. As shown in Table 6, we found that when using theintersection-based method the cost of using the distance-based
method per distributing vehicle could be kept to less than
1/100.

Table 4: CPU/ memory usage for information distribution from server in
mid-scale network

cost
of using the distance-based
method.
information
For Approx. 840 vehicles

Server in
mid-scale
network
CPU max
usage

Load test delay
verification(Average)

Server in
mid-scale
networkmemorymax usage

MQTTserver CPU
max usage

MQTTservermemorymax usage

Distribute to 70 vehicles simultaneously[Distribution methods]

Simulated
emergency vehicle
location
46.58%
58.14%
2.75%
2.42%
information/
also no increased
Distance-based MQTT server load with the intersectionmethod

based method. Table 4 shows CPU and memory usage when
SPaT information/
Distance-based
58.02%
8.77%
2.47%
distributing
information50.19%
to 70 vehicles.
methodSPaT information/
Distance-based
method

3.23%

2.38%

(2) Measuring communications traffic for PUSH distributions
We conducted a theoretical measurement of communications traffic discrepancies between the two methods when data
was distributed via the PUSH methods from a server in midscale network. We looked at SPaT information for which both
the distance-based and intersection-based methods were available.
As shown in Table 5, when the data distributing intersection
count was the same, the distance-based method distributedwith a higher frequency as data updating experienced no delay.
With the intersection-based method, distribution data volume
was reduced to less than 1/100 as data was only transmitted
when SPaT information was updated. Consequently, from theperspective of communications traffic, the intersection-based
method has the advantage.

Vehicle↓Mid-scale network area server

Table 5: Results of theoretical measurement of communications traffic for
SPaT information
Distribution
methodNumber ofintersection
per area

Distribution
frequency

SPaT information(Distance-based)

12(1km2）

1 second

12

120 seconds(Signal cycle length)

SPaT information(Intersection-based)

Table 6: Number of supportable vehicles and per-server cost calculation
Information

Azure D96ads v5
Supportable vehicles under
one configuration (one server)

Server cost per vehicle

Emergency vehicle
SPaT information(distance-based)

SPaT information(Intersection-based)

For Approx. 88,800 vehicles

188 yen/month

1.78 yen/month

2.3. Examination of functions needed for practicaluseThis section looks at functions and issues to be examinedwhen considering the social implementation of technologies
for distributing data to large areas, with a view to deploying
such technologies nationwide.
(1) Re-examination of requirements
Further reducing costs per information-receiving vehicle
will require re-examining requirements aimed at achieving
more efficient information distribution (distribution methods,
function allocation, etc.). The server load tests have shown that,for every type of data, distribution control per informationreceiving vehicle places considerable load on the server. As aresult, distribution methods need to undergo a re-examinationthat also factors in use cases. For example, this could mean to
apply the intersection-based method for SPaT information, orto distribute emergency vehicle information not to informationreceiving vehicles as shown in Fig.7 but to vehicles passing
within a certain size of area centered on an emergency vehicle.
➢ Distribution frequency: Whenever information source data is updated
Distribution radius cannot be➢ Distribution radius: 300 m (needs review)[Emergency vehicle location information]

set per vehicle, so is fixed

Information is not distributed to vehicles withno emergency vehicles in the vicinity

Transmission area
300m

DataCommunications Data distributed
receiving
per hourper hourvehicles928,800

Approx. 2.79 GB

7,740

Approx. 9.68 MB

258 (*)

Fig.7: Proposal method of information distribution to vehicles passing
within a certain size of area centered on an emergency vehicle*Average Vehicles per kilometer in Tokyo (fiscal 2022)

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information(2) Creation of a system with a view to practical implementation
Creating an information distribution system in mid-scale
network area with a view to at-scale deployment will requireconsideration of overall system architecture. Further research
into availability and scalability is needed, including, forexample, the addition of functions for specifying available
servers from multiple distributing servers as shown in Fig.8,
further boosting efficiency through application optimization,and implementing non-functional requirements such as
redundancy and security.

[References]
(1) "Yuji Aburakawa: SIP 2nd Phase:Automated Driving for Universal

Services—Mid-TermResults Report (2018–2020), pp.43-49, September
2021, https://www.sip-adus.go.jp/file/rd03/chapter2_s.pdf (accessed
November 2022)[Contacts]
Business Solution Division, Business Solution Department 2, Group 8, NTT
Communications Corporation, 2-chome-3-1 Otemachi, Chiyoda City, Tokyo
100-8019, 050-3463-5532, Contact: Shota Taki

Data source server

Available server
designation function

Distribution
server (Z)Distribution server (A)Intersection ID& areaCL application

Distributed
information
CL application

Select Distribution server via available server designation function

Fig.8: Proposal for designating multiple distributing servers in order toenable the use of available servers(3) Establishment of server in mid-scale network implementation guidelines
Based on the results of this research and development, we
identified areas requiring consideration ahead of creating andstudying server in mid-scale networks, and established
implementation guidelines for business entities who are
considering to distribute information in mid-scale network to
vehicles through V2N system. Such business entities may
include OEM and content providers.

3 Conclusion
As part of research and development efforts in fiscal 2021
aimed at promoting the use and expansion of distribution
system in mid-scale network nationwide, we examined thefeasibility of expanding and applying distribution-related
technologies and conducted verifications by way of a large-scale
field operational tests.
In the future, we plan to accelerate efforts to put the resultsof this R&D into practical use and apply the knowledge gained
from these activities toward a range of fields through guidelines,
among other things.

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3

Ensuring the Safety of Automated Driving

Technological Development and
Education for Enhanced Safety(Overview)
Osamu Hosaka, Hiroaki Kimura (Cabinet Office)1

Background and SignificanceEnsuring safety and reliability is the most important issue
in the practical implementation and deployment of
automated vehicles, and there is an urgent need to establish asafety assurance method for automated vehicles. In addition,communication between automated vehicles and othertraffic participants must be conducted flawlessly. Therefore,
SIP-adus (Cross-ministerial Strategic Innovation Promotion
Program (SIP) Automated Driving for Universal Services)
decided to address the following three issues for technologicaldevelopment for enhanced safety.(1)(i) Building of safety assurance environments in virtual environments(ii) S ustainable and effective measures against cyberattacks onconnected cars (cybersecurity)(iii) Establishment and dissemination of appropriate
methods for communicating the intentions of automatedvehicles to people, and effective education and awarenessmethods for people using automated vehicles andautomated driving services (safety education)
Currently, addressing the building of a safety assurance
method for automated driving is also the matter of greatest
interest globally, and various initiatives are in progress. SIPadus is also conducting safety assurance on public roads,
such as the FOTs (Field Operational Tests) in the Tokyo
waterfront area, and these driving tests on public roads andtesting of actual cars at testing sites are also important, but
evaluation via simulations that are reproduceable and that
can also generate critical conditions are essential as well.
Therefore, SIP-adus is particularly focusing on the evaluationof sensor performance, with the aim of building simulation
models which are highly consistent with real environments andcan be an alternative to test evaluations in real environments,and has commenced the development of a safety assurance
simulation platform in a virtual environment that can perform
evaluations under various conditions based on the relevant
models.
Regarding cybersecurity, in the first phase of SIP-adus,
the establishment of a method for evaluating the level ofcybersecurity protection against cyberattacks from outside
the vehicle was addressed, and an evaluation method for
during the development phase was developed. On the other
hand, because cyberattack technology continues to evolve, asystem to detect and monitor cyberattacks during vehicle
operation after a vehicle is on the market is also necessary.
Currently, Intrusion Detection Systems (IDS) for
cyberattacks against vehicles by third parties in bad-faith aregarnering attention as countermeasures against this issue.
The development of a method to evaluate the performance of
IDSs was addressed in the second phase of SIP-adus.
In addition, as a human-machine interface (HMI) issue,
research and development was conducted on the appropriate
indication and education methods to avoid communication
errors when automated vehicles encounter other vehicles,
pedestrians, etc. in mixed traffic.
The research and development of all three of these issues were
progressed in cooperation with the German Federal Ministry of
Education and Research (BMBF) under the Japanese-German
cooperation framework. [See Section 6 2) for more details]

2

Building of safety assurance
environments in virtual environments

Because the current evaluation method, which is centeredaround FOTs using actual vehicles on public roads, does not

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3 Ensuring the Safety of Automated Driving
﻿Technological Development and Education for Enhanced Safety (Overview)

Experimental Evaluation
Public Road

Virtual Evaluation
SILS／MILS(Software in the Loop／Model in the Loop)

Proving Ground
Connect

Connect

HILS(Hardware in the Loop)
Camera

Radar

LiDAR

VILS
(Vehicle in the Loop)

Construction of sensor models that are highly
consistent with real phenomena

Fig.1: Consistency verification of DIVP’s sensor model

modeled based on the principle of reflectance properties,
and consistency is verified by comparing the results withthose of actual vehicle tests. In addition, there are "virtualenvironment models" which correspond to cameras, radar,and LiDAR respectively to make the simultaneous evaluationof these sensors possible. (Fig.2)

Virtual model

Actual physical model

Verification
SourceLight

TargetTrans

Ray tracing

Environmental model
3D modelReflection/Refr
actionSpatial rendering
SourcePropagation

Recieve

Sensor Model
Perception

Recognition
Physical value
outputVerification

allow for the intentional setting of the necessary driving
environment conditions, which makes it difficult todetermine whether or not an automated vehicle meets therequired safety and to evaluate all of the various events that
occur on public roads using actual vehicles, there was anurgent need to develop a method to assure the safety of
automated vehicles under specific driving environment
conditions. In addition, it is considered necessary to develop
simulation tools that focus on sensor performance evaluationin order to improve the efficiency of safety assurance using
actual vehicles, which takes an enormous amount of time inthe current development of automated vehicles.
Therefore, SIP-adus program to develop simulation
models which are highly consistent with real phenomena
and can be an alternative to test evaluations in realenvironments, in order to conduct safety assurance withhigh reproducibility under various traffic environments.
(Fig.1)A consortium consisting of three universities (Kanagawa
Institute of Technology, Ritsumeikan University, and Toyota
Technological Institute) and ten companies (Hitachi Astemo,Sony Semiconductor Solutions, Denso, Toyota TechnicalDevelopment, Pioneer, Biprogy, Mitsubishi Precision, Soken,
Solize, and Yushin), led by Professor Hideo Inoue of the
Kanagawa Institute of Technology, and the all-Japan
organization commenced the building of the Driving
Intelligence Validation Platform (DIVP®), a safety assurance
environment platform in a virtual environment. This "virtualenvironment model" is a model that enables the safetyassurance of an automated driving systems by modeling thegeometric information and reflectance and spatial
propagation properties of objects under traffic environment
scenarios, and detecting them with a refined sensor model(Fig.2); by changing the various environmental factors suchas time of day, the weather, vehicles, and infrastructure, it ispossible to evaluate a system under any conditions.
In this model, in order to accurately simulate the sensor
outputs, the detection principles of each sensor and physicalphenomena in the electromagnetic wave band used are

Allocation of improvement requests

Consistency verification andenhancement research based on
sensor output comparisonsFig.2: Virtual environment model and sensor model

From FY2021, we have cooperated with the AD-URBAN(FOT project of Automated Driving system Under Real city
environment Based on Academic researcher’s Neutral
knowledge) step being undertaken by the Kanazawa
University consortium to share scenarios where there wasrecognition failure in FOTs of LiDAR and cameras, evaluate
the reproducibility in a virtual environment, verify thedifferences in recognition between the real environment andvirtual environment, and improve the accuracy of the model.
[See Section 3 2) for more details]
In addition, in order to ascertain the future needs ofcustomers and to identify issues regarding the continuous
operation of the DIVP data platform, a scenario package wasprepared that stimulated sensor weakness scenarios in theTokyo Waterfront City area, invited participants fromautomobile manufacturers and sensor manufacturers, andconducted a monitoring evaluation in two separate steps.
The results of the monitoring evaluation were generally

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3 Ensuring the Safety of Automated Driving
﻿Technological Development and Education for Enhanced Safety (Overview)

Organization of simulation types according to purpose

Customer
environment(existing Sim)

SILS/HILS

Scenario

Environment

Traﬃc flow
scenario

Map data

SpatialrenderingSensor
Perception
Recognition

Sensor model (true value model)<PointMILS/SILS

Want to utilize existing
assets and own scenarios

SDMG®
DIVP-Format

DIVP-I/F

Want a virtual environment output for
sensor development and recognition
learning

User control
model

User vehicle
model(MATLAB/Simulink, etc.)(CarMaker/CarSim/
ASM etc.)

Want to develop and evaluate systems
and sensors

DIVPⓇ "Environment, Space Rendering,Sensor" modelSensing Weakness
Scenario

Automated vehicles
Fusion/Control
Vehicle control

Reference
control model

Reference
vehicle model

DIVP-I/F

LegendConnection using standard IF

Fig.3: Connectivity between customer environment and DIVP

favorable, and the simulation experience conducted in STEP1 was rated higher than other simulators in terms of thereliability of the simulation and the adequacy of the assets. In
STEP-2, several companies from among the participants in
STEP-1 were selected and a detailed evaluation verification
was conducted on the connectivity of the system with various
existing simulation environments, the output of simulation
results when the scenario environment was arranged basedon the virtual environment that had been prepared, and theconnectivity of the system with the participants’ various
models and systems. (Fig.3)
Automated vehicles utilize a complex combination of
sensors such as cameras, radar, and LiDAR, and it is essential
to evaluate them via sensor fusion. In this case, evaluation of
combinations of various sensors is necessary, andstandardization of the interfaces is important to realize thisin a simulation. For this reason, the standards of the Germanbased standardization organization Association forStandardization of Automation and Measuring Systems(ASAM) were used to ensure compatibility with existing
simulations.
In addition, SIP-adus started a cooperative project called
the VIrtual Validation methodology for Intelligent Driving
systems (VIVID) with the German research project VIVALDI
in October 2020, and the standardization of safety assurance
systems and simulation interfaces for automated driving byorganizations such as the International Organization forStandardization (ISO) and ASAM while strengthening
international cooperation efforts was promoted throughthese activities. [See Section 6 5) for more details]
On the other hand, the establishment of safety assurance
methods for automated vehicles requires verification of thevalidity of scenario definitions and evaluation criteria. To
accelerate the practical implementation of safety assurance
technology, DIVP has cooperated with the Safety Assurance
KUdos for Reliable Autonomous Vehicles (SAKURA) Project
being conducted by the Ministry of Economy, Trade andIndustry and the Ministry of Land, Infrastructure andTransport, based on a proposal by the Safety Assurance
Subcommittee of the Japan Automobile Manufacturers
Association (JAMA), and established a working task force andsteering committee to jointly promote the project in FY2021.
As a result of the DIVP activities over the past four years,
the practical implementation of a platform that enables
streamlined scenario generation, recognition performance
evaluation, and vehicle control verification is in sight, andV-Drive Technologies, a new company (100% owned by
Biprogy) that provides DIVP’s research results as a tool chain,
was established in July and started general sales in September.

3

Cybersecurity

Automated vehicles need to acquire road traffic
environment data such as high precision 3D maps and traffic
signal information via communications. Cyberattacks are amajor threat to automated vehicles and other vehicles with
communication functions, and it is extremely important totake cybersecurity measures in anticipation of cyberattacks.
Several cases of cyberattacks on vehicles have already beenreported at international conferences, and Intrusion
Detection Systems (IDS) are attracting attention as acountermeasure against cyberattack methods, which
continue to evolve even after vehicles are put on the market.
On the other hand, since IDS is a countermeasure against
unknown cyberattacks, it is difficult to evaluate its
performance. Therefore, SIP-adus has worked on an
evaluation guideline for IDSs. In this project, in addition to
examining IDS in cooperation with an industry organization(JASPAR), IDS performance evaluation was conducted using
a testbed and actual vehicles, IDS evaluation methods were
established, and the formulation of IDS Evaluation
Methodology and Guidelines was completed. The results ofthis research were transferred to JASPAR in August 2022 andare planned to be used as industry guidelines in the future.
Although the number of vehicles with communication
functions is increasing each year, there have not been manyhacking incidents that concerned safety because they havebeen used mainly for entertainment purposes. However, thenumber of such incidents is anticipated to increase as
automated driving commences in the future and information

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﻿Technological Development and Education for Enhanced Safety (Overview)

obtained from communications is used for control.
Therefore, the active collection of threat informationfrom cyberattacks on connected cars was attempted, and thedevelopment of observation, collection, analysis, and storage
methods, as well as the definition of basic specifications for a
threat information sharing system to support initial response
activities was conducted.
The basic specifications of this threat information sharing
system will be transferred to an industry organization(J-Auto-ISAC) at the end of FY2022, and the future
improvement and utilization is also planned. (Fig.4)
Competitive Area

V-SOC*

Cooperative Area

Formulation of IDS evaluation methodology andguidelines (Activity a)The operational transfer of the
IDS evaluation guidelines to an industry association
was completed in August 2022.
Surveillance

Cooperation

PSIRT*

Initialresponse

information
sharing

Research on threat information collection
and initial response support for connectedcars (Activity b)The final goal isoperational transfer of the basic system
specifications, etc. for initial support forincident response to an industry
organization by the end of FY2022.

IDS

AttackAttackdetection

ThreatInformation
Sharing System

Attack* V-SOC: Vehicle-Security Operation Center. Monitors vehicles and responds in cooperation with the PSIRT when an incident is detected.
* PSIRT: Product Security Incident response Team. Receives incident reports from V-SOC and third parties and responds.

Fig.4: Scope and final goals of the activities (in red, within the dotted lines)

Regarding this project, based on the cooperation
framework between SIP-adus and BMBF, a cooperation
project called SAVE (Securing Automated Vehicles) wascommenced in October 2020 with the German research
project SecForCARs (Security For Connected, AutonomousCars), and international cooperation was promoted throughstudies, etc. of threat information sharing systems. [See Section
6 7) for more details]

guidelines. The initiatives focused on the following three
details as the cooperative area related to HMI.
Assuming movement and logistics services that use(1) 
automated vehicles equivalent to SAE Level 4TM, smoothcommunication methods that ensure the safety of
automated vehicles and surrounding traffic participants(pedestrians, bicycles, drivers of other vehicles, etc.) andthat allow them to clearly understand each other’s
intentions were derived. [See Section 3 5) for moredetails](2) An HMI was developed for the appropriate taking over ofdriving when the driving environment conditions deviate
or when the function of the automated driving systemdeteriorates, and education methods for drivers were
derived. [See Section 3 6) for more details](3) As a preliminary step before the introduction and spreadof vehicles with automated driving at SAE level 3TM or
higher, the knowledge that drivers, pedestrians, etc.
should acquire regarding driver assistance systems
equivalent to SAE level 2TM and effective education
methods for them were derived. [See Section 3 4) for
more details]
Regarding this project, cooperation with a German
research project was commenced in July 2019, based on thecooperation framework between SIP-adus and BMBF, andthe setting of research topics and verification of the validityof the research methods for the three issues mentioned above
was progressed. In addition, the results were widely
disseminated worldwide, and international standardization
was conducted while strengthening international cooperation
efforts. [See section 6 4) for more details][References]
(1)

Cross-ministerial Strategic Innovation Promotion Program (SIP)
Automated Driving for Universal Services R&D Plan, https://www8.

4

cao.go.jp/cstp/gaiyo/sip/keikaku2/4_jidosoko.pdf, (accessed 30th June

Safety education

2022)

Road safety is ensured by the three complementaryelements of people, vehicles, and the traffic environment, and
it is difficult to ensure safety through vehicles alone. Currently,
various driving safety support systems such as Autonomous
Emergency Braking (AEB) are widely used and are steadily
contributing to the reduction of accidents, but conversely,
accidents caused by overconfidence are sometimes seen. It istherefore necessary for all traffic participants to have a correctunderstanding of automated driving. Regarding this issue,
SIP-adus investigated the ideal HMI, including appropriate
indication and education methods, while taking international
trends into consideration, and conducted studies toward thedevelopment of the necessary technology and creation of[Contacts]
Secretariat of Science, Technology and Innovation Policy Cabinet Office,1-6-1 Nagata-cho, Chiyoda-ku, Tokyo 100-8914, 03-6257-1334, Contact:
Osamu Hosaka, Hiroaki Kimura

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Ensuring the Safety of Automated Driving

1) Development of Driving Intelligence
Validation Platform (DIVP®) for
Automated Driving Safety Assurance
Hideo Inoue (Kanagawa Institute of Technology)(Abstract) As automated driving systems become more complex, there is a need to ensure the safety of countlessdriving environments. However, safety validation on current automated vehicle relies on comprehensive results
evaluations in real driving environments and are quite costly in terms of people, material, money, and time. It is alsodifficult to test the physical perception and recognition performance limits of camera, radar, LiDAR, and other suchexternal sensors with respect to physical phenomena that occur in the real world. In other words, there is the issue of
how far to go in constructing systems to guarantee safety (How safe is safe enough?) Against this backdrop, thisresearch project will build an assessment platform for virtual simulations that can achieve "highly consistent modelingof driving environments, spatial propagation, and sensors" with real phenomena, as is needed to conduct automateddriving safety assurance. The goal is to enable detailed and efficient safety assurance for automated driving systems
under a large number of scenarios.
Keywords: automated driving, virtual environment, external sensor, virtual simulation, safety assurance

1 Project Background and Overview
The National Highway Traffic Safety Administration(NHTSA) of U.S. Department of Transportation has investigated
accidents involving self-driving vehicles and reported cases inwhich the sensors failed to detect objects and detected objects
were not properly recognized.(1)(2) Currently, a variety of different
approaches to safety assurance are being tried in countries
around the world. As a typical example, the PEGASUS project,founded by Germany's Bundesministerium für Wirtschaft undEnergie (BMWi) and its successor, the SET Level project,
propose validation and verification methodologies based on
driving scenarios.(3)(4)Through these activities, it is essential to utilize a simulation
evaluation environment to evaluate the safety of automatedvehicles under weather conditions that are difficult to reproduce
and accident/near-miss conditions that are dangerous toreproduce. In particular, whether sensors can correctly perceiveand recognize objects is important point and a simulation
infrastructure equipped with sensor models that are highly
consistent with real phenomena supporting these processes, isneeded. However, so far, there has been no full-scale researchand development of a simulation with high fidelity on sensors’
performance. As part of the second phase of SIP-adus, theDriving Intelligence Validation Platform ("DIVP®") project, a
research and development project undertaken by a consortiumof collaborating sensor manufacturers, software firms,

universities, and other organizations, was launched at the endof 2018. (Fig.1) The project aims to build a simulation platform
for automated driving safety evaluation in a virtual spacefocusing on sensor models that are highly consistent with actual
phenomena.
In this project, above-mentioned 13 organizations inindustry-academia collaboration (Fig.2), by connecting witheach other by utilizing their respective expertise, work on theconstruction of interface specifications for the safety evaluation
platform including automated driving control models in
addition to a series of virtual space models of "driving
environment-spatial propagation-sensors", with the aim ofcontributing to global standardization.

consistent sensor modeling
2 Highlywith real phenomena
External recognition sensors differ from standard vehicle
component models and play a dynamic role in connecting
driving environment models with automated driving control.
Conventional simulators put an emphasis on evaluating
whether or not system control functions properly, and sensor
models based on true value (normal function) are often used.
As mentioned above, automated vehicle safety assurance
requires identifying the strengths and weaknesses (limits) ofperipheral surveillance sensors and making improvements tosystem design, sensors, and sensory perception algorithms.

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図1 実現象と一致性の高いセンサモデルの必要性
Real vehicle test
Public Road

Virtual test
SILS／
／MILS

Proving Ground（Software in the Loop／／Model in the Loop））

Connect

Connect

HILS

VILS

（Hardware in the Loop））

Camera

LiDAR

Radar

（Vehicle in the Loop））

Highly Consistent Sensor Model

Source : Kanagawa Institute of technology, MITSUBISHI PRECISION CO.,LTD., DENSO Corporation, Pioneer Smart Sensing Innovations Corporation, Hitachi Automotive Systems, Ltd.

1

Fig.1: Necessity of sensor models highly consistent with real phenomenaRealenvironment

図2 DIVP® プロジェクトの構成

Real Physics
Based Virtualization

Open SCENARIO*2Test data Generator

Environmental
conditions

5

3

Temporalmodifications

Open DRIVE

Sensing weakness
scenariosearch algorithm(AI)

Virtual environment

Sensingweakness DB

Measurement & validation
Sensor model

Environment model
Environment2Road furnitureand rules

1

Road shape

Automated
control

SensorMeasurement & validation

Database
Sensing weakness
scenario database
construction

VehicleEnvironment

Space design

Visible light

Perception

Recognition

Camera modeling

Camera modelingSony Semiconductor
Solutions Corporation

Radar modeling*1

Test data
generating toolMillimeter-wave
Ray tracing*1

Recognition
Radar modeling*2

Perception

Fusion*2

Recognition

Perception

VehiclemaneuverRisk prediction model

Driving
Path planning

LiDAR modeling

Infrared light
Ray tracing

Performance Validation
 Intended performance Performance limits Sensing weakness
 Traffic disturbance Human errors*1

Perception

Path/Ray tracing

4 Moving object

Automated
driving model

VehicleMotion control

Recognition

Platform with standard I / F*1 Ritsumeikan finished Feb-2021, DENSO finished June-2021, Hitachi finished Sept-2021*2 TTDC, U-shin, Toyoda-univ joined Mar-2021

Fig.2: DIVP project structure

However, with true value-based sensor models, it is difficult toincorporate electromagnetic wave spatial propagation
validation results into models. This presents problems forincorporating into models the kinds of scenarios that sensors
struggle with. This project is taking the reflectance properties(retro, diffusion, specular reflection, etc.) and transmission
properties of millimeter wave radar waves, visible light used incameras, and near infrared light used in LiDAR, and is building
a physical model and designing a spatial propagation model for
ray tracing and other applications. Additionally, using
Peripheral structuressophisticated experimentation and measuring technologies, weare building a physical model for real phenomena that changes
based on peripheral environment effects such as rain, fog, andperipheral illumination such as sunlight. This project's focus on
incorporating spatial propagation properties as seen throughsensors into a model consisting of driving environments, spatial
propagation, and sensors based on electromagnetic principles
sets the project apart from other DIVP simulation platforms.
(Fig.3) Specific examples of each model are given below.

Space
Rain, fog, snow, etc.

Targets

Virtualenvironment
model seen
throughsensors

Camera

Radar

LiDAR

Light source
Ray tracing
Road surface
Precisely modelized sensor internals for more detailed perception output

Windshield

Image sensor

ISP*/recognition section

Photoelectric conversion
Photoelectric conversion
Raw signal processing
Raw Signal Processing

OutputInput

In-sensor
model

Lens

Fig.3: Sensor model for replicating spatial propagation (camera example)

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図6 精緻な反射を再現する3つのモデル（ミリ波レーダ）

2.1. Camera model
Rather than the RGB workflow which can show images tohumans, the camera model in the DIVP simulates spectral
properties that are input into CMOS and other semiconductors.
Moreover, sunlight has been formulated as a sky model,
allowing precise sunlight sources to be simulated by inputting
the time of day, latitude, and longitude. As shown below, objects
are given defined reflection properties and highly realistic
simulation images are achieved. (Fig.4)
図4 物標の反射特性の有無の影響
No Property

With Property

Flat, no color, no texture

Precise & real reflection duplication

Scattered model

Reflector model

RCS model

For small target (vehicle、person etc.)
Radar equation (distance 4th power)
Physical Optics approximation

For large target (Building, road surface etc.)
Friis equation (distance squared rule)
Geometrical Optics approximation

For analysis duration reduction
Radar equation (distance 4th power)

1st layer εr1 ，μr1

t1

Nth layer εrn μ，
rn

tn

2nd layer εr2，μr2
3rd layer εr3，μr3

Multi-layer model(Transfer Matrix Method)t2
t3

Input parameters

Dielectric constant,
Magnetic permeability, Thickness(from reflection characteristics)

Angle characteristics ofreflection characteristics

Bi-static RCS

Fig.6: Three models for replicating precise reflection (millimeter wave radar)

9

Fig.4: Impact from presence or absence of object reflection properties
Source : MITSUBISHI PRECISION CO.,LTD.

4This allows for replicating the darkness of a tunnel or thestrong sunlight backlighting seen when exiting a tunnel,
scenarios where obtaining visible light is problematic, and forvalidating that high dynamic range ("HDR") camera models
have sufficient visibility to discern objects. (Fig.5)
図5 HDRカメラの効果検証例

Normal (NML) Camera

High Dynamic Range (HDR) Camera

Fig.5: Sample image of Verification for HDR camera performance

Source ： Sony Semicondtor Solutions Corporion

2.2. Millimeter wave radar model
Millimeter wave radar is the sensor for which it is mostdifficult to create a model. We have defined three reflection
models according to the behavior of radio waves on illuminatedobjects, and use them for each object accordingly. The physical
optics approach is used as a setting model for small objects suchas vehicles and people, while the geometric optics approach isused as a reflection model for roads and other large objects. The
radar cross-section ("RCS") model uses predefined objects forpurposes such as shortening analysis time. Each model is used
for different scenario objects. (Fig.6)
The output of millimeter wave data is replicated well,including in the form of maps showing reflection intensity via
X-Y coordinates (heat maps), as well as X-Y reflection point
maps that show locations of strong reflection as target object
reflection points. (Fig.7)

Fig.7: Examples of millimeter wave radar model output

2.3. LiDAR model
Near infrared light, which is used in LiDAR, is a sensor thatis relatively easy to model due to its directivity properties. The
LiDAR model shown below can assess background light andother environmental disturbances by performing 360° scans. By
creating a model for scanning the near infrared light emitted by
LiDAR and precisely replicating things like object footprint
based on near-infrared light spread and the effects of sunlightand other background light, this project has made it possible toconduct simulations that are highly consistent with real
phenomena, even for LiDAR. (Fig.8)

Fig.8: LiDAR model

2.4. Sensor output consistency validation
For the inputs and outputs of each sensor model, this project
conducts quantitative consistency validations for simulation
models by comparing test results in real environment. (Fig.9)

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図9 現象実験ベースの一致性検証の取組み(Fig.10)(1) Traffic participants and other objects(2) Peripheral structures(3) Road surfaces(4) Surrounding environment such as sunlight source, rain, fog(5) Interface between sensor and environment (windshield,
Radar snow and ice deposits, etc.)

SystemIdentification
Simulation
Modeling

Gap Analysis

figure10 DIVP®’s virtual world model overview(4)Surrounding environment(1)Participants and Objects

Correlation(5)Interface

Light sources

Traffic participants

Interface between sensors and space・Camera: windshield
・LiDAR and Radar: sensor surface

Verification test(2)Peripheral
Static objects

Rain, fog and snow(3)Road surfaces
Changes due to weather conditions (e.g. reflectance)

Fig.9: Efforts involving test-based consistency validation

Results show a high consistency with light source-related
scenarios in which sensor use is problematic, includingnighttime and back lighting such as from sunlight, as well as
conditions such as rainwater deposited on glass surfaces. For
falling snow scenarios and other phenomena difficult toreplicate, we are validating consistency with models based onelectromagnetic wave reflection and propagation analyses via
test measurement.

Source : DENSO Corporation, SOKEN, INC, MITSUBISHI PRECISION CO.,LTD.

9

Fig.10: Virtual environment from the perspective of sensors
10

3.2. Environment model development process
Fig.11 shows the main components of the DIVP simulator
structure. In addition to the OpenSCENARIO® and OpenDrive®
data reading for scenarios defined in Germany's ASAM
standard activities, there are seven characteristics of thestructure: (1) a file defining the physical properties of materials
validated based on reflection principles (commonly known as
DIVP materials), (2) SDMG® (Space Designed Model
Generator), (3) database of events with sensing weakness
scenario and tools for compiling those events and generating avirtual environment model, (4) environment and space designmodel, (5) sensor perception model and recognition algorithm,(6) standardization of interfaces connecting inputs and outputs
for each model, and (7) 3D model for objects and other assets.
Measurement and validation of reflection principles
involves compiling a physical properties file (DIVP materials)
as a simulation model consisting of reflex properties that arehighly consistent with real phenomena. This is achieved bydevising measurement systems for visible light, near-infrared
light, and millimeter waves, and accurately measuring physicalproperties. (Fig.12)

a virtual environment model
3 Building
to address sensor detection weaknesses
3.1. Defining a virtual environment model
The European PEGASUS project defines six levels of driving
environment scenarios and provides a high precision 3D map
used for automated driving. These have defined driving
environment data useful for automated driving control (selflocation, traffic flow, routes, etc.), but there are few virtualenvironment models for which physical properties such asreflex, transmission, etc. from the perspective of externalsensors have been defined. As virtual environment architecture
as it relates to things such as sensor reflection properties, thisproject defines the following five areas and aims to build avirtual environment model that incorporates properties such as
electromagnetic wave reflection, transmission, and attenuation.
Scenario
Scenario description
Dynamic content

Rendering(7)3D model(1) DIVP : Defined material property values verified based on the principle of reflection

DIVPⓇ（OpenDRIVE, CRG）

(1)Physical properties model(Reflection, attenuation, etc.)(2)SDMG(3)Sensing weaknesses
Scenario database

Perception

Recognition

AD control

Vehicle control(Map, object)(OpenSCENARIO)

Static content

Sensor fusion

Physical property

User Model

User Model

User Model

User Model

Interface : ROS,FMI/FMU, Simulink/S-funktion(4)Environmental and(5)Sensor
space designing models
perception(3) Original database based on JAMA and SIP-FOT data

Sensorrecognition

SensorfusionAD control

VehicleModel(6) I/F DIVPⓇ: I/F capable of outputting spatial, perceptual, and cognitive models

Fig.11: Structure of process from sensing weakness scenario description to virtual environment models generation

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Fig.12: Millimeter wave measurement instruments
figure13 Space design model development process
Process1
Requirement
definition

Process0

Phenomenon analysis

 Analyze a phenomenon
 Investigation ontheoretical formula

Process2

 Measuring
 Definition ofmodeling target
phenomena Parameter Definition

𝐷𝐷𝐷𝐷𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚

Model development

 Investigation on modeling methods
 Reconstruction of theoreticalformula𝑁𝑁𝑁𝑁 𝑠𝑠𝑠𝑠 (𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 ) = � Model construction
 Implementation

𝑛𝑛𝑛𝑛𝑣𝑣𝑣𝑣

𝑁𝑁𝑁𝑁(𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 , 𝑣𝑣𝑣𝑣𝑗𝑗𝑗𝑗 )𝑗𝑗𝑗𝑗=1 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣𝑣𝑣𝑗𝑗𝑗𝑗 𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴

Theoretical formula of attenuation
0

Process4

Model design

Theoretical formula of particle size distribution

e.g. Rain attenuation and scattering

𝐾𝐾𝐾𝐾𝑟𝑟𝑟𝑟 = 10𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙10 𝑒𝑒𝑒𝑒 ∗ 103 �Process3

Reproductive
experiment𝛿𝛿𝛿𝛿𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝐷𝐷𝐷𝐷 𝑁𝑁𝑁𝑁 𝑠𝑠𝑠𝑠 𝐷𝐷𝐷𝐷 𝑑𝑑𝑑𝑑𝐷𝐷𝐷𝐷 [𝑑𝑑𝑑𝑑 𝐵𝐵𝐵𝐵 ⁄𝑘𝑘𝑘𝑘 𝑚𝑚𝑚𝑚�

Attenuation estimation

雨滴粒形分布からの推定減衰量
Estimated result from theoretical formula

実測減衰量

Real

𝛿𝛿𝛿𝛿𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝐷𝐷𝐷𝐷 =Attenuation

Theoretical formula of scattering
𝜆𝜆𝜆𝜆2
Re � 2𝑛𝑛𝑛𝑛 + 1 (𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛 + 𝑏𝑏𝑏𝑏𝑛𝑛𝑛𝑛 )
2𝜋𝜋𝜋𝜋
𝑛𝑛𝑛𝑛

Precipitation

Fig.13: Space model development process
Source : SOKEN, INC

133.3. Development process for space models for propagation, attenuation, etc.
Replicating sensor detection with precision requires
accurately replicating physical phenomena such as sunlightsources, rain, and fog. With cameras, for example, visible lightemitted from light sources it propagates space and reaches
object surfaces. After being reflected off of or passing throughsurfaces, propagates space and reaches the camera lens. Lightpassing through the lens undergoes photoelectric conversion,
becomes electrical signals, and gets subjected to different kindsof control. (Fig.3)
Light diffusion, attenuation, etc. Events which occur at thistime, such as light diffusion and attenuation, can have significant
effects on the light input into cameras and therefore requireprecise replication. This is the case with millimeter-waveband
radio waves in the case of millimeter wave radar, and with nearinfrared light in the case of LiDAR. This project is also definedby a development process for highly realistic propagation,
15
attenuation, and other space models, with a focus on five areas:(1) analyzing phenomena and studying theoretical formulas,(2) defining phenomena for modeling, (3) replicating andmeasuring phenomena, (4) designing detailed models based ontheoretical formulas, and (5) implementing models. (Fig.13)

3.4. Examples of nighttime road surface reflection
models (cameras）
Precisely replicating road surface reflection properties forheadlight light sources at night is important for camera and
LiDAR models. As light sources hit surfaces at a low incidence
angle when emitted from one's vehicle headlights, we optimized

reflectance as a retroreflective model based on test values. This
図15enabled
夜間路面の再現
camera simulation imaging highly consistent with realenvironments. (Fig.14)

Real

Simulation

Fig.14: Nighttime image replication via optimizationof headlight reflections on road surfaces

3.5. Examples of falling rain space models for LiDAR
This project is developing weather-related space models forsuch things as backlight, rain, and fog, which are difficult toreplicate and evaluate using real vehicles. For a space model for
falling rain, we are replicating and validating as a simulation
model the effects on LiDAR signals by fake points caused byraindrop reflection according to real phenomena based on
rainfall, as well as fake images caused by specular reflection on
wet road surfaces. (Fig.15)

3.6. Building on 3D asset models for virtual environments
As mentioned above, this project is partly focused onmodels for virtual environment from the perspective of sensors,
as well as sensor models to support those models. Through this112

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図16

雨がLiDARセンサに与える影響

LiDAR (Doubts due to rain)

LiDAR (Reflecting like a mirror) Rainfall
20mm/h Target vehicle inmirror on roadside Rainfall
180mm/h LiDAR error percept
vehicle in mirror

Fig.15: Effects of rainfall on LiDAR sensors (test)
Source : Kanagawa Istituteof technology, SOKEN, INC

Passenger vehicles (11 models)

Traffic signal

Motorcycles and special vehicles

Pedestrians and their belongings

歩行者

Traffic signs and construction equipment

NCAP dummies

Backlight

AnimalLarge vehicles (including towing)

Rainfall(Raindrops, Spatial Damping/Scattering, Doubt)

Night headlight

Fig.16: Replicable assets / space model expansion

has established to build up evaluation scenarios—(1) NCAP
and other such assessment, and (2) evaluations conducted via
modeling communities of the FOTs at Odaiba and C1
Metropolitan expressway (to be focused on sensing weakness
scenarios) —and work to create models consisting of driving
environments, space propagation, and sensor perception andrecognition. To create scenarios, it is important to establish
virtual environment units as package scenarios in line with
objectives, and conduct a series of reliable safety assurance
using these package scenario units. (Fig.17)

project, we will gradually measure the reflection properties ofobjects needed for various kinds of evaluation environments,
and will conduct comparative evaluations of real test values andsimulation values while expanding our scope to include DIVP
materials. For the 3D assets for the environmental models thatwill support these efforts, we are expanding these assets into alibrary that includes objects, roads, and peripheral structures.
Being expended upon are assessment scenarios from things likethe NCAP (New Car Assessment Programme), and highpriority asset models and space models for modeling of sensing
weakness scenarios with, i.e., the Odaiba region and C1: theinner circular route of the Metropolitan Expressway, where theautomated vehicle field operational tests (FOTs) are being
conducted. (Fig.16)

図18 DIVP®シナリオパッケージのロードマップ
Laboratory

PG : Proving GroundCG : Community Ground(Odaiba, Metropolitan highway C1)

Dynamic

Real traffic
environmental
Sensingweaknessconditionof DIVP simulator towards
4 Application
automated driving safety assurance
StaticNCAP

Just how many safety assurance scenarios can be planned
for remains a question to be answered, but assessment efficiency
can be greatly improved by establishing high-consistency
conditions in a virtual environment. For this reason, this projectBasicvalidation

Fig.17: DIVP package scenario roadmap
Source : DENSO Corporation, SOKEN, INC, MITSUBISHI PRECISION CO.,LTD.

18

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Scenario creation, unit validation for each asset / sensor

CPFA-50
L1:Road Shape

CPNA-25

Closed-Loop validation

CPNA-75

CPNC-50

Scenario creation, unit validationfor each asset / sensor

CPLA-25

CPLA-50

No slopes, homogeneous / solid pavement, no irregularities (slopes, cracks, manholes),no features that could cause false sensor detection in the surrounding area―

L2: Objects / Rules
L3: Temporalmodifications

―

―

―

Obstacle vehicle――

Pedestrian (adult)
runs and crosses theroute from the driver‘sside (Far side).
At a vehicle width of
50% without braking.

Pedestrian (adult)
walks and crosses
the route from thedriver's side (Nearside).
At a vehicle width of
25% without braking.

Pedestrian (adult)
walks and crosses
the route from thedriver's side (Nearside).
At a vehicle width of
75% without braking.

Pedestrian (child) runs
and crosses the routefrom an obstacle (small
and large vehicle) fromthe opposite side of thedriver's seat (Near side).
At a vehicle width of
50% without braking.

Pedestrian (adult)
walking forward in thesame direction.
At a vehicle width of
25% without steering
operation to avoid acollision after braking or
FCW operation.

Pedestrian (adult)
walking forward in thesame direction.
At a vehicle width of
50% without braking.

L4: Moving objects

4m

6m

4m

AEB

FCW

2m

2m

75％

1.5m

25％

25％

1ｍ

2m
1ｍ

10m

10m

1m

1m

1ｍ

L5: Environmental
conditions

Day

Day/Night

Day/Night

Day

Day/Night

Day/Night

Fig.18: NCAP evaluation environment modeling
図20

4.1. Application toward NCAP assessment
As with advanced safety systems that have automateddriving control such as AEB (Automated Emergency Break),
ACC (Adaptive Cruise Control), and ALKS (Automated Lane
Keeping System), assessment protocols are defined in detailunder projects such as Euro-NCAP and J-NCAP. Although
there is a small amount of variation with regard to traffic
accident circumstances among countries, it is important tohave protocols that incorporate accident information, including
with respect to pedestrians, vehicles, and turning left or right atintersections, as well as scenarios such as cutting in and cuttingout with automated driving ALKS. It is safe to say that thissimulation can effectively replicate conditions such as whenpedestrians suddenly step out into the street at night, a scenario
where real vehicles are affected by peripheral light sources. This
project is gradually creating models from NCAP scenarios, and
30 scenarios for existing Euro-NCAP protocols have been madeinto models. (Fig.18)
Shown below is an example of a simulation that incorporates
a sensor model for a scenario where a pedestrian steps out fromthe shadow of a parked car. (Fig.19) Data can be output tocameras, Radar, and LiDAR simultaneously for one assessment
scenario, enabling dynamic assessment.

NCAP車両陰からの歩行者飛出しシナリオ時のシミュレーション
Scenario of Pedestrian Emerging
from behind Vehicle

Camera Simulation

Fig.19: Simulation of a scenario involving a pedestrian stepping out from theshadow of a NCAP vehicle

20

4.2. 
Application toward real-world environment(Odaiba and C1 the Metropolitan Expressway)assessments
For the following package scenarios, we worked on building
an environment model for Odaiba and the C1 Metropolitan
Expressway, where the FOTs are being conducted. (Fig.20)
As virtual environments that provide a comprehensive
combination of real world environmental factors (driving
environment, roads, geographical features, dynamic objects,
weather, etc.), these locations are useful in assessing scenariosthat sensors struggle with. In coordination with other FOT
projects that are part of SIP-adus, sensing weakness condition
data (e.g., places, images, recognition output) generated inthese real-world environments can be relayed to DIVP
simulators, and can be sublimated to virtual environments thatare assessed by a greater number of users as Virtual Community
Grounds. The following shows examples of assessments
conducted in virtual environments.

Fig.20: Odaiba driving environment model (camera simulation)

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4.2.1. Applying toward white line detection weakness evaluation on road surfaces coated withheat resistant paint (LiDAR)

4.2.2. Applying toward signal recognition limit evaluation (identifying rainfall conditions)

Using the DIVP simulator for Kanazawa University's
As part of automated driving field operational tests, ourautomated driving system ("AD-URBAN System," part of theresearch has identified a phenomenon whereby LiDAR
AD-URBAN Project), we evaluated recognition limits based on
struggles to detect white lines on road surfaces coated with heat
the rainfall levels of a signal recognition algorithm. We
resistant paint in front of the Odaiba Telecom Center (datareplicated severe rainfall conditions that are not easily replicatedmeasured as part of "the 2nd phase of SIP-adus Research on thein real-world vehicle evaluation by making modifications torecognition technology required for automated driving
these conditions as simulation parameters. We then derived
technology (levels 3 and 4)" Kanazawa University (5). We
evaluation conditions that arrive at signal recognition limits. In
determined that this is due to road surfaces coated with heatso doing, we were able to efficiently evaluate other algorithm
resistant paint tending to have stronger retroreflection
and improvement conditions with good reproducibility, andcomponents, and therefore approaching the reflection
were able to demonstrate the effectiveness of the DIVP
properties of white lines, and incorporated these findings into
simulator. (Fig.22)
the simulation model. Fig.21 shows simulation results. While
LiDAR can detect white lines on normal road surfaces (red■ Evaluation of signal recognition using real vehicles
lines in the Fig.), the simulation clearly shows that white lines→ Unclear recognition limits due to failure to encounterare not being detected on road surfaces coated with heat
heavy rainfall
resistantpaint.
図23 LiDARシミュレーション（台場駅前付近）

Normal road surface (white line → detectable(red line))■ Evaluation of signal recognition by DIVP simulator→ Evaluation of recognition limits by varying rainfall
according to parameters

Signal recognition failure
due to raindrops
confirmed

Thermal shielding paint (white line un-detectable)

Fig.22: Signal recognition limit validation via DIVP simulator

Fig.21: LiDAR simulation (area in front of Odaiba Station)
After contrast correction

23

Lv 1

Lv 1.4

Lv 1.6

Lv 1.8Lv1.9 ( Performance limit )

Lv 2

LiDAR
image

High
Correlation value

Matching
correlation
distribution

Low

The higher the correlation value (red), the easier it is to estimate self-location.

Fig.23: Validating LiDAR recognition limits at the road surface wetness level

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4.2.3. Applying toward crosswalk recognition limit
evaluation (identifying road surface wetness
conditions)for how it can coordinate with other automated driving projects.

4.2.5. 
Evaluating safety when automated vehicles
turn right at intersections

As described in the previous section, we used a DIVP
simulator to evaluate the recognition limits of an algorithm formatching locations of sidewalks and other features through
LiDAR based on road surface wetness conditions in the ADURBAN system with points on an ortho map. We were able toreplicate wet road surfaces based on LiDAR output generated
via the DIVP and validate recognition limits according todifferent contrast levels. (Fig.23 Lv.1.9)

As another effort related to coordination with AD-URBAN,
we are working to replicate traffic conditions via DIVP
simulations from data obtained when real-world vehicles turnright at intersections, as well as replicate the ways in whichcameras, millimeter wave radar, and LiDAR perceive andprocess the world. This will lead to our developing performanceindicators for such things as recognition performance andsafety margin with respect to oncoming vehicles and otherobjects. As ours will be a synchronized simulation platform, it
should be able to handle safety assurance for both perception
capability and safety margin at intersections in a range of traffic
environments. (Fig.25) In the future, we will contribute tobuilding intersection evaluation scenarios and developing
algorithms for automated vehicles.

4.2.4. Applying toward evaluation of self-location
estimation algorithm robustness
The AD-URBAN system uses a self-location estimation
algorithm that conducts map matching using LiDAR. For thisrobustness verification, we created a virtual environment modelfor LiDAR output in the DIVP simulator that modeled a severe
condition wherein there were numerous parked cars lined up
along the road so as to obscure the road surface. As a result of
the assessment, we validated that AD-URBAN's self-location
estimation algorithm is always highly consistent with correct
values and has high robustness. (Fig.24) Consequently, as DIVP
can be configured with conditions that are difficult to replicate
in real-world spaces, it can contribute to system assessment.
It is therefore safe to say that SIP-adus is uniquely effective
DIVP® LiDAR & Camera Sim.

of the FOTs in the Tokyo
5 Results
waterfront area
5.1. Objectives and background
In preparation for commercializing DIVP and in order toevaluate the feasibility of using the technology in the business

LiDAR & Algorithm output

Effect on localizing accuracy

Parked vehicle
Hiding of the roadsurface occurs.
Estimated location(posterior probability density distribution)

Fig.24: Evaluation of self-location estimation algorithm robustness

Driving data

CameraMillimeter wave

Fig.25: Safety evaluation conducted via DIVP simulation for when vehicles turn right at intersections

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﻿1) Development of Driving Intelligence Validation Platform (DIVP®) for Automated Driving Safety Assuranceof OEMs and suppliers while encouraging its use in customer
environments, we executed Step 1: Simulation trial in November
2021 then Step 2: Simulation evaluation in January 2022 as partof the FOTs in the Tokyo waterfront area.

5.2. STEP1: Simulation trial as FOTs in the Tokyo
waterfront area
As shown in Fig.26, we conducted a trial use of a simulation
model that replicated in a virtual space, the environment of theTokyo waterfront city area, a field in the Tokyo waterfront area
FOTs. The usability of model and the results of our simulations
were evaluated. 81 people from 56 companies, including OEM
companies, took part in this simulation trial.

Thermal coated roadWest of the Aqua City
intersection
Shade of the trees andwhite line
Near expressway
junction (westbound)

Aomi 1-chomeintersection
Green Yellow

Red

Arrow

5.3. STEP2: Simulation evaluation as FOTs in theTokyo waterfront area

Pedestrian crossing(in black outfit)
Manhole on roadsurface (eastbound)Fig.26: Sensing weakness scenario in Odaiba environments

To conduct the trial, we set up a special portal site where we
mainly provided technical data concerning DIVP characteristics,
particularly its consistency with real-world environments, andvideos showing simulation results. Many participants weremade aware of our model through our efforts to notify the highconsistency of DIVP using data from consistency evaluationthat measured to replicate physical phenomena.
The portal site also provided information through webinars.
Through a questionnaire conducted at the end of the webinar,
participants ask questions about the feasibility of using the
DIVP simulator in their business. The results are shown in
Fig.27. SDMG®, a tool for generating environment models andscenarios, shows promise for use in not just research but vehicle
development and design, with the possibility for expanding the
図26

アンケート結果サマリ

Which services SDMG canbe used for ?

Which services Simulation
PF can be used for ?
Vehicle Use

Vehicle Vehicle Use
Sales
7%
3%
Research

5%
VehicleDevelop.
/ Design
48%

34%
VehicleDevelop.
/ Design
50%

5%Operation/ Planning

26

Fig.27: Questionnaire results

use of simulations in product development operations. We also
confirmed the ability for SDMG to be used in real operations.
Simulation platforms also show similar promise for use inresearch and vehicle development and design. In addition, we
learned that, for research applications, simulation platforms
could be used in processes ranging from evaluation toadaptation in new system and algorithm research anddevelopment, as well as vehicle development and design.
For the write-in questions, some questionnaire respondents
said that the simulators had "been shown to be consistent andextremely useful," while others said things like "simulators weretool for quickly achieving what I want to do" and "I expect to geta lot of feedback from the users' point of view," these answers
suggesting the potential for further development of simulators.
The questionnaire results also highlighted the fact that
promoting the use of simulators for safety evaluation would need
to be an area of focus going forward. Feedback shows good result
the use of physical simulators to sensing weakness evaluation, so
we will continue to coordinate with efforts such as the research ofevaluation indicators while clearly communicating how thissimulation platform can benefit safety assurance.

In order to prepare DIVP for participating companies' work
environments, in Step 2 we deployed DIVP into theseenvironments and validated their connectivity. Using scenariosand environments that we modified based on prepared virtualenvironments, we attempted to link the outcomes of conducting
DIVP simulations with participating companies' various
models and systems. Furthermore, to promote further adoption
in participating companies, we are also working to ensure
connectivity among multiple existing simulation environments.
Eight companies participated in Step 2, and we conducted
DIVP evaluation in line with their individual needs. Specifically,
we validated the viability of data generation for machinelearning, conducted comparative validation with realenvironment test results using the companies' own recognition
models, and software coordination with Simulink®. (Fig.28)
Applicable cases Sensor DIVP virtual environment output
Data
generation forAI
training■ Massive image generation (realistic images)
MultiplecamerasIn-house
Camerasensormodel
Millimeter
evaluation wave radar

Scenarios...

Details of implementation■ Recognition SW (AI) development andvalidation
Recognition
Machine learning
SW improves
performanceand efficiency■ DIVP (camera and millimeter wave)
space rendering■ Performance evaluation of proprietary
camera recognition models and millimeterwave models (OEMs and suppliers)■ DIVP (camera and millimeter wave)
space rendering and perception

SW linkage is realized, and various
outputs of DIVP and various
simulations are coupledResearch
43%

SW linkageon
Simulink

CameraMillimeterwave radar

Other
SW

DIVP

Other
SW

5%
Operation/ Planning

Fig.28: Examples of user needs as identified through the FOTs
in the Tokyo waterfront area

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Through these activities, we ascertained the needs of clientssuch as OEMs, suppliers, and universities for different types ofsystems with regard to the usage of DIVP virtual environments
and space rendering outputs. We have also conducted different
types of validations aimed at achieving automated driving
safety validations for DIVP virtual environments. Certain
validations are still being done based on requests fromparticipating companies.

cooperation and
6 International
standardization
Different countries are trying different approaches to safety
evaluation. It is a rivalry among powerful players: there is the
PEGASUS project completed in 2019 and SET Level projectthat followed on, both funded by BMWi, and there have also
been IEEE and ISO standardization and other developments by
the U.S.'s Mobileye and Germany's BMW based on SaFAD.
China is also planning its own safety assurance framework.
(Fig.29)
Meanwhile, due to a number of automated vehicle accidents
that have occurred in the U.S., the country's American

Automobile Association (AAA) needs to conduct proper,
objective safety evaluation that satisfy consumers, throughmeasures that include reducing accidents by deploying
automated driving technologies, and is disrupting the previously
system-based, system builder-oriented approach to safetyassurance.
While these efforts are happening overseas, DIVP project aspart of SIP-adus makes efforts for joint research on safety
evaluation through Japanese-Germany cooperation, while
taking part to ASAM (Association for Standardization ofAutomation and Measuring Systems) for standardization
activities based on the outcome of this cooperation.

6.1. Japanese-German cooperation VIVID project
DIVP and Germany's VIVALDI project are being jointly
conducted as an International cooperation project known as
VIVID, which aims to contribute to safety assurance for
automated driving based on sensor modeling. The project,launched in October 2020 with support from SIP-adus andGermany's Bundesministerium für Bildung und Forschung(BMBF), is currently developing a standardized automateddriving safety evaluation system and interface. (Fig.30)
At a joint meeting between Japan and Germany held in■
Study of the Safety
Envelope approach for AVs■Germany:▷ Germany, as chair, led thedevelopment of ISO 21448(SOTIF) and ISO 5083
(replacing ISO 4804 by
SaFAD) .
▷ Government-led projects:

PEGASUS, ongoing V&VM
and Set Level 4 to 5; Sim
evaluation of theeffectiveness of preventive
safety technologies of
VIVID:P.E.A.R.S., which is
being conducted incollaboration with Japan[Third-party evaluation organization]■: Develop related test
methods and standards for AVs.
■
: Develop standards for
scenario-based safety arguments■: Rating tests for LKA and
ACC■
Creation of "Framework for
Automated Driving SystemSafety■
Release of IEEE P2846(RSS-based standard)

See RSS, SFF for AV Safety Assurance

Based on RSS references■: Proceed with various
rule making based on RSS*2■: Develop standards forevaluation procedures for ICV*1 Level
3 and above in China▷SaFAD*3 => released in ISO 4804
▷Released in IEEE P2846=>Referenced to FRAV and VMAD
under GRVA in WP 29■:
Development of SFF*4■
China-ITS: Create
V1.0 of AV standard■: Develop China
SOTIF for intra-China traffic
environments=>Could be translated to ISO.
■ : Chair of WG9 to develop
ISO 34501-34504■: Launch of AVSC (Automated Vehicle Safety Consortium)
and IAMTS (International Alliance for Mobility Testing &Standardization)■: Release of UL 4600 (principles and processes forevaluating safety at the system level)

Fig.29: International trends study results

Figure 30*1 ICV : Intelligent and Connected Vehicle. *2 RSS : Responsibility-Sensitive Safety.
*3 SaFAD : Safety First for Automated Driving. *4 SFF : Safety Force Field
Source: Prepared by Deloitte Tohmatsu Consulting LLC based on publicly available informationJT1- Toolchain -JT2- Scenario -JT3- Sensor Model -JT4- Framework & metrics-

VIVALDI
DIVP

Outcome Build VILS under the DIVP
environmental model and transmit
data to the VIVALDI simulator via
OTA Proceed with preparations for a
joint study in view of standardizing
the data format and I/F

 Exchange sensing weakness
scenarios

Implement mutual data exchange
between the DIVP environmental
model and VIVALDI sensor model Agree on standardization of openmaterials

Proceed with preparations for a joint
study in view of standardizing thesensor model I/F Compare the process andevaluation method for mutual
understanding Proceed with the formulation anddefinition of AD safety assurance
evaluation standards as VIVID going
forwardFig.30: Organization for international cooperation and international standardization

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Berlin in June 2022, automated driving project stakeholders inboth countries were informed that the project had made majorachievements, including having had an impact on theimportance of sensors and environment models. (Fig.31)

scenario description, reflective properties for different
environment models, as well as I/F connecting different models
such as sensors, which are included as property in DIVP. As for
I/F related to camera input, our involvement to the organization
was ended up with standardizing the technology as OSI ver.3.0.
This represents an epoc-making achievement that largely
owes to the groundbreaking nature of DIVP and the winning of
supporters through the VIVID project. Further efforts will bemade toward research, development, and international
standardization with the goal of standardizing AD safety
evaluation. (Fig.32)

6.2. Participation in the international standardization organization ASAM (Germany)
In response to ASAM, through which a broad range ofstandardization efforts are being made with regard to automateddriving, DIVP project stakeholders are participating in major
working groups for technologies such as OpenDRIVE/OpenSCENARIO and OSI® (Open Simulation Interface). In
these working groups, we are issuing proposals forstandardization of such technologies linked to sensing weakness

図30

日独シンポジウム概要“German-Japanese Symposium on Safety Assurance forConnected and Automated Driving”

Representatives of funding institutions, key note speakers oflarge scale validation projects and event participants
Dr. Stefan Mengel
Head of division, electronics
and automated driving, BMBF
Mr. Reinhold Friedrich
Deputy Head, electronics andautomated driving, BMBF
Ernst Stöckl-Pukall,
Head of Unit Digitisation andIndustry 4.0, BMBK
Mr. Benjamin Engel
Global technology manager,
ASAM

Mr. Seigo Kuzumaki
SIP-Adus Program Director

Mr. Shigekazu Fukunaga
Director, ITS and Autonomous
Driving Promoting Office,
METI

Ministries
PJ
Research
Institutes
OEM
Mega
Suppliers

Vendor30

Figure 32

Fig.31: Organization for international cooperation and international standardization

Standardization status at the international cooperation
framework and ASAM

VIVID spirit of innovation
 Discussion on standardization of scenarios, materials, and I/F

 Overall image of standardization initiatives through collaboration with VIVID
Standardization process

Scenario
Sensing weakness

Geometric
movement(1) Scenario
Align with standard OpenScenario
 OpenDrive
Scenario format
standardization for
sensor weakness
 OpenScenario
 OpenLabel

Env. Model

Sensor Model

Current status
Scenarios

Fusion / AD Model
R&D

Property
PropertySpace
designPerception

Recognition

FusionSimulation technology and toolchain R&D

AD
Model

SDMG
SDMG

(2) Material
Standardization ofreactive property
format of object
surface for sensor
validation
 OpenMaterial

CameraRecognition I/F
RADAR
Materials
I/F

Commonality(3) Camera Perception I/FStandardized spectrum I/F
required for camera input
 OSI3.0(4) RADAR I/FStandardized sensor model
I/F retaining condentiality ofspatial design
 OSI4.0

VIVID

Complementarity
Safetyassurance
evaluation
strategyplanning

Definition ofstandardization
scopeStandardi
zationDiscussion
among experts

Agreement on
formatProposal
acceptance

✓ ✓

✓

....

Standardization
complete✓Fig.32: Japanese-Germany symposium overview

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7 Conclusion
As a part of SIP-adus, this project has seen research anddevelopment efforts made through industry-academiagovernment collaboration. Our goal is to capitalize on the
newness of models consisting of driving environments, space
propagation, and sensor, ensure connectivity with othersimulators, and develop core technologies for the efficient andwidespread execution of increasingly complex automateddriving safety validation. All DIVP project members fervently
hope that virtual environment simulation technologies that cancreate detailed digital twins of real environments will makeconsumers more confident about automated driving safety, andpromote more automated driving in the society.

[References]
(1)	ODI RESUME: U.S. Department of Transportation, National Highway Traffic
Safety Administration, 01/19/2017,https://static.nhtsa.gov/odi/inv/2016/
INCLA-PE16007-7876.pdf, (see June 2021)(2)	Nikkei Business: The Difficulties of Communicating the Limits of Technologies(2016), https://business.nikkei.com/atcl/report/15/264450/071500039/, (see June
2021)(3)	SET Level Project Office (Deutsches Zentrum fuer Luft- und Raumfahrt e. V.
(DLR), German Aerospace Center (DLR), Institute of Transportation Systems):
SET Level Project, https://setlevel.de/en/project, (see June 2021)(4)	PEGASUS Project Office (Deutsches Zentrum für Luft- und Raumfahrt e. V.
(DLR), German Aerospace Center, Institute of Transportation Systems):
PEGASUS METHOD An Overview, https://www.pegasusprojekt.de/files/tmpl/
Pegasus-Abschlussveranstaltung/PEGASUS-Gesamtmethode.pdf, (see June
2021)(5)	Naoki Suganuma (Kanazawa University): "the second phase of SIP-adus-Research on the recognition technology required for automated driving
technology (levels 3 and 4)", (2020)[Contacts]
Advanced Vehicle Research Institute, Kanagawa Institute of Technology, 1030
Shimo-ogino, Atsugi, Kanagawa 243-0292, 046-291-3277, Contact: Hideo Inoue(Professor/Director)

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3

Ensuring the Safety of Automated Driving

2 ) R esearch on the Recognition Technology Required
for Automated Driving Technology (Levels 3 and 4)
Naoki Suganuma, Keisuke Yoneda, Ryo Yanase, Akisue Kuramoto (Kanazawa University),
Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu University), Junichi Meguro (Meijo University)(Abstract) Automated driving equivalent to Level 4 in urban areas requires advanced and autonomous recognition
functions using on-board AI, as well as road facilities, communication facilities, and other infrastructure to support
these functions. At the same time, the installation of road and communication facilities throughout Japan requires
massive budgets, so it is necessary to study the minimum necessary infrastructure and recognition technologies.
Therefore, in this project, for the purpose of advancing discussions of future cooperative areas, we conducted
research and investigations on the recognition technologies and infrastructure that will be essential for automateddriving systems through public road tests of automated vehicles, chiefly through universities and other organizations
that are capable of publishing a certain amount of acquired data and the technologies used to acquire that data.
Based on the knowledge gained from the public road tests, we investigated the factors and issues that cause
recognition failures in (non-cooperative) automated driving systems, and evaluated the effectiveness of road traffic
environment data provided by the infrastructure.
Keywords: traffic light recognition, object recognition, Global Navigation Satellite System, localization, emergency vehicle recognition, virtualenvironment, recognition limits

1

2

Preface

Automated driving equivalent to Level 4 in urban areas
requires advanced and autonomous recognition functions
using on-board AI, as well as road facilities, communication
facilities, and other infrastructure to support these functions.
However, since massive budgets are required to install road andcommunication facilities throughout Japan, it is necessary to
study the minimum necessary infrastructure and therecognition technologies required under that infrastructure. In
this project, we collaborated with universities that are alreadytesting automated driving on public roads and with universities
that are conducting cutting-edge research on related elemental
technologies to develop the recognition technologies required
for an automated driving system equivalent to Level 4 incomplex urban traffic environments with a mixture of ordinary
traffic participants and other automated vehicles. In addition,
we evaluated the marginal performance of various recognition
technologies as well as the effectiveness of infrastructurecooperative driving.

Overview and Results of the FOTs
(Field Operational Tests)

2.1. Summary of the FOTs
This project aims to develop and verify recognition
technologies for automated driving systems and technologies tolink with infrastructure information such as V2I/V2N. We built
a test vehicle equipped with multiple LiDAR units, millimeterwave radar, cameras, and other sensors for the recognition of
the surrounding environment, GNSS/INS and other localization
sensors, and V2X on-board terminals, and then conducted
FOTs. Fig.1 (a) shows an overview of the test vehicle.
In July 2019, we began FOTs in the central part of Kanazawa
City, Ishikawa Prefecture, and in September 2019 we also began
FOTs in the Tokyo waterfront area. In the Tokyo waterfrontarea, the vehicle ran for 182 days over a three-year period
through FY2021, covering approximately 3,212.8 km in
automated driving mode. Fig.1(b) shows the automated drivingin the Tokyo waterfront area.
Among the data collected in the FOTs, the driving data
under adverse conditions that tend to cause recognition failures
are considered to be useful data for general researchers who
study recognition algorithms, irrespective of the implementersof this project. For this reason, from the sensor data collected in

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LiDAR
Three cameras
Four microphones(side of vehicle body) (for traffic light recognition)

LiDAR (for all directions)
Eight cameras(for all directions)
LiDAR(lateral monitoring)
Nine millimeter wave radar units(for all directions)

GNSS/INS

information transmitted by wireless communication based on
V2I and V2N makes it possible to recognize the traffic lightstate from a greater distance. However, in the examination inthis project, it was assumed that the traffic light recognition
distance required to pass through an intersection isapproximately 120m, and it was found that both methods havesufficient recognition distance. On the other hand, in situations
where traffic lights are not physically visible, recognition using
an on-board camera becomes difficult, and V2I/V2N are
considered effective in such situations.

Wheel speed sensor[m](a) Installation of sensors in automated vehicles

Recognition distance

350

More distant recognition is possible

300
250
200
150

After passing(out of visual range)

V2N

100

V2I

50

Camera0
540

535

the various areas in this project, a dataset (AD-URBAN Open
Image Dataset v1) that includes sensor data under adverse
conditions such as backlighting, background assimilation,
concealment, nighttime, rainy weather, etc., which tend tocause recognition errors, was constructed. It was made available
beginning at the end of March 2022 to domestic institutions forthe limited purpose of research activities.(1)2.2. Evaluation of the Effectiveness of V2I/V2N Road
Traffic Environment Data
In the FOTs in the Tokyo waterfront area, it was possible toacquire road traffic environment data provided by wirelesscommunications from infrastructure such as V2I (Vehicle to
Infrastructure) and V2N (Vehicle to Network). Therefore, in
addition to evaluating automated driving functions, we also
conducted and evaluated infrastructure-cooperative drivingusing V2I/V2N.
V2I/V2N provide various types of road traffic environment
data. In this project, we specifically evaluated the traffic lightinformation provided by V2I/V2N and the emergency vehicle
location information provided by V2N.
Fig.2(a) shows the comparison of traffic recognition
distance between V2I, V2N and camera based recognition. The
results show that the distance at which the traffic light can berecognized (the recognition distance) is approximately 150m
when the autonomous recognition technology using the onboard camera is used. At the same time, the traffic light

[s]

555

Elapsed time

560(a) Comparison of traffic light recognition distance between
V2I/V2N and camera(b) Automated driving in the Tokyo waterfront area

Fig.1: Overview of the automated vehicle developed in this project

550

545

Early deceleration

12
10
8

Suppression of
deceleration

6
4
2
0

6

4

2

0

-2

-4

-6

-8

-10

(b) Effectiveness of deceleration suppression in the dilemmazone using V2I with remaining time information
Fig.2: Results of evaluation of V2I/V2N traffic light information

In this project, we also developed a technology to facilitate
intersection entry by utilizing information on the remaining
time at traffic lights transmitted from V2I, and verified theeffectiveness of this technology. Specifically, as shown in
Fig.2(b), in the so-called "dilemma zone" where the traffic lightcolor changes from green to yellow/red just before entering anintersection and sudden deceleration is unavoidable, we
confirmed that the system can start deceleration before theactual traffic light color changes, enabling smooth decelerationand stopping before the intersection. Since sudden deceleration
in a dilemma zone always occurs regardless of whether driving
in automated or manual modes when the decision to enter anintersection is based solely on the current traffic lightinformation, the number of remaining seconds via wirelesscommunication is considered to be a useful piece of information
for safe entry into an intersection.

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Next, we show the results of an evaluation of the emergency
vehicle location information provided by V2N. In thisevaluation, simulated emergency vehicle locations were
distributed by V2N via a server, and their usefulness wasevaluated based on the information received by the vehicle.
Fig.3 shows an example of receiving the location of a simulated
emergency vehicle in the Tokyo waterfront area. The results
show that the location of the simulated emergency vehicle canbe determined smoothly at the most cases. At the same time, we
observed that an error of about 20m occurs in someenvironments, making it difficult to determine the location of
the simulated emergency vehicle in detail. Therefore, if we
assume that an automated vehicle must autonomously takeevasive action when approached by a simulated emergency
vehicle, it may be necessary to combine autonomous recognition
technology with on-board sensors to determine the detailed
location of the emergency vehicle. On the other hand, whenusing on-board sensors, we can assume that it is difficult toconfirm the approach of an emergency vehicle at a distance oroutside the visible range, and therefore, V2N simulated
emergency vehicle location information may be particularly
useful in such situations.
Displacement ofapprox. 20m
Behaves likemakinga left turning
rivorydlttua jec
Ac traPosition
information
accuracy
degradation

Taken from Google maps

Fig.3: Example of V2N simulated emergency vehicle location
information acquisition data

Evaluation of Recognition

3 Technologies

In the development of automated driving systems and theevaluation of their safety, it is important to confirm whether
automated driving functions operate correctly under actual
traffic conditions by driving automated vehicles on actual roads,
as was done in this project. On the other hand, evaluations in
actual road environments have limitations in terms of time,cost, and efficiency. In addition, it is difficult to evaluate themarginal performance of automated driving functions in realenvironments. For this reason, this project also evaluatedrecognition technologies using virtual environments such as
DIVP® (Driving Intelligence Validation Platform), which was
implemented as another project. In this section, we present an
overview of the various recognition technologies developed inthis project, as well as the results of the marginal performance
evaluation using the virtual environments.

3.1. D evelopment of Traffic Light Recognition Technologies and Examination of Conditions That Make Recognition Difficult
Accurate recognition of traffic light state is important forsmooth intersection crossing in automated driving. In thisproject, we developed a state-of-the-art recognition algorithm
that maximizes the performance of traffic light recognition by
conducting technological development combining conventional
pattern recognition technology and the latest AI technology inutilizing the latest cameras developed for on-board applications.
In addition, we also evaluated marginal performance in failure
scenarios as found in the FOTs using the virtual environment
developed in the DIVP project to look at situations whererecognition proved difficult in the FOTs.
(1) Development of Traffic Light Recognition Technologies
The development of an algorithm for recognizing traffic
lights by image recognition using an on-board camera wasconducted from the viewpoint of the use of digital maps and thedevelopment of several types of image recognition algorithms.
When a digital map is used, it is possible to limit the recognition
range in the image, and thus false positives can be suppressedand high-speed processing can be expected. For the multiple
image recognition algorithm, we considered a method ofpattern recognition that focuses on features such as brightness
and the circular shape of lighted areas in the input image, and amethod of semantic segmentation that uses deep learning. The
AI technology is expected to enable recognition that takes into
account not only the local brightness information but also thesurrounding objects. We evaluated the performance of thealgorithm developed in this way on driving data from the Tokyo
waterfront area. Fig.4 shows an example of recognition resultsof traffic lights using the developed algorithm.

Fig.4: Example of traffic light recognition results(2) Issues Identified in the Tokyo Waterfront FOTs
Next, given the results of the FOTs in the Tokyo waterfrontarea, we analyzed the technical issues in the recognition of
traffic lights, which are mainly caused by environmental factors,
and evaluated the algorithm. As a result, as shown in Fig.5, we
confirmed that the recognition performance temporarily
degraded under the following environmental conditions:backlighting, forward lighting, concealment, backgroundassimilation, and nighttime. On the other hand, in many
situations, the influence on the decision of whether to enter anintersection is considered to be limited because the effects are

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mainly temporary when approaching an intersection. At thesame time, due to the effects of backlighting, there are moments
when objects cannot be seen due to image saturation, which
may prevent appropriate decisions on whether to enter when an
event occurs at the start of an intersection. However, it was also
verified that the effects of such failures are limited in the image
when using an on-board grade camera, and therefore, it isdesirable to use information from other traffic lights when there
are multiple traffic lights at the intersection, or to use V2I/V2N
information when there is only a single traffic light at theintersection to assist in the decision. In the evaluation ofrecognition performance through FOTs, we demonstrated thatthe system can recognize green, red, and arrow traffic lights
within 120m, which is the target performance, more than 99%of the time by introducing a strategy of determining intersection
entry based on the status of multiple traffic lights.(2)(3) Evaluation of Marginal Performance in a Virtual
Environment
We worked to reproduce in a virtual environment the
problems that we have encountered in our FOTs. We reproduced
in a virtual environment situations such as the aforementioned
failure caused by backlighting and the rare situation shown in
Fig.6, in which sunlight reflecting off a building makes it
difficult to recognize a traffic light, and evaluated the consistencyof the reproduced images as well as the marginal performanceof the system. As a result, it was confirmed from the evaluation
results in the virtual environment that traffic light recognition
in rainy conditions, which did not cause recognition failures in
the actual FOTs due to only light rain, can fail under heavy
rainfall of several tens of mm/h (so-called downpour) (Fig.7).

3.2. Development of AI Technology Necessary forLong-Distance Object Detection
For safe and smooth automated driving, it is important toreliably detect vehicles, motorcycles, pedestrians, and otherobjects in the vicinity of the vehicle. For this reason, in thisproject we examined ways to improve the detection performanceof distant moving objects, which is an issue in moving object
detection.

(a) Concealment(b) Background assimilation(c) Nighttime(c) BacklightFig.5: Examples of scenarios in which traffic light recognition becomesdifficult, as confirmed by field operational tests in the Tokyo waterfront area(a) Actual environment(b) Virtual environment

Fig.6: Example of reproduction of reflected light from a building
in a virtual environment(a) Actual environment (light rain)(b) Virtual environment (heavy rain)

Fig.7: Evaluation of marginal performancein wet weather using a virtual environment

Development of Long-Distance Object Recognition(1) Technology
Based on the experience of the FOTs conducted to date, it isnecessary to recognize automobiles within 200m andpedestrians within 70m in urban traffic environments,
depending on the situation. Therefore, we developed arecognition algorithm to satisfy these requirements. First, since
the blurring of distant objects due to the on-board camera
being out of focus is a problem in image recognition using an
on-board camera, we developed an object recognition algorithm
that can deal with blurring, enabling more accurate detection of
distant objects with small pixel counts. In addition, since thesituations where long-distance recognition is required arelimited, such as at intersections to be passed, we also verified
that stable recognition is possible by using a digital map to limit
the recognition area in the image.
In LiDAR recognition, an object must be detected as a 3D
rectangular BOX from a group of observed points, but it is noteasy to stably recognize an object from a sparse group of distantpoints. Therefore, we verified that sensor fusion with therecognition results of camera images is effective in compensating
for the sparse groups of points in the distance. Using suchsensor fusion, we created a recognition algorithm that achieved
the target performance of 90% detection of automobiles within
200m and pedestrians within 70m in a normal environment.
(2) Issues identified in the Tokyo Waterfront Area FOTs

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Because object recognition by LiDAR and cameras
recognizes objects based on the overall image features of theobject based on the observed sensor information, recognition
can be difficult due to changes in environmental conditions
such as the arrangement of surrounding objects, degree ofconcealment, and weather conditions. Other issues include
background assimilation when an object is near a median strip
or a pole, and in the case of a camera, the effects of sunlight
conditions. In the case of causes of failures specific to certain
sensors, we can deal with them with the aforementioned sensor
fusion strategy, but in concealment scenarios, which are an
issue for both sensor types, recognition is inherently difficult
with on-board sensors. In the FOTs, we confirmed that the
failure scenarios in which concealment is a major factor
occurred during the judgment of approaching oncoming traffic
at right-turns at intersections. It is important to evaluate themarginal performance of the system in such situations because
the system must recognize vehicles farther away when passing
through intersections with many lanes.

under different conditions. Therefore, an evaluation of marginal
performance affected by blind spots was conducted using thevirtual environment being developed in the DIVP project. As
shown in Fig.8, the data generated is of a scenario in which thevehicle is turning right at an intersection and an oncoming
vehicle is approaching straight ahead in an environment inwhich there is also an oncoming truck waiting to turn right.
LiDAR and image data with different shielding rates weregenerated by changing the relative position of each vehicle.
The detection results of objects from the LiDAR and camera
sensor data for the intersection scenario generated in this way
were confirmed, and the marginal performance of each sensor
and sensor fusion was evaluated for different shielding rates asshown in Fig.9(a), (b), and (d). Fig.9(c) shows the distributionof the shielding rates of LiDAR and camera in the same frames.
From these results, we verified that the recognition performanceby sensor fusion will likely improve with different installation(3) Results of Blind Spot Evaluation in a Virtual Environment
In the FOTs, object recognition under the influence ofconcealment has been identified as an issue. However, becausesuch scenarios are encountered only momentarily whiledriving, it is difficult to create a large amount of driving data

Fig.8: Recognition of oncoming vehicles at intersections
in a blind spot environment

1.0

1.0

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

Shielding rate: LiDAR

(2)

5
0-2

-75

-50

50

25

100

75-

Shielding rate: Camera

0.0

125

-

100

150

-

125

-

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150

200

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(a) Detection rate by LiDAR

75

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125

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-17

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-20

175

(b) Detection rate by camera

High shielding of both resultsin many false negatives
100
1.0
80

60

0.8

Lower camera
shielding than LiDAR

0.6

0.4

40

0.2
20

Shielding rate: LiDAR

0.0
0
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20

40

60

80

100

(c) LiDAR/camera shielding rate distribution in the evaluation data

5

0-2

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25-

75

50-

100

75-

5

-12

100

0

-15

125

5

-17

150

0

-20

175

(d) Detection rate by LiDAR/camera sensor fusion

Fig.9: Object detection results by sensor fusion in a blind spot environment

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positions and observation methods for the sensors.

multi-GNSS, it was confirmed that a range of 30
cm@77%/1.5m@90% and 30 cm@57%/1.5m@92% could beachieved in Odaiba and Shinjuku, Tokyo, respectively. A
summary of the results is shown in Fig.10, which shows the
positioning method using vehicle trajectories as Conventional
1, and the method using Doppler to predict the initial RTK
search position as Conventional 2.
Next, we focused on determining locations that haveachieved a positioning accuracy of 0.3m or less, and confirmed
the possibility of achieving a positioning accuracy of 0.3m orless at 99% in the same evaluation locations. At the same time,
we examined the possibility of determining a positioning
accuracy of 0.3m, which is sufficient for automated driving, byutilizing the Centimeter Level Augmentation Service (CLAS),
which provides correction information broadcast from thequasi-zenith satellite MICHIBIKI. As a result, we were able toconfirm a positioning accuracy of 0.3m in 99% of the cases on acourse set in Odaiba, Tokyo.(3) A summary of the results isshown in Fig.11.

3.3. Development of Localization Technology Using
MICHIBIKI
In this project, while we used signals from GNSS (Global
Navigation Satellite System) satellites such as MICHIBIKI andINS (Inertial Navigation System), we also developed GNSS/INS
technology that can estimate positions applicable to automateddriving systems. In actual environments where automateddriving is used, there is a high possibility that the accuracy of
GNSS/INS will deteriorate because signals from GNSS cannot
be received. Therefore, we also examined the conditions underwhich automated driving systems using GNSS/INS can operate
stably.
(1) Study and Evaluation of GNSS/INS Applied to AutomatedDriving
In the development of GNSS/INS, we set two targets for theabsolute positioning accuracy required for automated driving: aposition estimation accuracy of 1.5m, which enables thedetermination of the travel lane, and a position estimation
accuracy of 0.3m, which makes possible the operation of an
automated driving system using only GNSS/INS. We also aimed
to achieve these goals by utilizing the quasi-zenith satellite
MICHIBIKI. First, to achieve a positioning accuracy of 1.5m, asa stable estimation method while still using a general-purpose
GNSS, we studied a method that maximizes the effectiveness of
GNSS Doppler. In addition, we developed a technique todetermine the achievement of 0.3m accuracy by utilizing
vehicle motion with a particular focus on the height direction.
In addition, to maintain highly accurate position estimationeven at locations where GNSS cannot be received, we conducteda study to improve the accuracy of dead reckoning (DR).
First, in the absolute positioning evaluation test using(a) Odaiba

1

North error [m]North［m］

0

-0.5

-1

−1500

−1000

−500

0

500

1000

East［m］(a) Results of reliability determination.

-1

-0.5

0

0.5

1

East error [m](b) Accuracy of positioning solution

Fig.11: Results of Reliability Determination in Odaiba

In addition, we improved the accuracy of DR, which isimportant for interpolating between points where highly
accurate position estimation is possible. First, we looked at theerrors in the DR sections and the factors causing them, andconfirmed that the initial azimuth angle has an effect in the caseof a 10-second DR. Next, to improve the performance of theinitial azimuth angle, we proposed a method to optimize theangle by using the FIX solutions for the past several tens ofseconds. In the evaluation test, we confirmed the effectivenessof the proposed method by achieving an error rate of 0.3m orless 87.7% of the time after 10 seconds of DR on an evaluation
course in an urban area (Fig.12).

90

90

80

+20％

87.7%

13% improvement
74.7%

70
60

60
50

● All conventional methods● High precision system in initial position● High precision system for initial orientation● High precision system for yaw rate
● High precision system for speed● Slip correction

40
30
20
10
0.2

0.3

0.4 0.5

Across Err［m］

1

Rate％

Rate［％］

0.5

100

100

0
0.1

0

−1000

(b) Shinjuku

70

500

−500

Fig.10: GNSS/INS absolute position evaluation results

80

FIX
Positive
Reference

1000

50
40
30
20

Conventional
methodProposed method

10
0
0.1

0.2

0.3

0.4 0.5

1

Across error［m］

Fig.12: DR error factor analysis and performance improvement

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Angle of incidenceθrRelationship between laser
Air

Direct reflection

Diffusereflection

Water

Reflectance(2) GNSS/INS Issues and Responses to Them
The results of this project showed that absolute positioning
performance required for automated driving can be obtained inenvironments where GNSS such as MICHIBIKI can be received,
and that a reliability determination of 30 cm can be made at 95%
or better in Tokyo waterfront areas such as Odaiba. However,even if CLAS could be received during the evaluation, thepositioning performance degraded at locations where thenumber of satellites decreases. For such locations, it is expectedthat complementary technologies such as map-matching usingroad infrastructure will be required to maintain positioning
accuracy.
In this project, we investigated a technology that can predict
positioning errors at arbitrary points using a virtual environment
by collecting high precision 3D map data and GNSS signal
errors as double differences. The developed technology is
expected to make it possible to predict where positioning
performance will deteriorate. Based on the above results, we
believe that safer operations of automated driving will bepossible by prioritizing the building of road infrastructurenecessary for location estimation in locations where positioning
performance is expected to deteriorate.

(2) Issues Identified in the Tokyo Waterfront FOTs
At the same time, the FOTs in the Tokyo waterfront area
revealed that the contrast between the white lines and theasphalt deteriorates on thermal shielding-coated road surfaces
and during rainfall, resulting in a loss of accuracy in matching.
As a countermeasure, we developed a method to correct thereflectance from the angle of incidence of the laser using theLambert model, which is a model of diffuse reflection of light(Fig.14). We have also confirmed that localization accuracy of
0.1m can be achieved by introducing a contrast correction
process that matches the entire LiDAR image to the contrast of
the map image.

Asphalt

Incident angle [deg]Correction

3.4. Development of Map-Matching Technology
LiDAR image before correction

Automated driving systems that use high precision maps
must be able to continuously estimate their own location withhigh accuracy. For this reason, we have developed a localization
technology based on map matching.
(1) Outline of the Developed Technology
As shown in Fig.13 , the localization technology developedin this project is realized by continuously estimating theposition by DR and map matching.(4) DR is capable of calculating
self-position at a high frequency by integrating velocity vectors
obtained from sensors, but it is not suitable for long-term
localization due to cumulative errors. To solve this problem, wedeveloped a method for robustly estimating accurate selfpositions by compensating for the cumulative errors in DR
through image matching (template matching) using roadsurface pattern images obtained in real time from LiDAR andreference map images created by acquiring road surface patterns
in advance.
Dead reckoning

Speed v

GPS position

Localization
Map matching

LiDAR image

Reference
Map imageFig.13: Overview of Localization technology

LiDAR image after correction

Fig.14: Correction of reflectance using Lambert reflection model(3) 
Evaluation of Marginal Performance in a Virtual
Environment
On the other hand, depending on the road surface material
and the wetness of the road surface, the contrast between thewhite lines and the asphalt may be significantly reduced,
resulting in incorrect localization. For this reason, we evaluatedthe marginal performance of self-localization by reproducing
scenarios that caused localization problems identified in the
FOTs using the virtual environment being developed in the
DIVP project.
In this evaluation, as shown in Fig.15, we changed thewetness of a normal asphalt road surface and a thermal
shielding-coated road surface, and compared the matching
results to obtain performance limits. In the DIVP virtualenvironment, the wetness of the road surfaces was defined infive levels: Level 0 is a dry road, Level 1 is a road where raindrops
have begun to seep in, Level 2 is a road where water has fully
seeped in and saturated the road surface, and Levels 3 and 4 area thin water film of 1-2 mm and a thick water film of several mm
or more, respectively. The results of the evaluation confirmedthat localization is possible even at the maximum wetness ofLevel 4 for a normal asphalt road surface. On the other hand, fora road surface with a thermal shielding coating, a wetness levelof 1.9 was confirmed to be the performance limit.

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Lv. 1
LiDAR image

Lv. 2
Matching correlation
distribution

LiDAR image

Matching correlation
distribution(a) Ambulance(b) Patrol car

Fig.16: Example of emergency vehicle detection
Performance limits for
localization between thetwo conditions
Lv 1.9Lv 2

LiDAR image(After contrast
correction)

Matching correlation
distribution

Correlation value

High

Low
Correlation peaks incorrect position

Peak of correlation
cannot be obtained

Fig.15:  Map-matching results near the performance limit during rainfall
on a thermal shielding-coated road surface

3.5. Development of Emergency Vehicle Recognition Technology
As stipulated in Article 40 of the Road Traffic Act, when an
emergency vehicle approaches, it is necessary to take evasive
action so as not to obstruct its path. For this reason, wedeveloped a technology for recognizing emergency vehicles
using on-board sensors.
(1) Recognition of Emergency Vehicles by Camera
For the image-based recognition of emergency vehicles, werecognized the vehicles themselves, such as police cars andambulances, and detected the flashing of their emergency
lights. In this project, YOLO v4 was used as the recognition
algorithm for emergency vehicles. First, YOLO v4 was trainedusing a dataset for object detection collected in the Tokyo
waterfront area. The Tokyo waterfront area dataset contains
seven classes of objects for detection, including general vehicles,
pedestrians, bicycles, and different states of traffic lights. After
training on the Tokyo waterfront dataset, we added classes for
emergency vehicles, and conducted additional training nineclasses. In training the first seven classes, the system acquires
features suitable for object detection using video acquired froma camera mounted on the vehicle as input. Then, by training
additional classes, features suitable for emergency vehicles canbe additionally acquired. Fig.16 shows an example of emergency
vehicle detection results. At the same time, it may be difficult to
detect an approaching emergency vehicle using only image
recognition based methods when the vehicle is far away or in anenvironment where it is physically difficult to see the vehicle
due to blind spots or other reasons. For this reason, a method to(2) Recognition of Emergency Vehicles by Microphone
For emergency vehicle recognition by microphone, in
addition to determining the presence or absence of an
emergency vehicle, it is necessary to determine the location of
the vehicle. For this reason, in this project we developed a siren
sound recognition technology and a sound source localization
technology. For siren sound recognition, features were extracted
from the sound data of the on-board microphones, and thesiren sound is identified by machine learning. In this project,
SVM (Support Vector Machine) was used as the recognition
algorithm, and in addition to the test vehicle driving data, datafrom ESC-50, which is a publicly available environmental
sound dataset, and voice data scraped online were also used as
the training dataset. Table 1 shows the evaluation results of theambulance siren sound detection technology developed in thisproject. The Precision index indicates a value closer to 1 when
there are fewer false positives, and the Recall index indicates avalue closer to 1 when there are fewer false negatives. Table 1
shows that the sirens were identified with an accuracy of morethan 90%.
Table 1: Recognition rate of ambulance siren soundPrecision

Recall

0.964

0.965

Recognition rate

In addition, we studied the phenomenon of the recognition
rate decreasing due to attenuation of the siren sound, increasein noise, and the Doppler effect depending on the distance tothe emergency vehicle and the vehicle's speed. Fig.17 shows agraph of the recognition accuracy as a function of vehicle speed
and distance to the emergency vehicle. The figure shows that
1.2
0 km/h1

Recognition rate

Lv 1.8detect the siren sound of an emergency vehicle using
microphones mounted on the test vehicle shown in Fig.1 wasalso studied.

20 km/h

40 km/h

60 km/h

300

350

0.8
0.5
0.4
0.2
0

0

50

100

150

200

250

400

Distance [m]Fig.17: Recognition accuracy based on own vehicle speed and distance128

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﻿2) Research on the Recognition Technology Required for Automated Driving Technology (Levels 3 and 4)

the recognition accuracy is 90% when the distance to theemergency vehicle is within 120m, but the recognition accuracy
decreases significantly when the distance to the emergency
vehicle is farther away. It can also be confirmed that therecognition accuracy generally decreases as the speed increases.
The test vehicle was equipped with four microphones forsound source localization (Fig.1). Using these on-board
microphones, we developed a technique for estimating thedirection and distance of a sound source by calculating the timedifference of sound arrival between the microphones.
To confirm the principle, the distance to the emergency
vehicle was fixed at 100m, the azimuth angle was varied whileboth vehicles were stationary, and the accuracy of the source
azimuth estimation by the two microphones was evaluated. As
shown in Table 2, it was confirmed that an azimuth estimation
with an error of about 10 degrees was possible under eachcondition.

[References]
(1)	AD-URBAN Open Image Dataset download page, https://github.com/
AdmoreKanazawa/open_data, (accessed 2022.08.31)(2)	K. Yoneda, A. Kuramoto, N. Suganuma, T. Asaka, M. Aldibaja, and R.
Yanase, Robust Traffic light and Arrow detection using Digital map with
Spatial prior information for Automated driving, Sensors, Vol.20, No.4,
2020.
(3)	Aoki Takanose, Yoshiki Atsumi, Kanamu Takikawa, Junichi Meguro,
Improvement of Reliability Determination Performance of Real Time
Kinematic Solutions Using Height Trajectory, Sensors, Vol.21, No.2,
2021.
(4)	N. Suganuma, D. Yamamoto and K. Yoneda, Localization forautonomous vehicle on urban roads, Journal of Advanced Control,
Automation and Robotics, Vol.1, No.1, pp. 47-53, 2015.

[Contacts]
Kanazawa University, Advanced Mobility Research Institute, Kakuma-cho,Kanazawa, Ishikawa 920-1192, 076-234-4714, Contact: Naoki Suganuma

Table 2: Accuracy of sound source azimuth estimation
with two microphones
True value [deg]Estimated position[deg]

Error [deg]

−36.2

−45.7

−9.5

9.4

1.9

−7.5

48.6

37.7

−10.9

4 Postscript
In this project, through universities as neutral research
institutes, we developed the recognition technologies required
for a Level 4-equivalent automated driving system in complex
urban traffic environments with a mixture of ordinary traffic
participants and other automated vehicles. We also evaluatedthe effectiveness of infrastructure-cooperative driving using
wireless infrastructure such as V2I/V2N through FOTs in theTokyo waterfront area. In addition, we reproduced in a virtualenvironment the problems of autonomous recognition
technologies based on on-board sensors that emerged in the
FOTs in the Tokyo waterfront area, and evaluated the marginal
performance of these recognition technologies.
In the future, for the introduction of automated driving
technology to society in various environments, including
prefectural and municipal roads, it will be important to evaluate
not only advancements in recognition and decision-making
technologies in automated driving systems, such as thosedeveloped in this project, but also their safety. At the same time,evaluations in actual road environments are limited in terms oftime, cost, and efficiency. For this reason, it will be important toconstruct frameworks for efficient and comprehensive safetyassurances using virtual environments, as was done in thisproject.

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Ensuring the Safety of Automated Driving

3) Research of New Cyberattack Techniquesand Countermeasure Technologies
Ken Okuyama, Naohide Waguri, Shinichi Kan, Yuki Imagawa (PwC Consulting LLC)
(Abstract) As connected cars and automated driving systems develop and spread, a lot of information of advanced
maps and the vehicles, people, and infrastructure facilities mapped onto them is transmitted to vehicles via external
networks. This situation causes cybersecurity issues. Furthermore, since the introduction of UN-R155/R156 to thesafety standards for road vehicles—agreed-upon regulations for cybersecurity and software updates—it is also
necessary to take countermeasures against cyberattacks from a legal perspective. To solve these issues, this study
focused on intrusion detection systems (IDS) as a new countermeasure technology against cyberattacks after
shipment, and developed IDS evaluation guidelines to serve as a baseline for evaluation and testing when introducing
IDS. During the current fiscal year, we are studying methods of collecting and accumulating threat information onconnected cars and conducting collection experiments using methods such as honeypots, seeking to create amechanism to support an initial response when an incident actually occurs.
Keywords: IDS (Intrusion Detection System), Honeypot, Threat Information, STIX/TAXII, S-BOM

1

Purpose of this research study andoverview of activities

In accordance with the research and development plan,
objectives and goals of the second phase of SIP-adus (Crossministerial Strategic Innovation Promotion Program (SIP)
Automated Driving for Universal Services): Research of New
Cyberattack Techniques and Countermeasure Technologies,
the "Formulation of IDS Evaluation Methodology andGuidelines" and "Research study on Threat Information andInitial Response Support for Connected Cars" are being
conducted from August 2020 to February 2023.

Background related to post-shipment cybersecurity
Legal side

Practical side

WP29 UN-R155 cyberattacks must be detected andresponded to, and it is necessary to explain that thecompany's vehicles can detect and respond to suchattacks.

There are no existing laws, regulations, or guidelinesthat specify what types of attacks should be detected andto what extent, making it necessary for each company todefine its own requirements.

Purpose and Policy of Activity A
Focusing on on-board IDS, we will investigate evaluation methods for evaluating the ability of IDS to detect attacks
and subsequently recover the vehicle and organize them into IDS evaluation guidelines to contribute to post-shipment cybersecurity measures in the automotive industry as a whole.
Development

Manufacture

Operation

Disposal

Detection Response Recovery

Subject of activity A
Improved post-shipment cybersecurity

Fig.1: Purpose and Policy for Formulation of IDS Evaluation Methodology
and Guidelines

2.1. Derivation of basic test cases

Formulation of IDS Evaluation

2 Methodology and Guidelines

As shown in Fig.1, our objective with this topic is tocontribute to post-shipment cybersecurity measures. We hopeto achieve this by creating IDS evaluation guidelines and thenproviding them to industry organizations as a baseline forOEMs when selecting, verifying, and operating IDS. The main
target audience is OEMs that have just started considering the
introduction of on-board IDS with the intent of raising the levelof post-shipment cybersecurity of their vehicles.

In order to organize the points to be evaluated in theselection and testing phases of on-board IDS, we analyzed
previous published cases of attacks and derived the cybersecurity
events from them. Among the cybersecurity events, we selected
Extraction

Information gathering and analysis
Data Source

Examples of Attacks

Cybersecurity events

Basic test cases

Papers
Products
Public documents

Analysis

FilterFilter conditions
1. An attack that occurred on a vehicle which has been shared in the past (2019-2021) (*1) and which
every IDS should be capable of addressing (*2), and/or
2. affects the basic movements of the vehicle (running, turning, stopping).

Fig.2: Derivation of basic test cases

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those that an IDS should detect at a minimum, and summarized
them as basic test cases. (Fig.2)

2.2. Basic test case testing environment
Based on the basic test cases, three types of testing
environment were considered, as shown in Fig.3. In light of theincurred costs, this study examined a test procedure based on
the assumption that testing would be conducted in either asimulation environment (Fig.4) or a testbed environment.
(Fig.5)

Simulation environment

Testbed environment

Vehicle (bench) environment

Test environment without
using actual ECUs. Reproduction of on-board
networks using software.

An environment built withthe minimum hardware
required to meet the test
requirements.

An environment in which
input devices, ECUs, andactuators equivalent to those ofa real vehicle are connected.

CGW

GW

While simulating an on-board NW,
the (attack) messages are input to the
IDS according to the test requirements.

IDS
Simulate on-board
NW with software.
Virtual ECU (CGW)
VirtualECU

VirtualECU

Control unit
ECU

ECU

Control unit

ECU

ECU

Actuator

Actuator

IDS

VirtualECU

Fig.3: Types of IDS testing environments

IDS

2.3. Testing of test cases using actual IDS equipment
The purpose of actual IDS equipment testing is to verify thevalidity of the test method of the basic test case (whether thetest can be performed using the predefined test method).
For this purpose, one OEM (hereinafter referred to as "OEMA") provided the communication data and ECU for the onboard network necessary for testing a basic test case. In addition,
we asked ETAS Corporation and Arilou Information Security
Technologies Ltd. to provide IDS adapted from their respective
companies for the specific vehicle model of OEM A. We
conducted testing according to the test procedures of the basic
test cases, and confirmed whether the test results matched the
expected values.

2.4. Results of testing
The procedures shown in the test method for all test
perspectives tested were performed for the IDS of the twocompanies under the simulation and testbed environments. It
was confirmed that there were no errors in the procedures andthat the IDS performed as expected.

2.5. Activities for social implementation
A technical review meeting was held with JASPAR (Japan
Automotive Software Platform and Architecture), the future
recipient of the IDS evaluation guidelines, in preparation for
the transfer of the IDS evaluation guidelines. The IDS evaluation
guidelines were finalized by responding to feedback from therecipient right up until the transfer, which—including
administrative procedures—was then completed in August2022.

Fig.4: Simulation environment

3

Research Study on Threat
Information and Initial Response
Support for Connected Cars

Our goals with this topic have been to define basic
specifications for methods of collecting and accumulating
threat information on connected cars and for initial response
support using threat intelligence, and to transfer our outputs toindustry organizations in 2023. "Threat intelligence" isinformation collected, analyzed, and accumulated to support
responses to cyberattacks and other threat. In some industries,
there are threat intelligence sharing activities.(1)Sharing threat intelligence is expected to be effective inpreventing chain-reaction damage caused by similar
cyberattacks, but the currently shared threat intelligence comesmainly from the IT domain.
Fig.5: Testbed environment

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3.Ensuring the Safety of Automated Driving
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3.1. Examination of basic specifications for information collection and storage

As a result of the current observation, we observed many
activities that match the characteristics of IoT malware (Miraietc.) as seen in IoT products, sending IDs and weak passwords
to telnet. This is considered an automated attack from a device
infected with malware and not an attack based on recognizing
the honeypot as an on-board device.
As part of the honeypot PoC, we have also been concurrently
studying efficient equipment search methodologies, and haveproposed two approaches: keyword search using an Internet
search engine to search websites for on-board unit products,
and a direct search using Censys*3 for keywords related to onboard unit.
The scope of the honeypot PoC up to FY2021 was honeypotsthat imitated aftermarket products among on-board unit, but in
FY2022, we are also studying the methodology for collecting
threat information using honeypots that imitate connected
systems as a whole.

In order for organizations in the automotive industry toobtain threat information more efficiently and proactively, we
conducted PoC (Proof of Concept) on collection methods otherthan public information collection, referring to examples of
implementation in the IT domain. During this activity, we
focused on honeypots*1 and playgrounds (one format ofCTF*2), and examined whether these methods can be applied tothe automotive domain.

3.1.1. Honeypot PoC
We created an automotive version of a honeypot used in the
IT field and conducted PoC. Specifically, we investigatedaftermarket products that can be detected via wide-areascanning, developed a honeypot prototype using thecorresponding products, and began cyberattack observation
experiments in late January 2021. Fig.6 shows an overview of
the automotive honeypot.

http
ssh

Cloud

telnet3.1.2. Playground experiments
Playground experiments were conducted to investigate
cyberattack methods targeting vehicles in connected systems,
as well as attacker's behavior and logs to determine if an attack
was targeted at a vehicle. These playgrounds were conducted ashacking contests in which participants report vulnerabilities
with the goal of exploiting the system, without the setting of any
specific flags.
For the playgrounds, we prepared an environment that
imitates a general connected system as shown in Fig.7. Although
there are various attack surfaces in a connected system, theattack surface in this playground is a user application or a web
portal site open to the public.

httpCollected
responses

ftp

ssh

Collected
responses

Devicedecovery
Systemtelnetftp

honeypot

MobileNW

honeypot

Fig.6: Overview of automotive honeypots

Playground environment(1) Attacks on web

applications via
the Internet

Attacker

Scope of honeypot
PoC(3) Attacks on AP

servers, etc. frominternal NWs, etc.

Attacker
AP Server
Web
Applications

Assumed attack
surface(5) Direct attack on vehicle
from internal NW
Connected
Server

Attacker

Drive
management
program etc.

(4) Attacks onconnected servers
from internal
NWs, etc.

DB(2) Attacks on DB fromDBinternal NW, etc. (Driving data,(User information
sensor information,systems)
operation records Attacker
etc.)
Attacker(6)
Direct attacks onvehicles via the Internet
Attacker

TCU/ECUAM on-board
deviceAutomated
driving
programs,
emergency callprograms, etc.

AM equipment
lot service, etc.

Fig.7: Playground environment*1 Equipment for observing attacks. A method to collect and observe access information and attack methods of attackers by disclosing a system on the Internet with
the intention of it being attacked.
*2 A method in which white hat hackers intentionally launch attacks against an environment that simulates a system to collect information on the availability of attacks
and their methods. A widely known method is to set a target (flag) for the attack and compete for points.
*3 C
 ensys is an IoT search engine. It can search for devices that are open to the public on the Internet.

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3.2. Methods of describing and sharing threat information
In order to collect and share threat information efficiently,
the threat information must have a certain level of structure anda method for sharing it must be formulated. As shown in Fig.8,
as vehicles become more connected and automated, there will
be more opportunities to adopt technologies used in IT
domain. We are conducting research focusing on STIX/TAXII,*4
which is the most popular protocol in the IT field and is capableof describing many types of information.
IT expert

IT domain

IT cybersecurity

Web Applications
Internet protocol

Connected car
cybersecurity

On-board unit
domainOn-board unit
systems expert

Vehicle functions

On-board systemcybersecurity

HardwareFig.8: Integration of automotive and IT domains

3.3. Utilization of threat information for initial response
In order to manage threat information and utilize it for an
initial response, it is necessary to determine whether the data
discovered and reported daily is relevant to the vehicles orsystems that will be managed. In particular, when open source
software (hereinafter referred to as "OSS") is used, a large
amount of vulnerability data is reported and frequent updates
are performed. In turn, this makes it necessary to constantly
update the relevant information and keep track of the version,increasing the difficulty in determining the relevance. Using
Software Bill of Material (S-BOM) to manage the components
included in software has been attracting attention as a solution
to these issues. Although the S-BOM can be created in-houseand used for vulnerability management, it can be usedthroughout the supply chain for a more comprehensive andefficient initial response. By creating an S-BOM and linking it
to other information sources, such as information sharing
systems, it is possible not only to determine whether or not theSupplier A

Complete vehicle manufacturer
SBOM

Information sharing
system information
sources

SBOM
On-board system A
Product nameSoft C1

Program D1
Program D2

Supplier B
SBOM

Supplier C
SBOMSoft C2

Program D2

Cooperation

Threat information

Advantages of S-BOM creation
Understanding of whether fleet
systems are vulnerable based on
the threat information collected
and shared
Smooth access to rele vantvulnerability information andinformation necessar y for
initial response in the event of
an incident

Fig.9: Use of S-BOM for initial responsecompany's vehicles and systems are vulnerable, but also tosmoothly access the relevant vulnerabilities and informationnecessary for an initial response in the event of an incident.
(Fig.9)3.4. Activities for social implementation
Our goals for this topic consist of two deliverables: basic
specifications for an information sharing system that
accumulates threat information in the automotive domain andcan be used to support initial response to cybersecurity
incidents, and an information collection guide that compiles
knowledge on attack methods and technologies for connected
systems as obtained from the honeypot and playground.
Technical review meetings with J-Automotive-ISAC, to which
the deliverables will be transferred, are being held on an ongoingbasis and approval has been received for the transfer of thedeliverables.
In order to cope with cyberattacks in the automotive domain,
it is important to take cybersecurity measures not only for
individual companies but also for the entire supply chain. In
order to make the deliverables easier to deploy in practice,meetings were held as necessary with J-Auto-ISAC, the final
recipient, in order to exchange opinions with the teams
conducting testing and research at the field level.
After the transfer, which is scheduled for December 2022, wewill continue discussions with J-Auto-ISAC for practical
deployment and additional activities.

4 Outcomes of the Project4.1. Development of IDS evaluation guidelines
For this topic, IDS evaluation guidelines were developed
mainly to contribute to accelerating the start-up of on-board
IDS development by OEMs/suppliers who are facing difficulties.
Regarding the basic test cases, test case procedures andexpected values were confirmed using actual equipment
through the collaboration of two companies, one OEM and one
IDS vendor, validating their suitability. The transfer of the test
cases to JASPAR has also been completed.
IDS for on-board units will be a typical technology andproduct that provides detection functions to cope with new
cyberattacks in the future, but we found through interviews
with some OEMs that this technology is not being studied at
the same level in the automotive industry as a whole. The targetof our evaluation was an off-the-shelf network monitoring IDS
using CAN communication as the target protocol. The maincontent of this project is testing and evaluation methods
focusing on detection functions, which is the main function of*4 STIX is a standardized language developed to describe threat information, while TAXII is a specified procedure for sharing threat information.

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IDS. It is expected that OEMs/suppliers will introduce
appropriate IDS by referring to these IDS evaluation guidelines.

4.2. Research study on threat information and initial
response support for connected cars
This topic aims to prevent cybersecurity incidents andminimize damage in the event of an attack on connected cars
and automated driving systems, which are technology that haveseen remarkable development in recent years. The basic
specifications for an information sharing system and an
information gathering guide for proactive threat information
gathering are being prepared.
The deliverables for this topic will promote information
sharing activities in the automotive industry, where connectedcars are widely used and new cyberattack methods arecontinuously reported, and will contribute to enhancing first
response capabilities. We hope that the proposed specifications
will be used to develop and implement information sharing
systems, and that the guidance will be used as a reference forproactive information gathering in addition to publicly available
information. We also expect that these sharing systems andinformation collection efforts will continue to be improved andadapted to the automotive industry through continuous
operation and review.

technologies from overseas and other industries, and tocontinue research not only on information gathering, but also
on methods of analyzing collected information and improving
its accuracy and precision. In addition, cybersecurity on theside of connected services (such as servers and platforms),
which has been investigated and handled independently by
OEMs/suppliers, may become a new area of cooperative area,
and is therefore considered to be an area of future research andfocus.
Based on the above, data cybersecurity activities have an
important role in the development of automated driving
systems, and are expected to contribute to the development ofcybersecurity activities in the industry.

[References]
(1)	Cross-ministerial Strategic Innovation Promotion Program (SIP) Ensuring
Cybersecurity in Critical Infrastructure, etc. (b2) Information Sharing Platform
Technology "Information Sharing Design Guide Construction Edition", Hitachi,Ltd. https://www.nedo.go.jp/content/ 100904081.pdf, (Reference 2022.05.31)[Contacts]
Technology Division, PwC Consulting, LLC, 2-1, Otemachi 1-chome, Chiyoda-ku,
Tokyo 100-0004, 080-4426-1658, Contact: Ken Okuyama

4.3. Conclusion
Since ensuring automotive cybersecurity may have an
impact on automotive safety, it is appropriate to ensure thatminimum cybersecurity standards are met and that thecommon threats faced as a whole must be made a cooperative
area for the entire Japanese industry, or at least actively shared,in order to improve the development and operational efficiency
of connected services and help Japanese companies to maintaintheir international competitiveness. It is also important tostrategically approach standardization organizations so that thecybersecurity measures and information sharing mechanismsthat have been established can be used to the advantage ofJapanese companies, allowing Japan to make recommendations
for future international standards and the development of
automotive cybersecurity.
The final results of this project also lead into our further
research and areas of focus. For the IDS evaluation guidelines,
it will be possible to create guidelines for the evaluation of IDS
other than off-the-shelf products, other protocols such as
Ethernet, testing and evaluation methods for non-functional
requirements such as performance that cannot be generalized
at this time, and guidelines relating to IPSs (Intrusion
Prevention Systems).
With regard to the basic specifications for information
sharing systems and the information collection guidelines, it isnecessary to catch up with and utilize methodologies and new134

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Ensuring the Safety of Automated Driving

4) Research of Education Methods for
Advanced Automated Driving Systems
Makoto Itoh, Huiping Zhou (University of Tsukuba), Yoshiko Goda, Masashi Toda (Kumamoto University), Maki Arame,
Junko Handa (Polytechnic University of Japan)(Abstract) This paper outlines the activities of "Research and Development of Knowledge that Drivers and Pedestrians
Should Acquire and Effective Educational Methods (Task C)" of the second phase of SIP-adus (Cross-ministerial
Strategic Innovation Promotion Program (SIP) Automated Driving for Universal Services). In Task C, based on themain research objectives, we have set three research themes for further developing educational methods: (1) proposalof an educational method based on individual characteristics, (2) proposal of a motivational method, and (3)development of complete educational materials that can be modularized with partial education in mind. In addition,using prototype teaching materials, we verified the effectiveness of providing general knowledge about automateddriving in advance using a driving simulator.
Keywords: education, training, motivation, knowledge, experience

1 Introduction
In order for the user to safely utilize an automateddriving system, the user must be able to make appropriate
judgments and take appropriate actions according to thesystem functions, the role of the user (driver), and thesituation in which he or she is placed. The human-machineinterface (HMI) installed in the vehicle should support theuser's judgment, and ideally, the HMI should support theuser in making appropriate judgments intuitively, regardlessof the user's knowledge. However, unfortunately, an ideal
HMI that can guarantee the user's appropriate judgment
and execution of actions has not yet been realized, and even
if it were technically feasible, it may not be possible to use
an ideal HMI due to cost constraints.
Therefore, it is necessary to consider how education andtraining should be conducted to provide users with thenecessar y knowledge and skills. This research anddevelopment is an effort to educate users on the safe use of
automated driving.

Proposal of educational methods

2 based on individual characteristics
In order to conduct education efficiently, it is necessary

to appropriately take into account the characteristics of therecipient of education, such as learning styles. In thisresearch and development, in addition to learning styles, we
focused on resilience characteristics, one of the individual
difference variables of personality characteristics, andexamined the effects on knowledge acquisition whentransitioning from Level 3 automated driving to manual
driving, taking into account the fact that automated driving
is a new learning experience.
Specifically, we analyzed the results of a survey of 3,240
participants conducted via the Internet during the firstphase of SIP-adus in relation to personality characteristics
and knowledge acquisition. Three types of educational
materials (leaflets, quizzes, and videos) were used, and theeffect of knowledge acquisition after learning with eachmaterial was compared.(1) A logistic regression analysis was
conducted using the binary variable of the ascending andnon-ascending groups as the dependent variable, and agroup of variables that were assumed to influence thedependent variable as the independent variable, using
propensity score matching. As a result, it was confirmed thatthe absorption effect of resilience characteristics differeddepending on the format of the teaching materials, and thatthe video teaching materials had the greatest potential toabsorb resilience characteristics.
In addition, a method that can absorb individual
characteristics and be effective in educating a large numberof trainees was investigated, as well as a method that can

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easily identify characteristics. As a result, we were able todevelop a shortened version of the learning style and career
resilience scale.(2) (3) Furthermore, we found that effective
teaching materials can differ depending on differences inlearning styles and career resilience. These results suggestthat adaptive learning is feasible if thre is enough time in a
education, where learning styles and career resilience can beeasily examined using a shortened version of the scale, andlearners are provided with more appropriate learningmaterials according to the results of the examination.

3 Proposal on motivation methods
The target audience for safety education is alltransportation users, and is diverse and wide-ranging interms of age groups, backgrounds, and learning styles. Manyof them are not interested in automated vehicles. Driver
education may include a course at the time of license
renewal, but it is necessary to devise ways to increase
learning effectiveness in a limited amount of time.
Therefore, in addition to teaching materials, a method toarouse interest in the learning content was studied. In thisstudy, we developed two types of motivational videos forsafety education ("Narrative" videos that focus on stories
that are expected to be emotionally moving, and "Factbased" videos that focus on objective facts), using the
motivational ARCS model. The results of formativeevaluation suggested that the two types of videos were able
to motivate at the same level.
In addition, in order to examine motivational methods
for automated driving education, we used two types ofmotivational videos we developed and examined theeffectiveness of motivational videos considering theinfluence of individual characteristics from a web-based
survey of 2,790 participants.(4) Propensity score matching
results verified the possibility that motivational videos
utilizing the narrative method can absorb individual
resilience factors compared to fact-based videos, and thatafter the fact scores increase with lower levels of resilience.
In addition, the use of narrative-based motivational videos
may be more effective when the individual's learning style istaken into account. It was also suggested that the use ofnarrative-based motivation may be able to absorb
differences in individual attributes such as gender, age,marital status, and whether or not they have children.
In addition, the methodology of group training toincrease motivation was also examined. We designed andimplemented an online training course, and found that thetraining improved knowledge about automated driving

systems and promoted attitude change.

4

Development of complete educational
materials that can be modularized withpartial education

One of the objectives of this project is to develop andaccumulate a variety of educational material modules thatcan be combined with each other so that users can learn
using educational materials that match their own
characteristics and learning styles. For the research, wedeveloped four modules including two types of motivational
videos as described in Section 3, and quiz materials andvideo materials for acquiring general knowledge about
automated driving. By developing a variety of teachingmaterials as short, micro-learning-enabled modules, it ispossible to learn each of them independently, or to combine
them according to learning goals or actual objectives. In
addition, a simple driving simulation system that enables
users to experience a driving takeover situation wasdeveloped as a web-based remote learning material. The
effectiveness of these teaching materials has been confirmed
through web-based surveys and verification experiments
using the simulator described in Section 5.
In addition, among the materials developed for the
research, the motivational videos and general knowledge
videos will be published as sample teaching materials for
actual use.

5

Experiments to verify educational
effectiveness for educational
opportunities

5.1. Educational opportunities
In this research project, we examined educational
opportunities for automated driving with a view to social
i mp l e m e nt at i o n . T h e r e a r e v a r i o u s e d u c at i o n a lopportunities, including mass media and websites, toprovide the knowledge necessary to use automated driving
or to interact with automated vehicles. Since the time andresources available for each of these opportunities arelimited, it is important to select the appropriate level of
specificity and granularity in line with the characteristics ofeach opportunity.
Opportunities to convey knowledge and information
about specific systems to actual users may include
opportunities for explanations at dealerships and car rental
offices and at the time of delivery, as well as the ability toconvey knowledge through in-vehicle systems. However,

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﻿4) Research of Education Methods for Advanced Automated Driving Systems

according to the ongoing research conducted to date in thisproject, (5) it is believed that some people generally havealmost no knowledge of automated driving at all, andtherefore it is necessary for them to have general knowledge
to be able to understand the explanations of individual
systems. Therefore, in this project, we decided to verify theeffectiveness of communicating general knowledge about
automated driving in advance.
5.2. Simulator-based verification experiment on generalknowledge
First, we decided to verify the effectiveness of the generalknowledge provision as a trial before the prototype teachingmaterials in Section 2. (Experiment 1) Based on the
instructional materials used in the knowledge provision
study conducted in the first phase of SIP-adus, we organized
the general knowledge about automated driving, created an
explanatory video, and had the participants watch it.
Approximately one month later, an experiment on thehandover from automatic to manual driving was conductedusing a driving simulator. To test whether generalknowledge is useful for several specific systems, two types ofexperiments were conducted: one that provided Level
3-equivalent functions only at low-speed following, and theother that provided Level 3-equivalent functions with nospeed limit (between-subjects factor). Neither of the two
types provides a function for changing lanes.
Fig.1 shows an example of the experiment results.(6) Thisis a scene in which a broken-down car is stopped in front of
the driver's lane and the driver needs to change lanes toavoid it. Since the automated driving system in thisexperiment does not have a lane change function, the driver
must take over in order to continue driving. Fig.1 shows theratio of the number of people involved in a collision to thenumber of vehicles involved in a breakdown. As shown in
Fig.1, the results suggest that if general knowledge about
automated driving is provided in advance, the driver may be

Accident rates in the DS experiment

0.30

0.25

0.15

0.10

0.05

Prior training available

No prior training

Prior training available

No prior training

Lv3 automated driving

Lv3 automated driving forwith no speed limit

low-speed following only

Fig.1: Example of experiment results

able to perform an appropriate takeover when RtI ispresented by the system in a situation where doing so isnecessary.
Next, a verification experiment was conducted using thevideo materials developed in Section 4, which wereshortened to the time expected to be spent on actual
education. (Experiment 2) In this experiment, an
explanation of the system used in the experiment, alongwith general knowledge, was given approximately one
month before the simulator experiment was conducted. As
a result, many participants were observed to be unawarethat they were being asked to take over driving in the firsthalf of the experiment, even though the RtI was presented
to them. The difference is that in Experiment 1, theexplanation of the actual system was provided on the day ofthe experiment, whereas in Experiment 2, it was provided
one month before the experiment. Although the usefulnessof providing general knowledge was confirmed inExperiment 1, it is necessary to promote understanding of
the provided knowledge in order to enable truly safe use of
the system.
In order to promote understanding and retention, we
reorganized the teaching materials so that necessary
explanations are given in a timely manner, and we
confirmed that in many cases, the participants were able torespond appropriately to driving takeover. In addition, we
also examined the two points of including a specific systemin the general explanation, and of providing even a
simplified experience of the driving takeover, and confirmedthat better results were obtained in the verification
experiment when a specific system was included as a casestudy. The results also indicated that the effect of theexperience of switching drivers may work in a more positive
direction, although the effect was not statistically significant.
5.3. Verification experiment on how education andtraining should be conducted when actually using thesystemEven if the same automated driving system is used in thesense of Level 3, the functions, their limitations, andsituations in which driving takeover is required differ
depending on the type of vehicle and system, such aswhether there is a speed limit or not. Therefore, when
actually using a particular system, it is necessary to haveknowledge specific to that system and acquire the skills to
respond appropriately in situations where driving takeoveris required.
One of the important issues here is how effective theexperience is. From the first phase of SIP-adus results,(7)(8)
we discovered that various experiments gradually allow thedriver to respond more skillfully. On the other hand, when

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﻿4) Research of Education Methods for Advanced Automated Driving Systems

the driver suddenly encountered a serious situation such as
loss of sensor function, there was a delay in intervention. In
addition, it was confirmed that experience with functional
failure, which is considered to be one of the most severe
situations requiring driver handover, is effective when
encountering actual situations, and that such an experience
may be effective over a long period of time.(9) From theabove, it is considered important to have the trainees
experience specific driving takeover situations in advance.
However, it is not always possible to devote enough time tothe test-drive experience in the midst of complicatedprocedures such as contracting when buying or selling avehicle. Rather, it would be effective to provide users with
an opportunity to freely learn on their own in their sparetime. Therefore, we conducted an experiment to verify theeffectiveness of using the web-based system developed in
Section 4 to allow users to experience a possible driving
takeover situation using the same HMI as the vehicle they
will actually use at a later date. As a result, it was found thatthe participants were able to take quicker action during theactual driving takeover by experiencing such a situation inadvance.
In the web-based learning, there were some cases inwhich the learners skipped watching the video materials
that they should have viewed, and as a result, there weresome cases in which their understanding was consideredinsufficient.

[References]
(1)	Arame, M., et al.: Effects of Learning materials about AutomatedDriving Level 3 Focusing on Driving Frequency: Verification by
Propensity Score Matching, Proceedings of 2020 Fourth World
Conference on Smart Trends in Systems, Security and Sustainability(WorldS4), pp. 454-460, 2020.
(2)	Goda Y, et al.: Development of a Short-Form Learning Style Inventoryfor Automated Driving Safety Education. Proceedings of IEEE
International Conference on Teaching, Assessment, and Learning forEngineering (TALE) 2020, pp. 847-851, 2020.
(3)	Arame, M., et al.,: Development of a Short-Form Career Resilience
Inventory for Online learning -Verification based on analysis results of
online learning for automated driving-, IEEE TALE 2022 (under review)(4)	Arame, M., et al.: Using Narrative based Video on Gaining Safety
Driving: Focusing on Career Resilience and Learning Style in
Automated Driving Level 3, Proceedings of Fifth International Congresson Information and Communication Technology, ICICT 2021.
(5)	The Second Phase of SIP-adus Research and Development in FY2019:
Research and Study on HMI and Safety Education Methods for
Advanced Automated Driving, https://en.sip-adus.go.jp/rd/rddata/rd03/
e211. pdf, (accessed 6/29/2021)(6)	Zhou, H., et al.: Influence of Prior General Knowledge on Older Adults’
Takeover Performance and Attitude Toward Using Conditionally
Automated Driving Systems, Proc. HFES Annual Meeting, 2021.
(7)	The First Phase of SIP-adus Automated Driving System Outcome
Report: Establishment of Guidelines by Data Collection on Human
Factors such as HMI, https://en.sip-adus.go.jp/rd/rddata/rd01/121s.pdf,(accessed 6/292021)(8)	Zhou, H., et al.: How Do Levels of Explanation on System Limitations
Influence Driver Intervention to Conditional Driving Automation? inIEEE Transactions on Human-Machine Systems, Vol. 51, No. 3, pp.188197, 2021.
(9)	Zhou, H., et al.: Long-term Effect of Experiencing System Malfunction
on Driver Take-over Control in Conditional Driving Automation.
Proceedings of The 2019 IEEE international Conference on Systems,
Man, and Cybernetics (SMC 2019), pp.1950-1955, 2019.

[Contacts]

6 Social implementation

Institute of Systems and Information Enginerring, University of Tsukuba
1-1-1 Tennodai, Tsukuba , Ibaraki 305-8573, 029-853-5502, Contact: Makoto
Itoh (Professor, itoh.makoto.ge@u.tsukuba.ac.jp)

Instead of simply summarizing the results of this researchand development in this report, they should be utilized forsocial implementation. For those who plan education, etc.,
this report is a manual that summarizes the knowledge that
contributes to planning and development of teachingmaterials. In addition, as described in Section 4, we arepreparing to release motivational videos and generalknowledge video materials as samples. We hope you willfind the manual and other materials useful.
Since this manual was compiled, we have continued toanalyze data obtained as a result of our research anddevelopment activities, and as further findings are obtained,
we hope to take the opportunity to revise it as necessary.

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3

Ensuring the Safety of Automated Driving5) R esearch on Communication between Low-Speed
Automated Transportation and Logistics Services
Vehicles and Surrounding Traffic Participants
Tatsuru Daimon, Masahiro Taima, Jieun Lee, Tomoyuki Furutani (Keio University)(Abstract) Aimed at addressing societal problems such as facilitating mobility for vulnerable road users, alleviating alack of drivers for transportation and logistics services, and reducing costs, technologies are currently being developed
for low-speed automated transportation and logistics service vehicles, and FOTs (Field Operational Tests) are being
done in rural regions. In contrast to traditional driver-operated vehicles, low-speed automated service vehicles needto see a number of issues addressed, including safety, security, and traffic efficiency, through better communication
with traffic participants, including pedestrians and other drivers. In order to achieve safe and reliable communication
between low-speed automated service vehicles and traffic participants, we analyzed communication characteristics
observed during the FOTs, and conducted experimental studies concerning communication methods (vehicle
behaviour, external HMI (eHMI), etc.) for relaying to traffic participants the intent and status of automated service
vehicles. Furthermore, through the field operational tests, we tested communication methods through eHMI identified
based on experimental studies, and confirmed the effectiveness of design recommendations for communication
methods.
Keywords: communication, expression of intent, eHMI (external human machine interface), vehicle behavior, traffic participant

1

Introduction

To ensure mobility for vulnerable road users in rural, or
depopulated areas, and to alleviate a shortage of drivers fortransportation and logistics services, research is being done
into the deployment and usage of transportation and logistics
services that make use of automated vehicles. At this early stage,
research is proceeding with the expectation that these services
will be used for low-speed travel in general road environments
and traffic situations where there are pedestrians, drivers, andother traffic participants. In some areas, including those where
FOTs are being done, human attendants who can manuallyintervene are needed for safety reasons in automatedtransportation or logistics service vehicles traveling at low
speed. However, technological advances should pave the way toa means of vehicle operation capable of handling situations andlimitations in areas of automated service vehicle deployment,
including means of operation where no human attendants, ortheir intervention, are needed. On the other hand, conventional
driver communication, such as gestures and eye contact, for the
purpose of ensuring safety and traffic facilitation for participantsin surrounding traffic, facilitates their understanding of thevehicle's intentions and status and their own judgment of theirown actions, and improves their sense of security. However,there is unlikely to be any active communication from humancrew member when automated service vehicles are being
operated without involvement by such crew member. These
research and development efforts(1) have focused on developing
and proposing design recommendations to support and achievesafe and seamless communication between automated service
vehicles and traffic participants. Through cooperation with
"automated service vehicle FOTs in areas primarily around
Michi-no-Eki (Roadside Stations) in rural regions,"(2) ("Michino-Eki FOTs"), we analyzed drive recorder video and identified
typical unsafe and inefficient means of communication between
automated service vehicles and traffic participants(3), shown in
Fig.1. For these typical cases, a focus was placed on methods
that included means of communication between traffic
participants and automated golf carts (6-passenger capacity)
traveling at up to 12 km/h along only electromagnetic induction
line, especially external human machine interfaces ("eHMI").
We conducted experiments in a virtual reality environment(VR experiments) using a head mounted display, experiments
using a driving simulator, and road experiments using anexperimental vehicle on campus and in other locations,researching and proposing means of communication for each

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3.Ensuring the Safety of Automated Driving
﻿5) Research on Communication between Low-Speed Automated Transportation and Logistics Services Vehicles and Surrounding Traffic Participantscase. In cooperation with Michi-no-Eki FOTs, we also
conducted FOTs relating to means of communication involving
eHMI, and evaluated the effectiveness of these means.

avoidance behavior, etc. when the eHMI gave them visual orauditory information about the intent of the vehicles, orrequests to avoid the vehicles.

2.3. Results of the Experiment(a) Approach/avoid(b) Cross(c) Overtake

Fig.1: Categories for communication with traffic participants

Concerning avoidance behavior after a pedestrian notices anautomated service vehicle approaching from behind, we
observed numerous cases of inadequate avoidance bypedestrians, including them not getting off the roadway ormoving over a little bit to the road shoulder when no eHMI was
deployed. However, when the pedestrians were given
information from an eHMI, many practiced proper avoidance.

Communication methods for

2 approach and avoidance situations

Sufficiently move out of the vehicle's path

100％

1

For approach and avoidance situations, we researchedcommunication methods through VR experiments that focusedon eHMI, for example, that included visually or auditorily
conveying vehicles' intent to pedestrians in situations where
automated service vehicles approach pedestrians from behind.

2.1. eHMI specifications
For eHMI used for approach and avoidance situations, as
shown in Fig.2, we developed the phrases "now moving" toindicate vehicle intent and "please clear the way" as a request forpedestrians, adding auditory cues to the existing visual cues.

No eHMIeHMI (visual)"Now moving"eHMI (visual)"Please clear the way"
"Please clear
the way""Now moving"

Percentage

80%
60%

2

Others

2

2

4

7

40％

5

20％
0％

Not move over enough or at all

6

6
4

1
No eHMI

VisualAuditory"Please clear the way"

VisualAuditory"Now moving"Fig.3: Pedestrian avoidance behavior upon first notice

We also analyzed how much time it took for experimental
participants who conducted proper avoidance and moved
completely off the roadway after noticing an automated service
vehicle to complete the avoidance process. Those given any sortof auditory cues took roughly 5 seconds to complete theavoidance process, whereas those given visual cues tended totake longer, although there was considerable individual
variation. In a retrospective report released after the experiment
was completed, many experimental participants did not knowthat automated service vehicles could not drive themselvesautomatically separated from electromagnetic induction line.
When no eHMI was used and an automated service vehicle

eHMI (auditory)"Now moving"eHMI (auditory)"Please clear the way"

Fig.2: eHMI specifications

had stopped before the experimental participants turned
around and noticed them, many said that they didn't knowwhat they're being told about. Even when the eHMI was used,

2.2. Experiment method
We built road environments modeled after non-intersection
segments in areas surrounding Michi-no-Eki (Roadside
Stations) and had experimental participants—posing as
pedestrians—look back and confirm the state of an automatedservice vehicle approaching from or stopping behind them as
the pedestrians walked down the left side of a non-intersection
segment. We then studied those pedestrians' understanding,people told us, for example, that "I thought I just needed tomove over to the shoulder a little and the automated service
vehicle would correct a little and avoid me," "‘Now moving’ isnot enough to prompt me to avoid vehicles," "(‘Please clear theway’) makes me feel like I am being forced to take action, likeI'm being admonished in front of others" (Visual cues) arealways shown, but I'm not used to looking for them and so I
don't notice them right away."

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3.Ensuring the Safety of Automated Driving
﻿5) Research on Communication between Low-Speed Automated Transportation and Logistics Services Vehicles and Surrounding Traffic ParticipantsCommunication methods for street

3 crossing situations

For street crossing situations, regarding automated service
vehicle behavior and eHMI-based communication methods
identified through VR experiments focused on non-intersection
segments and parking areas, we studied communication
methods through on-premises road experiments that involved
both the presence and absence of human crew members.

3.1. Specifications for test vehicles, deceleration behavior, and eHMI
The experimental vehicle was a golf cart capable of automateddriving, as shown in Fig.4. As with the Michi-no-Eki FOTs, thisvehicle was capable of automated driving along electromagnetic
induction line buried under the street. For the eHMI, we
installed LED panels on the dashboard under the windshield.
We created three types of information communicated topedestrians by the experimental vehicle: "After you" (request topedestrians), "Vehicle will stop" (vehicle intent), and "Vehicle isin automated driving mode" (vehicle control state). We alsoestablished three types of deceleration behavior for street
crossing situations based on experimental vehicle deceleration
start point and stop point when traveling at 12 km/h (as shownin Fig.5): normal deceleration, early deceleration, and early
LED panel・Human crew member present
・Human crew member absent

・After you
・Vehicle will stop・Vehicle is in automated driving mode

Fig.4: Experimental vehicle and eHMI specifications

・Normal deceleration

・Early deceleration

・Early stop

12km/h

0km/h

0

5

10

15

20

25

Distance between automated service vehicle and participant (m)Fig.5: Deceleration behavior types

30

stop.

3.2. Experiment method
To simulate the residential roads with few sidewalks in areas
around Michi-no-Eki (Roadside Stations), we set up
comparatively narrow on-premises roads. We had experimental
participants, as they waited on the road shoulder to cross theroad, observe the behavior of automated service vehicles,
eHMI, and the presence or absence of human crew member, as
the vehicle approached at 12 km/h from up the right side of thepedestrians. We then examined whether the experimental
participants perceived that the automated vehicle had given
way to them or not, and also whether they could judge to crossthe street safely.

3.3. Results of the Experiment
The following are the results of tests involving the presenceof human crew member under the same driving condition as
was present in the Michi-no-Eki FOTs. Fig.6 shows the timingwith which pedestrians decided to start crossing as theexperimental vehicle approached. When no eHMI was present,pedestrians made the decision to start crossing several seconds
after the vehicle stopped. When an eHMI was used, pedestrians
made the decision to start crossing at about the same time oreven before the experimental vehicle stopped. Some participants
made the decision to cross even earlier when the vehicle
conducted early deceleration, and participants tended to startcrossing after the vehicle started decelerating but before it
stopped when given the "After you" cue.
When no eHMI was used, participants' anxiety when
deciding whether to start crossing, was high when vehicles
decelerated or stopped in earlier timing, and conversely was
low when the vehicle conducted normal deceleration. When an
eHMI was used, participants' anxiety was lessened when thevehicle performed an early stop.
To summarize the results of the above, we expect that, instreet crossing situations, the recommended communication
methods will be to conduct with deceleration behavior such as
Timing of decision to begin crossing (sec)

To summarize the results of the above, we expect that, inapproach and avoidance situations, the recommended
communication methods will convey avoidance requests topedestrians using eHMI that provide either both auditory andvisual cues or only auditory cues.

■ Early deceleration■ Early stop

■ Normal stop

7.5
Golf cart stop timing

5.0
2.5
0.0
-2.5
-5.0

No eHMI"After you"

"Vehicle will stop""In automateddriving mode"

When human attendants are present

Fig.6: Deciding timing to start crossing when human crew members arepresent

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4 overtaking situations

As a communication method to reduce unsafe behavior bydrivers approaching from behind an automated service vehicle
and overtaking it, we conducted DS experiment to study the
impact that had on such drivers' understanding and overtaking
behavior when they are provided with vehicle state, type, andcaution information when they overtake the automated vehicle,
rather than directly conveying vehicles' intent to stop or yield.

4.1. Road environment and eHMI specifications
As shown Table 1, road environments were constructedaccording to different types of centerlines. As shown in Fig.7,
we established three types of specifications for eHMI used forovertaking situations, indicating them on the rear of theautomated service vehicles. These are "Low-speed travel /Automated vehicle" (alternately displayed in one-second
intervals) to indicate vehicle state and type, "Be careful when
overtaking" to alert drivers when passing vehicles, and "Bewareof surroundings / Please proceed" (alternately displayed in onesecond intervals), which includes intent to yield as a comparison.
Table 1: Studying communication according to centerline types
Road
environment

Goal of communications fromgolf cart to following vehicle

Golf cart vehicle state/behavior

Yellow
centerline

・Restrict overtaking
・Efficiently corresponding tofollowing traffic

・Maintain travel/driving state・Stop after deceleratingWhite
centerline

・Alert when overtaking
・Efficiently corresponding tofollowing traffic

・Maintain travel/driving state

4.2. Experiment method
After building a road environment modeled after nonintersection segments in areas around Michi-no-Eki, we had
experimental participants approach and overtake automatedservice vehicles traveling along roads in the area. We studied
the impact that was had on drivers' understanding, overtaking
behavior, etc., by automated service vehicle behavior, lighting
system operation (direction or hazard indicator lamp, stoplamp) and the presence or absence of eHMI. We set up
contactless eye tracking systems on the DS, measured theparticipants' gazes as they drove, and analyzed where theylooked from the time they approached the automated service
vehicle to when they started and completed their overtaking.

Low-speed travel↓↑

Automated vehicle(a) Low-speed travel/ Automated vehicle

Beware of surroundings↓↑

Be careful when overtaking(b) Be careful when
overtaking

Please proceed(c) Beware of surroundings/ Please proceed

Fig.7: eHMI specifications

4.3. Results of the Experiment
The following is a summary of the results of experimentfocused on white centerlines. Fig.8 shows time spent looking at
surroundings, starting from when participants approached theautomated service vehicle to when they started to overtake it.
Participants looked at their surroundings for about five seconds
when no eHMI was used, for around eight seconds when issued
a "Be careful when overtaking" alert by the eHMI, and about
five seconds when issued a "Low-speed travel / Automatedvehicle" alert. When issued a "Beware of surroundings / Pleaseproceed" alert, participants spent relatively less time looking attheir surroundings.
Time spent looking at surroundings (sec)with early stop and normal stop, and supplementally to convey
Communication methods forthe vehicle's intention to stop or give way to them.

11
10
9
8
7
6
5
4
3
2

No eHMI"Low-speed travel""Automated vehicle""Be careful when "Beware of surroundings"
overtaking""Please proceed"

Fig.8: Time spent looking at surroundings,
from start of approach to start of overtaking

Regarding participants' recognition of receiving the rightof-way from automated service vehicles, recognition was low
when no eHMI was used. Recognition was highest when theeHMI issued the "Beware of surroundings / Please proceed"
alert. Although "Low-speed travel / Automated vehicle" and "Be
careful when overtaking" do not explicitly express vehicles'
intent to yield both alerts yielded greater such recognition whencompared to situations where no eHMI was used. Compared towhen not using an eHMI, participants receiving eHMI alerts
felt less irritation from the time they approached the automatedservice vehicle to when they started overtaking it.
To summarize the results of the above, we expect therecommended communication methods will use eHMI to give
drivers information on vehicle state, type, when they overtake
an automated service vehicle.

FOTs to verify communication

5 methods using eHMI

In conjunction with Michi-no-Eki FOTs and with a focus on
methods of communication between an automated service
vehicle and traffic participants that were identified through VR,
DS, and on premises road experiments, we used actual traffic
environments to verify communication methods for crossing
and overtaking situations.

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As shown in Fig.9, we installed an LED panel to serve asa rear eHMI on the rear cargo bed of an automated golf cart
experimental vehicle used in the on-premises roadexperiments. Activation and deactivation of front and rearlighting on the eHMI were manually controlled by humancrew members. We configured the front eHMI to display"After you" and "Vehicle will stop" for crossing situations,and configured the rear eHMI to display "Low-speed travel /Automated vehicle" (alternately displayed) and "Be carefulwhen overtaking" for overtaking situations. We configured
the front eHMI to display these messages roughly
concurrently with deceleration start, and for the rear eHMI
to display the messages constantly. We also mounted several
drive recorders and cameras on the experimental vehicle tocapture traffic participants' expressions and behaviors. The
eHMI and messages were used in each FOT location after
application was filed and approved for a relaxation ofregulations.

eHMI (front)

eHMI (rear)Fig.9: Experimental vehicle and eHMI used in the FOTs

5.2. Characteristics of the FOTs area and road traffic
conditions
With support from the Road Bureau of the Ministry ofLand, Infrastructure, Transport and Tourism, Highway
Industry Development Organization, local governmentorganizations, and local transportation service providers, we
conducted FOTs around Akagi-Kogen Michi-no-Eki (Iinan,
Iishi District, Shimane Prefecture, hereinafter "AkagiKogen") and the Yamakawa region of Miyama (Miyama
City, Fukuoka Prefecture, hereinafter "Miyama"). The AkagiKogen service route has many residential roads and little
vehicle traffic. The Miyama travel route has national
highways and considerable vehicle traffic, including large
vehicle traffic, and there is a considerable speed difference
between these vehicles and automated service vehicles. In
all of the FOTs areas, parking areas existed at the start/end
points and along the routes, and pedestrians could
encounter situations where they crossed the path of theautomated golf carts in the parking lots.

5.3. Experiment method
Under the guidance of local government organizations andservice managers, the experimental vehicle was run for 10 days
during daytime hours in each FOT area. To ensure traffic safety,
we followed the guidance we were given and had theexperimental vehicle slow down and pull over to the left when
the human crew members detected the approach of following
vehicles so that following drivers could overtake theexperimental vehicle safely. Using drive recorders and cameras
mounted on the experimental vehicles, we recorded video dataof traffic participants' expressions and behavior while thevehicle was in operation. We chose eHMI message types basedon the number of observations to be made and vehicle operation
state, including displaying no message at all.

5.4. Results of the Experiment
To ensure that applicable instances of communication were
missed, we recorded all traffic participants that were present
within a certain radius from the test vehicles. In Akagi-Kogen,this was 33 pedestrians and 114 vehicles (four-wheel vehicles
and large vehicles), and in Miyama this was 13 pedestrians and525 vehicles. We established the road crossing situations as
those where pedestrians noticed the test vehicle and crossed theroad. For vehicle passing situations, due to the large number ofincidents recorded, we selected and analyzed incidents at
random.
For crossing situations, this paper analyzes observations
made in Akagi-Kogen. We calculated the time it took forpedestrians to start crossing after noticing the experimental
vehicle or after the experimental vehicle stopped. These results
are shown in Fig.10. Although the number of observations is
insufficient, when pedestrians were provided with no cues froman eHMI, they took at least five seconds to start crossing. Whencues from an eHMI were provided, they took within two
seconds.
For overtaking situations, this paper conducts analyses ofobservations made in Miyama. The analyses, which look atsituations where following vehicles approach and overtake theexperimental vehicle from behind, examine the traveling stateof the following vehicles just before attempting to overtake theexperimental vehicle. These analyses also take into account
6.0

Crossing timing (sec)

5.1. Experimental vehicle and eHMI specifications

Golf cart stop timing

4.0

2.0

0.0

−2.0

2

No eHMI

3"After you"5

"Vehicle will stop"

Fig.10: Pedestrian crossing timing in Akagi-Kogen

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Constant speed

Decelerate

Slow moving[References]

Stop

Oncoming vehicle
absent30

Low-speed travel /Automated vehicle

Oncoming vehicle
present

Speed upLow-speed travel /Automated vehicle3

Be careful when
overtaking

2

No eHMIBe careful when
overtaking

28

2 2

27

6

0%

4

1

4

No eHMI

2

6

20%

1

2

2

40%

60%

1

2
80%

100%

Fig.11: Travel state of following vehicles just before their start overtaking
in Miyama(1)	The Second Phase of SIP-adus Research and Development in FY2020: HMI andUser Education, https://en.sip-adus.go.jp/rd/rddata/rd04/e206s. pdf, (accessed
August 5, 2022)(2)	Commencement of Long-term Automated Driving FOTs in rural areas —Toward Formulating Standards for Road Spaces for Automated Driving, https://www.mlit.go.jp/common/001259382.pdf (accessed August 5, 2022)(3)	Tatsuru Daimon et al., Research on Communication between Low-Speed
Automated Transportation and Logistics Service Vehicles and Surrounding
Traffic Participants, SIP 2nd Phase: Automated Driving for Universal Services—
Mid-Term Results Report (2018-2020), pgs. 108-112, 2021

[Contacts]
Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku,
Yokohama, Kanagawa 223-8522, 045-566-1441, Contact: Tatsuru Daimon

whether or not there were oncoming vehicles. These results areshown in Fig.11. Most of the service routes in Miyama arenational highways, with vehicles traveling at speeds muchhigher than the 12 km/h or slower of the experimental vehicle.
Although the number of observations was low when there were
oncoming vehicles, we observed following cars decelerating
before overtaking the experimental vehicle when provided withcues from the eHMI, suggesting these cues encourage slowing
down before overtaking.
We also observed a small number of cases where, even whenthe eHMI provides cues, oncoming vehicles and following
vehicles interfere with each other. This could be due to the roadenvironment or to experimental vehicle stop timing or
positioning when yielding to following vehicles. A more indepth investigation is needed with regard to communication
methods that take into account possible interference between
traffic participants.

6 Conclusion
Aimed at achieving safe and efficient communication
between an automated service vehicle traveling at low speed
and traffic participants, this paper reports on the results ofvarious experiments conducted to evaluate the effectiveness ofcommunication methods and eHMI design recommendations,among other things, for different traffic situations. We observed
instances of unsafe or inefficient communication that is difficult
to collect data on through VR, DS, and on-premises roadexperiments and that relies on actual road environments andtraffic conditions. Furthermore, we were unable to conduct anadequate number of observations due to FOT time limitations.
Further FOTs and other studies will need to be done in order toobserve communication situations and evaluate the effectivenessof communication methods.

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3

Ensuring the Safety of Automated Driving

6) Research of HMI for Advanced AutomatedDriving Systems
Toshihisa Sato, Kunihiro Hasegawa, Yanbin Wu, Ken Kihara (National Institute of Advanced Industrial Science andTechnology (AIST)), Kimihiko Nakano, Yang Yo (The University of Tokyo)(Abstract) This paper outlines the efforts in "Development of evaluation methods of driver’s
OEDR (Object and Event Detection and Response) and HMI for enhancing driver’ takeover in a transition fromautomated to manual driving (Task B)" in the second phase of SIP-adus (Cross-ministerial Strategic Innovation
Promotion Program (SIP) Automated Driving for Universal Services). In Task B, we considered evaluation indices for thedriver's peripheral monitoring status before a takeover from automated to manual driving, and the effects of HMI on thedriver's understanding of the system. We conducted driving simulator experiments to study a method for thequantitative evaluation of the driver's OEDR in preparation for driving in a system-initiated driver takeover from Level 3
automated driving. From the experiment results, we identified an evaluation index for the driver's OEDR and determined
the time required for appropriate OEDR. In driver-initiated driver takeover situations from Level 2 automated driving, wefound that eye tracking are effective in evaluating the driver's attention state during Level 2 use. We also clarified the
HMI requirements so that the driver can understand the functional limits of the system appropriately and respond
appropriately before the functional limits are reached.
Keywords: human factors, driver takeover,evaluation methods, HMI ( human-machine interface) , driving simulator

1

Takeovers From Automated to
Manual Driving

When using automated driving, the driving mode may
switch from automated driving to manual driving due tocauses such as reaching a system functional limit or theboundary of the system’s operational design domain (ODD).
Here, we addressed the human factor issue by classifying
takeovers into two categories: system-initiated takeovers, inwhich the automated driving system announces the takeoverto the driver, and driver-initiated takeovers, in which thedriver takes over driving based on his/her own judgment
while the automated driving system is in use.
In system-initiated driver takeovers, when theautomated driving system is at Level 2, the driver needs tomonitor the system’s op erating status as well thesurrounding situation. Yet because it is not always the casethat the driver maintains a state of monitoring, it is essential
the system detects the state of the driver during automateddriving, and knows whether it is possible to switch driving
over to the driver. The first phase of SIP-adus addressed
different driver states, such as looking aside (not facing
forward),(1)(2) attention directed elsewhere (facing forward
but thinking about something other than driving and not
paying attention to the information necessary to drive),(1)(3)and dozing (decreased alertness). (1)(4)(5) The study was
conducted for evaluation indices for each state as well as theeffects of these states on driving performance during driver
takeovers. (6) Based on these findings, we also examined
methods for predicting performance after a driver takeoverbased on the driver's eyeball movements.(7) Furthermore, in
addition to estimating the driver's state during automateddriving and analyzing the effects on driving performance,
we also studied measures to prevent the driver's alertnesslevel from decreasing during automated driving.(8)(9)Efforts in the second phase related to system-initiateddriver takeovers focused on Level 3 use of an automateddriving system. We investigated a method for quantitatively
evaluating the state in which the driver is ready for thedriving task, i.e., the state in which the driver is aware of thesurrounding situation, in order to appropriately carry out
driver takeovers while the driver is performing non-driving
related activities (NDRA) during automated driving at Level 3.
For driver-initiated takeovers, we investigated a method
to evaluate the state in which the driver can appropriately
understand the system’s functional limits at Level 2
automated driving and respond appropriately. In addition,
we experimentally studied the requirements for HMI thatenables the driver to appropriately understand thefunctional limits of the system and respond appropriately.

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3.Ensuring the Safety of Automated Driving
﻿6) Research of HMI for Advanced Automated Driving Systems- We measured the driver's eye movement and head
movements during automated driving. Driving behavior
after switching to manual driving was also measured, andthe lane change success rate was calculated.

2 System-Initiated Driver Takeovers
2.1. Overview of Issues Addressed

2.3. S tudy of Methods for Evaluating Driver’s OEDR
Before a Driver Takeover

In Level 3 automated driving, in order to properly
perform a driver takeover while the driver is doing
something other than driving during automated driving, itis necessary to appropriately shift the driver's attention fromthe non-driving related activity to the driving task. To thispurpose, an effective way to prepare for the driver takeoveris by having the driver recognize the surrounding situation,such as the speed of his/her own vehicle, the movement ofother vehicles around him/her, and the state of the roadenvironment, before performing the driving task manually.
The key issue here is how to evaluate whether the driver isappropriately aware of the surrounding situation. Based on
this awareness of issues, for this topic, we addressed thefollowing issues. (Fig.1)
・Methods for evaluating the driver's awareness of thesurroundings prior to a change of driving
・Time required for proper OEDR・Validity of evaluation methods for OEDR by driver(comparison with standard evaluation methods forperipheral awareness)
・HMI requirements to promote proper OEDR

(1) Experiment conditions
In this experiment, in which 30 drivers participated, thefollowing conditions were set: a condition in which thedriver is given a one-minute warning before the takeover, acondition in which the driver stops playing Tetris andbecomes aware of his/her surroundings one minute before
the takeover, and a condition in which the driver changes
without such a warning and without becoming aware of his/her surroundings in advance.
(2) Results of the experiments: Driving performance afterdriver takeover
The percentage of drivers who were able to change lanes
within a specific section without colliding with othervehicles after the takeover was calculated, and thepercentage was significantly higher in the condition inwhich the driver became aware of his/her surroundings
before the takeover than in the condition in which thedriver did not become aware of his/her surroundings. The
results suggest that, in Level 3 automated driving, the driver
can perform a takeover more appropriately when switchingfrom a state in which the driver is doing something otherthan driving to manual driving by creating a preparatory
state for the takeover by becoming aware of the surrounding
situation.
(3) Results of the experiments: Evaluation index of driver's
OEDR
An analysis of the forward fixation rate after the start ofperipheral awareness based on the driver's visual behavior
as detected by an eye camera showed that the forward
fixation rate tended to increase for 20 seconds after the startof peripheral awareness, and stabilized at a higher value
from 30 seconds after the start of peripheral recognition to

2.2. Experiment Scenarios
Each of the four issues above was examined in driving
simulator experiments. The road traffic scenarios were thesame for all experiments and were as follows- T he vehicle traveled at approximately 60 km/h in thecenter lane of a motorway with three lanes in eachdirection in automated driving mode.
- D uring the automated driving, participants in theexperiment played a game (Tetris) using a tablet PC.
- A few minutes after the start of automated driving, we
initiated a driver takeover, and a lane change was
performed by manual driving within a specific section.

Transition from automated driving Level 3 to manual driving-Planned driver takeover
SystemDriver takeover request (RtI: Request to Intervene)

Automated driving Level 3

Driver takeover mode automated driving OFF (Level 0)

Providing information on the "monitoring of peripheral conditions”

Driver NDRA: Non-Driving Related Activities

Attentiveness: state of readiness
How to evaluate?

Preparation for driver takeover

Manual driving(Non-Driving Related Activities)

Results obtained
from the project's
experiments

The process of increasing situational awareness by paying
more attention to either the front or the periphery (mirrors).
➡ If a takeover is performed at this point, thelane change success rate is lower than that aftera more stable percentage of eye fixation to thefront and periphery.

Stable percentage of eye fixation to front and periphery (mirror) = state of being able to recognize both
➡If the driving is taken over at this point, the lane change
success rate is the same as that of manual driving.
Effective HMI requirements

・The point of a takeover (start of manual driving) can be determined.
・Assuming the posture for driving operations

Evaluation index: driver's eye movement, head movement

Fig.1: System-initiated driver takeover efforts and results

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﻿6) Research of HMI for Advanced Automated Driving Systems

the point at which the driver takeover was performed.
Furthermore, the forward fixation rate remained stable after
the driver takeover. The eye movement other than forward
fixation were directed at side mirrors, rearview mirror, andinstruments.
This result suggests that the forward fixation rate after
the start of OEDR can be an effective evaluation index of thedriver's peripheral monitoring, and that the driver can be
judged to be aware of the surrounding traffic situation when
the low forward fixation rate increases immediately after thestart of awareness and then remains stable.
2.4. Examination of the Time Required for Proper OEDR
(1) Experiment conditions
The experiment conditions consisted of the times taken
for the driver to become aware of his/her surroundings
before taking over driving. There were four experimentconditions set at 5, 10, 20, and 55 seconds, and a 0-second
condition (taking over driving from the state of playing agame), plus a manual driving condition rather than an
automated driving condition. There were 30 participants inthe experiment.
(2) Results of the Experiment
The rates of forward and mirror gazing were calculated
for the four conditions in which OEDR was studied, and thechanges from the start of peripheral awareness werecompared. The results showed that the changes from thestart of peripheral awareness were the same in allconditions, and that the rate of forward fixation increased
until 20 seconds from the start, after which it tended tostabilize. Put differently, no such stable shift was observed in
the 5-, 10-, and 20-second conditions. The results of thedriving performance after the takeover showed that the lanechange success rate in the 55-second condition was higher
than that in the 0-, 5-, 10- and 20-second conditions, andwas as high as that in the manual driving condition.
This result indicates that high performance was observedin the driver takeover after the stable shift in the forward
fixation rate, which supports the results in 2.3.
2.5. Study of Validity of Evaluation Methods for Driver
OEDR (Comparison With Standard Evaluation
Methods for OEDR)(1) Experiment conditions
The time for the driver to become aware of his/her
surroundings before the driving takeover was set to 55
seconds, and the verbal thinking method was used to obtainutterances of what the driver was thinking during that time.
In the DS experiment with 20 drivers, we obtained
utterances related to awareness of the surrounding situation,such as, "The flow of cars in the right lane is faster than mycar."(2) Results of the Experiment
From the obtained utterances, we tabulated the numberof utterances related to the situation in front and thenumber of utterances related to the situation in the
peripheral area. The number of forward/periphery-related
utterances from the start of the driver's peripheral awarenessand the change in the rate of gaze to the front/mirrors overthe same time period were compared. The number offorward/periphery-related utterances and the rate of eye
fixation to the front/mirrors showed nearly the samechange.
This result indicates that the change in the eye fixation
rate is large in the sections where the change in the numberof utterances is large. Conversely, the change in the numberof utterances is stable in the sections where the change inthe eye fixation rate is stable. Assuming that the number ofutterances indicates the degree of awareness of the situation,
the result suggests that the change in eye fixation rate may
correspond to the degree of awareness of the situation.
2.6. HMI Requirements to Promote Proper OEDR
(1) Experiment conditions
In order to examine HMI requirements for promoting
OEDR when asking drivers to become aware of theirsurroundings before driver takeovers, we set the following
conditions: a condition in which the driver is informed of
the takeover timing by being told "Takeover after x seconds,"
a condition in which a countdown is given in addition toclearly stating the takeover timing, a condition in which thedriver takes hold of the steering wheel and assumes thedriving posture while becoming aware of his/her periphery,
and a condition in which an alarm is sounded if the driver
does not become aware of his/her periphery. A total of 120
drivers (20 per condition) participated in the experiment.
(2) Results of the ExperimentComparisons of the times it took to complete lanechanges and of the collision rate showed that the results ofthe condition in which the driver is given a countdown in
addition to being informed of the takeover timing and thecondition in which the driver assumes the driving postureand takes the steering wheel during OEDR were better thanthose of the other conditions. At the same time, thecondition in which an alarm was sounded when the driver
failed to become aware of his/her periphery had no effect on
improving driving performance.
These results suggest that knowing the time of a driver
takeover and assuming a posture for driving operations are
effective in improving driving performance after the
takeover.

3 Driver-Initiated Driver Takeovers
Level 2 driver assistance requires the driver to be awareof the surrounding situation and of the system status during

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automated driving, and to take over driving when thesystem reaches functional limits. In this study, weinvestigated a method for evaluating whether the driverunderstands the system status appropriately. We also studied
HMIs that facilitate the driver's understanding of the system
to ensure that the driver can take over driving without fail.
3.1. Method for Evaluating the Driver's Understanding of
the System
We interpreted a driver state equivalent to manual
driving as the state in which the driver is able to coexist withthe system, i.e., he/she understands the system state. We
analyzed the differences in driver behavior during manual
driving and Level 2 driver assistance to investigate a method
for evaluating the state of system understanding.
A driving simulator experiment was conducted with 10
experiment participants,(10) driving in the second lane of aroad with three lanes in each direction by manual driving orLevel 2 driver assistance. We set up potential risk situations
in which the participants should pay attention to theirsurroundings while driving. The participants' eye
movements while driving were measured using a noncontact eye camera. Significant differences were observed in
the duration of eye fixation in several areas, with a tendency
for the participants to spend less time looking at the front
and instruments and more time looking at the peripheryand mirrors during Level 2 driver assistance than during
manual driving.
3.2. HMI to Facilitate System Understanding
Candidates for HMIs that would help drivers understandsystem detection failures and false detections were selected
and operated in a driving simulator environment. An HMI
that displays the recognition results of the traffic situation inreal time was used in a driving experiment with 18
participants.(11) (12) The results of a questionnaire showed
that, when the HMI display was present, drivers understoodthat the automated driving system may not be able torecognize objects other than vehicles, suggesting that the
HMI may promote driver understanding of the system.
3.3. HMI for Preventing Vehicle-to-Vehicle Accidents at
Signalized Intersections
The purpose of this experiment was to investigate therequirements for an HMI to prevent vehicle-to-vehicle
accidents near signalized intersections through appropriate
driver intervention during driving by Level 2 driverassistance. Two types of HMIs were proposed: one that
presents static environmental information based on map
information for the purpose of clearly indicating dangerous
locations (static HMI), and one that presents object
recognition information based on on-board sensor
information for the purpose of confirming the Level 2
driving support mechanism (sensor HMI). We evaluatedthe effectiveness of these HMIs through experiments usinga driving simulator. The HMIs proposed in this study
display the presented information on a heads-up display
fixed to the dashboard of the driving simulator.
The road environment used in the experiment simulated
a Japanese national highway, with two lanes in eachdire c t ion and an addit iona l r ig ht-tur n lane ne arintersections, resulting in three lanes. In the straight-ahead
scenario, the vehicle followed another vehicle ahead of it in
the second lane at 60 km/h using the Level 2 driverassistance system. The driver did not need to perform any
dynamic driving tasks such as acceleration/deceleration or
steering operations when the driver assistance system wasin operation. In the risk scenario, another vehicle appearedin the right-turn lane of the oncoming lane at a signalizedintersection, and when the driver's vehicle entered theintersection, the other vehicle suddenly started to turn right.
The situation was such that if the driver did not intervene indriving, a collision would have occurred. The Level 2 driverassistance system did not request any driver intervention,and the driver had to detect the hazardous event or anomaly
on his/her own and take the initiative to avoid the collision.
The four experiment conditions were a combination of thepresence and absence of static HMIs and sensor HMIs. An
ana lysis of t he exp er iment d at a showe d t hat t hecombination of a static HMI and sensor HMI resulted in thedriver intervention with the largest margin between vehicles
and significantly increased the driver's sense of driving
safety compared to driving without HMIs.(13) (14)In the right-turn scenario, the vehicle makes a right turnat a signalized intersection in Level 2 driving. In the risk
scenario, a motorcycle slips out from the oncoming lane
while the vehicle is turning right, and the driver must
intervene to avoid a collision with the motorcycle. The
driver intervention time and the collision margin time uponintervention were analyzed. The driver intervention timetended to be shorter and the collision margin time tended
to increase when the static HMI and the sensor HMI wereused together.
3.4. HMI for Accident Prevention During Traffic Signal
Changes at Signalized Intersections
During Level 2 driver assistance, in which control bysignal recognition is not performed, the driver is required toperform driver-initiated intervention to stop the vehicle
when the signal turns yellow on approach to a signalizedintersection when engaging follow control by adaptive
cruise control. The purpose of this experiment was toinvestigate HMI requirements to support safe travel at asignalized intersection when the signal light color changes
just before the vehicle enters the intersection during Level 2
driver assistance.
We proposed a static HMI that presents static
environmental information based on map information and148

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a dynamic HMI that presents dynamic environmental
information based on infrastructure information in addition
to static environmental information. The dynamic HMI
presented information such as the predicted color of thetraffic light ahead when the vehicle reaches a signalizedintersection. The information was displayed on a heads-up
display fixed to the dashboard of the driving simulator, andan audible notification was made when the information wasdisplayed. The effectiveness of the system was evaluatedusing a driving simulator experiment.
The road environment used in the experiment simulated
a Japanese national highway, with two lanes in eachdire c t ion and an addit iona l r ig ht-tur n lane ne arintersections, resulting in three lanes. In the straight-ahead
scenario, the vehicle followed another vehicle ahead of it in
the second lane at 60 km/h using a Level 2 driver assistancesystem. When the vehicle and the vehicle ahead of it
approach the signalized intersection where the risk scenario
occurs, the light changes to yellow. The vehicle ahead passes
through the intersection without stopping, but if the vehicle
following it continues to drive with the driver assistancesystem, the traffic light turns red near the stop line. We
analyzed the responses under three conditions: no HMI,
static HMI, and dynamic HMI. It was shown that when
dynamic HMI was used, the vehicle decelerated slowly andstopped with a margin of safety.
This is thought to be because traffic prediction support
information on the traffic signal enabled the vehicle toprepare for deceleration and to stop with a margin of safety. (15) (16)In the left-turn scenario, the vehicle turns left at asignalized intersection by driving at Level 2. In this risk
scenario, the traffic signal changes to yellow as the vehicle
and the vehicle ahead of it approach the intersection. The
preceding vehicle passes through the signal intersection
without stopping, but before this vehicle enters theintersection, the light turns red. If the driver does notintervene, he or she will run the red light. Analysis of the
stopping situation at the stop line and the driverintervention time after the signal change showed that theuse of dynamic HMI increased the number of times thedriver could stop in front of the stop line. In addition, thetime required for the driver to intervene tended to decrease.

Real Proving Ground, Proceedings of DSC 2019 Europe, pp.43-50, 2019.
(4)	Toshihisa Sato, et al.: Evaluation of Driver Drowsiness While Using
Automated Driving Systems on Driving Simulator, Test Course andPublic Roads, In: Krömker H. (eds) HCI in Mobility, Transport, andAutomotive Systems. Driving Behavior, Urban and Smart Mobility,
HCII 2020. Lecture Notes in Computer Science, Vol 12213. pp.72-85,
Springer, 2020.
(5)	Yanbin Wu, et al.: The Relationship Between Drowsiness Level andTakeover Performance in Automated Driving, In: Krömker, H. (eds)
HCI in Mobility, Transport, and Automotive Systems. Driving Behavior,
Urban and Smart Mobility. HCII 2020. Lecture Notes in Computer
Science, Vol 12213. pp. 125-142, Springer, 2020.
(6)	Damee Choi, et al.: Effects of cognitive and visual loads on driving
performance after take-over request (TOR) in automated driving,
Applied Ergonomics, Vol. 85, 103074, 2020.
(7)	Yanbin Wu, et al.: Eye movements predict driver reaction time totakeover request in automated driving: A real-vehicle study,
Transportation Research Part F: Traffic Psychology and Behaviour, Vol.
81, pp. 355-363, 2021.
(8)	Yanbin Wu, et al.: Effects of scheduled manual driving on drowsinessand response to take over request: A simulator study towards
understanding drivers in automated driving, Accident Analysis &Prevention, Vol. 124, pp.202-209, 2019.
(9)	Yanbin Wu, et al.: Age-related differences in effects of non-driving
related tasks on takeover performance in automated driving, Journal of
Safety Research, Vol. 72, pp.231-238, 2020.
(10)	Koichiro Inoue et al.: Transformation of driver mental models byadvanced driver assistance systems, 18th ITS Symposium, 2020.
(11)	Koichiro Inoue et al.: Effects of Presenting Object Recognition Results
to Drivers on Mental Models for Advanced Driver Assistance Systems,
29th Transportation and Logistics Sector Conference (TRANSLOG
2020), 2020.
(12)	B o Yang, et al.: Influences on Drivers’ Understandings of Systems byPresenting Image Recognition Results,IEEE Intelligent Vehicles
Symposium Workshops, 2021.
(13)	Saito, T. et al.: HMI requirements to support appropriate driver takeover
during level-2 driver assistance on prefectural and municipal roads at
signalized intersections, 19th ITS Symposium, 2021.
(14)	B o Yang, et al.: Influences of Different Traffic Information on Driver
Behaviors While Interacting with Oncoming Traffic in Level 2
Automated Driving, International Journal of Human–Computer
Interaction, pp.1-9, 2022.
(15)	Saito, T. et al.: HMI requirements to support proper driver takeover
during Level 2 driver assistance on prefectural and municipal roads,
30th Transportation and Logistics Sector Conference (TRANSLOG),
2021.
(16)	Yang, P. et al.: Investigation of HMI requirements for safe signalizedintersection passage during Level 2 driving, 58th Annual Conference of
the Japan Council of Traffic Science, 2022.

[Contacts]
National Research and Development Agency Human Mobility ResearchCenter, National Institute of Advanced Industrial Science and Technology(AIST), 1-1-1 Higashi (Central 6), Tsukuba, Ibaraki, Contact: Sato Toshihisa[References]
(1)	Yanbin Wu, et al.: Assessing the Mental States of Fallback-Ready Drivers
in Automated Driving by Electrooculography, Proceedings of 2019 IEEE
Intelligent Transportation Systems Conference (ITSC), pp. 4018-4023,
2019.
(2)	Toshihisa Sato, et al.: Evaluation of Driver Visual Distraction in
Automated Driving Systems in Driving Simulator, Test Course, andPublic Roads Experiments, Proceedings of 7th International Conference
on Driver Distraction and Inattention (Book of Abstracts, DDI 2021),
pp. 58-60, 2021.
(3)	Toshihisa Sato, et al.: Comparison of Driver Conditions in AutomatedDriving Systems and Transition Behaviors in Driving Simulator versus

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A Society with Automated Driving(1) Automated Driving Mobility Services in Regional Communities

Automated Driving
Transportation Services in Rural
Areas (Overview)
Yoshiyuki Kato (Highway Industry Development Organization)

Social Issues in Rural Areas and the Significanceof Automated Driving Transportation Services

1

Japan's population is declining and aging, and rural areasin particular are facing financial pressures and a lack ofskilled personnel. Public transportation services have been
particularly hit hard by these social conditions. The numberof people transported by bus services has been on a significant
downward trend across the board, with particularly severe
declines in rural areas. (Fig.1)(1) This trend is expected tocontinue in the future, making it difficult to maintain public
transportation services. Therefore, in rural areas, especiallyin rural areas, where people are increasingly dependent on
automobiles, these services may not be able to adequately
meet the mobility demands of the elderly after they relinquish
their driver's licenses.
105 Population (outside the three largest metropolitan areas) Number of passengers (three major metropolitan areas)

80

83 83
79

Rural areas

75 down about

78

87

76

23

22

H

21

20

H

19

H

18

H

17

H

16

H

15

H

14

H

13

H

12

H

96

96

89 89

95 94
90 91

Number of passengers
86 86
transported (nationwide)
Number of passengers (outside thethree major metropolitan areas)

25%

70

H

87

94

76 77

87

76 77

76

75

74

30

84 83

86

87

94

29

89 90

96

101

H

89

88

97

100

28

85

90

92 92

98

H

89

92

27

91

91

92

97

H

90

93

93

26

94

94

92

H

93

97 97

25

96

98

H

97 95
95

95

98 98

24

99 99

H

100 99H

100 100
100 100

H

100

Fig.1: Number of people transported by bus (with the year 2000 as 100)(1)

In the future, it is desirable to build a society in whichpeople who want to travel can travel to their destinations
when they want to and by the means of transportation theywant to use. Particularly in rural areas, it is also desirable tocreate a society in which people can travel stress-free without
relying on private vehicles. Automated driving transportation
services are expected to have a significant effect toward therealization of such a society.

2

Results and Challenges Obtained from the FOTs
(Field Operational Tests) in Rural Areas

2.1. Outline of the FOTs in Rural Areas
In order to respond to the challenges of an aging
population in rural areas and nationwide, a project called"FOTs of Automated Transportation Services Based on
Michi-no-Eki in Rural Areas" has been conducted since
September 2017, with the aim of introducing automatedtransportation services using automated driving technology
and ensuring the flow of people and things in rural areas. As
of July 2022, automated vehicles have been driven on publicroads at 18 locations throughout Japan as part of the FOTs.
Based on the results of FOTs with regard to the technicalaspects and business models, in November 2019, Michi-noEki "Kamikoani" (Akita Prefecture) became the first roadsidestation in Japan to provide a regular automated driving
transportation services to users under a system operated by
the local government and other local entities, and has
achieved safe operations to date.
In addition, automated driving transportation services wereintroduced in earnest at Michi-no-Eki "Okueigenji Keiryu no
Sato" (Shiga Prefecture) in April 2021, at the Miyama City Hall
Yamakawa Branch Office (Fukuoka Prefecture) in July of thesame year, and at Michi-no-Eki "Akagi Kogen" (Shimane
Prefecture) in October of the same year. (Fig.2)
2.2. Activities in each test field
For the FOTs of automated driving transportation services
based at Michi-no-Eki roadside stations, short-term FOTs

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Automated Driving Transportation Services in Rural Areas (Overview)[Social implementation] Started November 30, 2019

(Short-term FOTs)
18 locations

Kamikoani-mura, Kita Akita-gun, Akita-ken(Michi-no-Eki "Kamikoani"):

9 locations

Implementation period: December 9, 2018 - February 8, 2019
Vehicle used: Yamaha:

4 locations

frm FY2017-FY2018 :(*Including long-term FOTs and social implementation locations)(Long-term FOTs)

from FY2018(Social implementation)(*Including social implementation locations)from FY2019[Social implementation] Started October 4, 2021

Nishikatamachi, Tochigi-shi, Tochigi-ken(Michi-no-Eki "Nishikata")

Nagaoka-shi, Nagaoka-ken(Orataru Yamakoshi Restoration Exchange Center)

Hitachiota-shi, Ibaraki-ken(Michi-no-Eki "Hitachiota" andTakakura Community Center)

Nanto-shi, Toyama-ken(Michi-no-Eki "Taira")
Implementation period: September 1- October 10, 2020
Vehicle used: Yamaha

Gujo-shi, Gifu-ken(Michi-no-Eki "Meiho")

Implementation period: June 23 - July 21, 2019
Vehicle used: YamahaIna-shi, Nagano-ken(Michi-no-Eki "Southern Alps Mura Hase")

Ube-shi, Yamaguchi-ken(Kusu Komorebi no Sato)[Social implementation] Started July 19, 2021

Implementation period: November 5 - November 29, 2019
Vehicle used: Advanced Smart Mobility

Miyama-shi, Fukuoka-ken(Miyama City Hall Yamakawa Branch)
Implementation period: November 2 - December 21, 2018
Vehicle used: Yamaha

Implementation period: May 21 - June 21, 2019
Vehicle used: Advanced Smart Mobility

Takahatamachi, Higashiokitama-gun, Yamagata-ken(Michi-no-Eki "Takahata")
Implementation period: October 11 - November 12, 2021
Vehicle used: Yamaha

Niimi-shi, Okayama-ken(Michi-no-Eki "Koigakubo")

Taiki-cho, Hiroo-gun, Hokkaido(Michi-no-Eki "Cosmall Taiki")

Toyota-shi, Aichi-ken(Michi-no-Eki "Donguri no Sato Inabu")
Miyoshi-shi, Tokushima-ken(Michi-no-Eki "Nishiiya Kazurabashi Yumebutai")

Ashikitamachi, Ashikita-gun, Kumamo-ken(Michi-no-Eki "Ashikita Dekopon")[Social implementation] Started April 23, 2021

Otsu-shi, Shiga-ken(Michi-no-Eki "Imoko no Sato")

Tatehata-cho, Higashiomi-shi, Shiga-ken(Michi-no-Eki "Okueigenji Keiryu no Sato")
Implementation period: November 15- December 20, 2019
Vehicle used: Yamaha

Implementation period: January 27- March 15, 2019
Vehicle used: Yamaha

Fig.2: FOTs areas for automated driving transportation services based at such locations as Michi-no-Eki (roadside stations)〇 Tourism
Attracting tourists incooperation with
tourism DMO*, andresponding toinspection tours〇 Logistics
Utilized fortransportation toMichi-no-Eki roadsidestations (transportationof shipments)〇 Welfare
Cooperation with a
medical clinic attached
to a Michi-no-Eki(In front of the clinic in theroadside station
Michi-no-Eki roadside station)〇 Transportation ofelementary school students
Educational program for elementary
school students. Implementation of
measures to improve the acceptanceamong elementary school students*DMO: Destination Management/Marketing Organization

Fig.3: Future plans for multi-purpose use of automated driving transportation services□Christmas Illumination (Kamikoani).
The illumination (LED) is set up along the automated driving course and surrounding facilities.
Events are being planned, studied, and tried for people to enjoy facilities even in winter.

□Delivery lectures for the elderly (Miyama).
With the purpose of improving public
acceptance of automated driving services,
we gave lectures to the elderly, and also
explained how to use smartphones.

□Cooperation with facilities along the route, information
disseminated with social media (Okueigenji).
Proposal of a model course that ties up facilities along the route (fish farms, cafes)
and automated driving services. Disseminate information via social media, etc.

□Publicity by streamers, publication of Kawaraban, regional version of Jidoroku (Akagi Plateau).

Publicity by streamers designed by the Michi-no-Eki roadside station chief,
information dissemination in cooperation with tourist associations and roadsidestations, creation of Jidoroku with the original design of the roadside station chief.

Fig.4: Efforts by the regions to improve acceptance of automated driving transportation services

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Automated Driving Transportation Services in Rural Areas (Overview)

lasting approximately one week were conducted at 18 locations
nationwide to verify the following five items: (1) roads andtraffic, (2) local environment, (3) cost, (4) public acceptance,and (5) regional effects. In addition, with the aim of socially
implementing automated driving transportation services, we
conducted long-term FOTs of approximately one month atnine locations nationwide to verify the technical aspects(securing of driving space, driving management system) andbusiness model aspects (business implementation system,
collaboration with other businesses for multipurpose use,business profitability, etc.). Based on the results of the FOTs,
social implementation has been carried out where the various
aspects have been proven ready.
In the four regions where the system has been socially
implemented, efforts are being made to implement multipurpose use (Fig.3) for the purpose of improving business
continuity and to implement automated driving transportation
services (Fig.4) for the purpose of improving public acceptance.
In addition, studies are being conducted to contribute to theapplication to other areas throughout the country.
During the short-term FOTs, types of automated vehicles
other than the cart type were also used, but because of their highcost burden and the difficulty of driving them in narrow sections,in areas where the system was socially implemented, cart type(electromagnetic induction line type) vehicles were used.
2.3. Results and Issues from the FOTs
In areas where the system was socially implemented, stable
operations of automated driving transportation services over along period of time were confirmed. In addition, byimplementing the services as public transportation services
within the framework of private paid passenger transportation,
it was confirmed that even automated vehicles can provide
sustainable services within community-run operation systems
centered on the local governments.

At the same time, as shown in Table 1, in order to continue
providing public transportation services using automatedvehicles after the completion of this project, it is necessary to
examine business continuity, respond to system problems
and accidents, and build facilities that need to be handed
over to the local communities, and to otherwise ensure thatthe services are reliably handed over to the operators in areas
where social implementation is to take place. Because
automated vehicle transportation services are operated
throughout the year in the socially implemented areas, there
were cases where automated driving became difficult,especially during the winter season, and this became an issue
for the continuous provision of the services. (Fig.5)
Sensor collides with frozen snow on theroad surface. Sensor is disconnected
and automated driving is disabledElectromagnetic induction line broken
due to snow removal work, etc.
Automated driving services suspended▲ State of disconnection of electromagnetic induction line (Kamikoani)
Section where
snowplows cannot enter

Narrow section (between Yuzurihao entrance and Yuzurihao)

No snow removal system
has been established.

In front of garage (before snow removal)▲ Snow coverage on the automated driving route (Okueigenji)

Side of vehicle incontact withdeposited snow
▲ Snow coverage on the automated driving route (Akagi Plateau)

Fig.5: Challenges in winter driving

Table 1: Things that need to be passed on to the operating entity in the social
implementation area
NumberItems for study

State of progressFuture issues
Exploration of the possibility offurther multitasking
Study of measures to increase thenumber of users
Evaluation of ripple effects on the local
economy(1)

andStudy of multitasking Examinationofand business continuity implementation
initiatives
forof automated driving
multitaskingineach
servicesregion(2)Ordering of the items
that must be addressed
when operating
automated driving
services.

(3)

Clarify the responsein the event of a
How to respond when flow
assuming thea problem occurs in an failure,
caseof
a malfunction
automated vehicleof the automatedvehicleCoordinate with automated vehicle
manufacturers and local specialist
companies on how to respondConsideration and establishment of amaintenance system
The results of repairs and such arereflected in the vehicle operation
manual(4)

Assuming that an
Methods of responding accident has occurred,to accidents that occur the parties involved
during operation ofand the boundary
automated driving
points of responsibility
servicesfor automated driving
services are clarified

Verify the appropriateness of the
response flow by interviewing experts,
insurance companies, etc.
Clarify how to respond when an
accident occurs and reflect it in thevehicle operation manual(5)

Clarify facilities, etc.
that need to be handed
over to the community
for the operation of
automated driving
services.

Create manuals forautomated driving
services (vehicle
operation manuals) tobe referred to byservice providers andspecialized companies

Conduct workshops for local operating
entities and specialized companies
Updated based on opinions ofoperators

Creation of roadledgers (listing ofSurveying is carried out in each area
locations of
a ledger is prepared showing theinfrastructure facilities) andlocations of the automated driving
Clarify goods andsupportfacilities
facilities currently inuse.

3

Future Efforts to Expand Social Implementationof Automated Driving Transportation Services

Many local governments and private companies that areconsidering the introduction of automated driving
transportation services are interested in such services, butlack the know-how to proceed with such projects. We believethat we can support local governments and private businesses
that are considering the introduction of automated driving
transportation services by making use of the knowledge wehave accumulated through our FOTs to date. To this end, we
have established "Jidosapo" a contact point for inquiries
aimed at municipalities and private businesses that areconsidering the introduction of automated driving
transportation services. (Fig.6) Jidosapo portal site provides
an implementation manual for automated driving
transportation services and information on business flow,
etc. on the Web.
So far, Jidosapo has received inquiries from several local
governments and private companies about automateddriving transportation services, and in some cases, meetings

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4 A Society with Automated Driving(1) Automated Driving Mobility Services in Regional Communities
Automated Driving Transportation Services in Rural Areas (Overview)[Contents]

Highway Industry Development Organization, ITS and New Road Creation
Division, Plaza Edogawabashi Bldg. 2F, 1-23-6 Sekiguchi, Bunkyo-ku,
Tokyo, 03-5843-2917, Yoshiyuki Kato

Fig.6: Inquiries for automated driving transportation services (2)have been held to exchange opinions. In order to expand
social implementation of automated driving transportation
services, we at Jido Support plan to continue receiving
inquiries and updating the contents of the Jidosapo website.
This includes updating the implementation manual andinformation on available subsidy programs.

4

Conclusion

In order to address the challenges of an aging of society
nationwide, including in rural areas, a study titled "Field
Operational Tests of Automated Driving Transportation
Services Based on Michi-no-Eki and Other Facilities in Rural
Areas" was conducted from September 2017. Short-term
FOTs were conducted at 18 sites nationwide, while long-term
tests were conducted at nine sites. Based on the results ofthese FOTs, automated driving transportation services weresocially implemented in four areas nationwide.
In the socially implemented areas, it was confirmed that
it is possible to sustain the operation of the automated driving
transportation services within the operational system of thelocal government and other such local entities. On the other
hand, it became clear that there are things that need to behanded over to the operating entities in order to continue
providing the service after the project is complete. It isnecessary to ensure that the things required for service
provision are handed over to the local operators during theremaining period of the project.
To utilize the knowledge we have accumulated to date, apoint of contact for inquiries regarding automated driving
transportation services has been established. This contact
point will continue to be used to implement theimplementation of automated driving transportation services
in society.

[References]
(1)

Ministry of Land, Infrastructure, Transport and Tourism: FY2021(2)

Highway Industry Development Organization: "Jido Support," a portal

White Paper on Land, Infrastructure, Transport and Tourism, 2021.6
site for inquiries on automated driving services
https://www.hido.or.jp/jidosapo/, (2022.9)

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A Society with Automated Driving(1) Automated Driving Mobility Services in Regional Communities

1) Establishing the Environment for the
Deployment of Transportation Services
Relying on Automated Driving
Yoshiyuki Kato (Highway Industry Development Organization)(Abstract) This project aims for social implementation of automated driving services, which have been started in limited areas,in multiple areas throughout Japan for the purpose of permanent implementation of automated driving mobility services, by
examining the sustainability of the project, updating the manual on automated driving mobility services as needed, andcontributing to the expansion of examples of social implementation of mobility services with automated driving. First, we willaddress issues related to social implementation, such as securing road space and operation management, in rural areas where
there is little other traffic, as areas where automated driving mobility services can be introduced on prefectural and municipal
roads at the current technological level. Four areas that have been socially implemented and one experimental area where along-term field operational tests (FOTs) was conducted were selected as the target areas for verification. These target areas willsequentially begin offering automated driving mobility services from November 2019, and are implementing measures such as
collaboration with other businesses and improving the acceptance of local residents that will contribute to the sustainability ofthe business. This paper outlines the purpose of introducing automated driving mobility services, the areas selected for studies
for practical application, the vehicles to be used, the definition of requirements for social implementation, and the initiatives
being taken in each of the social implementation sites and long-term FOTs sites.
Keywords: public transportation, mobility services, social implementation, selected for studies

1

Purpose of introducing automateddriving mobility services

The objective of this project is to implement automateddriving mobility services, started in limited regions, in
multiple regions throughout Japan in view of its permanent
implementation. In addition, the sustainability of the projectwill be studied by solving local social issues in social
implementation and by horizontally developing measures tosecure driving space, which was considered a common issueduring the FOTs. Furthermore, we will update the manuals
on automated driving mobility services that we havedeveloped, and contribute to the expansion of examples ofpractical applications of mobility services with automateddriving, which is our ultimate goal.
For the social implementation of mobility services with
automated driving, we will first consider rural areas withminimal other traffic as areas where such services can be
introduced on prefectural and municipal roads at the current
technological level, with the aim of commercializing mobility
services with automated driving and logistics services, andsolve problems in social implementation such as securing
road space and operation management. In addition,
guidelines for the introduction of automated driving services
in rural areas and standards for road space for automatedvehicles will be developed for horizontal deployment
throughout Japan. To this end, in cooperation with local
governments and related businesses, the project will conduct

verification and the necessary studies and research toestablish a business model that will enable the continuous
operation of mobility services with automated driving.

2

Definition of target areas for practicalapplication, vehicles to be used, andrequirements for social implementation

2.1. Areas selected for the tests
In this study, four sites for social implementation using
electromagnetic guide wires and one site from the long-term
FOTs sites as an example of a magnetic marker were selected
for study from among the sites with FOTs conducted in
FY2017 and later, based on the selection policy described
below. (Fig.1, Table 1)[Social implementation]• In implementing this project, a highly feasible business plancan be developed to create a sustainable business model.
• In the region, there are entities that are expected to be thefuture providers of mobility services and can form apartnership as one of the central implementers of this project.
• Even with the current level of automated driving technology,
the driving environment is one in which social implementationof automated driving mobility services is considered possible.
We can work with local governments and other•
organizations to implement this project.

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1) Establishing the Environment for the Deployment of Transportation Services Relying on Automated Driving

Kamikoani-mura,
Kita Akita-gun, Akita-ken(Michi-no-Eki "Kamikoani")
Implementation start date: Started November 30, 2019
Vehicle used: cart type

Iinan-cho, Iishi-gun, Shimane-ken(Michi-no-Eki "Akagi Kogen")

Implementation period: Started October 4, 2021
Vehicle used: cart type

Takahata-cho, Yamagata-ken(around Takahata Town Hall)
Period: October 11 - November 12, 2021
Vehicle used: Cart type (magnetic marker)

Miyama-shi, Fukuoka-ken(Michi-no-Eki "Miyama")
Implementation period: Started July 19, 2021
Vehicle used: Cart type

Higashiomi-shi, Shiga-ken(Michi-no-Eki "Okueigenji Keiryu no Sato")
Implementation period: Started April 23, 2021
Vehicle used: Cart type

: Long-term FOTs sites: Verification of social
implementation (including planned)

Fig.1: Locations where FOTs were conducted
Table 1: Status of implementation of FOT locations
Features of long-term experiments/social
Operating
implementation tests
entity・Development of organizational structures with・K
 amikoani
management by local NPOs, Michi-no-Eki
Mobility(roadside stations), and local companies(1) Kamikoani ・Support for procedures for transfer of infrastructure
Service
Association
facilities to municipalities, etc.
(NPO)・Securing users by providing services in each of thefour seasons・Study of modes of operation that meet the needs of
local residents, tourists, cargo transport, etc.
(2) Okueigenji
・Establishment of a management structure that
・Higashiomi
Keiryu no
involves various stakeholders within the community
City
Sato
・Study of measures to improve profitability throughappropriate operational settings
・Building a sustainable management structure withexisting organizations and local volunteers
・Securing of stable income and consideration of(3) A kagi Kogen
・Iinan Town
services that are easy for residents to use
・Verification of service models to meet diverse
mobility demands
・Transportation service to and from service locations
and a service to meet and greet senior citizens
・Home delivery service to assist people with shopping(4) Miyama
difficulties and shopping behavior under COVID-19 ・Miyama City
pandemic・Study of an energy management system utilizing
power generation at a biomass center
・Securing means of transportation to support daily o・Noutings for the elderly
verification of・Development of mobility services linked to existingthe(5) Takahatatransportation that promote tourism and theoperational
economystructure will・Building mechanisms for mobility and service
be conducted.
linkages to promote local industry and economy
Test site[Long-term FOTs]• The project should introduce new technologies that willlead to the solution of social implementation issues
according to the region.
•
Although not a prerequisite, there are places where
automated driving FOTs have been conducted in the pastand data from these tests can be utilized.

Fig.2 Vehicles used

2.2. Vehicles used
In this project, we used a cart-type vehicle that travels
along a predetermined route (equivalent to Level 2 automateddriving) by sensing the magnetic force from electromagnetic
guide wires buried in the road surface. (Fig.2) In addition, atthe long-term FOTs site (Michi-no-Eki "Takahata"), a vehicle
that uses a magnetic marker to identify its own location wasused instead of the electromagnetic guide wire method toevaluate the feasibility of driving.
In conducting the FOTs, we are making necessary
improvements in cooperation with the vehicle manufacturer,
such as measures for user comfort, including measures toprevent cold and heat stroke, and the addition of functions
that contribute to management and operation.
2.3. Definition of requirements for social implementation
In order to contribute to the resolution of issues and theidentification of measures for the practical and permanent
introduction of automated driving mobility services, we are
studying the requirements that should be defined for the
FOTs environment. Specifically, the items required for the
introduction of mobility services with automated driving inrural areas are categorized into "items required for permanent
installation" and "items related to securing driving space for
automated vehicles." In addition, in order to comprehensively
verify the categorized issues, we have clarified the issues to beverified in FOTs in each region. The results of defining therequirements for social implementation are shown in Tables
2 and 3.

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Table 2: Items required in permanent automated driving mobility services
Verification items

Table 3 Items related to securing driving space for automated vehicles

Specific verification details

Verification items

1. Improve the overall environment for public transportation in
the village by reviewing its secondary transportation system(1) E stablish routes,
2. Transit support based on shared roles with key transit systems
diagrams, transit(community bus)
methods, etc. based on
3. Consider adding shortcut routes to improve convenience andthe needs of users, suchusage promotion
as cooperation with
4. Mobility services linked to community buses, mobility support
public transportationin disaster areas
and other vehicles.
5. Coordination with existing JR and other means oftransportation, dispersion of demand for existing transportation(2) D iversified fee
collection methods

1. Verification of the possibility of diversifying revenues such as
publicity income by setting up a monthly or child fee, small
volume freight transportation, etc.
2. Increase revenues through setting appropriate freight rates andrevenues from freight transportation, etc.
3. Investigate introduction of cashless services and linkage withlocal currencies(3) E xamine and verify thestable operation of amanagement system• Simplify reservation and operation methods and establish an
and a system for
operation manual in cooperation with the architecture projectmonitoring operation
status and making
reservations, etc.
1. Investigation of an operation plan with future operators, and theuse of the system for self-paid use, etc.
2. Reduce operating costs with service every other day, taking into
account the season, etc., and by using a reservation system, etc.
(4) S tudies of sustainable
3. Operate about 4 days a week, focusing on holidays whenbusiness feasibility
tourism demand is expected to be high
4. Add shortcut routes to promote convenience and usage
5. Home delivery service to residents along the route(5) C ompliance with
• Establish manuals for guide wires and vehicle maintenance, andvarious laws andprovide education and training to local companies, etc.
regulations

3

Status of efforts by region

3.1. Social implementation of automated driving mobility
services at Michi-no-Eki "Kamikoani"(1) Overview of the Project
Michi-no-Eki "Kamikoani" has been implementing the(2) M easures to ensure
traffic safety in areas
where non-cooperated
automated driving isdifficult

1. I nstallation of road markings, signboards and vehicle alerting
signboard using vehicle-to-infrastructure cooperation system
2. C
 onsideration of a separate structure and dedicated space forpedestrians and bicycles
3. Consideration of installing a waiting area for passing and overtaking
4. S tudy on the installation of measures to guide ordinary cars to
bypass and to control the speed(3) A ppropriate division ofroles between car androad for implementation• B ased on the results of (2) above, we are planning to compile a planfor the road environment, roadside environment, etc.

 enetration of services to diverse generations in cooperation with
1. P
educational institutions (fostering understanding and awareness of(4) Effective communication
automated driving services to multiple generations throughmethods (HMI) to
educational programs for children)
harmonize with
2. C
 onsideration of providing information (sound and light) on alerts
surrounding traffic
from the car through collaboration with SIP-adus (HMI Team), etc.
 valuation of the driving(5) E
environment

1. S tudy of operational conditions based on regional characteristics,
season, climate, etc.
2. S tudy on the impact of laying guide wires, RFID, etc. on pavement

service socially since November 2019 to revitalize local
communities and provide mobility support mainly for theelderly through automated driving. The service operates on aroute that connects the Michi-no-Eki, medical clinic, andother locations with three villages, using Michi-no-Eki"Kamikoani" as a base. (Fig.3) (As of July 2022, the service has
been in operation without accident for more than two years.
(2) Status of other business collaborations
In anticipation of the transfer of automated driving mobility
services to local communities, the possibility of collaboration
with tourism businesses and new collaborative projects arebeing studied to improve business continuity when the service
is transferred to a local operator. In addition, based on local
transportation needs, the project is transporting foodstuffs from
the Michi-no-Eki to the meal service center and collaboratingwith convenience stores along the operation route. (Fig.4)

Dogawa District
Dedic
not a ated secti
lloweod to e n (tempo
Assembly hallcomm nter w rary) w
here
unity ith the
ge
c
Appr
ox. 1.0ooperatio neral traffic
km o n of the loca is
ne w
ay
l

Kosawada District
Kosawada
Residence
Kosawada
Community Center
Kosawada Higashi
Kosawada
Post Office(1) M easures to secure
driving space using
vehicle-toinfrastructure
cooperation technology,etc.

Specific verification details
1. S ecuring permanent dedicated space in farm road sections(consideration of operation by making the space free of people, etc.)
2. C
 heck and study solutions for car and infrastructure issues such as
magnetic marker control, etc.
3. S eparation structure considering the speed difference from thenational highway, installation study of entrance/exit breakers

Dogawa Village

ClinicSenior Citizens Welfare Center(Council of Social Welfare)Lifelong Learning Center

Village officeMichi-no-Eki"Kamikoani"
Lodging andexchange center(Koanity)

Fukudate District

Fukudate Yamane

Familiarization through signage

Automated driving routes
Kosawada-Dogawa Route(5 km round trip, 43 min.)
Kosawada-Fukudate Route(4 km round trip, 35 min.)
Kosawada Loop Route(1.9 km round trip, 20 min.)Stop

Exchange Center

Fig.3: Operational route at Michi-no-Eki "Kamikoani" (Map source: NTT Spatial Information)

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1) Establishing the Environment for the Deployment of Transportation Services Relying on Automated Driving

Eigenji Taxi, a transportation company, as the operator andmanager, respectively, under the "Private Paid Passenger
Transport Service with the Cooperation of Transportation
Companies" system.
▲ Receipt of foodstuffs at
Michi-no-Eki▲ Automated vehicle arrives at a convenience store(2) Status of other business collaborations
In anticipation of the transfer of automated driving
mobility services to the region, and in order to improve thecontinuity of the business, measures are being implemented
to promote use, such as publicizing model courses incooperation with restaurants along the route, transporting
shipments to morning markets (mountain village markets)held at Michi-no-Eki, and supporting transportation forresidents to COVID-19 vaccination sites (clinics). (Fig.6)▲ Delivery of products
purchased at Michi-no-Eki▲ Delivery of foodstuffs at the foodservice center▲ Coming home with items purchased at a convenience store

Fig.4: Examples of initiatives linked to automated driving services(3) Efforts to improve social acceptability
The project is implementing both programs for residents
along the route and for tourists. Specifically, the project issoliciting nicknames for automated vehicles, holding
agricultural experience tours in cooperation with residents
along the route, and holding a photo exhibition of automatedvehicles.

(3) Efforts to improve public acceptance
The project is implementing initiatives targeting both
local residents and tourists. Specifically, the project isplanning automated driving tours in collaboration withrestaurants along the automated driving route, collaboratingwith traveling clinics for COVID-19 vaccinations, andcollaborating with a salon held at a Michi-no-Eki.
3.3. S ocial implementation of automated driving mobility services

3.2. S ocial implem entation of automated driving mobility
services at Michi-no-Eki "Okueigenji Keiryu no Sato"at the Yamakawa Branch Office of the Miyama City Hall

(1) Overview of the Project
Michi-no-Eki "Okueigenji Keiryu no Sato" has been
implementing the service socially since April 2021, with theobjectives of (1) regional revitalization and mobility support
mainly for the elderly via automated driving, and (2) service
utilization by tourists (visitors to the Michi-no-Eki, mountainclimbers, and campers). The service is based at Michi-no-Eki
Okueigenji Keiryu no Sato," and operates over a distance of 4.4
km (approximately 30 minutes) between the Michi-no-Eki
and the entrance to Choshigaguchi. (Fig.5) The automateddriving mobility service is operated by Higashiomi City and(1) Overview of the Project
The Yamakawa Branch Office of the Miyama City Hall has
been implementing the service socially since July 2021 withthe objectives of (1) creating a base for people to gather(LeFranc, etc.) and revitalizing human flow through atransportation service, and (2) building a safe, secure, andsustainable regional public transportation system in the future.
The service is based at the Yamakawa Branch Office of the
Miyama City Office and the Biomass Center LeFranc, andoperates on a route connecting the A-Coop Yamakawa store
and the Biomass Center LeFranc. (Fig.7) This location is▼ Warning signs

Michi-no-Eki▼ Signage on the route"Okueigenji Keiryu no Sato"

A Michi-no-Eki in a reused junior highschool building. It is a base forsupporting the daily lives of local
residents, and includes a city hallsub-branch office and a medical clinic.

Okueigenji Keiryu no Sato

Cross the bridge just outside
the Michi-no-Eki and you
will see a stop on your left.

Eigenji Campsite▼ Warning signs ▼ Road markings

In front of Hie Shrine

Approx. 2 min. walk
Kiwadakami
Kiwada Campground Resting Garden

Log village Miyama Campsite
Seiryu no Sato Auto Campground
Matsubara Auto CampgroundYuzurioguchi

LegendYuzurio

Automated vehicle stops
Campground
Driving route (about 2.2 km one way)

GSI Maps

Choshigakuchi Entrance
Choshigakuchi trailhead

Ikeda CampgroundRiverside Glamping Nuts

Fig.5: Operational route at Michi-no-Eki "Okueigenji Keiryu no Sato" (Map source: NTT Spatial Information)

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1) Establishing the Environment for the Deployment of Transportation Services Relying on Automated Driving○ Transportation support for events such as local meetings
・Meetings at salons at the Michi-no-Eki and meeting places in the villages along the route (Yuzurio, Kiwada)○ Transportation support for shopping at the Michi-no-Eki and using the clinic
・Promotion of shopping at the Michi-no-eEki and use of the on-site clinic (first and third Wednesdays)○ Transportation support for visiting friends' houses and facilities on the route・Short trips between friends’ houses and visiting facilities along the route (e.g., fish farms, shrines, etc.)○Holding meetings (salons) at community
associations and Michi-no-Eki
・Planning to start around July when vaccinationis underway (at meeting places in each village
and roadside stations), although it has beensuspended due to the COVID-19 pandemic○Promoting movement of Michi-no-Eki
and on-site clinics
・Cooperation with shopping use at Michi-no-Eki(Consider discount for purchase of a certain amount, etc.)
・Promote use through support for
vaccinations at clinics, etc.

○Promote transportation to roadsidefacilities, friends' homes, etc.
・Travel to friends' homes and roadsidefacilities using this service as anopportunity for local residents to get out
and about (support for short trips)

Salon for residents during long-term FOTs (held at a Michi-no-Eki)Use of on-site clinics (at time of vaccination)

Visits to fish farms by local residents, etc.

Fig.6: Status of other business collaboration efforts

operated under the "Private Paid Passenger Transport Service
with the Cooperation of Transportation Companies" system,with Miyama City acting as the operator and the transportation
operator (Setaka Taxi, which is commissioned to operate thecommunity bus) acting as the operator of the service.

In front of A-Co-op Yamakawa store
City hall Yamakawa Branch
Yamakawa Genkikan(2) Status of other business collaborations
At this location, a system called "Mobisuke" has been
introduced to comprehensively support automated driving
mobility services by performing various analyses such as farecollection, operation management, and boarding/exiting data.
Miyama City's automated driving mobility services areoperated as one of the community bus services, and since
there are transfers to and from the community buses, theentire community needs to have a bird's eye view of theoperation management. Therefore, "Mobisuke" was introduced
to the community bus service, and a system was constructedto check the operation status on a map. An overview of theoperation management system is shown in Fig.8.

Yamakawa Civic
Center

OubukanElementary
SchoolHaramachi
443

Matego(3) Efforts to improve public acceptanceIn order to operate automated driving services
sustainably, it is important to create momentum for theacceptance of automated driving in the community as awhole and to improve the public acceptance of automateddriving among local residents. In order to improve social
acceptance, it is necessary to make automatic driving known
not only to the elderly in the community but also to thecommunity as a whole. Therefore, as a program to promote

User
information

Reservation
information

Information on
getting on andoffService routeInbound (to A-Co-op Yamakawa)

Heikezakura
entrance

Outbound (to Lefranc)
Stop (proposed)

Lefranc

Community bus stop

Fig.7: Operational route at the Yamakawa branch office of the Miyama City
Hall (Map source: NTT Spatial Information)

Operation
information

Ticketinformation

Vehiclelocation
information

Real-time
video
information

Operation management system
User reservation system
Onboard device system
・Provide information to crew members ・Internet reservation
・Manage boarding and alighting ・Operation informationof passengers/fare payment
・Obtain the current location of the vehicle

Operation management system Vehicle monitoring system
・Management of boarding and alighting ・Voice interaction with crew
information and reservation information ・Camera monitoring inside and・Management of delay time and operation status outside the vehicle

Front camera
Interior
cameraFig.8: Overview of the operation management system

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1) Establishing the Environment for the Deployment of Transportation Services Relying on Automated Driving

Transport" system, with Iinan Town acting as the operator of
automated driving mobility service.

understanding of automated driving mobility services, we
held an on-site lecture on the mechanism of automateddriving for fifth graders on June 30, 2021, and for sixth
graders on November 16, 2021, at Oubukan Elementary
School along the route. (Fig.9)(2) Status of other business collaborations
For publicity and promotion of automated driving
services, the local community actively conducts its own
publicity in cooperation with Michi-no-Eki and tourist
associations. The means of publicity include posters, flyers,banners, social media, and handwritten publications, etc.
(Fig.11) In order to avoid one-way information transmission,
the company is working on publicity that takes into
consideration the interests and concerns of everyone fromchildren to the elderly by creating a coloring corner and aboard game with the route as a motif.
(3) Efforts to improve public acceptanceIn order to improve public acceptance of automateddriving mobility services among local residents, the project
is fostering affection for the vehicle by publicly soliciting
nicknames, and is also promoting user guidance, trial rides,and even registration of users at salons where elderly people
gather to facilitate their use when shopping and participatingin community activities.

ClassesFig.9: Onsite lecture

3.4. S ocial implementation of automated driving mobility
services at Michi-no-Eki "Akagi Kogen"(1) Overview of the Project
Michi-no-Eki "Akagi Kogen" has been implementing theservice socially since October 2021 with the following
objectives: (1) to build a sustainable management systemcentered in Iinan Town with existing organizations and local
volunteers, (2) to secure stable income such as commuter
passes, and to brush up the service (operation format) for easy
use by residents. The service operates along the Akana-jukuroute, which is based at Michi-no-Eki "Akagi Kogen," andcircles the Michi-no-Eki, Iinan Town Hall, and other locations.
(Fig.10) In addition, a shortcut (branch) is provided in thecenter of the route, and three routes are used: the northernroute, the southern route, and the all-around route. Thislocation is operated under the "Private Paid Passenger

Fig.11: Diverse public relations activities

Akana-jyuku route approx. 2.7 km/lap
Akana Elementary
SchoolSettlement (Akana area)
Inoue Clinic

Manpukutei

Shimoichi
KaikanTsuda Store
Akana Nursery
SchoolStop (9) is the nearest
Setoyama Castle Trailhead (information)
Nakaichikami Neighborhood
Association
Bank

BeautyparlorPharmacy

Yoshikawa
Yoshimori-do
Ikoi no Go
Kinukake

Akana Sake
Brewery

Fukushima residence
Post
officeMoebinoFukushima Residence (Community Space)

A Co-op[Shimane Prefecture]
Michi-no-Eki "Akagi Kogen"Iinan-cho, Shimane-ken

Akana Station

Iinan-cho

Akana Sightseeing
Peony Garden

Akagi Kogen

Hiroshima
Prefecture

Iinan Town Hall

Bus terminal "Akana Station"

A Co-op Akana Store

LegendMichi-no-Eki "Akagi Kogen" Direct produce market "Botan no Sato"

Akana
Community
CenterAkana-jyuku route
Stop
Main facilities, etc.

Fig.10: Route operated at Michi-no-Eki "Akagi Kogen" (Map source: NTT InfraNet)

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1) Establishing the Environment for the Deployment of Transportation Services Relying on Automated Driving

vehicle with a magnetic marker system and analyzed theoccurrence of manual intervention. (Fig.14) The number ofoccurrences of switching to manual operation and pauses due
to manual operation of the brake and steering wheel during
automated driving was 137 (0.57 times per trip, 0.76 times/km
over a total automated driving distance of 180 km) for a totalof 240 trip. Of these, 18 were due to stoppages caused by theautomated driving system (GPS and magnetic sensors).
In the aisles of facilities and parking lots, manualinterventions such as stopping to ensure safety and overtaking
off the path occurred due to the erratic behavior of othervehicles and pedestrians. On the other hand, no emergency
intervention to avoid collisions occurred.

3.5. Long-term automated driving mobility service FOTs
at Michi-no-Eki "Takahata"(1) Overview of the ProjectLong-term FOTs were conducted at Michi-no-Eki"Takahata" for 33 days from October 11 to November 12,
2021, to (1) confirm adaptability and operability as a meansof public transportation, (2) confirm the effectiveness andacceptability of short-distance mobility services and identify
issues in building a business model, and (3) verify thetechnology behind automated driving support. The route
started and ended at the Takahata Public Hospital, with the
Takahata Town Hall as the base. (Fig.12)
At this location, FOTs were conducted using an automatedvehicle with a magnetic marker system instead of theconventional electromagnetic guide wire system. The vehicle's
position is determined by repeated estimation and correcting
the estimated value using the magnetic marker. In addition,
the vehicle's travel route is set in advance, and the target
steering angle and speed are determined according to thevehicle's travel position by associating the vehicle's speed andstop commands. Fig.13 shows an image of the vehicle's own
vehicle location determination method and traveling method.

(3) Efforts to improve public acceptanceOn October 30, 2021 (Saturday), a seminar on automateddriving and a trial ride were held along the route of the
Vehicle sensors<2D
diagram>

Vehicles<Crosssectional view>
Magnetic markers

Detects
magnetic forcesabove
a certain
Permanent magnet to beinstalled on the road intensity at a
certainheightsurface
Magnetic
field▲ Image of magnetic marker reading(2) Technical verification of automated driving support

Fig.13: Image of magnetic marker reading

At this location, we conducted FOTs using an automated[South Route] Length approx. 1.4 km, 7 stops Takahata Hospital
Yamazawa, Ainomori Pharmacy, etc. Takahata Hospital[North Route] Length approx. 1.8 km, 5 stops Takahata Hospital
Takahata Town Hall, Super Kimura Takahata HospitalN

Route length (1 lap) Approx. 1.4 km

Town
library

Genkikan

reet

Town
library
York-Benimaru

hata
ad No. 7 Taka
Prefectural Route
Kawanishi Ro
Route length (round trip) Approx. 1.8 km
Created using
the GSI Maps

Approx. 1.0 km

Takahata Town
Hall

Takahata
Hospital

To Kawanishi

Approx. 0.4 km

Manual driving area

Genkikan

Hoshi
Pharmacy

Automated driving area

Approx. 1.1 km

Tsuruha Drug

Manual driving area

Approx. 0.7 km

Created using the GSI Maps

National
Route 399

Takahata
Town Hall

Automated driving area
Town Hall
St

reet

National
Route 399

Town Hall
St

Supermarket
KimuraTakahata
Hospital
Yamazawa DrugTo
Yamazawa
Ainomori
Pharmacy

Fig.12 Experiment route at Michi-no-Eki "Takahata" (Map source: NTT Spatial Information)N

Northern route

Length (round trip)
Automated driving section
Manual driving section

Approx. 1.8 km

Supermarket
KimuraApprox. 1.1 km
Approx. 0.7 km

NSouthern route

Length (1 lap)
Automated driving section
Manual driving section

Avoiding pedestrians crossing the street
Avoiding parking cars
Yielding the right ofway to oncoming traffic

Avoiding stopped cars
Overtaking avoidance

Takahata Town Hall

Library

Avoiding pedestrians crossing the street
Avoiding stopped cars
Avoidance of cargo-handling vehicles
Yielding the right of way to an oncoming car at
an intersection
Yielding the right of way for cars entering andexiting the parking lot
Overtaking avoidance
Genkikan Takahata Hospital

Created using
the GSI Maps

Approx. 1.4 km Takahata Town Hall
Approx. 0.4 km
Avoiding pedestrians
Approx. 1.0 km
crossing the street
Avoiding oncoming cars
when turning right
Avoiding parked cars
Avoiding vehicles enteringfrom the roadside
Overtaking avoidance
Library
Takahata
Hospital
Genkikan

York-Benimaru

TsuruhaDrug
Created using
the GSI Maps

Hoshi Pharmacy
Main intervention
pointsYamazawa
Aino no Mori Pharmacy

Main intervention
pointsFig.14: Manual intervention locations and main reasons (Map source: NTT InfraNet)

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1) Establishing the Environment for the Deployment of Transportation Services Relying on Automated Driving

automated driving FOTs with 78 elementary and junior highschool students and their guardians participating. (Fig.15)
The results of the questionnaire confirmed positive opinions
such as "I felt safe at low speeds," "I want to tell my family
about it," and "It would be good if it could be used at school
and in other places around me."▲ Information leaflet▲ Venue and test ride course▲ Jidoroku: Takahata version▲ Status of trial ride

▲ Exhibition description▲ Automated driving board game[Contacts]
Highway Industry Development Organization, ITS and New Road Creation
Division, Plaza Edogawabashi Bldg. 2F, 1-23-6 Sekiguchi, Bunkyo-ku, Tokyo
112-0014, 03-5843-2917, Contact: Yoshiyuki KATO

Fig.15: Automated driving seminar and trial ride4

Conclusion

In this project, we verified the practical application of theautomated driving mobility services at four social
implementation sites and one long-term FOTs site, with theaim of implementing the service, which had already been
started in limited areas, in multiple areas throughout Japanon a permanent basis.
The first social implementation of automated driving
mobility services in Japan began at the Michi-no-Eki"Kamikoani" in November 2019, followed by Michi-no-Eki"Okueigenji Keiryu no Sato" in April 2021, the Yamakawa
Branch Office of the Miyama City Office in July of the same
year, and Michi-no-Eki "Akagi Kogen" in October of thesame year. The above four areas use electromagnetic guidewire automated vehicles, but at the long-term FOTs site(Michi-no-Eki "Takahata"), we conducted FOTs using avehicle that uses a magnetic marker to locate its own position.
In social implementation at four locations nationwide
and the long-term FOTs at one location, various efforts arebeing made to improve the sustainability of the project,including collaboration with other projects and increasing
the acceptability of the project among local residents. In
addition, the long-term FOTs confirmed the feasibility of a
new type of automated vehicle. In the future, it will benecessary to organize the items to be handed over to the local
operating entities in order to continue providing automateddriving mobility services after the project is completed. In
addition, we will continue to work on horizontal development
utilizing the know-how cultivated through this project inorder to expand the number of cases of automated driving
mobility services.

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4

A Society with Automated Driving(2) Public Acceptance of Automated Driving

Initiatives for Fostering Public
Acceptance (Overview)
Yuichi Araki, Hiroaki Kimura (Cabinet Office)1

Background and Overall Strategy

In order for the public at large to accept the new
innovation of automated driving and use it with confidence,
it is necessary to promote a correct understanding of
automated driving, present quantified benefits, raiseawareness, and develop supporting systems such as
insurance. The SIP-adus (Cross-ministerial Strategic
Innovation Promotion Program (SIP) Automated Drivingfor Universal Ser vices) has focused mainly on thedissemination of information and the quantification ofeffects, while also promoting initiatives aimed at a wide
range of targets based on a long-term plan.
In terms of information dissemination, test-ride eventswere held in conjunction with the FOTs (Field Operational
Tests) in the Tokyo waterfront area, and online events were
held in conjunction with FOTs of an automated driving
mobility services in rural areas. Furthermore, whileclarifying the target audience communication has beenstrengthened through interactive information dissemination.
On the other hand, we have also worked on quantifying
the effects of automated driving on society in order toprovide a more complete explanation of the impact of thistechnology. We have developed a method for estimating thesocial and economic impact of the spread of automateddriving, and also developed a method for measuring theeffect of activities to foster public acceptance itself.

2

Information dissemination via theInternet, social media etc.

We have had a Website since the first phase of SIP-adus,
which has disseminated information primarily on research
results and the SIP-adus Workshop, as international
conference. It was pointed out, however, that the website isfor specialists and somewhat difficult for the general public
to understand.
From the perspective of fostering public acceptance, we
therefore opened the SIP-café, a community for considering
an automated driving society (Fig.1), in October 2019, in
conjunction with the start of the FOTs in the Tokyo
waterfront area and to disseminate information andpromote understanding among the general public andothers. Under the responsible editorial supervision of Kazuo
Shimizu, an inter nat iona l automot ive j our na list,
information on automated driving was actively andFig.1: SIP-café internet website top page (1)162

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4 A Society with Automated Driving(2) Public Acceptance of Automated Driving
Initiatives for Fostering Public Acceptance (Overview)continuously distributed through content relating toautomated driving, focused on the medium of video but
also including other easy-to-understand information on
automated driving for the general public in the form ofarticles and editorials by experts who are well versed in
automated driving. Since its establishment, the website has
consistently published an average of more than 10 articles
per month on automated driving related efforts by related
ministries and agencies, including the SIP-adus, as well as
articles by columnists and other experts on automateddriving, and information on efforts for automated driving
technology development by private companies.
We have also created other video content. This includes
videos on the future vision for society through therealization of automated driving and the details of thetesting conducted by the participants in the FOTs in theTokyo waterfront area. We have also disseminatedinformation in conjunction with each of the SIP-adus
projects, including other information on the FOTs in theTokyo waterfront area and rural areas, a portal site for
geographic data architecture (MD Communet®), an app
contest (KYOTO Raku Mobi Contest), and virtual safetyvalidation technology (DIVP®).
Interactive communication has also been promoted bylinking with social media such as Twitter and Facebook.
On the other hand, for the website for specialists, we
tried to disseminate the research results of the first andsecond phases of SIP-adus in English in a timely manner,with an awareness toward international cooperation. (Fig.2)

3Media /Public Events

3.1. Events for media & automated driving professionals
The SIP-adus has conducted various events for the
media and automated driving professionals, both in real life
and online, as shown in Table 1.

Table 1 : List of Events
Date
October 2020

Fig.2: SIP-adus website top page (Japanese and English) (2)Event Name
Webinar "Automated Driving Related Laws"

November 2020

Status Report Meeting, SIP-adus Workshop 2020

March 2021

Webinar "Automated Driving Level 3 Law Revision and TechnicalStandards"
Interim Results Presentation Event

April 2021

Test-ride event

June 2021

Webinar "Liability Issues in Automated Driving"

October 2021

Test-ride event

December 2021

Webinar "HMI and Driver Overconfidence"

January 2022

Webinar "Software Updates and Cybersecurity"

March 2022

Webinar "Cooperative Areas of Mobility Data Utilization and Data
Provision"

From May 2022

Automated Driving Live News (10 times in total)

September 2022

Test-ride event

March 2023

Final Results Presentation Event

In November 2020, our results were reported at theTokyo International Forum with the participation of policy
implementers in order to promote collaboration among thevarious projects of the SIP-adus. The session was held aspart of the SIP-adus Workshop 2020, and was broadcast
on l i ne w it h E ng l i sh m ate r i a l s and s i mu lt ane ou sinterpretation to more than 1,000 people in Japan andabroad.
In addition, a total of six webinars were held from
October 2020 on the following themes: "Automated Driving

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Initiatives for Fostering Public Acceptance (Overview)

Related Laws" in October 2020; "Automated Driving Level 3
Law Revision and Technical Standards" in March 2021;"Liability Issues in Automated Driving" in June 2021; "HMIand Driver Overconfidence" in December 2021; "Software
Updates and Cybersecurity" in Januar y 2022; and"Cooperative Areas of Mobility Data Utilization and Data
Provision" in March 2022.
In conjunction with the Tokyo 2020 Olympic andParalympic Games, a test-ride event for the general public
and an exhibition of the results of FOTs were scheduled tobe held in the Tokyo waterfront area in cooperation with theJapan Automobile Manufacturers Association, but thesewere cancelled due to the spread of COVID-19. As areplacement, the Automated Driving Showcase to Change
the Future was held in March 2021 in collaboration with the
Ministry of Economy, Trade and Industry and the Ministryof Land, Infrastructure, Transport and Tourism, combining
a symposium-style event and the presentation of interim
results in a hybrid format of a real and virtual venue.
This interim results presentation event was intended
mainly for operators and engineers involved in automateddriving services, but sought to provide the interim results of
the SIP-adus in a form that was also understandable to as
many people as possible, including the general public. The
exhibition featured exhibits of the results of technologicaldevelopment related to the building and distribution of
traffic environment information, including SPaT (Signal
Phase and Timing) information, merging lane assistanceinformation, and road traffic information using vehicle
probes. It also exhibited the vehicles used in the FOTs in theTokyo waterfront area and rural areas, actual equipment andvideo clips related to the construction of a geographic data
distribution portal, a safety evaluation environment in a
virtual environment, and other related topics. In addition,
online guided tours and virtual exhibits using a 3D walkthrough were also used, creating an event that responded tothe "new normal" of living with COVID-19. [See Section 4(2) 3) for more details]
Furthermore, in April 2021, a media test-ride event washeld in the Tokyo waterfront city area, staged with thecooperation of approximately twenty vehicles provided bynine companies who participated in the FOTs in the Tokyo
waterfront area. The event was well-received by the media,
who commented that they were able to deepen theirunderstanding of the technology and concept due to being
able to test-ride multiple automated vehicles at the sametime. With that in mind, we held another test-ride event in
the Tokyo waterfront city area in October 2021, again withthe cooperation of approximately twenty vehicles provided
by nine companies who participated in the FOTs in theTokyo waterfront area, while expanding the range of thetarget media. The introduction of the vehicles on YouTubeby a female journalist and tagging the content with "Work &Life" helped to expand dissemination of information on
automated driving to a diverse range of people, including
not only car enthusiasts but also people of child-rearing age
and others.
In September 2022, a test-ride event was held again in
the Tokyo waterfront city area under the theme of "Japanleads the world in traffic safety for the future Cohesive
Society that respects diversity." It was targeted at media,
Generation Z students, and local citizens, with thecooperation of vehicles provided by the participants in the
FOTs in the Tokyo waterfront area. The final results of thefive years of the SIP-adus will be presented to the generalpublic during the Final results presentation event in March
2023.
3.2. Citizen dialogue
Table 2 : List of Events
Date

Event Name

December 2018

Citizen Dialogue: Shodoshima-cho, Shodo-gun, Kagawa

August 2019

Citizen Dialogue: Ina City, Nagano

January 2021

Citizen Dialogue: Maebashi City, Gunma

March 2021

Regional Automated Driving Summit

June 2021

Citizen Dialogue: Yokohama City, Kanagawa

April 2022

Citizen Dialogue: Tochigi

From May 2022

Automated Driving Live News (total of 10 times)

Symposiums and citizen dialogues have been held asinteractive events with citizens, local government officials,
and related businesses, as shown in Table 2.
In particular, during the second phase of SIP-adus one
primary theme has been to solve local social issues, and sothese dialogues were held not only in Tokyo but also in theregional areas where the aim is to solve social issues throughautomated driving.
"A Plan for the Future of Japan: Shodoshima, Mobility
Services Tailored to Each Region" was held in December
2018 in Shodoshima-cho, Shodo-gun, Kagawa Prefecture.
"The Role of Automated Driving in Supporting the Future of
People, Towns, and Lifestyles: Toward the Realization of a
Town Where People Want to Live Forever" was held in
August 2019 in Ina City, Nagano Prefecture, where FOTs
had also been conducted by the SIP-adus. "Ideal Cities andAutomated Driving" was held in January 2021 in Maebashi
City, Gunma Prefecture. "Traffic and Mobility Required in
Urban Transportation: Toward Social Implementation ofAutomated Driving" was held in June 2021, in Yokohama
City, Kanagawa Prefecture. "Toward the Construction of
Sustainable Mobility Services that Respond to Local

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Initiatives for Fostering Public Acceptance (Overview)

Conditions: The Challenge of All Tochigi" was held in April
2022, in Tochigi Prefecture.
At the Regional Automated Driving Summit in March
2021, the leaders of Iinan Town, Shimane Prefecture; Eiheiji
Town, Fukui Prefecture; Kamikoani Village, Akita
Prefecture; Chatan Town, Okinawa Prefecture; andHigashiomi City, Shiga Prefecture spoke about their local
governments' initiatives and thoughts on automated driving.
This was followed by a two-part panel discussion on"Solutions to Local Issues" and "Next Generation Public
Transportation Systems and Operators' Efforts," featuring
stakeholders involved in automated driving in local
communities, public transportation operators, andautomated driving venture companies. The format ofconnecting various local stakeholders via the internet wasan event that could only be held online.
In addition, from FY2022, in order to reach a wider
range of people, including the members of Generation Z
who will be the future leaders of the new mobile society,since May we have been released ten episodes of AutomatedDriving Live News, with a mix of real discussion on avariety of topics from the perspective of users and the
younger generation and distributed live via Zoom andYouTube.

4

Quantifying the Effects of AutomatedDriving

We have organized and quantified the impact ofautomated driving in order to provide material for open
discussion of the benefits and potential risks of suchtechnology, based on factors such as the technology leveland penetration of automated driving.
Specifically, economists have compiled quantitativeevaluation methods for the social and economic impact of
reducing traffic accidents and traffic congestion, improved
the accuracy of simulations developed during the first phaseof SIP-adus to estimate the effect of reducing traffic
accidents, and compiled them into a method for calculating
the overall impact. In addition, we held a contest for
students to propose their visions for the future automateddriving society, aiming to disseminate information from theleaders of the automated driving society themselves basedon the results of this research. [See Section 4 (2) 2) for moredetails]
We also studied research as a medical-engineeringcollaboration on the evaluation of the effectiveness ofdriving support technology for people with visual field loss,
and clarified the effectiveness of advanced driving support

systems in reducing accidents involving people with visual
field loss to the same level or less as normal drivers, making
clear the importance of the field of vision in driving. We
held a number of webinars on the relationship between
visual field loss and driving, which is not widely known, andthe role of automated driving and advanced driving
assistance systems in this area; in July 2021, "TheIntersection of Visual Field Loss and Automated Driving";
in September 2021, "Driving Licenses and Visual Field Loss
- Considering Driving and Employment"; and in January2022, "Visual Field Loss and Automated Driving Systems."
We also started to develop a method for measuring theeffectiveness of public acceptance activities themselves, andconducted nationwide questionnaire surveys of 10,000
people in January 2020, January 2021, and January 2022 (incooperation with the Ministry of Economy, Trade andIndustry and the Ministry of Land, Infrastructure, Transport
and Tourism). By continuously conducting the same scalequestionnaire survey every year, we evaluated changes overtime and analyzed the questionnaire results to quantify thecurrent status of public acceptance factors (lifestyle changes,
learning, costs, inherent characteristics/technologicallimitations, response to accidents, etc.). In addition, KPI/KGI indices were developed based on the past questionnaire
surveys. The survey results will be used to evaluate efforts tofoster public acceptance and provide feedback on such
efforts in the future. [See Section 4 (2) 1) for more details]
In addition to this, opinions about buses were collectedfrom passengers such as wheelchair users and people withpoor eyesight, and then compiled and proposed to the Japan
Automobile Manufacturers Association (JAMA). The hopeis to make such vehicles kinder on people with disabilities
or reduced mobility and orientation who have highexpectations for automated driving. [See Section 4 (2) 4) for
more details][References]
(1)	SIP-café, a community for considering an automated driving society,https://sip-cafe.media/, (Ref. 2022.10.19)(2) SIP-adus homepage, https://en.sip-adus.go.jp/, (Ref. 2022.10.19)[Contacts]
Secretariat of Science, Technology and Innovation Policy Cabinet Office,1-6-1 Nagata-cho, Chiyoda-ku, Tokyo 100-8914, 03-6257-1334, Contact:
Yuichi Araki, Hiroaki Kimura

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4

Society with Automated Driving(2) Public Acceptance of Automated Driving

1) Research and Evaluations for Fostering
Public Acceptance
Yukiko Miyaki (DAI-ICHI LIFE RESEARCH INSTITUTE INC.)(Abstract) In the social implementation of automated driving technology, it is essential to foster public acceptance inaddition to the development of the technology and regulatory system. A correct understanding and a flexible andappropriate response by consumers will promote the early and effective use of the technology and ensure the safetyof the new road traffic system. The author, in cooperation with related ministries and agencies, has conductedconsumer awareness surveys on automated driving for several years, following up on the changes in awareness andexploring what information in which areas, and how to provide it, will lead to the fostering of public acceptance.
Based on the focus on ADAS in the interim report, this paper looks closely at automated driving as a passenger
transport service vehicle in local communities, and examines the relationship between the degree of acceptance of"lifestyle change," "learning," "cost," and "inherent characteristics and technological limitations," which are considered
to constitute public acceptance, and awareness within the community. This paper then discusses action evaluation
in fostering public acceptance and presents hints for fostering public acceptance of automated driving and theimportance of a co-creation system.
Keywords: Public acceptance, consumer awareness, community mobility, social impact, visualization of effects

1

Project Execution Flow and Survey
Contents

of driver's license) and securing the means of transportation tomaintain their lifestyles have become social issues. In many
regions, public transportation is being reduced and drivers arein short supply and are aging themselves, making the securingof sustainable means of transportation an urgent issue.

1.1. Background and objectives
In conjunction with the author's participation as an expert
committee member in a METI/MITI project since 2016, theDai-ichi Life Research Institute has conducted questionnaire
surveys on automated driving for the purpose of understanding
consumers. Based on these surveys, time-series data on
consumer behavior and awareness have been collected as a
METI/MITI project from the January 2019 (FY2018) survey
and as a joint survey by the two ministries and the Cabinet
Office SIP from the 2020 (FY2019) survey. Based on this data,
proposals have been made regarding the KPI/KGI evaluation
indices and processes for actions to foster public acceptance asan

SIP-adus(Cross-ministerial

Strategic

Innnovation

Promotion Program (SIP) Automated Driving for Universal
Services).
Various factors are behind the use of automated driving
technology in Japan, but the focal point is the aging society,which is said to be "era of 100-year-life". The baby boomer
generation, which experienced the rapid increase in dependence
on private cars, are now elderly, and whether or not they cancontinue driving from a safety perspective (e.g., the surrendering

In addition, even if public transportation is offered as analternative to people and communities that have long been
dependent on private cars, there may be circumstances where it
is not easy for their lifestyles to change, including it being
difficult to get to a train station or bus stop.
Two possible solutions to these issues using automateddriving technology are "extending driving life expectancy" inthe sense of increasing the safety of travel using private cars,
and the "creation of alternative transportation" that allows
people to continue living in their community without using
their own cars. (Fig.1)

Extension ofdriving life expectancy
A society where people can continue
driving even if their life is 100 years

Creation ofalternative transportation
A society where people can travel freely
and safely even if they do not have accessto a private vehicle

Improvement of driving support
functions in private cars so that thesurrendering of driver's licenses canbe postponed to a certain extent evenif physical functions deteriorate due
to aging = automated driving ofprivately owned vehicles
Introduction of automated vehicles
as public transportation that enables
travel within and between
communities without the use ofprivate vehicles = Automated drivingof service vehicles

Fig.1: Two solutions aimed at mobility in the era of the 100-year-life using
automated driving technology

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
1) Research and Evaluations for Fostering Public AcceptanceAs "SIP 2nd Phase: Automated Driving for Universal Services— Mid-Term Results Report (2018–2020) "focused on the“extending driving life expectancy" part, this paper focuses on
the "creation of alternative transportation" and discusses the"Questionnaire Survey on Vehicles and Automated Driving"that has been conducted four times. The second phase of SIPadus.

(%)
60

The survey was conducted as a Cabinet Office SIP survey(late January to early February 2022), which re-surveyed 20,631

59.4

58.2

55
50
45

40

1.2. Summary of the Fourth Questionnaire Survey

58.8

56.9

48.4
44.0

43.7
43.2

43.6

40.9
39.9

42.7

37.1
35

47.4

49.4

43.5
42.9

45.5
45.1
44.8

42.0

41.0

37.9

30
2019

2020

2021

2022

people as a sample of the men and women aged 18-69 fromI am concerned about means of transportation in my old age in the community I am living inacross Japan who participated in the internet survey by the

I cannot live without a car in my old age in the community I am living in

Ministry of Economy, Trade and Industry and the Ministry ofLand, Infrastructure, Transport and Tourism, and 10,357
responses were received and merged into one survey. In
preparing the questionnaire, in addition to collecting opinionsfrom the National Police Agency, there was collaboration with

It is inconvenient to travel by bus or train in my old age in the community I am living in
I want to continue living in the community I am living in, in my old age
I want to move to a different area if I do not have the means to move freely in my old age
I am thinking about the time when I can no longer use my current means of transportation in the future

Fig.2: Changes in awareness regarding mobility in old age

other contractors such as the University of Tsukuba and theservice vehicles are expected to be one solution to solve theUniversity of Tokyo.

driver shortage and bring mobility to communities, even if
their speed is slow.

2

Recognition of Mobility Issues in theCommunity and Public Acceptance ofAutomated Driving

2.2. Organizing Public AcceptanceIn the area of automated driving, in addition to theperspective of technological safety and the development of laws
and rules, another major challenge to be addressed is how to

2.1. Mobility Issues in the Community

make automated driving accepted and used effectively in society

The local transportation environment and issues play a majorfrom the perspective of public acceptance.

role in the use of automated driving technology as alternative

Regarding the public acceptance of automated driving, thetransportation. Regarding attitudes toward mobility in old age, aauthor created and analyzed composite scores consisting of thelook at changes in survey results since 2019 shows that while thefollowing four categories: (1) Lifestyle changes: acceptance of

number of people who say that they "want to continue living inchanges in lifestyle due to the spread of automated driving, (2)their current area in old age" has increased, the number ofLearning: acceptance of the burden of learning for the spread ofpeople who say that they "are concerned about transportation

automated driving, (3) Cost: acceptance of various cost burdens

means in old age in their current area," that they "cannot livein the spread of automated driving, and (4) Uniqueness/without a private car in their current area," that "moving in oldtechnical limits: acceptance of the characteristics and techniccal

age using buses or trains is inconvenient in their current area,"

limits and risks particular to automated driving. (Fig.3)

and that they "want to move to another area if they do not havethe means to move freely in their old age" has an increasing
trend. In addition, the percentage of respondents who "think
about the time when the means of transportation I currently use
may not be available in the future", which is data that has beenacquired since 2020, is also increasing, indicating that more
people are aware of mobility issues in their old age. (Fig.2)
The hurdles to using public transportation as an alternative
to private cars are not insignificant for people and communities
that are highly dependent on private cars. However, when we
think about the future, we must also assume a gradual shift to(1) Lifestyle Changes(2) Learning

Can the various lifestylechanges due to the spreadof automated driving be
accepted?

Can the burden ofvarious learning involvedin the widespread use of
automated driving be
accepted?

(3) Cost(4) Uniqueness/technical limits

Can the various cost burdens due to the spread of
automated driving be
accepted?

Can the particular characteristics and the technological limitations and risks ofautomated driving be
accepted?

Fig.3: Four elements for public acceptancesuch a lifestyle, and a public consensus is gradually emerging on
this issue. Under these circumstances, automated driving
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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
1) Research and Evaluations for Fostering Public Acceptance2.3. 
Public Acceptance Scores and Community
Awareness: Results of the Survey
Regarding the four acceptance factors, looking at theirrelationship with the characteristics of the community reveals
that there is a relationship between people's community
awareness and the acceptance.

party awareness in which one is involve in wanting to improve20

the community".

15

about their thoughts and ideas about their community, and10

these items are similar to the civic pride mentioned above.

5

ApplicableNot applicable

14.22

15.56

14.75

13.01

12.36

12.88

Cost

ApplicableNot applicable

13.35

12.80

Uniqueness/
technical
limitsLearning

015.07

Lifestylechanges

I am living in for a long time," for affirmative ("applies" and13.56

Uniqueness/
technical
limits(Points)

community I am living in," and "I want to live in the community

Cost

Pride Study Group), it is "a sense of pride based on a kind of

I am living in a better place," "I am aware of the issues in the

12.44

Fig.6: Community awareness and acceptance scores(I am aware of the issues in the community I am living in)

the Tokyo University of Science (representative of the Civic

the community I am living in," "I want to make the community

13.29

Learning

0

to their own community; according to Professor Kaori Ito fromwere compared for the four questions of "I like/am attached to

15.07

5

their city, but it is not merely a feeling of affection or attachment

In this analysis, the mean values of each acceptance score

15.62

10

important. Civic pride refers to the pride that citizens have inIn the third and fourth surveys, respondents were also asked

14.53

Lifestylechanges

In city planning, the concept of "civic pride" is considered(Points)
20
15.18
15

Fig.7: Community awareness and acceptance scores(I want to live in the community I am living in for a long time)"somewhat applies") and negative ("does not apply" and "doesTentatively assuming these items to be civic pride, it can be

not apply at all") responses and it was confirmed that when

said that in communities with high awareness, a relatively highoverall awareness of the community was high then acceptancepercentage of people will accept the changes in their lifestylescores were also high. (Figures 4-7)

that will be brought about by the social implementation of the(Points)
20
15.07
13.94
15

new technologies and mechanisms for automated driving,
accept the learning required for such changes, bear the
15.56

14.47

13.00

12.18

13.31

necessary costs, and accept the inherent characteristics and
12.74

technological limitations of automated driving.
In other words, it can be said that in pioneering the10

implementation of automated driving, it will be effective as a5strategy to introduce it first in areas with high civic pride and to

ApplicableNot applicable
Fig.4: Community awareness and acceptance scores(I like/am attached to the area I live in)

Uniqueness/
technical
limitsCost

Learning

Lifestylechanges0(Points)

20
15

15.15

15.65
13.96

14.44

13.06

12.20

13.35

12.76

10

How to express automated driving

3 technology as a solution

3.1. Arousing People’s Action Through Two Steps ofUnderstanding and AcceptanceIn the social implementation of new mechanisms andtechnologies such as automated driving, in addition to the5understanding of users, consumers, and residents, it is essential
Cost

ApplicableNot applicable

Uniqueness/
technical
limitsLearning

Lifestylechanges0"foster it as a local mechanism".

Fig.5: Community awareness and acceptance scores(I want to make my community a better place)to foster their awareness of "effective and safe use". It is tempting
to take a passive stance when introducing technology, assumingthat the technology will cover all safety issues.
However, as Goal 12 of the SDGs 'responsible consumption
and production' says, consumers of the future are expected to168

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
1) Research and Evaluations for Fostering Public Acceptancecontribute to the sustainability of society by being involved in

However, although there is a tendency to consider the 'How'"responsible consumption" in the form of effective use whileof "how to implement it" first, initially there should be a processsecuring safety. In addition, 100% safe technology is practicallyof first ascertaining the local issues (the 'Why') and then

impossible. There are significant risks even with existing

considering how automated driving technology can be used tovehicles, and users are expected to ensure greater safety in thesolve those issues (the 'What'), including the points of whatform of obeying the rules when using them. It is also necessary

kind of technology will be used to drive what kind of vehicle

to form a similar consensus for the introduction of automatedand in what manner will be used to drive the vehicle. (Fig.9)

driving. Regarding automated driving, which tends to be

In practice, it seems that such a process and approach is

expected to be a completed technology, it is important tobeing taken in communities where social implementation andunderstand its characteristics and limitations, and consider

FOTs (Field Operational Tests) are successful.

what methods of use will compensate for its technologicalshortcomings and issues.
Further, as shown in Fig.8, it is also necessary to take into
account the fact that "understanding" and "acceptance" aredifferent. First, as Phase 1, action is required to increase theamount of information about automated driving for consumers
and to raise their level of awareness and understanding.

What

Phase 1
Actions to increase people's
amount of information,awareness and understanding

However, the level of acceptance does not necessarily increaseas the level of awareness and understanding increases. Somepeople judge "non-acceptance" as a result of an increase in thelevel of understanding of automated driving. Accordingly, what
is required as Phase 2 is a process that seeks a path to solving

Why

Why is automated driving
technology necessary?
<As a solution
to social issues>

What is automated driving
technology?
<As possibility>

Phase 2
Actions thatinspirepeople’ s
How to overcome the issues in social implementation? behavior

How

problems by identifying specific issues from those who are"non-accepting" due to some issues or risks, finds parts that can

Fig.9: The ‘How’ derives from the ‘What’ and the ‘Why’

be complemented not only in terms of technology, but also byconcessions and cooperation among people in terms of theestablishment of usage methods and rules, and stimulating
consumers to take action themselves.
Awareness of automated driving: High

Problem study, countermeasures,
collaboration, resolution
Understandbut not accept

Phase 2
Actions that encourage
people's conduct

3.2. Visualization of the value and effect - social impact assessment
In order to promote understanding and acceptance, whenselecting the solution of "automated driving" as a result ofconsidering the ‘Why’ and the ‘What’, it is necessary to visualize
the value and effect of mobility and automated driving. In
particular, in the formation of a consensus on the investment ofUnderstand andacceptpublic funds, it is essential to consider from the perspective ofwhat degree of investment effect can be expected. From thisnon-acceptanceAcceptancepoint of view, regional public transportation basically operates=

Positive=

Negative

Not accept because of
not understanding

at a loss, and even if it were replaced by automated driving, it
Accept without
understanding Phase 1

Awareness of automated driving: Low

Actions to increase
people's amount of
information, awarenessand understanding

Fig.8: Two phases of understanding and acceptancecannot be expected to be profitable, at least in terms of farerevenue, and is unlikely to become profitable in the short term.
Also, it is more than a theory that if people give up theirprivate cars, they can apply the cost of purchasing andmaintaining them to public transportation. Private cars do not

In this process, it is necessary to understand the backgroundand issues of "why automated driving technology is being usedin our community". In addition, it is necessary to correctly
understand the current status of what can and cannot be donewith automated driving technology. Only when there is anunderstanding that automated driving technology can be a
promising solution to local problems can people face thechallenge of how to implement this technology.

demonstrate their value simply through transportation only;
their value as an object of desire and preference is also great, in
addition to the convenience and psychological security of being
able to drive it at any time.
In this way, while costs should not be taken in a narrow
sense, they are not always measurable quantitatively, so it isnecessary to take the social impact into account when
visualizing them.

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
1) Research and Evaluations for Fostering Public AcceptanceFor example, in addition to the economic benefits ofsense of protecting the sustainability of people's lives byimproved community accessibility, the invigoration of mobility

maintaining and sustaining mobility in the community, and thecan prevent frailty and extend the healthy life expectancy of theperspective of "utilizing" in the sense of creating various

elderly, thereby reducing medical and nursing care costs andspillover effects through the use of mobility. (Table 1)

manpower (mainly for family members). Many people also

It is the consumers themselves who will benefit greatly fromenjoy seeing and photographing mobility. If mobility becomes athe spread of automated driving. In light of this point, it iscommunity icon, it will not only increase the flow of people

thought that increasing the number of opportunities forfrom outside the community, but also improve commitment to

consumers themselves to be proactively involved and consider

the community. Thus, the enhancement of mobility will

how to incorporate and use the technology in their daily lives

improve the quality of life of community residents and citizens.

and how safe and effective social implementation can be

In order to put the effects of the introduction of automatedachieved (the ‘How’), and fostering an awareness of the issues asdriving technology, including the impact assessment, into thebeing their own issues, will lead to the creation of prompt andcontext of fostering public acceptance, it is necessary to visualize

safe mobility, and the realization of a sustainable society.

the effects financially from a multifaceted and long-term
perspective, from both the perspective of "enlivening" in the
Table 1: Ensuring sustainability and visualizing the effects of community mobility
Actions

Viewpoint

Steps for public acceptance - setting

4 KPIs and KGIs

Keywords

Viewpoint of "enlivening"community mobility
Maintaining its existence● Create and maintain means of transportation that fit the community
environment and needs● Development of mobility infrastructure that enables people tocontinue moving after they have surrendered their driver's license or
have lost physical functions due to aging, illness, disability, etc.
● Ensure safety during movement

This process is summarized in the action evaluation sheet in● Sustainability● Maintain daily life

Table 2. The purpose of this project was to establish and evaluate● Safety and security● Acceptance of inconvenience● Fosteringawareness of the use of diverse mobility by a diverse range of people

Viewpoint of "utilizing"
community mobility
Utilizing it● Recognition and visualization of the impact(financial and non-financial value) of mobility

should be set in the process of fostering public acceptance of● Economic Effects(1)Economic effects (direct and indirect)
Examples: Fare revenue, improved accessibility,community revitalization, reduced social security costs(2)Health Benefits
Examples: Disease prevention, maintenance and improvement of
mental health, extension of healthy life expectancy(3)Creating connections and fun
Examples: Creation of opportunities for face-to-face contact andcommunication, vehicles as objects of preference

what points should be kept in mind and what benchmarks● Extension ofhealthy life expectancy● Wellbeing● Improvement of qualityof life● Pleasant, fun,happy experiences● Awareness of inconvenience

automated driving. As part of this effort, a consumer survey was
conducted, and interviews and workshops have also been held.
The entire process has been classified into ten processes, anditems to be considered for each process have been set as
checkpoints.

Table 2: Action evaluation sheet
Activity item

Step

2

Foundation study

1

3
Transmission

4

5

7

Reception and diffusion

6

8

10

Goals

9

Creation and coordination of theoverall framework and strategies forthe individual projects(Frame & Strategy)

Checklist (2022 updated version)

1. Has an annual activity plan been formulated after devising a comprehensive medium to long-term
strategy based on the existing information, situation, and the previous fiscal year’s outputs?
2. Has a process with clear goals been formulated for each project?
3. Are projects cooperating so there is no waste or duplication?
(comprehensiveness, appropriate target selection, etc.)

Information collection and understandingof the targets and circumstances(Target Grasp)
Selection, editing, and processing oftransmitted information(Adaptation)

1. Has sufficient information (culture, characteristics, issues, etc.) collection and understanding been
conducted in advance about the targets (society, community, people, etc.) in which public acceptancewill be fostered?

Means, media, and venues for
information transmission(Means)

1. Have the most appropriate means, media, and venues been used for information transmission to thetargets?

Experience Opportunity Creation/UX(Experience)

1. Was an opportunity created for the target to personalize the issues through a realistic customer
experience in accordance with local issues and needs?

Feedback and bidirectionality(Communication)

1. Has the selection of appropriate information for the target audience been considered for information transmission?
2. Has appropriate information suited to the target audience been edited and processed for transmission?

2. Was the Society 5.0 perspective (fusion of the physical and virtual) considered?

1. Could the impact of information transmission be verified through the response of the targets andinteraction with them?
2. Were areas for improvement of the content and methods of information transmission discovered and
new ideas gained through the response of the targets and interaction with them?

Information diffusion and social
interest(Expansion)

1. Did the activities and information transmission lead to the diffusion of relevant information throughmass media, social media, etc.?
2. Did it create the effect of communicating information between people?
3. Were you able to elicit a derivative effect by improving the degree of satisfaction of existing users?

Consumer understanding(Understanding)

1. Verification of whether consumers' understanding (‘What’) of automated driving and ADAS functions has improved
2. Verification of understanding (‘Why’) about the background of introducing automated driving and
ADAS functions into society
3. Were consumers' intrinsic behavior to understand automated driving and ADAS functions aroused?

Consumption and use behavior(Use)

Social acceptance among consumers(Acceptance)

Phase 1
Why: Background/needs
What: Instillation of
mechanisms and rules

1. Did the consumers understand the social issues and their own situation, and do they show willingness
to adopt technology effectively and safely in their own lives?
2. Do the consumers show willingness to think about how they should act in order to effectively andsafely adopt technology into their own lives (‘How’)?

Phase 2How
* How to implement it in aform that suits the community* How consumers will be
involved and act to achieve this

1. Do consumers appear to be receptive to each of the factors that may arise from the introduction of
automated driving technology? (1) Lifestyle changes (2) Learning (3) Costs (4) Characteristics andtechnological limitations

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
1) Research and Evaluations for Fostering Public Acceptance

4.1. The process for Phase 1

culture, and history of the people living in the community, andSteps 1 through 7 correspond to the "Phase 1" referred to in
Figures 8 and 9.
Steps 1 and 2 are defined as the "foundation study" for the"creation and coordination of the overall framework andstrategies for the individual projects". Step 1 is to conduct the
management, such as ascertaining the overall picture of the SIP
until now and considering the strategy, and allocating thenecessary resources to the necessary sections while watchingover each project. Step 2 is "information collection andunderstanding of the targets and circumstances," which refers
to information collection and understanding of the targets(society, communities, people, etc.) in which public acceptanceis to be fostered. Until now, the author has attempted to gain an
overall understanding by collecting quantitative data throughnationwide surveys, but it will be more important in the future
to collect detailed information for each community.
Steps 3 through 5 are "transmission," which questions
whether the information content and appropriate media wereselected to match the target audience, and whether realexperience value was provided so that the target audience could
personalize the issues.
Steps 6 and 7 are "reception and diffusion," which measures
the impact and level of understanding of the information
through two-way communication with the target audience, andfurthermore, asks whether communication among the target

then to recognize what specific issues they are facing. Since
local media such as regional newspapers have significant
influence in regional areas, collaborating with such media toeffectively communicate the ‘Why’ and ‘What’ aspects shouldbe effective. In addition, since the media with high affinity
differs depending on the attributes and community, it is also
important to select the most appropriate media and process theinformation into the most appropriate form. It is important tocreate a system that uses a variety of media to effectively spread
information while creating connections among people andcommunities.
There are some cases in which regional automated driving
events attract only those who are involved in or interested in
automated driving, such as business operators and relatedparties, so ingenuity is required so that information reaches adiverse range of people and arouses their interest, such as byholding joint events with other fields.

5.2. Sharing information and case studies
In recent years, many FOTs of automated driving service
vehicles have been conducted throughout Japan, and some ofthem have progressed to social implementation, so it is also
important to systematically share such cases. Since the needs
and backgrounds of each community differ, it is natural that alarge part is tailored to the community in the implementation

audience has been created as a derivative effect.

of automated driving services, but there are many cases where

4.2. The process for Phase 2

goes without saying that successful examples should be shared,

examples from other communities can be used as references. It

Steps 8 to 10 correspond to "Phase 2" in Figures 8 and 9 andindicate, based on consumers’ understanding, that consumers
are proactively involved in utilizing it effectively and safely
themselves, and accepting the associated lifestyle changes,
learning, costs, inherent characteristics and technologicallimitations.

but also sharing cases that did not go well will add significant
value to the FOTs overall. Furthermore, the value of the FOTs
will be maximized by aggregating this information in acentralized manner and deploying it in a cross-cutting manner.

5.3. Consensus building for co-creation and complementation between people and technology
In the transmission of information regarding technology, it

5Transmission and sharing of informationand consensus formation toward public
acceptance of automated driving

5.1. Information transmission tailored to communities and targets
In regional areas, awareness of mobility issues is particularly
high, and the social implementation of automated driving
services vehicles as one of the solutions requires that thesignificance of this issue be transmitted through a variety ofmeans.
To this end, it is necessary to first understand the awareness,is necessary to share not only "what can be done," but also thetechnological limitations and specific risks to obtain a social
consensus. Just as human error is inevitable, system error is also
inevitable. In the same way that it is presupposed that there are
dangers that accompany conventional cars and that users are
expected to make efforts to reduce the risks by wearing seatbelts
and child restraints and obeying traffic rules, the process of
introducing automated driving into society must be conductedin a form where the users incorporate the technical limitationsof automated driving.
By adopting a style in which humans compensate for thelimitations and inadequacies of the new technologies, and byadopting an attitude of nurturing the technologies and

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
1) Research and Evaluations for Fostering Public Acceptancemechanisms throughout society, measures can be taken as aresult to resolve social issues at an early stage. Increasingawareness of "mobility," which is one of the major issues in theera of 100-year lives, will ultimately lead to the creation of safe,
secure, and sustainable mobility and contribute to maintaining
the quality of life and wellbeing of people.
Automated driving technologies are a "means" to this end,
and it is necessary to review the point that social implementationand fostering public acceptance are not "ends" in themselves,and for industry, government, academia, and the private sector
to collaborate toward co-creation.

[References]
(1)	Miyaki, Yukiko: "Toward Acceptance of Automated Driving in Society-Aiming to Solve Social Issues through Proactive Consumer
Involvement -," Automotive Technology, Vol. 73, No. 2, pp. 32-38, 2019.
(2)	Miyaki, Yukiko: "Toward Fostering Public Acceptance of AutomatedDriving," Academic Trends 2022.2 , pp. 100-104, 2022.
[Contacts]
Life Design Research Department, Dai-ichi Life Research Institute Inc., 1-13-1
Yurakucho, Chiyoda-ku, Tokyo 100-0006, 050-5474-8244, Contact: MIYAKI Yukiko(Director and General Manager, Excective Researcher, miyaki@dlri.jp)

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Society with Automated Driving(2) Public Acceptance of Automated Driving

2) Development of Assessment Methodology for
Socioeconomic Impacts of Automated Driving
Including Traffic Accident Reduction
Yoshihiro Suda (The University of Tokyo), Hiroaki Miyoshi (Doshisha University)(Abstract) The evaluation of the social and economic impact of automated driving is extremely important in terms offostering social acceptance, government policy making, and application to corporate management regarding automateddriving. Based on this recognition, the purpose of this paper is to present an overview of "Study of socioeconomic
impacts of automated driving including traffic accident reduction" and its successor project, "Research on assessmentof the impact of automated driving on society and the economy and on measures to promote deployment." This paper
first introduces two models (dynamic model and static model) of the "diffusion simulation" of automated vehicles, whichis the basis of the socioeconomic impact assessment, following overview of the impact assessment on road traffic,
transportation services, and industry and society, which were conducted using the results of the diffusion simulation.
Keywords: socioeconomic impact assessment, diffusion simulation, traffic accident, traffic simulation

1

Purpose and contents of the study

Impact on road traffic

Impact on transportation
services

AdviceInternational
cooperation

Impact on industry andsociety

Advisory committee

Socioeconomic Impact Analysis

Diffusion simulation

to understand how the speed of automated driving diffusion
and the resulting socioeconomic impact will differ dependingon economic incentives for automated driving and how
automated driving vehicles are introduced to the market.

Relevance of automated driving to SDGs

The practical application and expansion of automateddriving can contribute to solving social issues such as
reducing traffic accidents, reducing traffic congestion,
ensuring mobility for those with traffic constraints, andimproving driver shortages and reducing costs for logistics
and transportation services. Aiming to realize a society inwhich all people can lead a high quality of life through thewidespread use of automated driving, this research sets thefollowing two objectives and works to evaluate thesocioeconomic impact of automated driving: The first is tofoster social acceptance of automated driving. To promote
the practical application and social diffusion of services andvehicles that utilize automated driving technology in the
future, public understanding of automated driving is aprerequisite. In order to gain public understanding, it isnecessary to quantitatively understand the impact ofautomated driving on people's lives and the Japanese
economy in terms of both utility and potential risk. Second
objective is to contribute to government policy making andcorporate management. For example, it is extremely
important for government and corporate decision-making

Based on those purposes, the research shown in Fig.1 was
conducted in the first project, "Study of socioeconomic
impacts of automated driving including traffic accident
reduction." First, for the purpose of organizing basic
information, the relationship between automated driving
and the 17 goals, and 169 targets of the Sustainable
Development Goals (SDGs) set forth by the United Nations
was organized, and the significance of automated driving forsustainable society was confirmed. Next, a "diffusion
simulation" of automated driving vehicles was conducted touse its outputs as common basic figures when analyzing thesocioeconomic impact of automated driving. Finally,
quantitative analysis of the socioeconomic impact ofautomated driving from three angles were conducted: theimpact on road traffic, the transportation service sector, andthe industrial and social sectors.

Fig.1: Overall Picture of the Research Project

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In the second half of the project, "Research on assessmentof the impact of automated driving on society and theeconomy and on measures to promote deployment," various
simulation models constructed in the first half of the project
were improved, assumptions were reviewed, and the effectsof various promotion measures were analyzed.
The model developed in the projects has the following
five characteristics, and is able to analyze a wide range ofsocio-economic impacts in a theoretical and consistentmanner:1) Model building combining knowledge of trafficengineering and economics;
2) Common data on the socioeconomic environment(population, GDP, etc.) are used uniformly as simulation
assumptions;
3) Construct a simulation model for the diffusion of
automated vehicles based on microeconomic theory thattakes into account consumers' willingness to pay (WTP)
and the learning effect of production;4) Possible to estimate the diffusion rate of automatedvehicles in response to various diffusion promotion
measures; and5) Utilize the results of diffusion rate estimation as common
data for socioeconomic impact assessment.
Since the main objective of the first half of the project wasto construct a model, an advisory committee involving
academics from a wide range of fields, including engineering,
was established, and the study was conducted based on thediscussions in the study group. On the other hand, since thefinal purpose of the latter half of the project was todisseminate information to the public, the research was
conducted mainly based on the discussions in the Working
Group for Service and Business Implementation. In addition,"Simulation of the diffusion of automated driving vehicles"
and "Consumer choice changes regarding car ownership,use, and mobility" were studied in cooperation with aGerman research institute as part of the Japan-Germany
collaborative research. This Japan-Germany collaborative
research is based on the fact that the Socio-Economic Impact
Assessment was listed as one of projects of the JapanGermany joint research project at the Steering Committee
meeting between the Japanese Cabinet Office and theGerman Federal Ministry of Education and Research in
January 2019, conducted based on "Joint Declaration ofIntent on Japanese - German Cooperation on the Promotionof Research and Development on Automated Driving
Technologies" (January 12, 2019). For more detailed
information on the activities of the Japan-Germany
Cooperative Research, please refer to Section 6 (8) "SocioEconomic Impact" of this report.
In the following, the results of this study were presented,
focusing on the features and concepts of the simulation
model developed in this study.

2

Simulation of diffusion

The S-shaped growth curve model is a model often used
to simulate the diffusion of goods and services.(1)(2) However,when assuming a situation in which several levels of
automated driving coexist, as in this study, it is not
appropriate to simply use the parameters set by such amodel. In addition, the S-shaped growth curve model has anextremely simple structure, making it difficult to use whensimulating the effects of policy measures and OEMs' market
launch measures on the diffusion rate of automated vehicles.
Therefore, in this study, original diffusion simulation model
for passenger cars was decided to be constructed.
When considering the diffusion of automated vehicles,
the timing of introduction of SAE level 1 to 4 has alreadybeen discussed, but the feasibility and timing of theintroduction of SAE level 4 and SAE level 5 automatedvehicles that are capable of automated driving on all generalroads cannot be predicted. Based on these points, thediffusion simulation model in this study is divided into twoparts: a model for estimating the diffusion of automateddriving vehicles up to SAE level 4, for which the timing ofmarket launch can be predicted to some extent; and a modelfor estimating the diffusion of self-driving vehicles(equivalent to SAE level 4/5). The former is a dynamic modelthat captures the diffusion process of automated driving
vehicles while showing the year, and the latter is a static
model that estimates the diffusion rate of self-driving
vehicles in a steady state when self-driving vehicles are
realized.
The dynamic model classifies automated vehicles asshown in Table 1. In the first half of the project, automatedvehicles were classified based on the combination of SAE
levels and feasible roads, but this has been completely revisedin the second half of the project.
The columns in Table 1 indicate the functions of vehicles.
The functions are classified into three major categories: safe
driving support (S), driving support (D), and system driving(A), and then the functions are further classified in terms of
the roads on which the functions can be realized. The lasttwo digits of S20, D20, A25, etc. indicate the year when thefunction is expected to be introduced to the market. On theother hand, the rows in Table 1 indicate the categories ofautomated driving vehicles which are composed of
combinations of the above-mentioned functions. Here, thecategories of automated vehicles are classified into three
types: "driving support vehicles," "limited automatedvehicles," and "advanced automated vehicles." Driving
support vehicles are vehicles that have driving support
functions added to vehicles already on the market (S0 and

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2) Development of Assessment Methodology for Socioeconomic Impacts of Automated Driving Including Traffic Accident Reduction

Table 1: Classification of automated vehicles in the dynamic model
Driving safety assist
S20
S25

D20

ACC, LKAS
ForwardOmnidirectional on national
short distance long distance expressways
and limited
sensing1
sensing2
highways
Without
safety support
functions
Equipped withsafety support
functions only
Driving
supportvehiclesLimitedautomated
vehiclesAdvancedautomated
vehiclesDriving assist
D25
D30
A25
ACC, Lane
Lv.3 onchange assist on ACC, LKAS,
national
national
Lane change expressways
expressways andassist on
and limited
limitedordinary
highways
highways, LKAS
roads
duringon ordinary
congestion
roads

System driving
A35

A30
Lv.3 onnational
expressways
and limited
highways

A40
Lv.4 onnational
Lv.4 onexpressways
national
expressways and limited
and limited highways, Lv.3
on ordinary
highwaysroads

A45
Time ofmarketentry

Lv.4 onordinaryroads

S0

Existing

S1

△

D1
D2
D3
D4
R1
R2
R3
R4
A1
A2
A3

✓

Existing

✓

✓

✓

✓

✓
✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓
✓

✓
✓
✓

✓

✓

✓

✓
✓
✓
✓
✓
✓
✓

✓
✓

✓

✓

✓

✓

✓

✓
✓
✓

✓
✓

✓

✓

✓

✓

✓

✓

✓

✓

✓
✓

✓

✓

✓

✓

✓

✓

✓
✓

Existing
2025
2025
2030
2025
2030
2030
2030
2035
2040
2045

✓

1. Acceleration control for pedal error, lane departure warning, lane distance warning, AEB for vehicles (short distance in front), AEB for pedestrians, AEB for bicycles crossing the road
2. AEB for vehicles (all-around, long distance), AEB for bicycles (all-around long distance)✓: Equipped △ : Partially equippedS1). Limited automated vehicles are vehicles with SAE level3 functions added to driving support vehicles. Advanced
automated vehicles are limited automated vehicles with SAElevel 4 functions added. For each of these three types, theautomated driving category is set as a combination of feasiblefunctions.
In the static model, on the other hand, two types ofvehicles are set for simulation analysis: automated vehicles
that realize SAE Level 2 on all roads, in which humans are
the main driver of the vehicle, and automated vehicles thatrealize self-driving.
(1) Dynamic model
The dynamic model estimates the number of vehicles ineach of the automated driving categories shown in Table 1
for passenger cars, logistics services (trucks), andtransportation services (e.g. buses) every five years from2025 to 2050. The estimation method differs for each of thethree categories, but this article introduces the estimation
method for passenger cars. The estimation model forpassenger cars has been completely reconstructed following
the review of the automated vehicle categories conducted in
the latter half of the project.
In the simulation for passenger cars, the number of carsneeded in each year is first calculated (A). By subtracting thenumber of remaining cars of the previous year from A, thenumber of new cars in each year is calculated. The
composition of the number of new cars in each year byautomated driving vehicle category is determined by the
consumer's automated driving vehicle category choicemodel shown in Fig.2. In the first step of the selection model,
the composition of new cars in the three groups of "driving
support vehicles," "limited automated vehicles," and"advanced automated vehicles" is determined under theexogenously given price of each category. In the second step,
the composition ratios of each automated driving vehicle
category among the groups selected in the first step aredetermined. The parameters of this model are set by
conducting a consumer's stated-preference survey (Webquestionnaire) targeting approximately 7,500 consumers.
On the other hand, the price of each category of automateddriving vehicles, which is exogenously given to the automatedvehicle category selection model, is determined by theexperience curve effect of production. The experience curve
effect refers to the phenomenon in which an increase incumulative production leads to an improvement inproduction technology and a decrease in production cost.

First step selection
Advancedautomated vehicles

Limitedautomated vehicles

Driving support
vehiclesSecond step selection
D1
Ｄ1

D2
Ｄ2

D3
Ｄ3

D4
Ｄ4

R1

R2

R3

R4

A1

A2

A3

Fig.2: Model of Consumer Choice Behavior Regarding Automated Vehicle
Categories

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2) Development of Assessment Methodology for Socioeconomic Impacts of Automated Driving Including Traffic Accident Reduction

1．
Privately owned manual vehicle
Parking lot

Destination

Privately owned self-driving vehicles
2．
Destination

Parking lot

Automaticparking

3．
Single ride self-driving taxi

Destination

Waiting
4．
Share ride self-driving taxi

Destination

Waiting5．
Public Transportation

StationStationDestination

6．
Walking and/or Biking
Destination

Fig.3: Traffic modes in the statics model(2) Statics model
The static model differs from the dynamic model in thatit only includes passenger vehicles and estimates thestationary state ownership and use of vehicles when selfdriving vehicles (equivalent to level 4/5) are realized.
The model assumes the introduction of self-driving taxis
that use self-driving vehicles as well as share automated taxis
that charge lower fares due to shared rides and can estimate
how the use of each transportation mode is affected byvarious factors such as their fares and waiting times.
This model was constructed using the results of aconsumer preference survey (Web-based questionnaire)
conducted in the "Analysis of the Impact on theTransportation Service Sector" section of "Changes inConsumer Choice Structure Related to Car Ownership, Use,and Mobility. As shown in Fig.3, the survey set up six
transportation modes, and investigated how travel time,
travel cost, and waiting time for dispatch of an automatedtaxi (including share taxis) affect ordinary citizen mode
choice by weekday, holiday, purpose of travel, and travel
distance zone. The static model utilizes as its core thetransportation mode choice model constructed using theresults of this survey.

3

Socioeconomic impact assessment

3.1. Road traffic(1) Traffic accidents reduction
Two analysis methods were developed for the analysis of
the reduction effect of traffic accidents. The first method is

the one used in the first half of the project, in which we
obtained the results of estimates of the effect of the automatedvehicle diffusion on the number of accidents, as well as
fatalities and injuries from by a measure of SIP automateddriving "Visualization of the Traffic Accident Reduction
Effects through Automated Driving and Driving Assistanceproject" and adjusted those using ITARDA traffic accident
macro data. In the measure, the reduction effect of traffic
accidents was estimated using the agent-based simulation
method.
The second method is the one used in the latter half of the
project, which calculates the diffusion rate of driving safety
support functions such as AEB and LKAS from the diffusion
rate estimation results for each category of automatedvehicles, and estimates the traffic accident reduction effectby referring to the accident-avoidance rate for each driving
safety support function set by the Advanced Safety Vehicle(ASV) Promotion Plan Phase 6 Report.(3) Since the accidentavoidance rate in this ASV report was calculated under ideal
conditions, the effect calculated by this method should beunderstood as the accident reduction potential of automateddriving, but it has the advantage of allowing more accident
types to be estimated than the first method.
Whichever method is used, the unique feature of thisstudy is that the monetary value of the traffic accident
reduction effect, including the non-financial losses of thetraffic accident perpetrator, is included.
There are two major differences between active safety
technologies (e.g., safety support systems and automateddriving) and passive safety technologies (e.g., airbag systems)
in terms of benefits to society. First, while both technologies
share the same benefit of "reducing injury to user himself,"

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2) Development of Assessment Methodology for Socioeconomic Impacts of Automated Driving Including Traffic Accident Reduction

active safety technologies also reduce injury to the otherparty to an accident as an additional external economy. The
second difference is that users of active safety technology
also reduce the psychological burden (non-financial loss) of
the perpetrator according to the reduction of bodily injury
to the other party to the accident. The first benefit can be
estimated by using the amount of economic loss per traffic
accident victim obtained from the Cabinet Office survey(4)and the reduction in the number of traffic accident victims
due to the diffusion of automated vehicles. On the other
hand, the second benefit cannot be estimated because there
is no data of "the amount of economic loss of the perpetrator
per victim of a traffic accident." However, the ability to avoid
becoming a perpetrator is extremely important in evaluating
the value of automated driving. For this reason, this study
newly estimated the psychological burden (non-financial
losses) of being a perpetrator monetarily through a webbased questionnaire survey.
This Web-based survey consists of a perpetrator survey
and a victim survey. In the perpetrator survey, the WTP(one- year usage fee) for a device that reduces the probabilityof killing the other party (one person) by 50% (or 90%) due
to his/her own driving error (assuming his/her fault ratio is100%) in a four-wheeled vehicle accident was asked in a
double-bounded dichotomous choice. On the other hand,the victim survey asked about the WTP for a device that
reduces the probability of death by 50% (or 90%) due to theother party's driving error (assuming zero fault) in a fourwheeled vehicle accident. The results of this analysis showedthat the median WTP did not differ significantly between
the perpetrator survey and the victim survey. This study
incorporates the results of this survey into a monetaryvaluation of the traffic accident reduction effect of automateddriving.
(2) Reduction of traffic congestion and CO2 emissions
After setting assumptions on characteristics of automatedvehicles during such as following and free driving behavior,lane change conditions and decisions, and reaction delaytime, etc., traffic simulation on the road was conductedconsidering the diffusion rate of automated vehicles
calculated by the simulation of diffusion (dynamic model).
And the coefficients of a model,(5) in which Electric Vehicles(EV) can be considered, were used to estimate the effect of
reducing traffic congestion and the resulting reduction in
CO2 emissions.
Specifically, the study consists of a two-stage structure;
the first stage is traffic simulation that considers the spreadof automated driving to estimate the intensity of congestion
reduction and CO2 emission reduction; and the second stage
is a nationwide extrapolation.

Traffic simulations were conducted on ordinary way, andtwo-lane/three-lane sections of expressways, mainly
considering the sag areas on expressways. In particular, thethree-lane section was selected because it was the section
where much knowledge was obtained in the "Visualizationof Local Traffic CO2 Emissions" in the first phase of SIP. In
addition, the estimation of the CO2 emission reduction
effect was conducted taking into account the diffusion ofEVs.

3.2. Transportation services sector(1) Ensure mobility for people with transportation
constraints and in areas with inconvenienttransportation such as depopulated areas
In rural areas, services originally provided by businesses
and residents are now being performed by the local
government due to the declining birthrate and aging
population, the exodus of young people to urban areas, andthe deterioration of business management and the resulting
withdrawal of businesses. For example, services such as
traffic safety awareness and publicity and crime prevention
patrols are provided by neighborhood associations andresidents' groups in urban areas but are often provided asadministrative services in rural areas. Such administrative
services require a large amount of labor and expense, but theservices currently provided by the local government are notconsidered highly sustainable because of the growing
shortage of human resources in the local government.
Therefore, one of the most important issues in rural areas is
how to maintain administrative services while saving laborand costs. From the viewpoint of administrative services,
this study examines which fields of application automateddriving technology can be introduced and how it is expected
to be utilized in rural areas, based on the actual usage ofvehicles used for administrative services. In addition, theeffects of the introduction of automated driving technology
and its issues were discussed through dialogue with local
government officials in mountainous areas.
(2) Drivers for logistics services
The number of truck drivers in supply and demand in the
future were estimated under multiple GDP transition
assumptions, and the number of driver shortages in logistics
services was calculated as the difference between the two.
Specifically, firstly the future number of drivers who will
provide logistics services was estimated, using the numberof truck drivers by age group, the percentage of drivers who
continue to work (continuation rate), and the percentage of
new drivers in the younger age group (new driver
employment rate). Next, the number of truck drivers indemand in the future was estimated by obtaining the value

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2) Development of Assessment Methodology for Socioeconomic Impacts of Automated Driving Including Traffic Accident Reductionof production and imports by item and by category fromfuture GDP, estimating the volume of freight generatedusing this value of production and imports, and estimating
future freight vehicle transport tonnage and freight vehicle
kilometers traveled.
Based on the results of interviews with experts involvedin the logistics industry, a Level 4 automated vehicle
operation permit scenario for logistics services shown in
Fig.4 was set up, and the amount of driving that can be
replaced by automated operation and the number of drivers
that can be reduced by automated operation were estimated.

25% expressway traffic
coverage case
L4 is available between Tokyo and Nagoya,and between Nagoya and Osaka.
Excluding areas with narrow shoulders suchas the Metropolitan Expressway.
Equivalent to 6.3% of total length ofJapanese expressway.

50% expressway traffic
coverage case
L4 is available on Tokyo-Fukuoka andmajor expressways in Tokyo
metropolitan area.
Equivalent to 17.6% of total length ofJapanese expressway.

75% expressway traffic
coverage case

50% expressway traffic coveragecase (refer to the permitted routes
for double-connected trucks.)

Considering the network, set up an
extended permitted area, including theCentral Highway and other roads.
Equivalent to 34.8% of total length ofJapanese expressway.

Set up with reference to the permittedroutes of double-connected trucks.
Can connect remote areas.
Equivalent to 17.7% of total length ofJapanese expressway.

Fig.4: Level 4 Automated Vehicle Operation Permit Scenarios for Logistics
Services

3.3. Industrial and social sectors(1) The automotive industry and the industry as a whole
due to changes in the automobile ownership structure,etc.
The realization of automated vehicles requires theincorporation of many sensors and software, and the inputs
required for production are different from those of
conventional vehicles. In this study, how changes in thecomponents of automobiles required for automated driving
will affect the automobile industry and the Japanese economy
was analyzed, using an Input Output Table with reference tothe methodology of a previous study(6) on electrically driven
vehicles.
Specifically, first, the components required for automateddriving and the industry sectors for those components wereidentified and estimated the increase in component costs byautomated vehicle category and by input industry sector.
Next, based on this cost information and the number of newvehicles per automated vehicle category obtained from thediffusion simulation (dynamic model), an input coefficient
table for Input Output Table was identified considering thefuture shift to automated driving. Using this input coefficient
table, the impact of the shift to automated driving on the

production value and the number of employees in the"passenger car sector," the "automotive parts and accessories
sector," and all industrial sectors in Japan was analyzed. In
addition, a sensitivity analysis was also conducted when thecost of automation is changed.
(2) Contribution to productivity improvement
To achieve sustainable growth in an era of declining
population, it is necessary to sustainably increase laborproductivity, and technological progress or innovation isimportant for this purpose.
To increase labor productivity, economists believe thateither an increase in the capital equipment ratio (the amountof capital per worker) or an increase in total factor
productivity (TFP) through technological progress andinnovation is necessary. Here, the effect of automated drivingon labor productivity and total factor productivity in theJapanese economy was analyzed. In general, factors that
increase the rate of increase in total factor productivity canbe categorized as improvement in the "quality of capital,""quality of labor," and "management efficiency," etc. The
main factor that increases total factor productivity due toautomated driving is thought to be the quality of capital. As
shown in Section 3.2 (2), "Drivers for logistics services," if
the introduction of SAE Level 4 automated driving makes it
possible to replace drivers who are in short supply with
automated driving, this will indeed correspond to the effectof improvement in the quality of capital. In this study, using
the results in Section 3.2 (2), the increase in labor productivity
and total factor productivity due to the introduction of
automated driving was estimated separately for commercial
use and private use.
On the other hand, an example of "improved management
efficiency" is the use of automated driving cabs for mixed
freight and passenger transport. If institutional conditions
are met and both passenger and freight transportation canbe provided, automated driving cabs will be able to efficiently
transport people and goods in the same vehicle by using an
advanced dispatch system. This would result in an increase
in total factor productivity, mainly due to improved
management efficiency.

4

Conclusion

This paper presented an overview of the "Study of the
impact of automated driving on reducing traffic accidents
and on others" and its successor project, "Research on
assessment of the impact of automated driving on society
and the economy and on measures to promote deployment."
The analysis outlined in Sections 2 and 3 was based on

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discussions with automobile manufacturers, the logistics
industry, and other related parties, and several dissemination
scenarios were set up and estimated for the purpose of
fostering social acceptance of automated driving, and for use
in corporate management and government policy making.
Although it is difficult to evaluate quantitatively, the"Mobility Business Innovation Contest (M-BIC) 2022"(7) was
held as a co-host event of the SIP-adus Workshop to extract
possible examples of how automated driving change people's
lives and society in the future. In addition, this event wasused to disseminate information about this research projectto the public.
The results of this research will be disseminated to theworld through presentations at international conferences
and meetings, and through submission and publication ininternational journals.
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Implications for Transport Planning, Victoria Transport Policy
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Kuhnimhof, B. Lenz, P. Phleps, Autonomous driving: the impact ofvehicle automation on Mobility Behavior, Institute for mobility
Research,2016,
https://www.ifmo.de/publications.html?t=45(accessed Jun. 25, 2021)(3) "Advanced Safety Vehicle (ASV) Promotion Plan Phase 6 Report" by
the Advanced Safety Vehicle Promotion Study Group, Automobile
Bureau, Ministry of Land, Infrastructure, Transport and Tourism,
2021,
https://www.mlit.go.jp/jidosha/anzen/01asv/report06/index.
html, (accessed Aug. 24, 2022)(4) Director General for Policies on Cohesive Society, Cabinet Office,Report of the Research on Economic Analysis on the Damage andLoss of Road Traffic Accidents (2017) (in Japanese), https://www8.cao.
go.jp/koutu/chou-ken/h28/index.html (retrieved May 25, 2021)(5) Mitsuo Yasushi, et al., Navigation System to Predict EV Driving Range,
Pioneer R&D 20, pp. 1-7, 2011.
(6) Takayuki Mase, Impacts of Producing Electrically Driven Vehicles on
Japan’s Industrial Output, Central Research Institute of Electric Power
Industry (SERC Discussion Paper), SERC18001 (2019), https://criepi.
denken.or.jp/jp/serc/discussion/download/18001dp.pdf (in Japanese,
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(7) M-BIC Official Web site 2022, http://webpark5061.sakura.ne.jp/2022/,(accessed Aug. 21, 2022)[Contacts]
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba,
Meguro-ku, Tokyo 153-8505, 03-5452-6680, Contact: Shoichi Suzuki

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Society with Automated Driving(2) Public Acceptance of Automated Driving

3) Projects to Foster Public AcceptanceTadashi Hirota (DENTSU MEITETSU COMMUNICATIONS INC.), Hiroshi Kimura (SC-ABEAM AUTOMOTIVE CONSULTING)(Abstract) Japan faces a mountain of issues related to mobility, including lack of transportation options in isolatedareas as the population becomes older as well as a shortage of drivers in the logistics industry. As such, there arerising expectations that social implementation of automated driving technology can solve these issues. One priority
for social implementation is the R&D of automated driving technology and the fostering of public acceptance alongwith constructing the system infrastructure. Therefore, as an activity to foster public acceptance, SIP-adus (Crossministerial Strategic Innovation Promotion Program (SIP) Automated Driving for Universal Services), form its firstphase, have taken efforts to promote proper understanding of automated driving in society through communicationand dialogue related to automated driving with citizens, local government officials, and business operators in the
traffic industry. We also widely shared the activities of local governments and businesses related to social
implementation of automated driving and promoted cooperation across Japan to learn about issues and successful
case examples. This document provides an overview of these activities.
Key words: Public acceptance, citizen, dialogue, region, test ride event, exhibition

1

Information Transmission, Media
Activities, and Videos

1.1. Website construction/operation: SIP-café
Fostering public acceptance of automated driving means
raising awareness and delivering objective and fair information
related to automated driving to a wide range of ordinary
citizens. Based on this concept, we constructed a website as themain pillar to our activities. Construction of this website would
fulfill the role of sharing public information and organizing
content in one place. It also served to ensure that the compiled
information remained there.
The public homepage was named "SIP-café" based on the
motif of a cafe where people and information intermingle inorder to give the general public as a sense of familiarity. (https://
sip-cafe.media)
The top page uses illustrated animation and dynamic
movement to catch the eye. The illustration is based on the ideaof how people’s lives will change after social implementation of
automated driving. However, the illustration has a soft andcomforting tone and color palette to make it easy for the generalpublic to accept. (Fig.1)
At first, the website had 5 categories of pages: "SIP-adus
Information", "Automated Driving News", "Automated Driving
Guide", "Automated Driving Glossary", and "Columns". FromFY 2021, a page for "Automated Driving Journey" was added.
The "SIP-adus Information" page transmit information andtrends of agencies related to SIP-adus, such as events for thegeneral public and FOTs (Field Operational Tests) aimed atcommercial use of automated driving. It functions as
information transmission that is the main purpose of this site.
The "Automated Driving News" is not limited to SIP-adusrelated news, but rather sends a wide-range of information,
including news about safety driving support technology,
automated driving technology, and new vehicles (passenger
cars, trucks, buses) that have adopted this technology.

Fig.1: SIP-café top page image

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The "Automated Driving Guide" and "Automated Driving
Glossery" are pages that explain basic knowledge and terms
related to automated driving. These pages aim to fulfill the roleof "encyclopedia" and "dictionary" to improve literacy amongpeople with no expert knowledge.
The "Columns" aims to stir interest in the automated driving
by publishing easy-to-read articles on this topic.
The "Automated Driving Journey", which was added later, isfor serialized reportages of FOTs carried out at various local
areas in visiting each test sites to give more realistic view to theaccounts.
In terms of these articles, 108 were published in FY2019, 203
in FY2020, 174 in FY2021, and 62 by July 2022 for a total of over
500 articles. These articles receive over 10,000 views a monthand play a role in widely transmitting information.

1.2. Introduction of activities for social implementation and R&D of automated driving on "SIP-caféon Tube"We engaged in activities to deliver information related toefforts for automated driving technology development andsocial implementation by industry, academia and government
to the general public through videos for easier understanding.
In order to make these videos more casually accessible whenever
and wherever viewers in broader generations are, the movies
are made available to the public on the "SIP-café on Tube" inYouTube as video-sharing website. Not only the activities that
SIP-adus had led such as "Memoirs of Automated Driving
Trials", "Citizen Dialogue" and "FOTs in the Tokyo waterfrontarea" to be mentioned below, but also those for development ofrelated technologies and new products by automotive
manufacturers and suppliers have been included in 144 sharedmovies as of the end of August, 2022, as shown in Fig.2.

1.3. Memoirs of Automated Driving Trials
As the number of local governments and businesses
conducting FOTs for social implementation of automateddriving is increasing, we began the "Memoirs of AutomatedFig.2: Test ride Advanced Driving Assistance System report:
Toyota MIRAI Advanced Drive (published June 20, 2022)

Driving Trials" project in FY 2021 that aims to foster public
acceptance of automated driving by grasping realistic issues in
implementation and voices of residents, local governments, andbusiness operators in as many regions as possible andtransmitting this information to a wide range of society.
Specifically, as shown in Table 1, the project interviewed key
persons in each region taking on FOTs for automated driving
and released reports and videos summarizing the results on the
SIP-café website for the SIP-adus. As seen in Figure 3, theseinterviews sought stories of on-the-ground struggles andcreative techniques related to social implementation that could
be a good reference for other regions who attempt to take on
implementation.
Table 1: Automated Driving Journey interview report(as of October 1, 2022)
FY2021 interview report
Higashiomi, Shiga / Automated driving services experience based in "Okueigenji
Keiryu no sato"
Miyama, Fukuoka / Automated driving services based on clean energy andcommunities facilities
Kamikoani, Akita / "Kamikoani" in Akita prefecture establishing automateddriving services
Iinan (Akagi Kogen), Shimane / Automated driving services for children who
hold the key to build the future
Takahata, Yamagata / FOTs of automated driving services in core area in Takahata
town
Chatan, Okinawa / Driving experience in "Mihama Shuttle Cart" early model ofautomated driving services in tourist areas
Nishi-Shinjuku, Tokyo / FOTs in Nishi-Shinjuku for practical implementation of
automated driving buses in "Future Tokyo" Strategy - 2025
Eiheiji, Fukui / Eiheiji-cho, Fukui prefecture utilized abandoned railway tracks
for "Realization of Mobility Space Services" for level 4 automated driving
Kasugai, Aichi / Automated driving services for comfortable driving and theresolution of issues in Kozoji, Kasugai City New Town
Yokohama, Kanagawa / Can this save Japan !? "C+pod Yokohama" Super compact
EV experience with short-term car rental
FY 2022 interview report
Fujisawa, Kanagawa / FOTs at Shonan i-Park "Future of healthcare MaaS"
Nishi-Shinjuku, Tokyo / "Future of Tokyo" Strategy - Automated driving FOTs forinfrastructure cooperation by Taisei Corporation, and others.
Nasushiobara, Tochigi / "Unprecedentedly elaborated planning" - Uniqueinitiative for automated buses in Tochigi prefecture

1.4. Up-to-date current status report (webinar) on
technology for automated driving implementation: Technology seminar
In order to foster public acceptance, it is ultimately necessary
to enhance literacy and recognition by general consumers.
However, it is difficult to achieve this directly. As of now,
activities to foster public acceptance have been developed
targeting "early adaptors" as called in marketing.
In order for a new system like automated driving to beestablished in society, there are not only technological themes
but also various surrounding issues that can be considered. We
designed and conducted technological seminars to generate
opportunities for early adopters to learn about the status ofthese issues and what solutions are being developed to addressthem.
The first seminar was based on the theme of "issues with

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自動運転見聞録“類を見ない計画性”ユニークな栃木県の自動運転バスの取り組み
2025年度の本格導入に向けて，県，市町，事業者が一体となって進めている地域がある．栃木県だ．どのような考え方なのか，ロードマップ，全体計画，組織などの詳細について，
栃木県県土整備部交通政策課 主査 安生真人氏，那須塩原市市民生活部生活課 課長 鈴木正宏氏，那須塩原市市
民生活部生活課交通対策係 主査 山田慎太郎氏，那須塩原市市民生活部生活課交通対策係 主査 平野純氏に聞いた．

are expected to be generated by the sharing, linking, andutilization of a wide range of data corresponding with the
popularization of connected cars. The speakers were Mr. Matsui
of Sumitomo Rubber Industries and Mr. Iso of NTT Data.

1.5. Awareness of relationship between visual field
loss and driving through "visual field loss webinar"
We held a total of three webinars in FY 2021 regarding therelationship between driving and visual field loss, which isn’t
widely known, and the expected contributions of automateddriving and advanced driving assistance technology. We invitedresearchers and specialists in this field to the webinars and took
efforts to raise awareness of these issues.
◼Session 1 (July 8, 2021)
Theme: "Crossing of visual field loss and automated driving"
Overview of discussion: The discussion was carried out onthemes such as: What is visual field loss? What is the impact on
driving? What future initiatives are needed as social issues to
respond to demands of general drivers.

県下でのノウハウ共有のために
楠田 栃木県ABCプロジェクトとは何ですか？
栃木県 自動運転システム（Autonomous）の「A」，路線バス（Bus）の「B」，挑戦（Challenge）の「C」の頭文字からとっています．
「無人自動運転移動サービス導入検証事業」のことで，県民が親しみやすいように命名しました．

Fig.3: Automated Driving Journey:
Activities for automated bus in Tochigi prefecture(Published July 27, 2022)

responsibilities during automated driving." It presented a legal
perspective based on criminal responsibility, ethical issues, andkey issues in the Road Traffic Act, which was revised in April
2020. The speakers were Mr. Makino of the National Police
Agency and Professor Imai of Hosei University.
The second was taken place with the theme "HMI andoverconfidence of drivers" to explain how drivers should
communicate with evolving technologies, how much theautomated technology can understand drivers' intent, and how
vehicles can be in the next generation. Mr. Hiraoka as speciallyappointed professor from the university of Tokyo and Mr.
Tochioka of Mazda motor corporation.
The third seminar focused on "software updates andcybersecurity." It explained environmental changes, importance,
issues, and initiatives surrounding software updates, andcorresponding cybersecurity issues. The speakers were Mr.
Asahi of Toyota motor corporation and Mr. Niikuni of the
National Traffic Safety and Environment Laboratoty.
The fourth seminar was based on the theme of "cooperative
areas of data provision and effective usage of mobility data." It
was a lecture on cutting-edge data utilization as new services◼Session 2 (September 2, 2021)
Theme: "Driver’s licenses and visual field loss - Consideringdriving and employment of disabled persons"
Overview of discussion: Introducing cases of companies thathave already begun efforts to support employees with visual
field loss, the discussion was proceeded for the themes such as
how the driving licenses framework should be based on thevisual field loss and the techniques demanded of companies
related to employees with visual field loss, as shown Fig.4.
◼ Session 3 (January 11, 2022)
Theme: "Visual field loss and driving assistance system Current and future technology-"
Overview of discussion: Studies to extend driving life
expectations, and development status of technologies for

第2回 視野障害について考えるSIP-adusウェビナー「運転免許と視野障害〜有病者の運転と就労を考える」

質疑応答〈視聴者の皆様からのQ&A〉

Fig.4: Discussion at No. 2 visual field loss webinar

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
3) Projects to Foster Public Acceptancepreventive safety, driving assistance and automated driving
were introduced. The panel discussion in the second half of theevent was focused on expectations and issues from users' view
points.

1.6. Reaching a wide-range of generations through"Automated driving LIVE News"In FY 2022, "Automated driving LIVE News" was held tentimes (expected as of end-August, 2022) as a monthly live show
about hot topics related to automated driving. In FY 2022, as
the final year of the SIP-adus, we have kept working to create
and operate contents more attractive for general users andyounger generation to enlarge the number of viewers including
the generation Z who will be the leader of the future mobility
society, for a successful conclusion of the project.
◼ Program 1 (May 6, 2022)
Theme: "A world where towns, roads, and cars talk together- Infrastructure cooperated traffic system with automateddriving"
Overview of discussion: Following experts' session to
introduce governmental policies and latest FOTs activities
related to technologies supporting automated driving such as
infrastructure cooperation technologies and communication
technologies, a talk session with younger generations was
carried out.
◼Program 2 (July 4, 2022)
Theme: "Future to be realized by mobility data plus
something - Road traffic environment data web portal, MD
communet®, and Activities taken place in Kyoto"
Overview of discussion: The discussion was carried out
following introduction of MD communet, which is a web portal
opened to the public in April 2022 to promote matching
between users and creation of new business with shared
information, as well as "KYOTO Raku Mobi contest," which is a
new app idea contest to solve mobility issues and tourist-relatedtransport issues in Kyoto.
◼ Program 3 (July 21, 2022)
Theme: "Facing mobility for elderly relatives – Elderly
第3回 自動運転Liveニュース「高齢家族の移動と向き合う─視野障害や認知機能低下にみらDriving Issues due to visual field loss and cognitive decline -"Overview of discussion: The experts talked about accounts
related to "visual field loss and car driving", including measurestaken through driving behaviors, importance of early detection,
possibility of driving assistance with technologies, and also
efforts for elderly driving issue, then the discussion followed
this as shown in Fig.5.

2

Exhibition/Symposiums

2.1. Dialogues with residents through "Citizen Dialogues"
As shown in Table 2, from FY 2017 we conducted "Citizen
Dialogues" to provide a place for dialogue aimed at exchange of
opinions with citizens and promotion of understanding forsocial implementation of vehicles and services that utilize
automated driving technology. Citizens of all ages and genders
participated in these dialogues, as well as various stakeholders
such as public transportation business representatives,
academics, and public officials as discussions were held basedon richly diverse perspectives. From FY 2020, in considerationof the COVID-19 infection prevention measures, the discussion
and Q&A with listeners occurred online using online
conferencing systems. Also, at end-FY 2020, as an interim
report for the past activities as seen in Fig.6, we invited persons
Table 2: List of Citizen Dialogues (as of October 1, 2022)
Fiscal year
Format
FY2017
Symposium
Symposium
FY2018FY2019FY2020

FY2021
FY2022

Symposium

Event location
Koto Ward, Tokyo (Tokyo Motor Show)
Institute of Industrial Science, The Universityof Tokyo
Koto Ward, Tokyo (Tokyo International
Exchange Hall)

Citizen dialogue

Shodoshima, Kagawa prefecture

Symposium

Koto Ward, Tokyo (TFT Hall)

Citizen dialogue
Symposium
Citizen dialogue(online)Regional AutomatedDriving Summit(hybrid event)
Citizen dialogue(online)Citizen dialogue(hybrid event)Ina City, Nagano prefecture
Koto Ward, Tokyo (Tokyo Motor Show)
Maebashi City, Gunma prefecture
Local governments across Japan (Kamikoani,
Akita prefecture, Eiheiji-cho, Fukui
prefecture, Chatan, Okinawa prefecture, etc.)
Yokohama City, Kanagawa prefecture
Tochigi prefecture (*)*Prefecture organized FOTs at its 10 cities and towns.

Fig.5: Discussions at Automated Driving LIVE News No. 3
Fig.6: Discussions at Regional Automated Driving Summit(held March 25, 2021) held

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
3) Projects to Foster Public Acceptancefrom local governments, traffic businesses, and startup
businesses from across the country who are engaged in FOTs
for automated vehicles, shared their activities, and held a "local
automated driving summit" to discuss issues.
For the discussion, we used a graphic recording to do
visualization that anyone could intuitively understand, publish
on websites such as SIP-café, etc., and share with local
governments in venue areas so that the information could be
used widely in public awareness activities for citizens even after
the event. (Fig.7)
グループディスカッション②

これからのまちや移動の理想像と自動運転

グループ A

戦略的イノベーションプログラム（SIP） 自動運転

市民ダイアログ 群馬県前橋市

Fig.7: Maebashi citizen dialogues (held on January 17, 2021)
graphic recording results

2.2. Share the achievement of the program: Joint testride events
For fostering social acceptance, as one of our efforts to makemedia interested in automated driving, we consider that it isimportant to offer a test ride opportunity to ride on automateddriving for media reporters and journalists. In the second phaseof SIP-adus, we originally planned a test-ride event andexhibition with the cooperation of the Japan Automobile
Manufacturers Association (JAMA) and support of OEM
companies on the Tokyo waterfront city area where the Olympic
and Paralympic Games Tokyo 2020 would be held. But, under
the COVID19 pandemic, as the games were postponed, ourevent was also rescheduled to 2021. On the other hand, though
the test-ride and exhibition were originally considered as just
one-time event, for social circumstance declining directcommunication opportunities but increasing importance ofcommunication through media, we eventually have a plan evenfor third event aiming for further appeal to media reporters andjournalists.
This plan change was started from cancelation ofcollaboration with JAMA then followed by ask each of
automobile manufacturers to provide exhibits to the event
which wiill be organized only by SIP-adus. Under thecircumstances in which various events where automobile
manufacturers comme together, such as Tokyo motor show,
were cancelled, we aimed to offer a place worth publicly
reporting as an event attracting automotive manufacturers,
suplliers, software develop companies and universities.
The first joint test ride event was held on April 20-21, 2021 in

one part of the parking lot of Ooedo Onsen Monogatari, a hot
springs facility in the heart of the Tokyo waterfront area.
Targeting media and journalists, we encouraged media coverage
by information releases to media companies and public notices
for the Automobile Journalists Association of Japan.
The exhibitors were consited of 8 organisations such as
Valeo, Kanazawa University, Subaru, Tier IV, Toyota, Nissan,
Honda, showing the total of 18 vehicles as test-ride vehicles andexhibits on the venue. And also asking support to TOYOTA
Motor Corporation and HINO motors as participant to the
FOTs in the Tokyo waterfront area, their automated bus basedon "SORA" with fuel cell were carried in to the venue to exhibit.
At the time, there were concerns about trends in COVID-19
infections. However, thankfully, infections had dropped aroundthe time of the event and it went smoothly by adapting infection
countermeasures at the venue and confirming with emergency
contacts.
The number of the visitors from media and journalists were
the total of 106 in 2 days event with test-ride schedule fully
reserved. As the ride to the commercial vehicles by automatedmakers had attracted many people under the condition of 90
minutes ride per trip, meaning only 5 trips maximum in a day,we struggled to make orders for them in advance.
As unexpected event, Mr. Shinji Inoue, as then Minister ofState for Science and Technology Policy of Cabinet Office cameto and walk around the venue to see and talked with exhibitors
at each of their booth.
The rating by reporters and journalists are good overall withcomments such as: "it was helpful to well understanddevelopment policies and originality of each exhibitor inseamlessly switching the test-ride vehicles to ride and evaluate,""it was good occasion to make access to automated technologies
developed by suppliers," "the event showed realistic image withnot only test vehicles but also latest model of vehicles already inthe market equipped with automated technology." This gave us
confidence to the result of the event as meaningful effort.
In addition to being broadcast on Nihon TV and TV Asahi,
it was also picked up by online news outlets like the Kyodo
News and Nikkei xTECH, creating opportunities for a wide

Fig.8: Commemorative photo at first event venue

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range of consumers to be exposed to the information. (Fig.8)
The second event was held for three days from 18 to 20
October, 2021 at the Tokyo International Cruise Terminal andits second parking lot, along with nearby Aomi F1-S zone. The
19th and 20th were for media as we did at the previous event,and the 18th as the first day of the event was reserved for SIPadus members who came to the venue for the occasion of theirregular meetings exceptionally scheduled to be held there forthis test-ride.
The timing of the event was settled to position the event as
an activity to open a new season with FOTs in the Tokyo
waterfront area from November 2021. And also following theamendment to the Road Traffic Act/Road Transport Vehicle
Act in April, the month of November, when the AEB installation
was made compulsory for new vehicles in accordance with the
newly adapted WP29 regulation ahead of the world, was thebest timing to appeal "the global leading Japanese safetyphilosophy" to the world.
There were nine companies who participated in the
exhibition: Suzuki, Daihatsu, Toyota, Nissan, Honda,
Continental, Valeo, Tier IV, and BMW for a total of 20 vehicles
as the test-ride vehicles and exhibits. (Fig.9)

Fig.9: Commemorative photo at second even

Also in the Aomi F1-S Zone venue, a press room wasintroduced for the convenience of media reporters andjournalists when taking a break or refreshments, as well as forbriefing held twice a day for the first time. The one-hour briefing
was for presentations on SIP-adus and seminars about newly
standardized AEB (Automatic Emergency Breaking)installation and legislative trends by officials from relevant
department of the Ministry of Land, Infrastructure, Transport
and Tourism, invited for the event.
Further, as another new attempt, we established a visual field
loss experience area in the press room for visitors to experiencevisual field loss by a driving simulator during their free time
between test rides and briefings.
An exhibition with experience of AEB and pedal
misapplication prevention system was also new in the event. A

rather large space of 70m x 18m was reserved for this exhibition
to experience the pedal misapplication prevention system by
Toyota and the AEB systems by Suzuki and Daihatsu. (Fig.10)

Fig.10: Test-ride on vehicle with AEB

The total of 104 reporters and journalists came together inthe two days for press, and the event was featured in articles by
24 publishers including online car magazines such as "ahead"and "cliccar", and "IT media Web" as online business magazine
among others.
The third test-ride event was held for three days fromThursday 29 September to Saturday 1 October, 2022 at Aomi R
zone in the Tokyo waterfront city area. As conclusion andtransition of activities of the second phase of SIP-adus, theevent was planned and carried out under the theme "Japantakes a global leadership role of traffic safety in the new cohesivesociety with inclusion and respectful diversity"
The Saturday event was planned to attract a generation ofundergraduate and graduate students, who represent thegeneral public and will be leaders of the society when theautomated driving technologies are commercialized..
The exhibits and explanatory sessions by the event organizer
were more enhanced than before, with the core exhibits by JARI
(Japan Automobile Research Institute) who is working on
creation of a real place for the performance evaluation and by
DIVP® of a virtual space, associated with other exhibits for V2N
that had been studied at the Tokyo waterfront area and those by
HIDO (Highway Industry Development Organization) who
lead FOTs in rural areas. These were closed by offeringsomething useful even only through watching and talking
together at the venue, other than test-ride experience.

2.3. Show achievements of research activities: Results presentation event and magazine book(tentative)
As mentioned in previous sections about the test-ride events,
we had preliminary plan to carry out an event at the Tokyo
waterfront city area, one of the venues of the Olympic and
Parlympic games Tokyo 2020. Other than test-ride event, we

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had a plan to show achievements made through the second
phase of SIP-adus. This was realized in March 2021 as "theShowcase of SIP automated driving changing the future - theinterim results presentation event of the second phase of SIPadus"This was to demonstrate latest status of researches proceededby SIP-adus for practical implementation of automated driving
in an easy to understand manner, and to offer opportunities tofeel current environment where these achievements werematerialized. For these, we started from organizing andcategorizing each of researches then worked to define story
structure of the exhibition. As a result of this, five major themed
areas were settled.
"Welcome Zone (Society5.0 and automated driving)" as theentrance showed the future society concept with Society5.0
through realization of automated driving society and introduced
outline of SIP-adus activities, along with a message video from
Mr. Kuzumaki, the program director of SIP-adus, projected ona large screen.
"01 Technologies to realize automated driving society" wasmainly for exhibits on studies of cutting-edge technologies
related to automated driving, such as studies at the Tokyo
waterfront are to generate traffic environment information for
infrastructure-cooperated automated driving, those to create
virtual environment for automated driving safety evaluation,
and also research studies on Intrusion Detection System (IDS)
against cyberattack. In this area, test vehicles used throughstudies and a symbolic exhibit with a large display to offer easyto-understand description on the FOTs at the Tokyo waterfrontarea, such exibits helped visitors to deepen their understanding
with arosed interests to the technologies.
The zone "02 Social implementation of automated driving"
had exhibits on FOTs for social implementation of automateddriving mobility services in depopulating rural areas andstudies for sustainable business models of such services, andalso exhibits concerning to creation of MD CommunetⓇ, a web
portal for future multi-purpose use of geographic data linked totransportation environment with broadly adapted automateddriving. A golf cart-type automated vehicle used in rural area
FOTs was displayed together with a movie on studies carriedout in remote areas to facilitate understanding like other zones.
In the zone "03 Automated driving familiar to people", theexhibits related to the society in which all the people can movearound safely and securely, introducing studies on knowledges
to learn and effective education methods about automatedvehicles and popularizing driver-assistance vehicles, as well as
studies on effectiveness of advanced driving assistance systemfor people with visual field loss.
Lastly, "04 SIP-adus keeping an eye toward the society" wasfocused on communication activities promoted by SIP-adus torealize automated driving society and other efforts to enhanceindustry-academia-government collaboration and global

cooperation.

Fig.11: Exhibitions

We introduced 4 key pillars: i) generation and transmissionof transport environment information, ii) establishment of
virtual environment for safety evaluation, iii) creation ofevaluation method for cybersecurity (IDS), iv) development of
web portal for geographic data distribution. Also technologicaldevelopments for generation and transmission of transport
environment information such as merging assistanceinformation, traffic information utilized vehicle probe data andothers were shown along with activities such as FOTs in theTokyo waterfront area and in rural area for automated driving
services, and others to disseminate social acceptance and tostrengthen international cooperation.
As shown in Fig.11, we deployed various exhibits such as
actual vehicles and equipment, explainer videos, and others tofacilitate understanding and access to information incomplementing figures and descriptions on walls, while
exhibitors gave explanation of details of their activities tocommunicate with visitors as two-ways exhibitions. In Fig.12, it
is shown that the venue guided tour of around 50 minutes wereorganized. A party of ten visitors could visit each zone with aguide who assists visitors to communicate with exhibitors anddeepen their understanding.
As the event was taken place under COVID-19 pandemic,
we applied infection prevention measures in the venue,
requesting visitors to indicate their contact information, tosanitize hands, and also to check their body temperature, while
always controlling the number of visitors inside the venue.
Further, for companies and individuals who might hesitate tovisit the venue, we created a website with 3D virtual venue
walkthrough. All wall graphics, texts, videos displayed in thevenue were accessible online. Online guided tours were alsoavailable on the web as "virtual guided tour", which enabledonline visitors to watch the venue and to communicate withexhibitors on the venue through a web meeting system. (Fig.12)
On the website, we applied various measures to encourage

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
3) Projects to Foster Public Acceptanceonline participation, making TV show-style video with hosts
walking through and showing the venue, and adapting live
streaming camera moving around the venue in regular basis totransmit online atmosphere of the actual venue. These measuresagainst COVID-19 pandemic are considered as effective evenafter the pandemic for remote participation.

3

Conclusion

Many citizens had viewed or taken part to the events
described above, of which the articles were published by many
medias. We can say that this contributed to foster public
acceptance of automated driving to a certain extent. On theother hand, fostering public acceptance can be completed not
only by SIP-adus activities, but also by continuous effort to beproceeded by researchers and experts for the future as theirissue. Relationship between our-own lifestyle and mobility
must be always thought by each of us as citizens to take actions,considering how automated driving can contribute and give
impact to this relationship. Based on these reflections, we aregoing to enumerate what should be put importance even after
the program to foster public acceptance as we found throughthe SIP-adus activities.

3.1. Expand accessibility by utilizing digital media
Fig.12: Scene of guided tour

A total of 1,097 people participated to the event at the venue
and online. As getting media coverage, articles on the event
were published by Response., cliccar, carview!, Nikkan Kogyo
Shimbun, NEXT MOBILITY, Yahoo! News, and others. Also
visit by researchers and SIP-adus related persons encouraged
mutual communication among them as did so exchange ofexhibitors.
We are preparing "Final results presentation event", as of thetiming of writing of this article, to hold in around March 2023
as the end of the second phase of SIP-adus. This must be aculmination of SIP-adus activities, as we keep this in mind toplan the event. A book as a collection of overall SIP-adus
activities, in progress as of this timing, will be also publishedand distributed at the event venue.
An objective of publication of the book is to review SIP-adus
activities as a project which have led its roll and purpose toachievements in reality, then to clarify what derived theachievements, which would broadly help people who want toinnovate to find a hint. Another is to structuralize whole of the
project as a successful case of effective collaboration amongindustry, academia and government or ministries and agencies
by strong will of "individuals" who aim to achieve something.
We have selected and structuralized topics among events of SIPadus and its related industries in and outside of Japan from prestarting of SIP-adus to FY 2022, then interviewed key persons who
led major topics in ministries and agencies, companies oruniversities. Based of this, we are going to make a story to beremained in telling how the ideological backbone of the program
have been developed over the period of two phases of SIP-adus.

SIP-café, which was inaugurated with a concept of
information transmission essential to foster public acceptance,
contributed to enhance communication on automated driving
offering space to collect, transmit and amass information, as we
recognize. On this website, we effectively transmitted
information in taking advantage of digital media, which enabled
us to make articles public in short-time after getting information
with many photos or link to videos.
Further, because of COVID-19 pandemic not only in Japanbut also outside Japan since around spring 2020, we needed toshift to online activities building even in this project utilizing
digital media. For example, communication events such as"Citizen Dialogue", "Automated driving LIVE News", andwebinars, or exhibitions for result presentation events, these
events were carried out using digital distribution system or on
hybrid meeting style of in-person and remote attendees. Digital
media enabled us to provide event experiences for remoteattendees which are not so far from those in real venue. Like
this, acquiring new contact point to participants as "remote
viewing", we were able to provide an entrance to automateddriving for people who had difficulties to attend to in-person
event as we've done. This can be said one of contributions
toward fostering public acceptance.

3.2. Deliberations as "one team" - the entire community uniting as one
What we found through activities mentioned above such as"Memoirs of Automated Driving Trials" and "Citizen Dialogue/
Regional Automated Driving Summit", that a key is "one team"of whole local stake holders, while citizens understand mobility
issues in their town as their own problem, they cooperate with

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
3) Projects to Foster Public Acceptancelocal government and business operators to talk about anddiscuss together how mobility in their own town should be,then through implementation of such services, all participants
continuously take part to improvement of this in checking
operation. In contrast to ready-made or government-mademobility service provided to citizens, what we discovered was"best practice" with co-realization of ideal mobility to improve
quality of life in community, with more engaged citizens as aresult of construction and continuous use of a system in whichcitizens can voice their opinion about local mobility services as
involved member. Technological development and institutionalframework are indispensable to realize social implementationof automated driving, but as mentioned above, frankcommunication among citizens, local government and business
operators must be sustained as system to make sustainable local
mobility service more realistic.
[Contacts]
DENTSU MEITETSU COMMUNICATIONS INC.104-0061, Ginza, Chuo Ward,
Tokyo, 2-16-7, Kosan No. 1 Building 4F, 03-5565-4331, Contact: Tadashi Hirota (t.
hirota@dmc.dentsu.co.jp)
SC-ABeam Automotive Consulting, 2-3-2 Otemachi Place East Tower, Otemachi,
Chiyoda-ku, Tokyo 100-8601, 03-6285-4600, Contact: Hiroshi Kimura (kimura@sc-abeam.com)

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4Society with Automated Driving(2) Public Acceptance of Automated Driving

4) Research for Automated Driving Bus
Friendly to Persons with Disabilities or
Reduced Mobility and Orientation
Keiji Adachi, Soichiro Shibata, Ai Ikenaga, Ryo Tachikawa (NTT DATA INSTITUTE OF MANAGEMENT CONSULTING, Inc.)(Abstract) Our aim is to realize a "Cohesive Society" in which people with disabilities, who until now have not necessarily
been able to fully participate, can actively participate and contribute to society. In the 1990s, Low-floor busses appearedon the market, and measures such as electric ramps for wheelchair access have been studied. In the near future,
automated driving is expected to reduce traffic accidents and ease traffic congestion. Even in a world where automatedbuses are widely used, however, it is necessary to aim for automated buses that can be used by people with disabilities
or reduced mobility and orientation including elderlies, rather than simply leaving them behind. We therefore conducteda research to identify design requirements and considerations in order to achieve such automated buses. The research
was conducted basically with participation of people with such reduced mobility, including behavioral observation ofparticipants and bus drivers when they have troubles and a review meeting after making and testing mockups. As aresult, as recognized problems, something related to getting in and off the bus, locking the wheel chair, among others arereported. For these, we clarified requirements and considerations necessary to implement and put into practical use
devices such as automatic ramp, wheelchair locking devices, and others
Keywords: Automated driving buses friendly to road users with limited mobility, inclusive society, people with disabilities, barrierfree, participation of users

1

Study Overview

1.1. Background and objectives
Buses are an important means of transportation for people
with limited mobility, such as people with disabilities, theelderly, and people using baby strollers (hereinafter referred toas "road users with limited mobility"). In the early 1990s, buses
equipped with wheelchair lifts were studied,(1) and in the late
1990s, low-floor busses were introduced by various
manufacturers. At that time, low-floor busses were a fully-flat
type with no steps inside at all, and some were equipped with anelectric ramp for wheelchairs, but their high price and small
number of seats prevented their wide acceptance.(2)In response, the Ministry of Land, Infrastructure, Transport
and Tourism established standard specifications(3) and acertification system(4) for low-floor busses, with the aim of
reducing their manufacturing costs and promoting their safety
and convenience. The standard specifications have continued tobe revised to make buses more friendly to road users withlimited mobility.(5)In regard to the SIP-adus (Cross-ministerial Strategic
Innovation Promotion Program (SIP) Automated Driving for
Universal Services), it is also stated that one goal is to realize asociety in which mobility of road users with limited mobility isensured and all people have a higher quality of life.
Therefore, after researching and analyzing the needs of road
users with limited mobility as well as domestic and international
trends, we conducted a study with the aim of formulating
implementation requirements and considerations concerning
the interior layout of automated buses which road users withlimited mobility can use more freely.

1.2. Research methodology
Through interviews with and behavioral observations ofroad users with limited mobility, we collected and organized
the value of using buses and the problems with using buses foreach type of road users with limited mobility. Based on the
results, ideas for bus layout were discussed in expert interviews
and workshops. Some of the ideas were then developed into a
bus mockup, virtual reality (VR), and illustrations, and a review
meeting was held (Fig.1), so that road users with limited
mobility and experts can test these ideas.
Based on the feedback obtained thought the review meeting,a report "design requirements and considerations" wascompiled. A study committee consisted of road users of limited
mobilities and experts from bus and other related industry
groups was established to provide advice on the overall study.
This report describes the requirements for each design, the

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Fig.1: A review meeting(Left: VR evaluation (cane and short leg orthosis user);right: mock-up evaluation (low vision)

feedback obtained from bus operators and participants to theresearch, and the specific requirements and considerations
based on said feedback.

2

Summary of problems

Based on the interviews and behavioral observations of theroad users with limited mobility and experts as mentioned
above, we extracted the problems for each type of mobility
limitation when road users with limited mobility take a bus andthose for bus operators. The details are shown in Table 1.

During the interviews, we asked not only about the problems
faced when using the bus, but also about the value of using thebus for road users with limited mobility. We found that using
the bus has two primary values: as a valuable means to getaround during daily life, and as a gateway to the world to expand
their range of activities. The following is a partial list of ideas
brushed up after obtained through the review meeting, out of
all ideas discussed based on the above mentioned problems andvalues.

3

Design requirements and considerations

3.1. Automatic ramps
3.1.1. Overview
When wheelchair users get on and off the bus, the driver has
to manually move the ramp in and out, which takes time. The
installation of an automatic ramp that can be moved in and out
at the touch of a button (Fig.2) would solve these problems.

Table 1: Road users with limited mobility and bus drivers' problems when using buses (extract)
Classification

Description of problems when using buses
Wheelchair users feel bad for other passengers when the driver takes time to set up a ramp and tosecure the wheelchair for their getting on and off the bus, so they hesitated to do so.
Electric wheelchair users
Securing a wheelchair next to the current folding seats narrows the aisle.
Scared of sudden stops and centrifugal forces when taking a curve.
PhysicallyDifficulties getting on and off the bus when there is a gap between the ground and the floor of the bus.
handicapped
Users with canes andSometimes feels rushed because they have to secure their brace before the bus even stops.
personshort leg orthosis
Narrow space in front of the seat does not allow legs to stretch out to unfasten the brace.
Sometimes difficult to press the stop button because their fingers aren't strong enough.
Upper limb disability
Hard to hold on to the railing when they also have luggage.
users
Difficult to prepare fare payments etc. without sitting down.
Difficult to find a bus stop if there are no braille blocks.
Blind white cane users
Unsure if the reason for the bus stopping is because of arrival at a bus stop or just due to a red light.
Confusion due to different buses, such as forward doors, rear doors, pre-payment, post-payment, etc.
Hard to find a seat when it's crowded. Feel bad about asking others to give up their seats.
Visually
Blind guide dog users
Don't know where to stand without disturbing other passengers.
impaired users
Not knowing where the stop button etc. is located, having to grope around for the location.
Unable to see which seats are available.
Especially in the evening, it is difficult to press the stop button because of the similar colors of the
Low vision users
button and the handrail.
Unable to tell if this is a bus to desired destination.
Unsure if the reason for the bus stopping is because of arrival at a bus stop or just due to a red light.
Deaf users
Rely on displays for information, but it's hard to see when sitting toward the back.
Unable to hear sounds, so unable to tell if IC card payment has succeeded.
Hearingimpaired users
Unable to prepare for alighting without knowing the time and distance to the next bus stop.
Hard-of-hearing users
Hard to hear announcements.
Difficult to get information to understand an emergency situation.
Don't know what to do when the display and the actual bus stops are out of sync.
Confused by and unable to cope with changes in boarding methods, such as different doorway
Intellectual/developmental disability users
locations.
No ruby characters provided for kanji characters, so stop signs, etc. are unreadable.
If they feel sick, it is considered bad manners to lie down and rest in the bus, so they get off the bus to
Mentally challenged users
rest, but don't know where they can rest.
Announcements are psychologically taxing, with fast voices, mechanical noises, and loud noises.
Hard to hear announcements.
Difficult to get on and off the bus when there are steps. Also, the swaying of the bus makes them feelElderly users
unsteady.
Can panic because it takes a long time to get off the bus. Standing up before the bus stops leads toaccidents involving falling over.
Unaware of a dedicated space for strollers. Never seen anyone using it.
Baby stroller users
Can panic because they cannot sit down or get off quickly enough due to carrying the stroller or dealing
with their child. Feel apologetic to the people around them.
Many accidents involve passengers standing up and falling before arriving at the bus stop, making thedriver feel uneasy during driving.
Panic because it takes so much time to fold the seats, secure wheelchairs, and deploy the ramp. Many
Bus drivers
people are reluctant to use wheelchair safety belts, which is dangerous.
Announcements and responses to passenger inquiries can be made while the bus is stopped, allowing
the driver to concentrate on driving.

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passengers. Since the automatic ramp is effective not only forwheelchair users but also for the elderly, and because it may
cause confusion when a driver or a system has to make adecision each time whether or not to extend the automatic
ramp, it is desirable to extend the ramp for all passengers.
(2) Improvements to bus stops (boarding and alighting areas)
The standards for facilitating mobility require that bus stop
areas are designed to allow wheelchair users to smoothly board
and alight from the buses that stop adjacent to the areasIn
reality, however, physical constraints result in bus stops without
sidewalks or on sloping roads due to drainage ditches. In orderfor the use of automatic ramps to spread, it is necessary to
improve bus stops so that automatic ramps can land on stableground.

Fig.2: Image of automatic ramp

3.1.2. Feedback obtained from bus operators andresearch participants, etc.
While some people said that the reduction of time requiredfor deployment of the ramp was an advantage, others said thatthe slope of the ramp could be concern and that visual andauditory information was necessary when deploying theequipment.

3.1.3. Examples of specific requirements(1) Make the slope more gradualThis is because someone on the bus, not bus conductor,
possibly need to support the wheel chair users when bus isautomated without any conductors in the future.

3.2. Simple folding seats
3.2.1. Overview
When wheelchair users get on and off the bus, the driverneeds to fold the foldable seats, which takes up additional time.
Because of the time required, some wheelchair users havecommented that they feel apologetic toward the otherpassengers and the driver. Both the drivers and the wheelchairusers feel the need for improvements.
The simplicity of the folding seats (Fig.3) creates spacewithin the bus, making it easier for wheelchairs and strollers to
get on and off.

(2) Automatic ramp deployment/retraction with sound andlightsThis is especially required for the visually or hearing
impaired, who may not otherwise know whether the automatic
ramp is completely deployed or not yet.
(3) Slope formed with a single board
If the automatic ramp is stacked in two layers, wheelchairs
may get stuck on the step in the middle of the ramp.
(4) Add roughness on the slope surface to an extent that will
not interfere with walking
For the visually impaired, getting on and off the bus bymeans of a ramp, instead of the present situation of getting on
and off by means of steps, would otherwise make it difficult forthem to distinguish the boundary between the inside and theoutside of the bus.

3.1.4. Considerations(1) Operational industry guidelines for automatic ramp useneed to be created
In order to use automatic ramps safely, it is necessary toestablish operational industry guidelines for whether automatic
ramps should be deployed for all passengers or only for specificFig.3: Image of a simple folding seat

3.2.2. Feedback obtained from operators, concerned
parties, etc.
While some commented that these seats provide both spaceand seating as required and are easy for wheelchairs and stroller
users to use, some of the participants commented that it wasdifficult to find a seat for a person with white cane, and difficult
to sit on a folding seat while holding it down with their hands.

3.2.3. Examples of specific requirements(1) Foldable seats should be sideways to the direction of travel
If the folding seat is oriented in the direction of travel, theseat cannot be folded to provide more space due to the positionof the backrest.

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4.Society with Automated Driving(2) Public Acceptance of Automated Driving
4) Research for Automated Driving Bus Friendly to Persons with Disabilities or Reduced Mobility and Orientation(2) When not in use, the seat surface is in a lowered position.
A visually impaired person using a white cane judges
whether a seat is available by the feel of the cane in their hand.
Guide dogs are trained to place their head on an available seat.
Therefore, when a passenger is not using a folding seat, the seatshould be lowered.
(3) Open and close at the touch of a button
If the chair is heavy or requires a lot of force to close, it may
be perceived as a chore to fold it up and utilize the space.

(Image of the clasp when affixed)

3.2.4. Considerations(1）Minimize risk of accidents like finger entrapment
In the past, the driver folded the seat, checking carefully toperform the operation safely. In the proposed system, thepassengers fold the seat for themselves by pressing a button. The
new design must therefore prevent passengers from getting
their fingers caught in the seat or otherwise injuring themselves.
(2) Loss of handrails
While the folding seat provides more space, it also requires
fewer vertical grip bars, leaving fewer places for standing
passengers to hold onto. Efforts to alleviate congestion and theinstallation of suspension straps and ceiling handrails are
required.
(3) Safety Considerations
If a person with a cane or short leg orthosis sits on a sidewaysfacing seat, other passengers may trip over them, causing injury
to themselves or other passengers. It is necessary to consider
the safety of other passengers as well, such as by making
announcements in the car to warn them.

3.3. Wheelchair locking devices
3.3.1. Overview
When a wheelchair user gets on the bus, the driver needs tosecure the wheelchair to the floor with a three-point belt after
the aforementioned ramp is deployed and retracted and the seatfolded away, which is all very time-consuming.
A wheelchair locking device (Fig.4), which is easy to bothsecure and release, will be installed on the floor of the bus. It canbe released by pressing a button mounted on the wall next tothe device.

3.3.2. Feedback obtained from operators, research
participants, etc.
Operators expressed hope that the time required to secure awheelchair could be shortened and the burden on the operator
to secure the wheelchair reduced. Users commented that theywanted floor makings to lead them to the correct position, thatthey stumbled over devices installed on the floor, and that theircanes got caught on them.

Fig.4: Image of wheelchair securing device

3.3.3. Examples of specific requirements(1) Add floor guides for wheelchair users
Floor markings would make it easier for wheelchair users tosecure their wheelchairs.
(2) Securing device installed with a lid under the folding seat
This will mitigate the risks that white canes used by thevisually impaired or canes used by the elderly may get caught in
the wheelchair locking device, or other passengers tripping
over it.

3.3.4. Considerations(1) Installation of fasteners on the wheelchair
In order to use such a wheelchair locking device, a clamp
must be installed on the wheelchair. In a society where suchwheelchair locking devices are installed on buses, it will benecessary to design a system and specifications for both thewheelchair and the locking device so that the device can be
attached regardless of the type of wheelchair.

3.4. Content of on-board display
3.4.1. Overview
When road users with limited mobility who need a long timeto get off the bus take an unfamiliar bus route, they are unable
to prepare for getting off the bus with sufficient time to sparebecause they do not know where they are or how soon they will
arrive at their destination, and they can panic when they arriveat their destination bus stop. People with mental disabilities andwho are prone to changes in their physical condition can feelanxious about using the bus because they do not know at which
bus stop they can get off to rest if they feel unwell during the bus
ride. Stroller users feel inconvenienced by the lack of
information around bus stops because they need to take care of
their infants. This system solves these problems by displaying
detailed information on the on-board display. (Fig.5)

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4) Research for Automated Driving Bus Friendly to Persons with Disabilities or Reduced Mobility and Orientation

Next Stop

Shimbashiekimae

2 minHamarikyumae

7 min

Tsukiji
5-chome

We are here

Hamarikyumae
2 minHamarikyu Rest Area

Toei BusCity 01
Express

Hamarikyu-mae Hotel

Oedo Line

Next Stop

Shimbashiekimae

1 minHamarikyumae
Stopping

Arrived
6 min

Tsukiji
5-chomeHamarikyu-mae

Arriving Shortly
Please do not get out of your seat until the bus comesto a complete stop.

Hamarikyu Rest Area

Next Stop: Tsukiji 5-chome

Hamarikyu-mae Hotel

Map data source: Google

Fig.5: Image of the content displayed on the on-board display

3.4.2. Feedback obtained from operators, research
participants, etc.

Red: Braking

Blue: Arrival at stop

Operators commented that they could concentrate on
driving because they would need to respond to fewer inquiries.
While participants commented that it relieved their anxiety
about their current location and the next stop, others said that
it would be better to obtain information from tablets or otherdevices.

3.4.3. Considerations(1) Selection of information to be displayed, provision of
information via apps etc.
While it is good if information can be obtained via thedisplay, too much information makes it difficult to receive eachpiece of information slowly and accurately, so it is necessary toconsider the provision of information using apps held by thepassengers.

3.5. R eason for stopping is indicated by lights inside
the bus3.5.1. Overview
Hearing-impaired persons and the elderly may not be able tounderstand the reason for the bus stopping, due to inability ordifficulty in hearing announcements on the bus. To solve thisproblem, the reason for the bus stopping is indicated by thecolor of a light installed inside the bus. (Fig.6)

3.5.2. Feedback obtained from operators, concerned
parties, etc.
Some users commented that while this does make it easier tounderstand the reason for stopping, and increases safety, colorand brightness need to be taken into consideration.

Fig.6: Image of on-board light display

4

Results and summary of the study

4.1. Design layout proposal to solve the problems ofroad users with limited mobility and drivers
This project focused not only on solving the problems ofroad users with limited mobility but also the problems ofdrivers.
Bus operators have been making efforts to promote buses
that are easy for road users with limited mobility to use byintroducing low-floor busses and continuously studying andworking on improvements. For this study, we obtained feedback
from road users with limited mobility on (1) automatic ramps,(2) simple folding seats, and (3) wheelchair securing devices,with the intent of allowing them smoother boarding andalighting.
In addition, bus operators and drivers make announcements
to prevent accidents aboard the bus. Nevertheless, such
accidents occur, especially among the elderly and road users
with limited mobility, due to being in a hurry to get off the bus.

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The content of onboard displays and reasons for stopping being
indicated by a colored light, as described in sections 3.4 and 3.5of this report, are considered to enhance the communication of
information to road users with limited mobility, contributing to
the reduction of impatience to get off the bus and the preventionof accidents involving falls inside the bus.
This project studied the design requirements andconsiderations assuming a situation in which automated buses
will be widely used in the future and so drivers will not always
be able to provide support to passengers. These requirements
are applicable not only to future automated driving buses, butalso to solving the problems of current scheduled buses.

4.2. Design requirements with consideration ofuniversal design
We carefully made a design to solve an issue for a user so as
not to generate another fresh issue for other users.
For example, for the visually impaired, the automatic ramp
makes it difficult to see the boundary between inside andoutside of the bus, the folding seats make it difficult to spot
empty seats when they are closed, and the wheelchair securing
device poses a risk of a cane getting caught in it. It is easy to
imagine that such physical layout changes would causeinconvenience to visually impaired people who have difficulties
for spatial recognition. As shown in the requirements andconsiderations in this study, it is necessary to design the space
so that visually impaired people can use it safely; for example,
by using materials and unevenness of the floor surface, alerts
using sound, and the removal of exposed hazards.

4.3. Future considerations
The committee confirmed that the Japan Automobile
Manufacturers Association would start measuring workability
and safety using the mockups (automatic ramp, wheelchair
securing device, and folding seats) created by this project, andthat they would like to continue studying other ideas,
incorporating new opinions based on the requirements
described, with a view to proposing them to business operators.

5

Conclusion from Masayuki Kawamoto,chairperson of this study projectThis study began with the question of how everyone,
including road users with limited mobility, can use public
transportation by themselves when public transportation
becomes fully automated in the future and drivers and othercrew members are no longer on board. The introduction of
automated driving technology does not mean a sudden shift to
fully automated driving, but a number of careful steps must be
taken. It is an important point of view that the driver of public
transportation, such as a bus route, should be able to concentrateon the driving itself and drive safely at the same time, whileallowing road users with limited mobility to make full use of

public transport without concern.
Furthermore, this survey have reaffirmed that, regardless of
the introduction of automated driving, all public transportation,
including existing public transportation, must provide
reasonable consideration for road users with limited mobility,
and that there is still much room for improvement from thecurrent situation. Of course, various measures and the efforts ofmany predecessors have lowered the barriers for wheelchairusers and others to use public transportation. However, while
many of these measures have been hardware-based, the use of
the IoT, although remarkably popular in recent years, has beenslower to take root. Many bus transportation businesses areplaced in a very difficult business environment, and it is difficult
to introduce new equipment that requires a large investment.
On the other hand, many road users with limited mobility now
also have smartphones, and it is no longer so outlandish to
making using such devices a prerequisite of taking the bus. Of
course, there are many challenges in making such applications
easy to use and understand; AI is not the simple and convenientanswer to everything. What is important is to steadily identify
true needs from the user's, rather than from the provider's,
point of view, and to make repeated improvements from there.
We hope that this survey will serve as a guidepost along thelong road ahead.

[References]
(1) Bus Gear Terminal: Looking Back on the History of "Low-Floor
Busses" (1) How a "European Tour" Changed the Times https://www.bus-gear.com/terminal/busgear_contents_detail.htm?L=BCSDe
tail&BCD=P65&ID=A10208465176, (referenced 2022.08.05)(2) Bus Gear Terminal: Looking Back on the History of "Low-Floor
Busses" (3) Why Did "Domestic Full-Flat Buses" Not Become
Popular? https://www.bus-gear.com/terminal/busgear_contents_
detail.
htm?L=BCSDetail&ID=A10309749413, (referenced 2022.08.05)(3) Ministry of Land, Infrastructure, Transport and Tourism: Report on
the Development of Standard Specifications for Next-generation
Prevalent Low-Floor Busses in 2002, https://www.mlit.go.jp/
jidosha/topics/nsbus/nsbushome.htm, (referenced 2022.08.01)(4) Ministry of Land, Infrastructure, Transport and Tourism:
Establishment of a Certification System for Low-Floor Busses withStandard Specifications, https://www.mlit.go.jp/kisha/kisha04/09/090119_.html, (referenced 2022.08.01)(5) Ministry of Land, Infrastructure, Transport and Tourism: Partial
Revision of the Guidelines for Approval of Standard Specification
Low-Floor Busses, https://www.mlit.go.jp/report/press/jidosha07_
hh_ 000185.html, (referenced 2022.08.01)[Contacts]
Healthcare Implementation Group, Advanced Technology Strategy
Unit, NTT DATA INSTITUTE OF MANAGEMENT CONSULTING, Inc.,
JA Kyosai Bldg. 10F, 2-7-9 Hirakawacho, Chiyoda-ku, Tokyo, 03-32217011 (switchboard), Contact: Soichiro Shibata (Manager)

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5

Data Connection and Use to Achieve
Society 5.0

Building and Designing a
Geographic Architecture(Overview)
Raita Hiraoka, Hiroaki Kimura (Cabinet Office)in 2019 we began the development of "cross-field" and "field-

1

by-field" data coordination infrastructures, as well as the

Backgroundconstruction and testing of an architecture based on aframework for deepening the views and understanding among

In the society that we should aim for as Society 5.0, newbusiness models will be born through the advanced fusion of
cyberspace and physical space, and innovations born from theutilization of big data and AI will create new value in various
fields, leading to a paradigm shift in the economic and social
system. In order to realize such innovations, it is important tocoordinate data not only by field, as has been the case in thepast, but also across fields.

In SIP "Big-data and AI-enabled Cyberspace Technologies,"

all stakeholders, ensuring interoperability among theseinfrastructures,anddeveloping

technologies

andstandardization that enable mutual coordination andcooperation (hereinafter referred to as the Society 5.0
Reference Architecture; Fig.1). This initiative is being
expanded to smart cities, geographic information-related
fields (automated driving, agriculture, disaster prevention, andinfrastructure), and personal-related fields. In the SIP-adus(Cross-ministerial Strategic Innovation Promotion Program(SIP) Automated Driving for Universal Services), we havebegun constructing an architecture for geographic data.(1)Organizations

Implementing agencies, councils,
governing bodies, etc.

Businesses

Services, solutions, agreements
and contracts ecosystem, FOTs, etc.

FunctionsIndividual functions to realize
services, solutions, etc.

Data

Datasets, vocabularies/
codes, data catalogs, etc.

Data coordination

Data collection, data integration, data
cleansing, IoT device management, etc.

AssetsSensors, actuator hardware,
networks, etc.

2

Activities in SIP-adus

In the SIP-adus, we put together an architecture ofautomated driving fields related to geographic data (hereinafterreferred to as the Automated Driving Architecture) (Fig.2)
based on the Society 5.0 Reference Architecture.
Since the first phase of SIP-adus , we have been working on
the generation and distribution of high precision 3D maps anddynamic road traffic environment data necessary for automatedetc.

Laws, regulations, etc.

eld
s,

Rules

Fi

Vision, scope, etc.

Semantic axis

Cybersecurity, authentication

Strategy and policy

Temporal and spatial axes

driving, but cost reduction is a major issue for their widespreaduse. On the other hand, it is highly possible to reduce costs by
utilizing 3D point group data and vehicle probe data, which are

Fig.1: Society 5.0 Reference Architecture

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Building and Designing a Geographic Architecture (Overview)

Functional
layer

Driving
assistance

Automated
driving
Emergency
vehicle assistanceTransportation
plan

Transportation
management

Bus location

Data
layer

Vehicletrajectory

Asset
layer

Radar

Images ofsurroundings
ABS activation

Baggagedelivery
Travel plan

Vehicle data

ETC gate

Road regulations

Traffic signal
light color

Bus route map

Railroadroute map

3D point groups

Traffic lights

CameraLiDAR

Transfer guideRoad management

2D mapGNSS

and other functions that promote matching of needs and seeds.

Signal controls
Congestion
forecast

Navigation

G sensor

DSRC device
Traffic
counter

Portal functions

Update map

Pedestrian
positionthe creation of new services by enabling diverse users to utilize
road traffic environment data for various services through an
open portal web site called "MD communet" (https://info.adus-

Railway
car location
Baggagelocationarch.com/) (Fig.4), the portal website was established and madeMeteorologicalinformation

available in October 2020. After that, efforts were made to

Taxi stand

expand the number of users, chiefly through data providers,
Dynamic

Smartphone

ElectronictagMeteorologicalsatellite

MMS
VehiclesThis portal supports the promotion of data distribution andMobility
supportSemidynamic

and in April 2021, the website was made available to the generalpublic with 11 participating companies and organizations.

Semi-static
StaticTraffic infrastructure

People, otherFig.2: Automated Driving Architecture

the data layer for generating such road traffic environment data,
in other fields as well.
Business
matching

We believe that the problem is an imbalance between data
users who do not know that data exists and data providers who

Datasearchdo not know the needs for their data. To address this, we

Needs/seeds
registration

decided to work on the construction of a portal site with datacatalog and data search functions.
In addition, since it is important to create examples of datautilization using this portal site, we held an "App Contest to Solve
Issues Related to Transportation in Kyoto, a Tourist City" tobroaden the scope of data utilization efforts and to gather
opinions on how to improve the usability of the portal site.
In addition, we promoted the broader utilization ofgeographic data in collaboration with other SIP-adus projects,
such as the use of probe vehicle data to improve logistics
efficiency and support road management operations.
In 2020, we surveyed and analyzed examples of mobilityrelated data utilization in Japan and abroad, and based on the
results of interviews with data providers, service providers, datacollectors, and academic experts, we held discussions at a study
group consisting of experts and compiled draft guidelines fordata handling in the mobility field as well as a proposal for
public-private data coordination.

Technical
supportUse
cases

Communications

Servicedevelopment
supportFig.3: MD communet, a data matching website

To promote the use of the portal site, we have improved theuser interface by incorporating the opinions of "MD communet"
users, and have otherwise worked to make it a portal site that iseasier to understand. We have held various events to enliven
matching activities, including an "Idea Creation Event" inNovember 2021, a "Business Matching Event (MD com Match)"
in March 2022, and an "Idea Creation Workshop" in March
2022, and have worked to create many examples of portal
utilization. As of the end of July 2022, 43 companies andorganizations had registered to participate, and 7,316 catalog
data items had been posted. [See Section 5 1) for more details]
When we look at trends overseas, we see that the EU made2.1. Construction of the Portal for Road Traffic Environment Data "MD communet®"

it mandatory for member states to establish National AccessIn constructing the road traffic environment data portal,use of mobility data, and stipulated that data on transportation

while assuming services in the functional layer based on theautomated driving architecture, we aimed to aggregate,visualize, and catalog data layers such as road traffic environment
data to serve as a trigger for matching data providers and data
users, thereby enabling one-stop browsing. (Fig.3) In addition,
the system has an advanced data search function that uses not
only keywords but also area and purpose of use as keys, searches
for similar data using machine learning, a suggestion function,

Points (NAPs) in 2017 in order to encourage the collection andand travel must be provided through NAPs by the end of 2030.
In Germany, the Federal Ministry for Digital and Transport
started operating a mobility data platform called Mobilithek inJuly 2022, which will serve as an NAP. Like "MD communet," itis a portal site with data cataloging and search functions.
As described above, efforts to promote the utilization oftransportation data are becoming more active overseas, and it isnecessary to develop "MD communet" in Japan. Even after the

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5 Data Connection and Use to Achieve Society 5.0﻿
Building and Designing a Geographic Architecture (Overview)completion of SIP, NTT DATA will operate "MD communet,"and plans to improve usability, enhance services and content,
increase awareness, and create usage examples.

Recommends less crowded spots by estimating
the sightseeing spots around the pre-entered
destination based on congestion statistics data
(API).

Nearbyrecommendations

Destination

Calculates congestion based oncongestion statistics data

Facilities that offer points, goshuin stamp, and otherbenefits in Teburan app

Fig.4: MD communet website

Fig.5: KYOTO Raku Mobi Contest

2.2. Holding of App Contests

The SIP-adus Award Winning Apps

In order to create use cases that help to solve transportationrelated issues by utilizing road traffic environment data andmobility-related data, we held a contest (the first "App Contest
to Solve Tourism and Transportation Issues," nicknamed the"KYOTO Raku Mobi Contest") in October 2020 to solicit apps
and ideas for solving tourism and transportation-related issues
faced by Kyoto, a world-class tourist city. The contest was
conducted with the cooperation of the Kyoto City Transportation
Bureau and other operators, and included data on stations,stops, routes, timetables, and fares for public transportation
systems such as buses and railroads; data on temporary luggage
storage and delivery services and stores for logistics; congestion
forecasts for sightseeing facilities; information on sightseeing
spots; center guide service API, and congestion statistics data.
Although there were excellent applications (Fig.5) and ideas in
the first KYOTO Raku Mobi Contest, they were not put into
practical use due to the fact that the data could not be distributed
continuously after the contest. However, the contest did
generate momentum to open up bus information, especially in
the Kyoto City Transportation Bureau, and expectations for thesecond contest were raised. Municipal and private bus
companies in Kyoto City will create bus information in GTFSJP format and register it in "MD communet." Information suchas probe vehicle data from logistics companies and tire
manufacturers will also be provided on "MD communet." The2.3. Development of Support Systems for Local Automated Driving Services
We clarified common issues in socially implementing
automated driving services in rural areas, and developed acommon reservation and operation management system called"Mobisuke" (location management of vehicle positions, safety
monitoring using camera images inside and outside the vehicle,
reservation

management,

boarding/exiting

information

management, etc.) with an interface that is easy to use foreveryone, including the elderly. In developing this system, we
conducted field operational tests (FOTs) in collaboration with
the initiatives of Higashiomi City, Shiga Prefecture; Miyama
City, Fukuoka Prefecture; and Takahata Town, Yamagata
Prefecture, in addition to Kamikoani Village, Akita Prefecture,and Iinan Town, Shimane Prefecture, which are promotingsocial implementation of automated driving services under the
SIP-adus, to improve functions based on feedback from service
providers and users.
In the future, while providing "Mobisuke" as an inexpensive
and convenient system, we aim to commercialize support
services, including the system, in the interests of strengthening
interregional cooperation and with a view to utilizing thecommunication function of the road traffic environment data
portal.

Issues" is scheduled to be held in December 2022. Through this2.4. Improving Logistics Efficiency Using Probe Vehicle Data

contest, we aim to raise awareness of the road traffic environment

With the aim of improving cargo wait times in transportation

second "App Contest to Solve Tourism and Transportation

data portal and obtain feedback from data users who entered
the contest, which we will use to make improvements to theportal. [See Section 5 2) for more details]

operations, improving work times for checking daily inspection
items based on vehicle and probe data, improving operation
schedules and control by real-time understanding of load
weight and tire information, and making other improvements
demanded of the logistics industry, we clarified an architecture

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5 Data Connection and Use to Achieve Society 5.0﻿
Building and Designing a Geographic Architecture (Overview)for future data coordination/utilization in which data obtained[References]

from logistics trucks is utilized to improve logistics efficiency.

(1) The Second Phase of SIP-Automated Driving for Universal Services Research andDevelopment Plan, https://www8.cao.go.jp/cstp/english/04_autodrive_rdplan.
pdf, (accessed 2022.06.30)

In order to improve the wait times for cargo, we obtained
and analyzed the data from the carriers' actual vehicles. Bothcarriers and consignor companies shared their awareness of thecircumstances behind the occurrence of wait times for cargo.
We analyzed the causes of wait times and considered measures[Contacts]
Secretariat of Science, Technology and Innovation Policy Cabinet Office, 1-6-1
Nagata-cho, Chiyoda-ku, Tokyo 100-8914, 03-6257-1334 Contact: Raita Hiraoka,
Hiroaki Kimura

for improvement. (Fig.6)
Vehicle data

Shippers

Motion management data

Trucking companies

Fig.6: Schematic of vehicle and motion management data collection and sharing

To improve work times for checking daily inspection items,
truck manufacturers' own vehicles were used to check the statusof trucks for daily inspection items remotely by identifying
event data that contributes to the checking of daily inspection
items from vehicle and probe data. In addition, we proposed
standardizations of data formats for cooperative areas of vehicle
information such as probe data, and provided information(data catalogs) to "MD communet" to promote further
utilization of data in the future. [See Section 5 3) for moredetails]

2.5. Utilization of Probe Vehicle Data in Road Management
As examples of the use of probe data collected from vehicles,
we studied the detection of slippery road surface conditions
caused by bad weather conditions such as rain, the detection offlooding on roads and the provision of alert information, as wellas the use of the information for road maintenance management
by estimating the amount of rutting from the amount of water
film detected on the road surface.
In cooperation with OEMs and tire manufacturers, wecollected information on ABS operation, vehicle skid control
operation, tire spin control operation, wiper operation status,
tire grip level information, and other data from vehicles inmotion. The accuracy of generated detection information basedon statistical processing was verified through FOTs on actualroads. [See Section 5 4) for more details]

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Data Connection and Use to Achieve Society 5.0

1) Design of Geographical Data Architecture— Building and Promoting a Traffic
Environment Data Portal Site
Naoki Iso (NTT DATA Corporation)(Abstract) The data collected for automated driving, such as high precision map data, road traffic data, and vehicle probes, areexpected to be used as road traffic environment data in various other industries as well as the automotive industry. In order torealize a mechanism to allow this, we constructed and released "MD communet®," a portal site for road traffic environment
data that aggregates data in the mobility field and links it with other fields. Aiming for the continued operation of MD communet,
we have also promoted activities to spread the service, such as acquiring more member companies, increasing awarenessthrough various promotion, and creating offline/online matching opportunities. In addition, we have developed service examples
that effectively use road traffic environment data, which is the key to social implementation of this system, in combination withinformation from other fields, and have studied the support functions necessary for service development.
Keywords: Road traffic environment data, Society 5.0, data coordination, business matching, data utilization service creation

1

Construction and release of "MDcommunet," a portal for road traffic
environment data

development with a focus on the implementation of three major
functions: the promotion of business matching, the expansionof data, and UI/UX improvements based on user feedback.

1.1. Value provided by the portal site
The goal of this program is to contribute to solving social
issues, such as reducing traffic accidents, reducing traffic
congestion, ensuring mobility for road users with limited
mobility, and improving the shortage of drivers and reducing
costs for logistics and mobility services, which will be achieved
through the practical application and expansion of automateddriving, ultimately creating a society in which all people enjoy
high quality of life. This reflects our belief that contributing to
the practical application of automated driving is required torealize a safe and secure society, solve social issues, and create
new business value for these purposes.
To contribute to achieving the goals of this program, we
have created "MD communet," a portal site for road traffic
environment data, as a place where people, information, anddata can gather and create various ideas and services. As shownin Fig.1, this portal site aims to promote the utilization of data
and the creation of new businesses through the portal site byaggregating a wide variety of road traffic environment data inthe mobility field from around the world, and by creating
various opportunities for users to communicate with each other.
For this reason, after equipping the portal site with the basic
functions required from a catalog search site, we advanced

Matching
Automated driving
MaaS
Logistics/
transportation
Road environment

Catalog listing/searchNeeds/seeds
registration

Communications
Aggregate catalog dataon portal site

Matching support

Fig.1: Image of business creation through the portal site

1.2. Business matching on the portal site
As mentioned above, the development of this portal site'sfunctions focused on proactive search and communication
functions to facilitate business matching.
Fig.2 shows an overview of the functions provided byportal sites. The main functions provided by the portal site
are shown below.
(1) Data catalog registration and search functions
This function allows users to register catalogs and publish
them for other users, and provides various search methods
so that users can easily find the data they are looking for.
Specifically, keyword search, conditional search, and area
search functions have been implemented on the top screen,
and re-search and faceted filter search functions have been
implemented on the search results screen.

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

Data catalog site
Catalog

Data information
registration screen

Data information
catalog

Data processing tool
registration screen

Data processing tools
catalog

Data request
registration screen

Data request catalog

Index

Extraction ofcharacteristics

Data user

CADDE
Connectors

SIP4D Catalog*CKAN support*CKAN support

Clustering

Activity
history
Data
Analysis

Data provider

Data user

Recommendation

G-Spatial Information
Center Catalog

Clusteranalysis

CKAN API

Presentation ofsimilar data

Registration andcollection functions

SearchData provider

Fig.2: Overall portal site functions(2) Communication functions
This function is designed to promote the discovery of
new data usage methods and direct business matching bydirectly connecting data providers and users.
Specifically, we implemented a function to evaluate posted
data and a message function to enable direct communication
among users.
(3) Recommendation function
This function recommends catalog data from the system
in order to provide users with unexpected discoveries.
Specifically, we implemented a function that provides
recommendations based on user access information, and asimilar data function and clustering function that uses
machine learning.
(4) Associative word support function for search terms
This function presents the user with related keywords
that are associated with the search term.
Specifically, we implemented a function that enables theretrieval of related keywords from an external repository(DBpedia) for each search term. This means that related term
information is not stored internally and the retrieval
destination can be set flexibly.
(5) Company profile
A function that allows each company to post theirachievements and activities, providing users with information
about the company itself and creating expectations formatching from perspectives other than those of the actual
catalogs.
Specifically, we implemented a function that allows
companies to introduce their initiatives and post text anddata such as editorials and use cases on the website fordissemination and promotion.
(6) Linked Data creation
A function that enables the operator to obtain sources for
information to be disseminated to member companies basedon user activity logs and data relevance.
Specifically, we implemented a function that enables

notification of recommendation information to member
companies by converting the metadata for registered catalog
data and logged-in user activity logs into Linked Data, which
can then be searched by management.
1.3. Portal site data expansion
In order to further promote business matching throughthe portal site, it is essential to expand the available data. As
a response to this requirement, data expansion was
implemented in terms of both the volume and types ofregistered data.
In terms of data volume, we also implemented linkage withexisting data platforms, such as the G-Spatial Information
Center and SIP4D. Furthermore, by implementing the
CADDE connector in the SIP inter-program data coordination,
as shown in Fig.3, it is possible to efficiently obtain datacatalogs from other programs.
In terms of data types, in addition to data informationand data requests, data processing tools can also be posted,
thereby expanding the data while increasing the number ofstakeholders involved in the creation of new services.
As data expansion progresses and the probability ofmatching increases when highly related data and logs arelinked, it is expected that strengthening both will create asynergy in the promotion of business matching.
1.4. Portal site UI/UX improvement
After the opening of the portal site to the public,
opportunities for business matching are expected to increase
due to the additional member companies and theaccompanying expansion of the data catalog, and the role ofthe portal site as a place where more data, people, andinformation can be gathered is expected to become more
important. In addition, for the purpose of attracting moremember companies, UI/UX improvement has been
continuously implemented in conjunction with various

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

CADDE

MD communet

Service planner(MDC member)

DisplayMatching function
Collect
MDC catalog
CADDE Catalog
Save

SearchSearch results
Connector
coordination
SearchReturnSearch results

Present data ID
GUI

SearchReturnReturnSearch function

SIP Disaster
Prevention

SearchData providers inother fields
Data
registration

SIP otherfields

MDC catalog
CADDE Catalog

CKAN

CKAN collaboration

G-Spatial
Information Center

Data providers in
Data other fields
registration

Fig.3: Example of additional development for enhanced functionality
Concept for periodic metadata harvesting function

promotion activities, such as studying the optimization of
the system lines to link from the promotional activities
websites to the portal site, as shown in Fig.4.
Promotional website

Portal site

important prerequisite, and we specifically aimed to create
allies for this project. First, in order to identify the appealing
points of MD communet, which are vital in the creation of
new allies, we organized the worldview and values to beachieved (as shown in Fig.5). This was based on our objectives
and the project concept. We also defined the ecosystem that
we wish to form through this project.
Ecosystem formed by the project✓ We will utilize the assets and professional strengths of the various companies and organizations participatingin this project to form an ecosystem for the creation and promotion of data utilization businesses.
Service
Data

Fig.4: Optimization of lines from promotional activities websites

ServicePF
Data

2

Undertakings to promote MDcommunet

Asset provision(data, services, etc.)

Portal site

Business integration

2.1. Outline of activities

Service integration

PF

Data arrangement/reference–Roles we would like to ask project
participants to cooperate with.

Fig.5: Ecosystem formed by the project

In order to continue to operate MD communet, it isimportant to create new services and values by gaining wide
recognition, making allies, and promoting data utilization
through matching. In the process of creating allies, it isnecessary not only to find partners who agree with MDcommunet's initiatives and can create together, but also tobuild an operation and promotion system that will enable
users to conduct transactions smoothly. In the promotion ofdata utilization, it is necessary not only to collect a wide
range of road traffic environment data, but also to create
encounters with unknown data by publishing characteristic
data from both the public and private sectors, leading to thecreation of new services and values. Therefore, in FY2019
and FY2020, we focused on building a network, expanding
the data to be posted, and increasing awareness.

2.2. Creating allies for MD communet
For the continuous operation of the portal site, "matching"including personal relationships with each party is the most

Next, we surveyed and organized the stakeholders
necessary for the realization of the ecosystem in the public
sector and in the private. First, we visualized the flow ofvehicles and people on the road, assumed use cases in whichroad traffic environment data would be used, and investigated
the flow of data collection and distribution. As shown in Fig.6,
we classified stakeholders into the public and private sectors.
For the public sector, we assumed public offices and public
interest incorporated associations that own or have jurisdiction
over road traffic environment data. For the private sector, we
assumed businesses and service providers that offer telematics
services. The relationships among these stakeholders wereorganized and candidate service providers selected.
In FY2019, we conducted interviews with the mappedcandidate businesses to understand their level of interest inand sympathy with the world view and service contents thatthe portal was aiming to provide, their level of interest in andconfidence in their own business using the portal, and theirrequests for the portal's functions and data handling, with

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

*Specific company names have been omitted

Business
category

Related company categories
Map/navigation
Logistics (home
Manufacturer (map information, delivery, transportation,navigation)business vehicles)

System/rule

Transportation(bus, taxi)Road/
infrastructure・・・

Academia andgovernment

・・・

・・・

Data
utilization/
Serviceprovision

・・・

・・・

・・・

・・・

・・・

・・・

・・・

Platform
operation

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・

・・・
・・・

Data
provision

・・・

・・・

・・・

・・・

・・・

・・・
・・・

・・・

〜

・・・

〜

・・・
・・・
・・・

・・・

LegendUser attributes

B to B to C
(including G to B
to C)

FY2021
4Q
Undisclosed
Undisclosed
Undisclosed
Undisclosed
Undisclosed

B to C (including
G to C)

Undisclosed
Undisclosed

Undisclosed
Undisclosed

FY2022
2Q

1Q

3Q
Undisclosed

Undisclosed

Undisclosed Undisclosed

Undisclosed

Undisclosed

Undisclosed

Undisclosed

Undisclosed

Undisclosed Undisclosed Undisclosed

Undisclosed Undisclosed

Undisclosed Undisclosed Undisclosed
Undisclosed
Undisclosed Undisclosed
Undisclosed Undisclosed
Undisclosed

Undisclosed

Undisclosed—Candidate—Implemented

Undisclosed Undisclose Undisclosed
Undisclosed
Undisclosed
Undisclosed
Undisclosed Undisclosed Undisclosed Undisclosed Undisclosed
LegendUndisclosed
Undisclosed
Undisclosed Undisclosed—Candidate Businesses—Key parties—Requested by SIP officials
Undisclosed
Undisclosed Undisclosed—Already implemented (revisited)
Undisclosed Undisclosed—Interview candidates

Fig.6: Mapping of candidate businesses

the aim of utilizing them in future promotion activities andportal site functions. Through these interviews, we were able
to obtain suggestions for future development of portal
functions and promotion activities such as portal sitemanagement, which we fed back into the development of theportal site and made an issue to be considered in the next
fiscal year. In FY2020, we considered the importance of
aggregating road traffic environment data from various
companies and publishing it as catalog data, so we decided toapproach potential data providers. As a result, we were able
to obtain membership from nine companies and twoorganizations that support the MD communet initiative.
After the public release in FY2021, in parallel withpromotional activities for the MD communet, we actively
approached data providers that possess characteristic roadtraffic environment data, and the data users and dataprocessors who are necessary for creating services. As a resultof various promotional activities, we were able to increase thenumber of members to over 60 companies. (Fig.7)
More than 7,000 items of catalog data were obtained via
the provision of catalog data from members. In addition,
through continuous efforts to reach out to business operators,
we were able to obtain the prospect of acquiring advanced
data such as vehicle probe data. We were able to include
characteristic public-private sector catalog data, including awide variety of vehicle probe data and cross-sectional traffic
volumes provided by the National Police Agency.

※50音順

Fig.7: List of MD communet members (partial)

2.3. Activities to raise awarenessIn FY2020, we also actively produced content andconducted various promotional activities to raise awarenessand interest in MD communet in preparation for the releaseof the portal site.
First, in conjunction with the release of information in
October 2020, a promotional website was created andreleased as a tool to introduce MD communet to candidate
businesses and to gain widespread recognition and interestin the future development of the site. (Fig.8) The purpose ofthis website was to inform visitors about the value andfunctions of the portal site and to motivate them to registeras members. The portal site was designed to provide an
overview of the services offered, catalog information, usecases, support details, membership registration methods,
and other information as the entrance to the data catalog, so

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Sitethat visitors can understand the services and be guided tomembership registration and inquiries after becominginterested. In designing the site, we aimed for a site structurewith a UI that would not be bothersome for both users who
know about MD communet and those who don't. After the
release of the site, we worked to increase awareness throughvarious press releases and linkage with the SIP-adus programrelated websites, and also used the website as an introductory
tool when conducting promotional activities.

matching companies at the event.
Idea generation event/business-to-business matching event

Online seminars at SIP-café

Fig.10: Examples of past events

2.4. Toward social implementation from FY2023

Fig.8: Promotional website

Next, a promotional video was produced to promote
understanding of MD communet. (Fig.9) In addition to being
aired at various events, the video was released on the SIP-café
website, and was intended to attract interest and lead people
to the promotional website. This led to an increase in thenumber of visitors to the website from SIP-café, which in turnled to an increase in the level of recognition.

In order to continuously promote the creation of new datadriven businesses in MD communet, we will take both cyberand real approaches to the issues and concerns that members
have in creating services by utilizing data, and provide thenecessary matching and technical support for the creation ofsaid services. At the same time, MD communet will continue
to expand its membership and promote data utilization by
sharing use cases to help people visualize data utilization andby communicating the value of MD communet in an easy-tounderstand manner. Both cyber and real support will be
provided. We will continue to operate and grow a system thatcan promote the wider distribution and service utilization ofroad traffic environment data, as a platform that can bepromoted through public-private partnership.

3

Testing Project Promotion

3.1. Overview

Fig.9: SIP-café news release

In addition to the production of the above content,
activities aimed at raising awareness of MD communet
through events were held. (Fig.10) In order to increaseawareness of MD communet and appeal to the possibilities ofusing mobility data, events were held using idea generation ina semi-closed environment to rediscover the value of mobility
data, experience idea generation methods, and meet withpotential future partner companies. With the cooperation of
the Graduate School of System Design and Management at
Keio University, a wide range of companies involved in themobility industry shared various ideas through lectures andworkshops. The event also created opportunities for interaction
among the companies, and provided the possibility of

Based on the architecture pertaining to geographic data in
the field of automated driving, we conducted FOTs (Field
Operational Tests) using use cases to solve social issues extracted
from the study results of the FY2019 projects in the first phase of
SIP-adus, "the field of services for logistics companies" and "thefield of end-to-end multi-modal navigation services." As a result,
we found the possibility for new value-added services throughthe exchange of data among users.
Furthermore, in FY2020, based on a survey of issues facedby the logistics industry, we extracted candidate data that canbe used in cooperative areas related to the initiatives and issuesof logistics companies, with the aim of collaborating andutilizing cooperative data in the logistics field that will lead tosolutions to social issues common to the industry.
In addition, based on discussions with the participants
for the research and testing project for improving logistics
efficiency based on architecture utilizing information fromvehicle probes and other sources, we examined the issues
related to the usefulness and deliverability of the above data

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Sitecandidates and the concept of service creation using theportal and based on the direction of the problem solving.
From FY2021, we explored the scope of possible
collaboration and utilization of vehicle probe data, which had
been an issue related to the availability of data due to its highconfidentiality. As a result, we were able to construct a scheme
to link and utilize data by bringing together a digital tachographmanufacturer that generates vehicle probe data for data
providers, a trading company that plans service creation forservice providers, and a data-holding delivery company that has
business issues for service users, and conducted FOTs to realize
service creation. In addition, in order to further promote data
coordination and utilization, we conducted testing that
contributed to solving issues faced by local governments inaddition to those faced by private companies.

incorporating multiple use cases and conducted functional
evaluation and problem identification by having participantsin the experiment use a service application that implemented
all use cases, as shown in Fig.12.

3.2. Fots overview

User information registration

User registers information
related to their own car

Navigation to the entrance of thedelivery destination and displayof rest spots along the route

Receives a reminder

By having drivers register
their vehicle information,
it is stored as driver
attribute information.

In addition to the route to the delivery
destination, rest stop locations are also
displayed. (The route factors in the widthof the car according to the vehicle type.)

When driving in front of atraffic sign or on a road where
there is no record of driving, awarning will be issued.

Fig.11: The field of services for logistics companies
Registration of travel spots

Catching up with dynamic information(1) Creation of services through data utilization by FOTs
participants
We set up use cases to solve social issues identified basedon the results of the study in the first phase of SIP-adus, andparticipants led FOTs.
Regarding social issues, in "the field of services forlogistics companies" we set the provision of a safe and secure
driving environment for truck drivers, including the need to
avoid dangerous routes when considering safe driving. In"the field of end-to-end multi-modal navigation services," wehave set the goal of providing stress-free transportation
support by combining personalized transportation means(including automated driving) with means of transportation
and route guidance that match user's personal data and are
flexible to changes in weather and congestion conditions.
The purpose of the "services for logistics companies" is toverify the extent to which the use of the road traffic environment
data portal can provide value, such as providing a safe and secure
driving environment and improving the working environment,
through data and data utilization methods that data users might
have been unaware of. In line with this objective, we conducted ademonstration of an application for cooperative provision andutilization of geographic road traffic environment data amongusers, as shown in Fig.11 Interviews were conducted with truck
drivers, operation managers with truck driving experience, andtraffic information service providers.
The purpose of the "end-to-end multi-modal navigation
services" was to verify that by using a road traffic environment
data portal, it is possible to provide support for a more stressfree form of mobility that includes more personalized meansof transportation (including automated driving) by providing
dynamic information through data and data utilization
methods that data users might have been unaware of. To
achieve this objective, we set up an FOTs scenario

By asking users to register theirgender, number of people, andgenres of interest, the system
accumulates personal data.

Refers to user's personal
data and recommends
spots that are popular oreasy to use accordingly.

The system collects dynamic
information, such as weather
information, operation information,and congestion compiled from Twitter,and notifies users of better spots.

Fig.12: The field of end-to-end multi-modal navigation services

As a result of the FOTs for the services for logistics
companies, we confirmed that route guidance that takes into
account static information such as the provision of a safe andsecure driving environment for truck drivers, loading docks,
rest areas, road information, and traffic signs, especially on
unfamiliar routes or routes where the road environment haschanged, can contribute to improvements. Regarding theusefulness and scope of the cooperative area data, we confirmedthat the data is useful as a cooperative area for public purposes,
and that there are cases where there are significant advantages tosharing the data depending upon its nature.
In the FOTs for the "end-to-end multi-modal navigation
services," we implemented a function that provides flexible
transportation and route guidance in response to changes indynamic information such as weather and congestion
conditions, while also taking into account information such as
user's personal data. In addition, the information collected via
the application, such as the results of actions taken and the timespent in the area, can be effectively used as cooperative area data
for marketing and other purposes. We therefore uncovered thepossibility for data utilization in other fields via data in thecooperative domain being distributed through the portal.

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

Preparation (Probe data acquisition → processing → drive recorder settings)(1) Acquisition of driving records for the past several days
(2) Processing
Obtain probe data and location information for delivery
destinations from the service provider's customers

Create shipping destination profile

OfflineCooperation

Estimate delivery
entrance
Estimate parking
position

Development scope

Estimate parking
direction
Estimate driving route
Delivery entrance → Parking position(3) Drive recorder settings

Service provider's existing service
Delivery destination is set tothe drive recorder.
OfflineCooperation

Real field testing
Testing step 1
VehicleService provider testing

Delivery destination
Several stores based on actual
data (planned for Kansai area)

Audio route guidance

Audio guidanceexampleTurn left [xx]m ahead and enterfrom the delivery entrance.

Estimate delivery
entrance

Please back your car into the dedicatedparking lot at the delivery entrance.
Turn left after proceeding straight and back yourcar into the dedicated parking area.
Estimate driving route
Delivery entrance → Parking position

Estimate parking
direction
Estimate parking
position

Results of testing
We plan to evaluate functionality, effectiveness of introduction, and future issues for service improvement.

Fig.13: Overview of delivery destination profiling test(2) Testing of vehicle probe data coordination andutilization among private companies
A digital tachograph manufacturer, a trading company,
and the operator of MD communet collaborated to propose
the creation of a service utilizing vehicle probe data for delivery
companies (data holders), who are the direct customers ofdigital tachograph manufacturers, for the purpose of
improving the efficiency of delivery companies' operations.
Although the high confidentiality of vehicle probe data posed
problems regarding its availability, permission to use the data
was obtained for the sole purpose of using it to improve theefficiency of customer operations.
In setting the issues of delivery service providers, among
the issues recognized by the digital tachograph manufacturer
and trading company, we selected issues related to operational
efficiency that also have a high affinity with vehicle probe
data, such as the positions of delivery entrances and parking
lots at delivery destinations not being organized and drivers
having to park multiple times or drive around the perimeter.
The content of the FOT was to analyze and process thevehicle probe data to detect the location of the delivery
entrance, parking location, parking direction, and route fromthe delivery entrance to the parking location for each store at
the delivery destination, manage them as delivery destination
profiles, and test whether the information provided to drivers
can contribute to operational efficiency. (Fig.13) The analysis/
processing of the vehicle probe data was conducted from thestandpoint of service development support, which is one of
the roles of MD communet management.
As a result of the FOT, we were able to confirm theusefulness of vehicle probe data in this area by confirming
the contribution to a certain degree of operational efficiency
and the issues that need to be addressed for practicalapplication. We also confirmed the usefulness of planningsupport and data processing support in the process of service
creation as part of MD communet operation.
(3) Resolving issues faced by local governments
In order to further promote data coordination andutilization, we examined the possibility involving not only
private companies but also local governments and using
geographic data to resolve issues faced by local governments.
Based on the characteristics of data utilization, we selected
the theme of utilization in EBPM (Evidence Based Policy
Making) for local governments, and examined a plan togenerate and utilize objective evidence by combining
multiple instances of geographic data.
Specifically, we investigated in depth the issues related tosnow removal and snow clearance in Yokote City, Akita
Prefecture, a problem shared with every region that suffersfrom heavy snowfall, and the issue of illegal cab parking in
Kyoto City, Kyoto Prefecture, a problem shared with othertourist destinations, and established use cases to solve theseissues using geographic data.
3.3. Common efforts through testingThrough the testing, we found that the creation of services
by combining multiple geographic data sets required technicalknow-how to handle data in different formats at the same time.
In order to continue to create services in the future, it isnecessary to lower the hurdles mentioned above. We believe
it would be possible to speed up development and reduce
costs related to data utilization in the next and subsequent
years by creating a template as a general-purpose function
for processes, such as importing and processing, allowing forthe handling of data of different formats simultaneously, as

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﻿1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

Template
Data and creation functions
Potential roadflooding areas(Ministry of Land,
Infrastructure, Transport
and Tourism)・・・
Actual traffic data
(Pioneer)

Point data・・・

Use case introduction
content for Yokote City

Mesh data

Line data<Server side>
・Preprocessing
・Import processing
・Data processing
・Risk location detection
process<Client side>
・Visualization
・Routing (normal)
・Routing (considering risk)
・Display of various data

Network data
・・・Data that can beused fordemonstrationsclassified by data type

User (issue holder)

MD communet management
dissemination promotion tools anddemo apps

Design・Processing flow
Program・Samples byfunction

Data applications
Understanding concept
Idea
generation

Service provider
Realization of service
creationFor

Understanding ofdata/promotion of utilization
・Overview of functions

Finding problems/solution ideas

Service idea generation

Prototype apps
・During disaster
Data test

TargetFor

xx creation function group

・・Road network data
(HERE)

Published on MD communet
promotional website

Use case introduction
content/during a disaster・・・
1 km mesh analysis
volume (SIP4D)

Purpose and means

Deliverables

Shortening ofdevelopment time
Service developer
Understand development concept

For members Services for MD communet members
・Disclosure of design products
・Program provision
Service development
Shortening of period

Data provider
Data applications
Understanding applications

Fig.14: Geographic data utilization templating study

shown in Fig.14. We then created a design and program
based on this idea.
In addition, because there were cases in which users were
not able to start using geographic data because they did nothave a clear image of what the data would be used for, wecreated a demo application that allows users to experience
what the data can do.

4

Future Initiatives

In FY2022, the final year of the second phase of SIP-adus,
we carried out activities focused on preparations for social
implementation. After the second phase of SIP-adus ends in
FY2023, MD communet will see social implementation.
After social implementation, the MD communet willcontinue to serve as a hub for public-private partnerships
and continue the various activities that have been
implemented during SIP-adus. This is expected to increase
the number of allies, promote further utilization of data andcreation of new services, and solve social issues.

[Contacts]

NTT DATA Corporation, Smart Business Management Department, Social
Innovation Division, Social Infrastructure Solutions Business Headquarters,
Toyosu Center Building, 3-3-3 Toyosu, Koto-ku, Tokyo 135-6033, 050-55462450, Contact: Ayako Nakajima

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5

Data Connection and Use to Achieve Society 5.02) Resolving Social Issues in Cities Popularwith Tourists
Noriyuki Hayashi, Naohisa Komiyama, Yurie Toyama, Takahiro Kashiwa, Hiroki Ozu, Wataru Uchida (Mitsubishi ResearchInstitute, Inc.), Kazuhiro Noguchi, Taro Morita, Natsuha Makino, Taigo Mitsuhara (MRI Research Associates, Inc.)(Abstract) From the perspective of creating use cases to solve transportation-related issues in urban areas by utilizing
road traffic environment data, we held a contest (an App Contest to Solve Tourism and Transportation Issues (the"Kyoto Raku Mobi Contest") to solicit applications and ideas to solve social issues related to tourism and transportation
faced by Kyoto City, a world-class tourist city with many tourist resources. The contest was conducted with thecooperation of the Kyoto Municipal Transportation Bureau and businesses involved in transportation, logistics, andtourism, and organized and provided data including data on stations, stops, routes, schedules, and fares of buses,
trains, and other public transportation systems; data on services and stores for temporary baggage storage anddelivery services in the logistics sector; and information on tourist facilities and locations, past congestion statistical
data and future congestion forecast data, and map APIs in the tourism sector. Through the implementation of thecontest, we raised awareness of the portal site for road traffic environment data, and through discussions andcoordination with the various involved parties, we realized the collection and utilization of data to be posted on the
portal site (MD communetⓇ).
Keywords: public transportation data, GTFS-JP, tourist city, app contest, MD communet

1

Overview of the Project

The SIP-adus (Cross-ministerial Strategic Innovation
Promotion Program (SIP) Automated Driving for Universal
Services) is working on the generation and distribution of roadtraffic environment data that is indispensable for automateddriving, toward the realization of an automated driving society.
This project took place in the tourist city of Kyoto and aimed tobuild an ecosystem that promotes matching between
information owners and users so that a variety of users can use
road traffic environment data in a variety of services. Specifically,
the project planned and conducted two "Kyoto Raku Mobi
Contests," an App Contest to Solve Tourism and Transportation
Issues, and coordinated the development and procurement ofroad traffic environment data for use in the contests, with theaim of building momentum for the utilization of the data.

2

1st Kyoto Raku Mobi Contest

2.1. Planning and Preparation to the Launch (FY2019)
Based on the assumption of several use cases for solving

issues related to the movement of people and goods in urban
areas, we considered the planning of the "Kyoto Raku Mobi
Contest" as an app contest to solve issues using road traffic
environment data (various data on transportation, logistics,
and facilities), with the city of Kyoto, which faces issues
concerning tourism and transportation in particular, as the
subject.
In the consideration of the use cases, multiple use cases
were considered for services related to transportation andlogistics in urban areas, targeting various entities with different
attributes (residents, tourists, government and transportation
operators, etc.). Specifically, use cases that apply to tourists wereconsidered, for example, (1) guidance of sightseeing routes thatavoid the traffic flows of residents, (2) the recommendation of"empty-handed sightseeing" in which tourists can store theirluggage, (3) suggestions of destinations that include congestion
forecasts, and (4) guidance services that take into accountcongestion conditions and transportation service operation
conditions. (Fig.1)
Based on the consideration of the use cases, various data that
could be used to solve issues related to transportation andlogistics in the tourist city of Kyoto were organized into the three
categories of "transportation," "logistics," and "facilities", and an
explanation of the purpose of the project and a request forprovision of the data was made to the relevant parties such as theentities that hold such data, and the necessary data was procured.

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﻿2) Resolving Social Issues in Cities Popular with Tourists(9) (Walk) Use the route
guidance and walk to thenearest bicycle parking lot

Bicycleparking lot
Kinkakuji Temple(10) (Bicycle share) Easy route toyour accommodation with no stairs
Route with many stairs(8) (Car share) Fares canalso be paid in the app
Toei Kyoto
Studio Park
Recommendation of
Nijo Castle
highly-recommended (7) (Car share) Fares
sightseeing spots based can also be paid inon the user's attribute the app
information.
Congestion* Search, reservation, and payment are
all possible on a single smartphone app* Real-time weather information canalso be obtained, giving peace of mind
for travel by foot or bicycle

Accommodation(3) (Transport by truck)
Only the luggage is sent
to the accommodation.
Location information iscontinuously acquired.

(5) (Transport byrickshaw) Souvenirs
purchased along theway will also be sent tothe accommodation(6) (Transit bus)
Multilingual route
guidance. Passengers
Kiyomizu Temple(4) (Bus) Travel by thebest route and mode(2) Store luggage and go to
Movement of goods
sightseeing spots empty-handed of transportation
Movement of people(1) Arrive at
Kyoto Station Kyoto Station© OpenStreetMapcontributors

Fig.2: "Kyoto Tourism Assist" (tentative name)(1)[Local residents][Tourists]

Teburan Supporters
Teburan medal display

Button to read a QR CodeScan the city's QR code

Fig.1: Example of use case study (tourist case)

2.2. Contest Management to Judging and Awards(FY2020)
Based on the above consideration, the "Kyoto Raku Mobi
Contest" was planned and held from February to November
2020 to solicit apps and ideas that would contribute to solving
tourism and transportation issues in Kyoto City. In addition toproviding contest participants with road traffic environment
data and APIs, we explained the contents and specifications of
the data, conducted lectures and opinion exchanges on issues
related to transportation and tourism in Kyoto City, andprovided mentoring to individual contest participants.
The "Kyoto Raku Mobi Contest" consisted of two divisions,the app development division and the app idea division, andcalled for app products or ideas that contribute to solving issues
in Kyoto. After conducting a documentary review of thesubmissions and testing and evaluation by monitors, we held afinal judging session on Saturday October 17, 2020 and awarded
seven prizes at an award ceremony on Saturday November 7,
2020.
The "Walking Town Kyoto Award" for the top prize in theapp development division was awarded to "Kyoto Sightseeing
Assist" (s name) (Fig.2), an app that supports the sightseeing
activities of visitors to Kyoto by utilizing various road traffic
environment data, including data based on the "General Transit
Feed Specification Japan" (GTFS-JP) provided by the secretariat.
The "SIP-adus Award" for the top prize in the app idea division
was awarded to "Teburan - an app that makes you want to walkaround empty-handed" (Fig.3), which was an idea for an app
that discovers and provides new value of sightseeing spots when
visiting empty handed. Subsequently, meetings were held withthe creator of the winner of the "Walking Town Kyoto Award"
for top prize in the app development category, and work wascommissioned for the completion of the app towards social
implementation.

Number of visits to Kyoto
The number of visits to
Kyoto is displayed
Teburan point display
Display number ofpointsNumber of Teburan stamps
display
Number of Teburan stamps
collected

Stamp

Stamp

* (including Teburan Supporters)

Kyoto City residents
For Kyoto residents,"Resident" is displayed* Message function button* Luggage storage button* Map display button* Kyoto Friend button* Goshuincho button* Settings button

Fig.3: "Teburan - The app that makes you want to walk around emptyhanded"(2)3

2nd Kyoto Raku Mobi Contest

3.1. Planning and Preparation (FY2021)
Since FY2021, a portal site (MD communet) for searching
and matching data related to road traffic environment data has
been built and its use has commenced. As an example of solving
a regional issue using road traffic environment data, theplanning of the "2nd Kyoto Raku Mobi Contest" as a contest to
promote data utilization using MD communet was studied,with the aim of forming an ecosystem for data provision andutilization in the tourist city of Kyoto.
In this contest, contest participants were asked to set andspecify an issue that the contestant expected to resolve
themselves and which targeted local residents, tourists, logistics,
transportation, or tourism businesses, or local governments,
and propose an app or idea that would solve the issue. As
described below, the types of road traffic environment data
provided in the contest was expanded, so unlike the first contest,
the issues (use cases) expected to be resolved were not limited
to specific items. Local governments and businesses involved in
the contest were asked to propose "example issues" from theperspectives of public transportation, the region, tourism,
logistics, etc., and the information was disseminated to thecontest participants.
As in the first contest, data for the second contest wasorganized into the three categories of "transportation,"

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﻿2) Resolving Social Issues in Cities Popular with Tourists"logistics," and "facilities," and we then coordinated andprocured data from entities that possess such data in order tofurther expand the types of data.
Among these, for transportation data, we collaborated with
Kyoto City to develop data from transportation operators so it
could be provided in a standard data format (e.g., GTFS). In
addition to utilizing GTFS-JP, which was developed on a trial
basis for the future openness of bus data by transportation
operators, dynamic data such as the location of buses and trains
was developed and distributed on a trial basis. (Fig.4)
Transportation sector
Data contents
Data on Kyoto Municipal Subway stations, routes(lines), timetables, fares, etc.

Data provider
Kyoto City Urban Planning Bureau

GTFS

Data on Eizan Railway stations, routes (lines),
Eizan Electric Railway Co., Ltd.
timetables, fares, etc.
Data on Kyoto Municipal Bus stops, routes (lines), Kyoto City Urban Planning Bureau
timetables, fares, etc.
"Walking Town Kyoto" Bus and Train Transfer Information
Kyoto Municipal Bus vehicle location API
Dissemination System Consortium

GTFS，GTFS-RT
GTFS-JP

Kyoto bus stops, routes (lines), schedules, and fares Kyoto Branch, JTB Corporation

GTFS

Data on Yasaka Bus stops, routes (lines), timetables, Yasaka Bus Co., Ltd.
fares, etc.
Data on Kyoto Municipal Bus vehicle locations,etc.
Kyoto City Park-and-Ride Parking Lot data

GTFS-JP, GTFS-RT"Walking Town Kyoto" Bus and Train Transfer
Information Dissemination System Consortium
Kyoto City Urban Planning Bureau

Fig.4: Road traffic environment data (traffic data) provided by thesecretariat in the second contest

We also coordinated so that the secretariat could provide
data on logistics, facilities, and tourism.In particular, it was
decided to expand the data in the field of logistics bycollaborating with providers of data already available on MD
communet. (Fig.5)
The field of logistics
Data contents

Data provider

Data format

Location of data provision

Location data (individual vehicle
Yazaki Energy System Corpora- CSV
MD communet
point sequential data) and origintion
and destination data of logistics
vehicles in the 100 square kilometersof the main area of Kyoto City
(To be published
Sumitomo Rubber Industries, Ltd. in the future)(To be provided in the future)
Sensing Core (Kyoto Office)Vehicle driving data (statistics)

Aioi Nissay Dowa Insurance Co., CSV
Ltd.

MD communet

The field of facilities and tourism
Data contents

Kyoto City sightseeing spot
information

Data provider

Data format

Location of data provision

Kyoto City Tourism Association XLSX(DMOKYOTO)

MD communet

Congestion forecast information Kyoto City Tourism Association XLSXaround popular tourist spots(DMOKYOTO)

MD communet

Information on Wi-Fi spots in
Kyoto City

MD communet

Kyoto City Tourism Association XLSX(DMOKYOTO)

Yahoo Japan Corporation
Data (estimates) on human flows around tourist
attractions in Kyoto City (2nd Kyoto Raku Mobi Contest)
Location guidance service API

Zenrin Co., Ltd.

Data on temporary baggage storage and delivery Secretariat of the 2nd Kyoto
service stores and coin-operated locker locations Raku Mobi Contest

CSV

MD communet

API

Other data

CSV

Other data

Fig.5: Road traffic environment data (logistics and facility data) provided
by the secretariat in the second contest

In the second contest, the website and contents were
designed to encourage participants to be aware of and use MD
communet. In collaboration with NTT Data, a link to MD
communet was placed on the contest’s website and related
content was posted in the "Events" and "Columns" sections of
MD communet as reference information for participants. In

MD communet

Other data

Certification
Via MD communet

Data download(immediate)
Staying on the login screen after
downloading data
Data download URL

Fig.6: Cooperation between the contest and MD communet(3)(4)

addition, we coordinated a policy that the road traffic
environment data provided in the contest would in principle beposted in the MD communet catalog during the period of thecontest. (Fig.6)
In addition, following on from the first contest, we
coordinated with relevant entities including local data providers
and local governments to involve them in the contest. In
addition to Kyoto City as a cooperating entity, we expanded thenumber of sponsors, who were mainly local businesses in
Kyoto.

3.2.Contest Launch and Operation to Judging andAwards (FY2022)
The "2nd Kyoto Raku Mobi Contest" was planned andimplemented taking into account the above consideration. As
with the first contest, it consisted of two divisions, the app
development division and the app idea division, and called forapp products or ideas that contribute to solving issues in Kyoto.
A press release regarding the second contest was sent out
and entries were accepted from Monday, April 25, 2022. Road
traffic environment data and mentoring was provided to contest
participants to encourage them to consider submitting entries.
Entries were closed on Monday, October 24, 2022, and thesubmission period closed on Monday, October 31. A document
review was conducted and the final judging and awards
ceremony was held on Sunday December 11, 2022. As with thefirst contest, awards were given including the "Walking City
Kyoto Award (Kyoto Mayor’s Award)" for the top prize in theapp development division and the "SIP-adus Award (Cabinet
Office Secretary-General of Science, Technology and Innovation
Policy Award)" for the top prize in the app idea division, and it
is planned to commission the winner of the "Walking City
Kyoto Award" with the aim of social implementation within
FY2022.

4

Outcomes of this Project

The app and ideas developed through this contest were a
step toward the contest participants solving specific issues byutilizing road traffic environment data. Because the intellectual

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﻿2) Resolving Social Issues in Cities Popular with Tourists

property rights concerning the entries in the contest in principle
belong to the creators of the inventions, etc. (i.e. the contest
participants), it is possible for them to continue their efforts
toward social implementation after the contest has ended.
Through this project, discussions were held with local
governments and data providers for the distribution andutilization of road traffic environment data in Kyoto, and waslinked to the creation of momentum for the development andprovision of data in standard formats, including dynamic data
(e.g., GTFS-RT). In the future, it is necessary to promote
discussions with the related entities so that data can becontinuously developed and distributed in the region even after
the contest is over.

[References]
(1) Teruki Matsuoka: Kyoto Raku Mobi Contest Submission Materials(2) Team Teburan: Kyoto Raku Mobi Contest Submission Materials(3) Mitsubishi Research Institute: 2nd Kyoto Raku Mobi Contest,
https://web.contest.adus-arch.com/, (accessed on 29th August 2022)(4) NTT Data: MD communet, https://info.adus-arch.com, (accessed on 29th August
2022)[Contactts]
Mitsubishi Research Institute, Inc., Smart Region Headquarters, 10-3, Nagatacho
2-chome, Chiyoda-ku, Tokyo 100-8141, 03-6858-2722, Contact: Noriyuki HAYASHI210

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3)Research to Realize More Effective
Logistics System with Probe Vehicle Data
Yukihiko Akao, Masaaki Kanazawa, Daijiro Sato (NX Logistics Research Institute and Consulting, Inc.)(Abstract) This study conducted FOTs (Field Operational Tests) on data acquisition and utilization for three use cases:(1) ascertaining the occurrence of waiting time and sharing with related parties, (2) checking daily inspection items
using vehicle data, and (3) measuring load weight and tire data to ensure legal compliance and safety, and identified
the issues towards implementation and considered countermeasures, with the aim of utilizing various types of data
related to vehicle and probe information to improve operational efficiency and ensure safety in truck logistics. Throughthe verification of the hypotheses via the FOTs and discussions with carriers and related businesses based on the
results of the FOTs, each use case was evaluated highly from the perspective of its usefulness in improving theefficiency of trucking operations and ensuring safety. On the other hand, since each use case is still at the concept
level and the considerations towards implementation are still in their infancy, it will take a considerable period of time
before practical application, as there are various technical, business, and legal issues to be addressed in the detailed
considerations. In order to create the environment and momentum towards practical application, it is necessary to
promote awareness of each use case and to create related organizations and businesses that will serve as promoters.
Keywords: trucking operation efficiency improvement, transportation operation safety, waiting time, daily inspection, load weight,
tires, vehicle signal data

1

This study is scheduled to be completed by the end of
December 2022 and is still in the process of discussion andexamination at the time of writing this report. This paper isbased on the results of the FOTs and considerations up to theend of August 2022.

Outline of the Study

1.1. Contents of the StudyIn this study, FOTs using vehicle and probe information forthree use cases were conducted, and the issues to be addressed
towards implementation and who will be in charge of future
promotion were considered, with the aim of utilizing various
data, including vehicle and probe information, to improve
operational efficiency and ensure safety in truck logistics, witha view to the practical application of automated driving
technology in truck logistics in the future.
The three use cases are: (1) ascertaining the occurrence of
waiting time and sharing with related parties, (2) checking daily
inspection items using vehicle data, and (3) measuring load
weight and tire data to ensure legal compliance and safety.
Through the test study of these use cases, the extent to which
the acquisition and utilization of vehicle and other various data
can be achieved at present was confirmed. Based on this,identifying the image of the final goal and the issues to beaddressed with the aim of practical application of these
initiatives in the future, as well as consideration of the roles tobe played in promoting future implementation, are in progress.

1.2. Background of the Study
The truck logistics industry is facing a growing shortage oftruck drivers due to working environment problems such as
long working hours. There is concern that this will have anegative impact on the Japanese economy, as logistics will not
be able to adequately meet the future demand for freighttransportation. One effective solution to this problem is
expected to be the practical application of automated driving totruck logistics in the future.
On the other hand, inefficient truck logistics operations,such as long waiting times before loading and unloading, are atypical example of the background to the working environment
problems of truck drivers. This inefficiency problem cannot be
fundamentally solved by automated driving, so the factors
should be analyzed and countermeasures should be consideredseparately from automated driving.
It is also true that there are safety and maintenance concerns

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regarding the practical application of automated driving intruck logistics, and it will be necessary to consider maintenancemeasures and safety assurance measures for a new era based on
the assumption of saving manpower through automateddriving.
In considering how to address these issues, we became awareof the problem of whether the use of vehicle and probe
information can be linked to concrete measures. Based on thisawareness, three use cases were studied and FOTs wereconducted with the aim of correcting inefficiencies and ensuringsafety in truck logistics operations.

1.3. Positioning of This Study with a View to Implementation
Three FOTs were conducted to confirm the present technicalfeasibility of the concepts related to the use cases and to evaluate
their usefulness in operational practice by gathering theopinions of carriers. Therefore, the FOTs and considerations
related to the three use cases are still in the early stages oftechnological consideration, and there are various issues thatneed to be addressed before they can be materialized andimplemented in the future. This study will present the main
issues that need to be addressed for practical application of eachuse case. In addition, it is planned to explain each use case toentities and government agencies and request them to consider
measures to deal with these issues and promote their practicalapplication, and to encourage them to implement promotion
measures.

2

Details of the FOTs for the Three Use
Cases

2.1. Ascertaining the Occurrence of Waiting Timeand Sharing Among Related Parties
2.1.1. Details of the FOTs
This is an initiative to collect and analyze information to
study measures to reduce waiting time for cargo by utilizing
information obtained from information terminals installed ontrucks owned by trucking companies and from their in-housesystems, with the aim of improving productivity and long
working hours at logistics sites. Specifically, the occurrence of
waiting time for each vehicle, work day, pickup location, anddelivery location is confirmed by collecting and analyzing theoperation management data of trucks in actual operation, andvehicles and locations where long waiting times (i.e., waiting 30
minutes or more at one location) frequently occur are identified.
The usefulness of sharing the data which identifies the actual
status of waiting times between carriers and shippers inpromoting initiatives to reduce and improve long waiting times

was tested.
According to the Ministry of Land, Infrastructure, Transport
and Tourism's "Survey of Truck Transportation Conditions"(2021), drivers spend 94 minutes waiting for cargo during each
operation (12.6% of the total time per operation). Assuming
200 workdays per year, the annual waiting time is estimated to
be about 310 hours. Reducing such unproductive waiting timeis essential to improve the long working hours of drivers.
To this end, it is necessary for shippers and carriers to sharean awareness of the actual conditions of waiting for cargo, suchas "how many times long waiting occurs at which pickup/
delivery locations," and to discuss the causes of such waiting
and implement improvement measures. However, shippers are
generally unaware of the occurrence of trucks waiting to carry
the shipper’s cargo. On the other hand, many carriers do notaccurately ascertain and record the time spent waiting for cargoat pickup and delivery destinations, and are not able tocommunicate the actual status of waiting to shippers.
Therefore, we believe that the first step needed towards
reducing waiting time is to ‘visualize’ the actual status of waitingtime as numerical data and to have a common understanding
between carriers and shippers. Based on this idea, this testing
project analyzed the occurrence of waiting time based on theoperation management data from the digital tachographs oftrucks used in actual operation, and presented this data analysis
to the carrier and their shipper, and discussions were held toattain mutual recognition about the status of waiting time aswell as consider the causes of waiting and improvement
measures. The ultimate goal is to reduce the waiting time ateach location to less than 30 minutes through the spread ofthese initiatives.
The subject of this test study was processed food products,
which have particularly long waiting times. With the consent ofa transporter that mainly handles processed food products, andtheir largest customer who is the shipper of the goods, operation
management data related to the transportation and delivery ofcargo from the shipper company was acquired and theoccurrence of long waiting times for cargo was analyzed fromthis data. The results of the analysis showed that 374 (57%) of
the 656 vehicles in operation during a two-month period waited
for a load, and that the average waiting time (the average for thevehicles that waited for a load) was 43 minutes per vehicle perday, with a maximum waiting time of 5 hours and 46 minutes in
one day. In addition, 157 vehicles (24%) waited for a long time,
and the average waiting time for these vehicles was 1 hour and23 minutes per vehicle per day. The destinations with an average
waiting time of one hour or longer were listed (Fig.1), and thedate and time of waiting was clarified for the destinations withparticularly frequent waiting.
After explaining this and the other details of the analysis

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Location

No. of Ave. wait Max. wait Date of
Total wait occurmax. wait Day
time
time
time
rences
time
Thu.
Sat.
Sat.
Sat.
Wed.
Thu.
Tue.
Sat.
Sat.
Fri.
Tue.
Wed.
Fri.
Wed.
Fri.
Wed.
Thu.
Mon.
Fri.
Wed.
Fri.
Mon.
Sat.
Thu.

Fig.1: Analysis of locations, days of the week, and times
with high waiting times

data to the transportation company that provided the data andthe company that shipped the cargo, opinions were exchangedon the actual situation of waiting times and target sites, and arebeing used in the analysis of the factors behind long waitingtimes and the consideration of countermeasures.

2.1.2. Results of the FOTs and Future Issues
When we asked the carrier and the shipper that cooperated
in this testing project for their opinions on the usefulness of thetruck carrier sharing such data with the shipper, highevaluations were received such as, "we could numericallyconfirm the status of waiting for cargo, and also obtain new
insights" and "it is very meaningful for both sides to share datathat enables understanding of the overall picture of theoccurrence of waiting times and exchange opinions", and theresults showed that such visualization of the logistics on site canpromote discussions between carriers and shippers to eliminate
waiting time. In particular, there was an issue that it is difficult
for the shipper company to proactively address drivers' long
working hours because the waiting time occurred not at theirown facilities but at the facilities of their customers andconsignee destinations. If the sharing of data with truck
transportation companies enables the shipper to approach theconsignee for improvement based on specific data, theeffectiveness of the system is expected to be very high, as it willlead to improvement efforts from both sides of the supply chain.
On the other hand, a problem that needs to be addressed forthe implementation and spread of this initiative is whether
carriers are capable of cleansing, correcting, analyzing anddocumenting the operation management data. In other words,
the correction and analysis of operation management datarequires a considerable amount of time on the administrator,
placing a different burden on the carrier. In addition, many

managers at carrier businesses may not have the experience orskills to analyze the data. This is the biggest issue for the
implementation and dissemination of this use case.

2.2. Checking Daily Inspection Items Using Vehicle
Data
2.2.1. Details of the FOTs
This is an initiative to verify the feasibility, usefulness, andissues of using truck vehicle and probe data to check inspection
items, instead of checking by the driver, for the daily inspections
carriers are obliged to perform by law before the start ofoperation.
It is estimated that the time required for daily inspections,etc. that carriers are obliged to perform is 25 minutes per
operation, or approximately 83 hours per year. Although there
are several items that cannot be confirmed via vehicle data,
many carriers hope that even a portion of the time required fordaily inspections can be made more efficient. In addition, theopinion was often heard that qualitative benefits such as thecorrection of variations in the level of daily inspections among
drivers and a reduction in the psychological burden of drivers is
expected. Furthermore, in view of the future implementation of
automated driving, studying the reduction of the manpower
required for daily inspections should also be progressed. An
empirical investigation of this use case was conducted based on
the awareness of the above issues.
The Japan Automobile Manufacturers Association (JAMA)
has standardized the "data items provided by vehicle and probe
data" and the "production rules for the API used by users toacquire data from the back-end of heavy-duty truck
manufacturers" and progressing the establishment of a systemthat allows data users to acquire and use common data items
from each truck manufacturer using the same API if the service
menu is the same for that user. Based on this, how event datathat contributes to the confirmation of daily inspection items
can be extracted from vehicle and probe data, etc. wasconsidered, assuming the use of the API.
First, daily inspection items that can be expected to bechecked using vehicle and probe information were identified.
Of these items, those that could be checked using vehicle signalsthat could be obtained from the vehicle model of heavy-duty
trucks used for testing by the truck manufacturer thatcooperated with the FOTs were selected as the target items for
the FOTs. Specifically, the following eight items were selected.
(1) Parking brake signal, (2) Engine speed signal(3) Accelerator pedal opening signal(4) Windshield washer switch signal(5) Windshield wiper switch signal(6) Air tank pressure signal(7) Cooling water low level signal

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Monitor screen

Vehiclesignals

Provideddata

Manufacturer’s
side virtual
back-end server

Communication
unit
User’s sideOn-board device

Fig.2: Diagram of vehicle data acquisition and on-screen display of daily inspection items(8) Lamps and lights signals (vehicle width lights, front lights,
tail lights, number lights,
turning indicators, emergency flashing lights, reverse lights)
In the FOTs, the truck manufacturer provided vehicle signal
data for the above items. Fig.2 shows the sequence of this.
By using this vehicle signal data to display the items related
to the FOTs on the monitor screen shown in Fig.3, the possibilityof being able to use the truck vehicle signal data to confirm therelated daily inspection items was confirmed.

According to our research, the time required for the eightitems covered in this study accounts for approximately 40% of
the total time required for daily inspections, and it is estimatedthat the expected reduction of annual time per person throughimplementation of this is approximately 32 hours. In the future,
if truck manufacturers can provide data based on the API
production rules (both ECU and actuator signals may berequired depending on the daily inspection items), including
other items related to daily inspections that could not be
ascertained in the FOTs, the range of daily inspection items thatcan be inspected based on vehicle data will be expanded and the
reduction in time required for the daily inspections can also be
expected to increase.
Furthermore, if an algorithm is established that can
automatically determine the suitability of the results of daily
inspections from the vehicle data and the results are recorded
electronically, it may be possible to replace visual inspections
with inspections based on vehicle and probe data. As this is
expected to help prevent accidents caused by failure to performdaily inspections, inspection errors and inadequate checking, itis therefore desirable to confirm consistency with laws,
regulations, and public notices, and to consider any necessary
revisions together with the establishment of the technology.

2.3. Measuring Load Weight and Tire Data to Ensure
Legal Compliance and Safety
2.3.1. Details of the Load Weight Measurement FOTs
Fig.3: Sample of a monitoring screen

2.2.2. Results and Future Issues from the FOTs
Using the vehicle data obtained from the vehicles used in the
FOTs, the appropriateness of the relevant daily inspections
could be determined by visually confirming their condition on
the monitor screen. In addition, the disconnection of a brakelight, which was intentionally reproduced as a malfunction,
could also be confirmed on the monitor screen. Therefore, if
vehicle and probe data utilization and API production rules are
established, and if the provided data items can be utilized
remotely on a monitor screen, it may lead to a reduction in theworking hours and workload of drivers and maintenancemanagers.

This is an initiative that verified the usefulness in preventing
overloading, in which the driver measures the weight of theload using a load weight meter when loading and unloading
during actual operation, and confirms the load weight datafrom the driver's seat, while the sales office manager also
ascertains the relevant data via a terminal.
Overloading is an extremely dangerous practice thatinterferes with safe driving, such as braking and steering. For
this reason, carriers generally take care to avoid overloadingwhen loading and unloading cargo, but some shippers may
deliver cargo to carriers without knowing the weight of the
cargo, resulting in overloading. On the other hand, carriers
cannot refuse to accept cargo from a shipper unless they haveascertained that the cargo will be overloaded.

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Data confirmation in the office

Email text & dataattached as a csv file
PortablePC

Display unit image
On-siteconfirmation via
measurement &display screen(shaft weight andtotal value)

Installation of demonstration display unit

Fig.4: Diagram of loading weight measurement and checking

Based on this, the possibility of preventing overloading
through the carrier using a load weight meter to ascertain theweight of loaded cargo and gross vehicle weight when loading
the cargo at a delivery destination and keeping records of theresults was verified. This initiative aims to prevent theoccurrence of unintentional overloading in the course ofoperations.
In the FOTs, axle-weight sensors that measure the load
weight and devices for checking and transmitting the load
weight data were installed on four heavy-duty trucks used in
actual operation. The drivers measured and confirmed the load
weight each time they loaded and unloaded the trucks during
operation. The measured load weight data was also sent to thesales office by email so that the manager could ascertain thedata (Fig.4 shows an overview of this sequence). The abovementioned measurement of load weight, data transmission, andconfirmation by the driver and manager was repeatedly
performed.

2.3.2. 
Results of the Load Weight Measurement
FOTs and Future Issues
Through the above tests, it was confirmed that the load
weight meter can measure generally accurate load weights. It
was also verified that the driver can check the load weight data
each time cargo is loaded or unloaded, and that the data wasappropriately transmitted to the operation manager.
After the tests, interviews were conducted with the transport
operator (managers and drivers), who gave a good evaluation of
the load weight meter, including the following.
・For the operator, the load weight meter helped with safe
operation by preventing overloading.
・For the operator, it is possible to use the data to negotiate
rates with shippers.
・It is useful to be able to ascertain the axial weight on eachaxis.
On the other hand, it was pointed out that it is necessary tolink with devices such as digital tachographs to save labor inoperation and to arrange the sharing of data. In addition, an
opinion was provided that, as it is assumed that data sharing
will lead to collaboration in order to reduce drivers' working
hours, this could lead to increased efficiency through matchingand joint transportation and delivery. Another opinion was thatthe accumulation of load weight data will lead to managementby shipper and vehicle, and that if the data can be linked to fuelconsumption and mileage obtained from the vehicle’s
information, it will be possible to determine fuel consumption
according to actual vehicle weight. In particular, many held theopinion that linkage with digital tachographs is desirable fromthe viewpoint of labor-saving device operation, centralized
management of the terminals from which information to bechecked by operation managers is obtained, and the effective
utilization of data through linkage with fuel consumption anddistance traveled, so it can be said that this is a main issue forthe future.

2.3.3. Details of the Tire Data Measurement FOTs
In this initiative, as with the load weight meters, tires
equipped with a TPMS were installed on heavy-duty trucks
operated by four carrier companies to ascertain tire pressure
and temperature data, and it was confirmed whether it ispossible for this data obtained by the tire manufacturer’s server
could be shared with the carrier company’s operation managersof the carrier companies.
Tire-related problems account for the largest number ofvehicle breakdowns, and carriers have a strong awareness of the
problem of tire maintenance conditions. However, mattersrelated to tire condition that can currently be monitored using
signal data are limited to air pressure and internal tiretemperature, so the initial goal is to eliminate tire failurescaused by poor air pressure maintenance (excluding thosecaused by sudden events or force majeure).
Both tire pressure and temperature values remained basicallynormal during the period of the FOTs, but a tire puncture
occurred in one of the vehicles cooperating with the testing,and as shown in Fig.5, an alert was issued and the operation
manager was notified by email when the tire pressure decreased
below the threshold value. The operation manager noticed theemail and contacted the truck driver, who stopped the vehicle
and changed the tire on site. One mistake in the response could
have resulted in damage to the vehicle and the spread of damage
to the surrounding area due to scattering of tire fragments, butsuch damage was prevented and transportation time loss was

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minimized.
Pressure (kPa)

Pressure

1200

Temperature (°C)
Temperature
60

Tire puncture occurred at this time
50

800

40

600

30

400

20

200

10

0

0

8:04
8:13
8:22
8:31
8:41
8:50
8:59
9:08
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9:36
9:45
9:54
10:03
10:12
10:21
10:31
10:40
10:49
10:58
11:07
11:16
11:26
11:35
11:44
11:53
12:03
12:12
12:21
12:30
12:40
12:49
12:58
13:07
13:14
13:23
13:32
13:41

1000

Time

Fig.5: Data showing air pressure reduction at the time of the tire puncture

2.3.4. Details of the Tire Data Measurement FOTs
When interviewing the carrier companies regarding the load
weight measurement, they were also asked about tire datameasurement, and most considered tire maintenance to be themost important factor in preventing accidents, and they highly
evaluated the usefulness of checking and managing tireconditions using data obtained while the truck is in motion. In
addition, many of them expressed an expectation for the use ofdata on tire wear and predictive signs of wheel breakage as
future tire data acquisition items.
On the other hand, as with the load weight meter, many held
the opinion of wanting linkage with digital tachographs or asystem to acquire tire data from digital tachographs. Also,
others wanted a system for direct communication to the truck
driver of information about the occurrence of an abnormality,
and that frequent transmission of data is unnecessary and that
notification only when an abnormality occurs or periodic
provision of information once every few months would be
sufficient.

3

Issues to Address to Promote Practical
Application

3.1. Issues in Practical Application of the FOTs’ Concept
As noted at the beginning of this paper, each use case is awork in progress, and there are various issues to be addressed in
the future for their practical application.
The main issues are as follows.

3.1.1. Ascertaining the Occurrence of Waiting Timeand Information Sharing
It is necessary to create a person in charge of organizing
basic data for analysis and preparing data analysis materials.

Regarding the analysis of waiting time, it is necessary to matchthe basic operation management data with other related data inorder to correct the required time and occurrence location, but
it seems that there are many carriers who find it difficult toperform such work and create analysis materials for theoccurrence of waiting time. For this reason, it is necessary to
promote the creation of a business operator (assuming a paid
service) that can perform the task of organizing and analyzing
data on behalf of carriers. It is also necessary to create anenvironment in which carriers can request such data analysis
providers to prepare analytical data on the actual status of
waiting time occurrence.

3.1.2. Checking Daily Inspection Items Using Vehicle Data
As mentioned above, JAMA has standardized the "data items
provided by vehicle and probe data" and the "production rules
for the API used to acquire data from the back-end of heavyduty truck manufacturers" and progressing the establishment ofa system that allows data users to acquire and use common data
items from each truck manufacturer using the same API if themenu is the same for that user. This use case is based on theassumption that this mechanism will be established, and it isexpected that this mechanism will be materialized first. It isstrongly hoped that a wide range of vehicle signal data items
related to daily inspection items, including items not includedin these FOTs, will be selected as common data items provided
by all truck manufacturers.
In addition, daily inspections must be conducted inaccordance with the "Vehicle Inspection Standards" and "Guide
for the Inspection and Maintenance of Motor Vehicles"
established by the Ministry of Land, Infrastructure, Transport
and Tourism. After confirming the consistency of the methodof checking daily inspection items using vehicle data signals
with these regulations, it is desirable to also discuss the revisionof these regulations as necessary.
It is also desirable to establish an algorithm that can
automatically determine the appropriateness of the results ofdaily inspections and to electronically record the results of suchdeterminations in the future. Although these are long-term
technological development topics, it is hoped that procedures
for acquiring truck vehicle signal data will be established andput into practical use, and that the Vehicle Inspection Standards
will be revised in the future, in order to create an environment
in which each company can engage in research and development.

3.1.3. Measuring Load Weight and Tire Data to Ensure Legal Compliance and Safety
Regarding both of these, there is a strong demand fromcarriers to be able to obtain information centrally from digitaltachometers and to be able to link it with other data, rather than216

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﻿3)Research to Realize More Effective Logistics System with Probe Vehicle Dataobtaining the information independently. In this case, it isdesired that it be possible to acquire and link data from thedigital tachographs made by any company in Japan, and not justthose made by a specific manufacturer.
In addition, regarding tire data, research and development
initiatives are being performed to obtain information on tire
wear and wheel breakage risks through linkage with truck
vehicle signal data. The establishment and practical applicationof procedures for acquiring truck vehicle signal data by JAMA,
as described in the previous section, is also an important issue
for this research and development topic.

3.2. Necessity to Create Future Promoters Towards
Practical Application
Regarding "ascertaining the occurrence of waiting time andinformation sharing", it is planned to consider ways to create abusiness entity responsible for organizing basic data andcreating analysis materials, and to request the cooperation oftruck carrier business associations in promoting awareness of
this use case. Regarding "checking daily inspection items" and"measurement of load weight data and tire data", the exchangeof opinions with related companies is being conducted so that it
can be taken up as part of the specific items being considered inthe "Logistics MaaS" promoted by the Ministry of Economy,
Trade and Industry.
It is expected that both the private sector and businessorganizations, as well as the government and other publicagencies, will support the implementation of all use cases.

[Contacts]
Department, NX Logistics Research Institute and Consulting, Inc., 2 Kanda
Izumi-cho, Chiyoda-ku, Tokyo 101-0024
Research & Consulting Unit 1, (General, Tire Data Measurement) 03-5829-1423,
Contact: Yukihiko Akao
Research & Consulting Unit 2, (ascertaining waiting time and information sharing)
03-5829-1405, Contact: Masaaki Kanazawa
Research & Consulting Unit 3, (Confirmation of daily inspection items,
measurement of loading weight) 03-5829-1420, Contact: Daijiro Sato

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4)Utilization and Application of Probe Data
to Road Maintenance and Management
Hirokazu Ichikawa, Atsushi Takenouchi, Naohiro Uchiyama, Shinichi Nedu, Kazuki Tokunaga (Pacific Consultants Co., Ltd.)(Abstract) This project was conducted for the purpose of studying potential generation of data that can be utilized inroad maintenance and management, as well as information useful in automated vehicle driving, such as information
related to poor weather and road conditions, based on probe vehicle data. As probe vehicle data used for studies, wetake up data that can currently be acquired, such as ABS, traction control systems, Electronic Stability Controls, andwindshield wiper operation data, and data that may be acquired in the future, such as road grip level data and water
film thickness estimates. By grasping the correlation between probe vehicle data and various meteorological data thatcan grasp the precipitation and organizing in along the same units for each category, we plan to consider the potential
for data generation and the effectiveness of this data in automated driving support and road maintenance.
Keywords: Probe vehicle data, road management, water film thickness, road conditions, vehicle control log

1

Purpose of project

During our researches in Section 3 3); Technological
Development for Lane-specific Road Traffic Information Using
Vehicle Probes, we improved the detection accuracy of thecongestion tail for each lane by utilizing direction indicator
information. As such, it may be possible to further utilize probevehicle data. On the other hand, data on road conditions during
rain and precipitation information may hold the key for use inroad management operations support and information delivery
for automated vehicles to issue a take-over request in enoughtime, as well as automated driving decisions in poor weather.
Meteorological data provided in real-time has a resolution of
250 meter mesh units in High-resolution Precipitation
Nowcasts. If it is possible to grasp road conditions andprecipitation data from probe vehicle data received fromcommercial vehicles (OEM, etc.), it may be possible to utilize
more detailed information.
This project aims to explore potential generation of data thatcan be utilized in road maintenance and management, as wellas information useful in automated vehicle driving, such as
information related to poor weather and road conditions, basedon probe vehicle data.

2

Overview of deliberations

We extracted and studied probe vehicle data that may be able
to detect status of water film due to rain and precipitation
conditions, degree of danger in driving due to water films, andflooding to / roughness of, paved roads. Specifically, thefollowing data was examined as probe vehicle data for our
researches: data that can currently be acquired, such as ABS(Anti-lock Braking System), Traction Control System (TCS),
Electronic Stability Control (ESC), and wiper operations
information, and data that can be acquired in the future, such as
road grip level data and water film thickness estimate value. We
also studied potential utilization by testing the correlation ofthis probe vehicle data and various meteorological data androad conditions where the status of precipitation could begrasped. (Fig.1)
1. Gather probe vehicle data
Data A: Gather ABS data, etc. (ABS, TCS, ESC, windshield wiper data)
Data B: Gather water film thickness, road grip level data (conduct test
vehicle drives in rain)

2. Compare evaluation of probe data and testing data
(1) Confirm correlation between grip level and water film thickness and ABS data, etc.
(2) Confirm correlation between grip level information and water film thickness andthe status of precipitation(3) Confirm correlation between ABS activation frequency and precipitation status

3. Deliberate use for automated driving supportand road management
Fig.1: Research procedures

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﻿4)Utilization and Application of Probe Data to Road Maintenance and Management

Further, the scope for gathering probe vehicle data, etc. andtesting its potential utilization was No. 1 Haneda Line andWangan Line (Fig.2) as expressway areas and National RouteNo. 357 (Shinkiba, Edo Ward to Funbashi City) (Fig.3) as a
general road.
As a use case, support for road maintenance and management
and automated vehicle driving is expected, as shown in Table 1.
Shiodome

Tokyo Waterfront
City area

techniques, and data organization examples.

3.1. Probe vehicle data
(1) Vehicle control records (ABS, TCS, etc.)
We gathered information (Table 2) on windshield wipers,
ABS, TCS, and ESC activation, which are thought to change
based on precipitation and the status of road water film, grasp
the correlation between this data and precipitation amount, etc.
and consider potential utilization for road management. We
gathered the following data in the below period and locations.
・Data acquisition period: Four months from August to
November 2022
・Data acquisition area: Two lines on Shuto Expressway(Haneda line, Wangan line)・Data acquired: Number of passing vehicles that data can beobtained from, number of activations of wipers, ABS, TCS,
and ESC in a 100 meter zone
Table 2: Overview of vehicle control devices

HanedaHaneda Airport

Source: Added to ⒸNTT InfraNet

ABS

Quickly controls the degree of braking and prevents slips andspins on slippery roads due to rain or snow

TCS

Prevents tire slip that occurs at sudden acceleration on slippery
roads

ESC

Prevent vehicle from going outside or inside lane when turning
around curves

Fig.2: Target line (expressway area)

National Route No. 357(Shinkiba to Funabashi)

Source: Added to ⒸNTT InfraNet

Fig.3: Target line (general road area)
Table 1: Expectation of Use Cases
・G rasp poor weather and road conditions early on, determine
Automated
whether in or outside Operational Design Domain (ODD), anddriving
implement safety mechanism (to decelerate, implement earlysupportmanual assistance requests, etc.)
・D eliberate necessity of asphalt repairs (to prevent splashingRoadpedestrians, extract areas that are easy to slip, such as sudden curves,management
etc.)
support・Urge caution of flooding, etc. based on information boards, etc.

(2) Road grip level information
By analyzing the tire rotation signal, etc., it is possible toidentify the status of grip with the road. The relationshipbetween the slip rate and force differ based on the degree ofslipperiness of the road (Fig.4). This slope is found from the tire
rotation signal, etc. to detect the slipperiness of the road (roadgrip level). If a water film appears on the road due to rain, theroad becomes more slippery. This data enables us to grasp thedanger of potential hydroplaning. We have considered theusefulness of this information.
We acquired the data in the following period and locations
by the following data acquisition method.
・Data acquisition period: Two months from August to
September 2022
・Data acquisition area: Two lines on Shuto Expressway
Tire μ (friction coefficient) -s (slip rate) characteristic
Driving force

Maximum driving force

Regular driving area

Slip rate
High μ3

Gather probe vehicle data and meteorological data

Following is an overview of data needed to test theeffectiveness of probe vehicle data utilization, data gathering

Maximum
control force

Medium μ
Low μ

Control force"Slope" changes
based on road

Source: Materials provided by Sumitomo Rubber Industries, Ltd.

Fig.4: Road grip level estimate

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﻿4)Utilization and Application of Probe Data to Road Maintenance and Management(3) Water film thickness estimates
We use the information of driving force of vehicles driving
on roads to estimate the water film thickness. In addition to
verifying estimate potential, we have grasped the correlation
between estimated water film thickness and precipitation
volume, etc. We acquired the data in the following period andlocations by the following data acquisition method.
・Data acquisition period: Two months from August to
September 2022
・Data acquisition area: Two lines on Shuto Expressway(Haneda line, Wangan line) and National Route No. 357
・Data acquisition method: Estimate water film thickness
through driving vehicles in the rain with devices on-boardthat can acquire necessary data, such as driving force andresistance, etc.

4

After organizing the gathered meteorological data and probevehicle data so that units match in each zone, we have grasped
their correlation. Specifically, we have conducted the following
testing. (Fig. 5)(1)Verify the correlation between the grip level information,
the water film thickness, and the frequency of activation ofABS, TCS, and ESC(2)Verify the correlation between grip level information, thewater film thickness, and the status of precipitation(3)Verify the correlation between frequency of activation ofABS, TCS, and ESC and the status of precipitation
In addition to the above, we used dashboard cameras and
CCTV images to confirm the status of water film generationand grasp the appropriateness of water film estimate for thegeneration of actual water film.
Information source

3.2. Meteorological information

ABS, TCS,
ESC activation
frequency(1)
Probe vehicle data B(Road grip level
information)
High-resolution
Precipitation Nowcasts,
rain gauge(precipitation)(3)

Water film thickness(2)
Precipitation status

Probe vehicle data A(windshield wiper information)

Dashboard cameras/CCTV
images, etc.
(Road/water film status)

Fig.5: Information source and generated information

Fig.6 shows the relationship of the wiper activation detection
rate every ten minutes of vehicles driving on the No.1 Haneda
line and Wangan line and the precipitation during the rain on
August 13, 2022. In this case, we can see a degree of correlation
between the high-speed activation of windshield wiper
100％
80％
60％
40％

Wiper activation detection rate/precipitation volume (per ten minutes)

Weather on Saturday, August 13, 2022:
Rain (typhoon)
10

Other
Intermittent
activation
Low speed
activation
High speed
activation
Precipitation
volume (mm)

8
6
4

20％

2

0％

0

18:00
17:30
17:00
16:30
16:00
15:30
15:00
14:30
14:00
13:30
13:00
12:30
12:00
11:30
11:00
10:30
10:00
9:30
9:00
8:30
8:00
7:30
7:00
6:30

(2) Roadside rain gauges
We gathered precipitation data acquired by rain gauges set
up by road managers to ensure safe and smooth road traffic andgrasp the relationship between it and probe vehicle data. The
following data will be acquired in the following period andlocations.
・Data acquisition period: Four months from August to
November 2022
・Data acquisition points: Rain volume gauges in National
Route No. 357 zone (six areas)・Data details: Hourly precipitation (mm/h), Accumulated
precipitation (mm)Generated information

Probe vehicle data A(ABS, TCS, ESC data)

Wiper activation detection rate(1) High-resolution Precipitation Nowcasts
The
High-resolution
Precipitation
Nowcastsis
meteorological information provided by the Weather Service
Support Center that aims to strengthen monitoring and forecast
ability of localized heavy rain storms. It provides high precisionand detailed radar images and precipitation volume forecasts.
The data of High-resolution Precipitation Nowcasts include
both rain rate intensity and five-minute rainfall rate. Utilizing
both, we have grasped the relationship with the probe vehicledata.
・Data acquisition points: Four months from August to
November 2022
・Data acquisition points: Rain volume gauges in National
Route No. 357 zone (six areas)・Data details: Rainfall rate intensity (mm/h), five-minute
rainfall rate (mm)Probe vehicle data and meteorological
data comparison and evaluation

Precipitation volume (per ten minutes)(Haneda line, Wangan line) and National Route No. 357
・Data acquisition method: Acquire road grip level throughdriving vehicles in the rain with devices on-board that canacquire necessary data, such as tire speed signals, etc.

Fig.6: Windshield wiper activation detection rate/precipitation volume
relationship example (Saturday, August 13, 2022)

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activation detection rate and precipitation. However, wiper
activation detection rate is a figure that applies to the entire
target zone, while precipitation is a value observed in the
Amadeus Haneda observation area. Potential correlation
between windshield wiper activation detection rate andprecipitation within a certain range has been presented. In the
future, we will verify the localized correlation of other probe
data using high resolution precipitation nowcasting, etc. shownin Fig.7.
Saturday, August 13 11:30 Rain rate intensity(mm/h)

Saturday, August 13 11:30 Five minute rainfall
rate (mm/5 minutes)

Source: Ⓒ OpenStreetMap contributors
Add data of Weather Service Support Center and High-resolution Precipitation Nowcasts inbase map of https://www.openstreetmap.org/copyrightFig.7: Examples of analysis datafrom High-resolution Precipitation Nowcasts(Saturday, August 13, 2022, 11:30 a.m., Left: Rain rate intensity,
Right: Five minute interval of precipitation volume)

5

Conclusion

We plan to organize the acquired probe vehicle data andmeteorological data for the 100 meter zone unit. After that, we
will grasp the difference in the data acquisition status of probevehicle data during rain and sun, verify the correlation withobserved meteorological data, and attempt to grasp feasibilityof information generation, detectable phenomenon by probevehicle data, detection accuracy, and resolution. Based on theseresults, we are planning to organize data that can be utilized in
automated driving support and road management.

[References]
(1) Japan Automobile Manufacturers Association: "Producing safer cars...
Preventative safety techniques to prevent accidents..." https://www.jama.or.jp/
library/children/encyclopedia/encyclopedia5.html (accessed 2022.08.30)(2) Forecast Department, Japan Meteorological Agency: Technological information
related to delivered materials No. 507 - Improvements to High-resolution
Precipitation Nowcasts
High-resolution Precipitation Nowcasts-, February 27, 2019(3) Japan Meteorological Agency: Manual on Codes ( in Japanese based on
International Codes, Volume I.2, Annex II to the WMO Technical Regulations)8, No. 23 March 1990 (revised December 12, 2018)(4) Japan Meteorological Agency: Manual on Codes ( in Japanese based on
International Codes, Volume I.2, Annex II to the WMO Technical Regulations)
Appendix No. 36, March 1997 (revised January 12, 2022)[Contacts]
Pacific Consultants Co., Ltd. Digital Services Division, DX Promotion Division,
Traffic System Office, 3-22 Kanda Nishiki-cho, Chiyoda-ku, Tokyo, 03-6777-4476,
Contact: Naohiro Uchiyama

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Promoting International Cooperation

Overview

Manabu Umeda (The University of Tokyo, Collaborative Research Coordinator for SIP-adus)

1

International cooperation activities

Automated driving technologies has been improved
dramatically with the participation of various industries,
including companies in the information services and startups
in addition to current automobile manufacturers. Automobile
manufacturers are actively producing and selling cars both inJapan and other global markets. Various activities such as
automotive technologies development, infrastructure,
standardization, legislative issues, and Field Operational Tests(FOTs) are being addressed throughout the world.
As the automotive industry plays a crucial role in Japan, inorder to maintain the international competitiveness of
automobile industries and its related businesses, it is necessary
to take the initiative in standardization and regulatory activities,
and to work toward international harmonization. Consequently,
the strengthening of international cooperation defined as oneof the four major activity pillars of the second phase of SIPadus, and information on its research outcomes were actively
communicated to other countries. Furthermore, investigative
research and other activities necessary to strengtheninternational cooperation were addressed for the purpose ofproviding open research and development that would induce
active discussions, as well as creating opportunities for social
implementations.
Along with Japanese-German cooperation launched in thefirst phase of SIP-adus, Japanese -EU cooperation was addressed
through exchange of views regularly in the second phase of SIPadus. We also sought the opportunities to cooperate with the
U.S. by continuous information exchange through international
conferences. To promote this international cooperation

activities with regard to automated driving, we broadly
enhanced the cooperation and sharing information amongexperts of universities and research institutes in Japan acrossthe humanities and sciences, and supported joint research
activities with research institutes in the U.S. and Europe. In
addition, we addressed to launch a new association that would
enable these international cooperation scheme to continueeven after the second phase of SIP-adus.

2

Setting focus themes and developing aframework for international cooperation

2.1. Setting focus themes for international cooperation
At SIP-adus, the seven focus themes described in sections
2.1.1 to 2.1.7 were identified by enhancing the international
cooperation activities addressed in the first phase of SIP, andtheme leaders responsible for promoting international
cooperation activities in each research fields were assigned.

2.1.1. Dynamic Map
Digital map databases with a layered structure built ongraph network representation of roads will be expanded widely
including highly detailed description of the road structure andits surrounding environment. The databases will be dynamicallylinked to real time information from an integrated sensor
systems onboard the vehicles and semi-real time informationfrom V2X communications. The development of such databases
is only possible through collaboration across industry sectors
and public agencies. [See Section 6 3) for more details]

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Overview

2.1.2. Human Factors
The shift between levels of automation will depend on thedriving environment and the driver’s condition along the trip. It
is important to design automated vehicle systems to effectively
communicate with the driver, so that the situational awarenessof the driver is maintained and the transition between the levelsof automation is properly performed. [See Section 6 4) for moredetails]

2.1.3. Safety Assurance
How safety is assured is an extremely critical aspect of
implementing automated driving technologies into the society.
Reliable and efficient test methods will be required in order toassure the safety for automated driving. Ensuring the safety ofcomplex electronic systems and software requires virtual testing
environments in cyberspace. Various evaluation methods forvalidation, modeling, and simulation are under development.
[See Section 6 5) for more details]

2.1.4. Connected Vehicles
High levels of automation require a larger range of
observation of the driving environment. The deployment ofconnected vehicle technology will be advantageous toautomated driving systems. Proximity will be sensed byintegrated sensors onboard the vehicle, and physically blind
vehicles will be notified through vehicle to vehicle (V2V)
communication. Furthermore, providing traffic signal andother infrastructure information to automated vehicles throughvehicle to infrastructure (V2I) communication facilitates therealization of smoother and safer automated driving. [SeeSection 6 6) for more details]

2.1.5. Cybersecurity
A connected car is a car that has become capable ofproviding various services, which enhance its convenience andsafety, through DSRC and cellular communication. However, aconnected car is also at risk from cyberattacks, and new types ofcyberattacks are regularly reported at international conferencessuch as BlackHat in the field of vehicle cyber security.
Cybersecurity measures will have to continue to be reinforced
to keep systems secure in the future. Therefore, it is essential toshare information and promote collaboration among industry
and government across the conventional boundaries between
sectors. [See Section 6 7) for more details]

by automated driving technologies and understanding its latentrisks will be essential for automated driving technologies to be
widely accepted in public. Considering the level of automateddriving technologies and its dissemination status, having an
open discussion for its effects and latent risks by automateddriving technologies is necessary, through guiding the analysis
and quantitative effects of the impact of automated driving withrespect to reducing traffic accidents, decreasing CO2 emissions,and mitigating traffic congestion, defining its long-term visionin Japan. [See Section 6 8) for more details]

2.1.7. Service and Business Implementations
In the central districts of large cities with high-density travel
demand, a pedestrian-centered multimodal transportation
network is anticipated to offer efficient and sustainable mobility.
Innovative transportation systems featuring automated driving
technologies and on-demand operation will reduce travel time
while making trips comfortable for passengers, and enhance
efficiency for operators. In contrast, small size vehicles withenhanced driver assistance for personal use are also anticipatedto provide aged or vulnerable road users and to encourage themto actively engage in social activities.
Various activities regarding new mobility services andmobility innovation by utilizing automated driving technologies,
are being carried out worldwide. [See Section 6 9) for moredetails]

2.2. Developing a framework for international cooperation
In the second phase of SIP-adus, along with experts on all
the focus themes mentioned above, an international
collaborative research coordinator was assigned under thesteering committee of SIP-adus to act as an overall window forinternational cooperation activities. In addition, with the goalof developing a long-term international cooperation frameworkwith overseas research institutes beyond the second phase of
SIP-adus, meetings named "Alliance for promoting mobility
innovation" were held, and the framework for international
cooperation shown in Fig.1 was established. This "Alliance forpromoting mobility innovation" provides opportunities for theexchange of information and views among the research teams,
universities' research centers and research institutes in Japan,
and provides opportunities to promote research anddevelopment activities in academic fields.

2.1.6. Impact Assessment
Demonstrating the economic and social benefits provided

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SIP-adus (Steering Committee)

PD, Cabinet Oﬃce, related ministries, NEDO

International Cooperation Working Group (WG)
Governments
outside Japan
GermanyEUand others

Overseas university,

Theme leader

Cabinet Oﬃce

(related ministries as necessary)

Research anddevelopment (program)

Collaborative researchcoordinator for SIP-adus

Alliance for Promoting
Mobility Innovation

research institutes,
private companies,
etc.

[Themes with ﬁnalized speciﬁc cooperation items]

Standardization bodies

Fig.1: The SIP-adus framework for international cooperation activities

3

Overview of activities to promoteinternational cooperation

3.1. Building networking of experts and Collecting
information for international research trends
through international conferences
The international cooperation theme leaders for SIP-adus
actively participated in international conferences such as ITS
World Congress, Transportation Research Board (TRB) in U.S.,
or EU-CAD (Connected Automated Driving) in Europe, toengage and consolidate their expert network throughinformation exchange with overseas experts, and collected thelatest trend research information. In addition, a collaborative
research coordinator for SIP-adus built connections withgovernmental organizations such as the U.S. Department ofTransportation (USDOT) and the Directorate-General for
Research and Innovation (DG-RTD) of the European
Commission, and these connections contributed to establish a
new research network by introducing experts or research
project coordinators through those governmental organizations
based on the requests from each experts, along with acquiring
the latest project information regarding automated driving
research in U.S. and Europe.

3.2. 
Japanese-German and Japanese-EU Cooperation Activities
Japanese-German cooperation activities have been carriedout between the Japanese Cabinet Office and the German
Federal Ministry of Education and Research, based on the Joint
statement to enable scientific exchange in automated driving
and to identify areas for potential research and development
activities on Science, Technology, and Innovation. As a result, it
was agreed to start joint research activities in the fields of"Human Factors" and "Impact Assessment" in 2019, and afurther joint research activities in the fields of "Safety Assurance"and "Cybersecurity" were agreed in 2020, thus JapaneseGerman joint research activities were carried out in total 4
research topics. Japanese-German cooperation has produced
various outcomes in each research topic, including contributions
to international standardization activities, joint bookpublications of research results, and exchanges of young
researchers through joint workshops, that demonstrated theimportance of Japanese-German cooperation, because bothcountries have the automobile industries as a core industry. A
joint brochure to summarize the research cooperation outcomeshas been published.
With respect to Japanese–EU cooperation activities, anagreement to consider cooperation opportunities between theexisting projects, the Horizon 2020 in EU Framework
Programme for Research and Innovation and the SIP-adus
research project, was reached with the Directorate-General for
Research and Innovation (DG-RTD) of the European
Commission, and potential cooperation opportunities werestudied. As a result, 3 cooperation activities with the project
under the Horizon 2020 framework were carried out by holding
joint workshops, etc. In Europe, a new research and innovation
framework, "Horizon Europe" was launched in 2021, and it isexpected to initiate further cooperation activities with new
research projects regarding connected automated driving. [SeeSection 6 2) for more details]

3.3. SIP-adus Workshop
The SIP-adus Workshop have been held since the first phaseof SIP as an international conference, in order to enhance
Japanese initiatives in the context of automated driving researchand development, to promote the technologies developed in

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Overview

Japan, and to contribute harmonization for international
standardization (Fig.2). It has been held annually in November
since 2014 (was held in October in 2022), and the Workshop is
now recognized as an international conference on automateddriving research hosted by Japan.
The Workshop was composed of plenary sessions open to
the public and breakout sessions that aim for deeper discussions
between invited experts, and the sessions consisted of the focus
international research themes. Due to the COVID-19 pandemic,
the SIP-adus Workshop 2020 and 2021 were held as a webbased conference, while the SIP-adus Workshop 2022 was held
as an in-person conference in Kyoto. [See Section 6 1) for moredetails]

Fig.2: Memorial photograph taken at the 2022 SIP-adus Workshop

3.4. 
Deploying globally opened, Large-scale Field
Operational Test environments
Large-scale FOTs were conducted in the Tokyo waterfront
city area encompassing the Tokyo waterfront subcenter area,
the Haneda airport area, and the Metropolitan expressway thatconnects Haneda airport and the waterfront city area. The
purpose of this FOT was to find solutions to technical issues forapplication of infrastructure cooperative data, such as merging
lane assistance, traffic jam information, and traffic signal
information, as well as to encourage the development of
automated vehicles, enhance international cooperation andstandardization, foster public acceptance, and promoteadvanced technologies.
Deploying a globally opened test environment to conduct
the FOTs in the Tokyo waterfront area enabled automobile
manufacturers and suppliers from outside Japan to participate
in the FOTs. Basic technologies for the realization of automateddriving were validated in the real traffic environment on publicroads.

3.5. Activities for International Standardization
SIP-adusisstrengthening
collaborations
withstandardization bodies to bring products and services into
global markets smoothly and quickly. We are working withorganizations such as the Japan Automobile Manufacturers
Association (JAMA) and the Society of Automotive Engineersof Japan (JSAE), as well as the Japan Auto Parts Industries

Association (JAPIA), the Japan Electronics and Information
Technology Industries Association (JEITA), the UTMS Societyof Japan, the Association of Radio Industries and Businesses(ARIB), and the ITS Info-communications Forum (ITS Forum)
to promote standardization based on both "de jure" standards
in ISO, IEC, and ITU etc., and "de facto" standards throughcooperation with global industry standardization bodies.

4

Future Initiatives Toward Promoting
International Cooperation

In SIP-adus, with the view that international cooperation isimportant, an international cooperation framework was
established, and international cooperation activities werecarried out at the both expert and government level, as well asassignments of theme leaders in charge of international
cooperation on focus themes, and an assignment of collaborative
research coordinator.
Activities toward social implementation of automateddriving technologies have been carried out worldwide, like theU.S., Europe, and China, but these are mostly still at the pilot
test phase. In order to realize and spread the automated driving
technologies into the society, it is important to share lessens andlearned through various pilot activities in the world, and toharmonize internationally in cooperative areas, includinginternational standardization.
In promoting international cooperation activities, it isessential to build trusted relationships with overseas
governments and research institutions, and continuing anddeveloping the relationships with overseas governmental
institutions and experts built through the international
cooperation activities under the SIP-adus are expected after thesecond phase of SIP-adus. In fact, the German Federal Ministryof Education and Research and Directorate-General for
Research and Innovation of the European Commission, whichhave been engaged in Japanese-German and Japanese-EU
cooperation activities, have strongly requested the continuationof cooperation activities with Japan after the completion of thesecond phase of SIP-adus.
Since the beginning of the second phase of SIP, SIP-adus has
been targeting to build a continuous international cooperation
framework, and it has been studied and made an arrangement
to establish the new association which enables to discuss andcoordinate with overseas research institutes by governmentindustry-academia collaboration and serves as an window forinternational cooperation activities regarding automateddriving technologies in Japan, by utilizing an academia networkin "Alliance for promoting mobility innovation". As a result,"Mobility Innovation Alliance Japan" was newly established inJuly 2022, taking over the framework of "Alliance for promoting

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mobility innovation" under the SIP-adus. Mobility Innovation
Alliance Japan aims to enhance research activities about
mobility innovation in Japan, which contribute to the social
economic growth and academic research development.
Mobility Innovation Alliance Japan co-hosted the SIP-adusWorkshop held in Kyoto in October 2022 with the Cabinet
Office, NEDO, and Doshisha University. In addition, They also
co-hosted the "Mobility Business Innovation Contest (M-BIC)
2022" which was held as a side event of SIP-adus Workshop
2022, with Mobility innovation collaborative researchorganization, the University of Tokyo (UTmobI) and Mobility
research center, Doshisha University.
In the future, the new association "Mobility Innovation
Alliance Japan" will be expected to serve for the related
government ministries and agencies as a one-stop service
window for international research activities related toautomated driving, etc., with overseas institutes. They are also
expected to act as a bridge between industry-government andacademia, as well as expanding academia network, and topromote international collaborative research activities byconsidering and proposing new international research topics.

[Contacts]
Mobility Innovation Collaborative Research Organization (UTmobI), the Universityof Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, 03-5452-6801 Contact:
Manabu Umeda

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1) SIP-adus Workshop
Megumi Funahashi (New Energy and Industrial Technology Development Organization (NEDO))(Abstract) SIP-adus Workshop 2022 was held in Kyoto on October 11. It was the ninth workshop since 2014, and thelast workshop of SIP-adus. The event saw researchers meet face-to-face and discuss issues for the first time in three
years. This international conference, organized by Japan and established to communicate information and research
results to stakeholders in Japan and overseas, to build networks with overseas researchers, as well as to develop
leaders capable of acting in an international capacity, has seen its profile increase with every iteration and has served
to facilitate SIP-adus (Cross-ministerial Strategic Innovation Promotion Program (SIP) Automated Driving for Universal
Services) international cooperation activities. Cooperative ties formed through this workshop among researchers inJapan and overseas, and newly developed connections with younger generations, will be carried on in the future
development of automated driving technologies and the process of their social implementation.
Keywords: S IP-adus Workshop, international cooperation, international conference, Japanese-German cooperation, Japanese-EU
cooperation1

About the SIP-adus Workshop

1.1. SIP-adus Workshop Aims
The SIP-adus Workshop has five main goals: 1.
Communicating the results of automated driving technologyrelated R&D in Japan to overseas stakeholders; 2. Sharing thelatest information about the development of these technologies
overseas; 3. Building a network among those engaged in
automated driving technology development in Japan andoverseas; 4. Communicating this information to stakeholders inJapan; and 5. Developing leaders who can act in an international
capacity through these activities.
One of the four pillars of the SIP-adus program isstrengthening international cooperation. Because of the need toproliferate products in the vast and growing domestic andinternational automobile markets, and to further strengthen
competitiveness, automated driving technologies will need tobe more readily accepted not just in Japan but also overseas.
Key to this endeavor will be active efforts by Japan tointernationally standardize the technologies it develops and tocommunicate those technologies to the international
community. SIP-adus therefore exists to establish themes to beprioritized for international cooperation, appoint international
cooperation coordinators to further this cooperation, exchange
information beyond borders with respect to internationallyshared issues concerning automated driving, internationallystandardize solutions to these issues in Japan, and thoroughlyshare information about activities and achievements. The SIPadus Workshop was launched as an international conference
organized by Japan to promote these activities by working toachieve the five goals mentioned above.
Held for the first time in 2014, the SIP-adus workshop saw
experts come together from Japan and overseas every autumn
up until FY2022, the final year of SIP-adus, raising its
international profile with every iteration. Through theworkshop, automated driving experts gathered under one roof
and worked to achieve these goals, broadly sharing information
and building international networks through discussions about
R&D and service commercialization.

1.2. SIP-adus Workshop Structure
The SIP-adus Workshop comprises the Plenary Session,
which is open to the public, and the Breakout Workshop, whereinvited experts can discuss issues.(1)(2)During the Plenary
Session, speeches are given by ministers of state in charge of SIP,
government officials from the U.S. and Europe, and the SIPadus program director, and experts present on research in theirfields. Ministries and agencies also talk about the national
policies in their respective departments. In the Breakout
Workshop, information shared during the Plenary Session
feeds deeper discussions through sectional meetings covering
different areas of expertise. Moderating the three days ofpresentations and discussions are the SIP-adus international
cooperation coordinator, as well as leaders of different key

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themes for international cooperation. Providing overall
leadership for the Breakout Workshop, these individuals
leverage the connections they form every year with researchersin Japan and overseas and arrange for participation by speakers
and discussion members.

2

History of the SIP-adus Workshop

2.1. Workshops up to FY2019
Starting with the first session in November 2014, the SIPadus Workshop has seen its profile and number of participants
grow every year. The nationalities of its participants has also
grown diverse: the 420 participants from nine countries in 2014
grew to more than 500 people from 23 countries and regions in
2019. Between 50 and 60 people give talks at the Plenary Session
every year, with about half coming to speak from overseas.
These international cooperation coordinators and theme
leaders for key issues concerning international cooperation
actively participated in Japan-EU and Japan-German researchcooperation and international standardization activities in theirfields, as well as in international conferences held overseas.
These efforts boosted SIP-adus's profile and forged important
relationships.
This cooperation revolved around key themes for the SIPadus international cooperation for that year. The first session
focused on five themes: the Dynamic Map, Human Factors,
Next Generation Transport, Connected Vehicles, and Impact
Assessments. Since then, the discussion themes have been
evolved by adding technical domains such as Security,
Connected Vehicles and FOTs (Field Operational Tests), and byincluding Regional Activities to show study trends in different
countries and regions, primarily those in Europe and the U.S.,
as appropriate to overall program activities and R&D priorities.
In addition to the plenary session, a poster session was held toshare achievements made in each field, along with a test ride ofautomated vehicles in certain years. (Fig.1) These events served

Fig.1: From the FY2015 test ride

to communicate the results of Japan's automated driving
technology developments and SIP-adus.

2.2. Changes Brought by COVID-19
In FY2020 and FY2021, the decision was made to hold theworkshop online as COVID-19 caused restrictions on people's
movement inside and outside the country. The two-day plenary
session was conducted in Japanese and English withconsideration for Japan time as well as U.S. and European time,with the goal to make it easier to participate in and watch. A
video recording of the workshop was made available for a onemonth period afterwards.More than 1,000 people watchedthese online events as they happened, and nearly 45% of those
who participated in the 2021 workshop (Fig.2) did so for the
first time. Despite the inability to meet face-to-face, theseworkshops ended up getting out the SIP-adus’s message to more
people than ever before, while highlighting the latent needs ofpeople who had always been interested in the workshops but
could not make it to the venue. Moreover, the 2020 workshop,
which consisted only of the Plenary Session, reconfirmed theimportance of holding the Breakout Workshop, where experts
can get into more in-depth conversations. However, participants
agreed that the online sessions, while allowing forcommunication through one's online network, did not allow
for participants to understand one another as smoothly or
deeply as they could face-to-face.

Fig.2: All speakers at the SIP-adus Workshop 2021 (held online)

2.3. 
Final Year of the Second phase of SIP-adus(2022)In FY2022, the best way to hold different events is being
reevaluated with a nod to COVID-19's having become endemic.
For the SIP-adus Workshop 2022, the event was held in person
for the first time in three years in order to bring back face-toface meetings. Rather than Tokyo, where past workshops havebeen held, the event was held in Kyoto, an international city that
represents Japan. Both the Plenary Session and Breakout
Workshop were held with an emphasis on facilitating directinteraction among participants. (Fig.3) This workshop
emphasized communication among experts based on theexperience of holding two workshops amid the COVID-19

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outbreak, and prompted the decision to provide video of the
Plenary Session later online for the many who were unable to
make it to the venue.
In addition to the seven themes of Human Factors，Impact
Assessment，Dynamic
Map，Connected
Vehicles,Cybersecurity，Safety Assurance，Service and Business
Implementation, added to the program this year ― the final
year of the second phase of SIP-adus ― was a panel discussion
by research representatives from the U.S., Europe, and Japanthat looked back on nearly 10 years of automated driving
development and looked forward to future developments.
Additionally, Mobility Business Innovation Contest 2022
(M-BIC)(3) Interim presentation session was co-hosted at thevenue and mutually shared event announcement. Including
event guide support by students of Doshisha University asworkshop venue, these efforts served to build new connections
with a generation of people who will drive greater involvementwith automated driving.

3

Although FY2022 will be the final year for SIP-adusWorkshops, these achievements will be perpetuated not only infuture meetings but also in the relationships among researchers
and the technical research conducted in Japan's automateddriving industry.

[References]
(1) SIP-adus website, https://en.sip-adus.go.jp/, (accessed October 21, 2022)(2) Takahiro Tanaka et al: SIP-adus Workshop, SIP 2nd Phase: Automated Drivingfor Universal Services—Mid-Term Results Report (2018–2020)" pp. 179-182,
2021(3) M-BIC website, http://webpark5061.sakura.ne.jp/2022/, (accessed October 21,
2022)[Contacts]
Robot and Artificial Intelligence Technology Department, New Energy andIndustrial Technology Development Organization (NEDO), MUZA Kawasaki
Central Tower, 15F 1310 Omiya-cho, Saiwai-ku Kawasaki City, Kanagawa 212-8554,
044-520-5241 (direct line), Contact: Takahiro Tanaka (project manager), Megumi
Funahashi

SIP-adus Workshop Achievements andLegacyThrough nine iterations held from 2014 during the firstphase of SIP-adus to 2022 during the second phase of SIP-adus,
the SIP-adus Workshop has become widely known worldwide
as an international conference for automated driving in Japan.
Throughout this time, SIP-adus members have monitoredsocial developments and accordingly re-examined how theevent should be held and what its content should be, while
continuing to share information every year about current R&D
trends and communicating the projects' achievements. Themeleaders convened researchers for each topic of discussion andmanaged these discussions, and relationships built throughthese discussions resulted in new networks for conducting
subsequent research and workshops. These outcomes represent
a full achievement of the original goals for the SIP-adusWorkshop.

Fig.3: From the SIP-adus Workshop 2022

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Promoting International Cooperation2) Japanese-German and Japanese-European
Cooperation
Manabu Umeda (The University of Tokyo, Collaborative Research Coordinator for SIP-adus)(Abstract) As intergovernmental international cooperation activities in the second phase of SIP-adus, collaborative
activities with the German Federal Ministry of Research and Education (BMBF) and Directorate-General for Researchand Innovation (DG-RTD) of the European Commission were conducted by receiving offers for joint research throughthe frameworks of Japanese-German cooperation and of EU funded project. In the Japanese-German cooperation, asteering committee that made decisions on collaborative research activities in the field of automated driving, approved
joint research plans in the four research fields of human factors, socioeconomic impact, safety assurance, andcybersecurity related to automated driving technology, and collaborative activities were conducted. In the JapaneseEU cooperation, offers were received to cooperate with projects under Horizon 2020, an European Framework
Programme for Research and Innovation funded by the European Commission, and cooperation activities with mainly
three projects under Horizon 2020 were conducted.
Keywords: Japanese-German cooperation, Japanese-EU cooperation, Horizon 2020

1

BackgroundIn the second phase of SIP-adus, enhancing international
cooperation was defined as one of the four major activity pillars.
In response to the automated driving Field Operational Tests(FOTs) carried out globally and various international
discussions, SIP-adus encouraged international joint researchand other cooperation activities with overseas research
institutes in the field of automated driving.
Many research projects regarding connected and automateddriving, including the PEGASUS project under the initiative ofGerman government and projects under Horizon 2020
Framework Programme for Research and Innovation, arecarried out in Europe, and offer of joint research through theframeworks of Japanese–German cooperation and the EU
funded project were received to SIP-adus. In response to thesecircumstances, the SIP-adus program established a framework
to support intergovernmental cooperation activities andconducted international cooperation activities as the JapaneseGerman cooperation and Japanese-EU cooperation.

2

Overview of Japanese-German Cooperation

Japanese–German cooperation activities on automateddriving were carried out between Japanese Cabinet Office and
the German Federal Ministry of Education and Research(BMBF), based on the Joint Declaration of Intent on Japanese– German Cooperation of the Minister of State for Science andTechnology Policy of Japan and the Federal Minister of
Education and Research of the Federal Republic of Germany on
the Promotion of Research and Development on AutomatedDriving Technologies on January 12, 2017.
In order to promote the research and development for
automated driving technologies by Japanese-German
cooperation, a steering committee was established to makedecisions on Japanese–German cooperation activities forautomated driving. The committee consists of related members,
specialists, and other representatives of the Cabinet Office,Ministry of Economy, Trade and Industry in Japan, BMBF, andGerman Federal Ministry for Economic Affairs and Climate
Action, as well as other Japanese and German agencies involved.
An expert workshop for exchanging information and opinions
among experts of related research fields, and a coordinating
secretariat that arranged and supported the cooperation
activities, were formed under the steering committee. Thus, theJapanese–German cooperative structure shown in Fig.1 wasestablished.

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・・・・

Fig.1: Japanese-German cooperation structure

The first Japanese-German cooperation expert workshop to
discuss specific cooperative research topics was held in
November 2017, by taking the opportunity of the SIP-adusWorkshop. Since then, the workshops were held as an annuallybasis, and a total of six expert workshops were held by October
2022. In the workshops, experts from various research fields
such as human factors, socioeconomic impact assessment andsafety assurance of automated driving participated, and active
discussions were held among the experts.
The first Japanese-German cooperation steering committee,
which discussed specific collaborative research topics andapproved cooperative research activities, was held in January
2019. Since then, a total of six meetings were held by January2023.
In addition, in order to enable smooth communication
between Japanese and German experts at each joint research
topic, a list of contact persons for each research topic was jointly
created and shared among related members in November 2019.
The Japanese-German cooperation activities have produced
various outcomes in each research topic, including contributions
to international standardization activities, joint bookpublications of research results, and exchanges of young
researchers through joint workshops, that demonstrated theimportance of Japanese-German cooperation, because bothcountries have automotive industries as a core industry. A joint
brochure to summarize the research cooperation outcomes has
been published.

2.1. Japanese-German Cooperation in Human Factors and Socioeconomic Impact Assessment
Through the expert workshops held on November 2017 and
September 2018, discussions were held regarding research anddevelopment of human factors and socioeconomic impact
assessment of automated driving. As a result, the joint researchplans for those two topics were approved at the first steeringcommittee in January 2019.
In the field of human factors related to automated driving,
Japanese and German academic researchers jointly investigateddifferences in interpreting communication signs between Japan
and Germany, and how to address the differences when it exists,
with regard to communications with outside environments
which were essential to automated vehicles.
In the field of socioeconomic impact assessment, researchers
worked on developing methodologies to scientifically andquantitatively calculate the impact of the safer and more
efficient traffic flow, reduction of traffic congestions, anddecreasing fatal traffic accidents that would be brought by therealization of automated driving technologies, as well as
researching differences between Japan and Germany in termsof fostering of public acceptance for automated driving
technologies.

2.2. Japanese-German Cooperation in Safety Assurance and Cybersecurity
At the second steering committee in November 2019,
members agreed to study the opportunities for new joint
research topics of safety assurance and cybersecurity, addition
to the above-mentioned joint research activities. And joint
research plans for both topics were approved at the thirdsteering committee in May 2020.
In the field of safety assurance, researchers developed
evaluation methods for validation, modeling, and simulation toassure the maximum level of safety for automated driving.
In the field of cybersecurity, researchers developed methods
to detect and eliminate potential security threats of modern
automated vehicles at an early stage, starting already in thedevelopment process.

3

Overview of Japanese-European Cooperation

Based on the recommendations for international
cooperation with non-European nations including Japan, theU.S., and Asia and Oceania countries in the Horizon 2020, EU
Framework Programme for Research and Innovation of theEuropean Commission, the meeting between SIP-adus andDirectorate-General for Research and Innovation (DG-RTD)of the European Commission was held to exchange the opinions
for future cooperation in April 2018. Since then, theopportunities of specific Japan–EU cooperation activities werestudied.
At the meeting with the European Commission (DG-RTD)
in November 2019, it was agreed to consider cooperation
opportunities by coordinating between already existing
European Horizon 2020 projects and SIP-adus research
projects, and since then, cooperation activities were conductedwith the three Horizon 2020 projects described below.
Different from the Japanese–German cooperation, theJapanese–EU cooperation did not intend to establish new joint

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Fig.2: Japanese-EU cooperation structure

research projects. It featured promoting cooperation activities
by coordination between existing Horizon 2020 and SIP-adus
projects.
In order to promote Japanese-EU cooperation activities, thecooperative structure shown in Fig.2 was agreed with theEuropean Commission (DG-RTD) to enable the coordinating
secretariats to work together for monitoring and supporting
cooperation activities based on coordination between Japanand EU projects.
The first meeting between the coordinating secretariats washeld in May 2020, and subsequently held regularly on aquarterly basis. In the meeting, the latest status of cooperation
activities between projects was shared and the opportunities forpotential new cooperative research areas were discussed. The
first high-level meeting including related government officials
was held in March 2021, with the release of the first Annual
Status Report 2020, which was summarized the Japanese-EU
cooperation activities in 2020. And the second and third highlevel meeting were held in July 2022 and February 2023,
respectively. An Annual Status Report was issued annually
since 2020.
In addition, a joint session with the European Commission(DG-RTD) was planned and held at the ITS World Congress topublicize the Japanese-EU cooperation activities. The joint
sessions were held in Hamburg, Germany in 2021 and in Los
Angeles, the United States in 2022, and the initiatives of the SIPadus program and the Japanese-EU cooperation activities were
presented.

3.1. 
Cooperation with the European HADRIAN
Project
In the field of research and development of human machineinterfaces (HMI) to enhance the safety of automated driving,

SIP-adus experts of human factors research project regularly
exchanged the information with experts of the European
HADRIAN project. A joint workshop was held in September
2022 to exchange views among the experts.

3.2. Cooperation with the European HEADSTART
Project
In the research field of safety assurance, cooperation
activities with the European HEADSTART Project wereconducted through the agency of the European Commission(DG-RTD). Since September 2020, meetings were held jointlywith the Ministry of Economy, Trade and Industry-directed
SAKURA Project to exchange information and discuss specificfuture cooperation topics related to safety assurance.
As an outcome of the cooperation activity, a white paper
towards the harmonization of safety assessment methods ofautomated driving was published jointly by the European
HEADSTART Project, the SAKURA Project, and the SIP-adus
program in December 2021.

3.3. Cooperation with the European SHOW ProjectIn the research field of realization of automated driving
services in urban areas, the Mobility Innovation Collaborative
Research Organization, the University of Tokyo (UTmobI) andITS Japan concluded a non-disclosure agreement (NDA) with
the European SHOW project, which conducts large-scaledemonstrations of mobility services focused on urban areas in
September 2020. In October 2021, an memorandum of
understanding (MOU) was concluded among the members,
and cooperation activities with the SHOW project are currently
ongoing.
Joint workshops were held in June and July 2022 to exchange
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realization of Level 4 automated mobility services.

4

Towards the Future

In the second phase of SIP-adus, the Japanese-German andJapanese-EU cooperation activities were conducted underdifficult circumstances like no face-to-face meetings allowed
due to travel restrictions caused by COVID-19. Despite difficult
circumstances, by utilizing online conferences and joint
workshops, etc., lots of exchanges and cooperation activities
among experts were successfully conducted, and various
outcomes including the exchange of research data, joint bookpublications, and the publication of white papers were obtained.
In fact, the German Federal Ministry of Education andResearch and Directorate-General for Research and Innovationof the European Commission, which have been engaged inJapanese-German and Japanese-EU cooperation activities, havestrongly requested the continuation of cooperation activities
with Japan after the completion of the second phase of SIPadus. It is expected that the trusted relationship with Japan built
on the cooperation activities will be maintained, and theJapanese-German and Japanese-EU cooperation activities willcontinue and expand beyond the second phase of SIP-adus.
In Europe, the Horizon 2020 framework program forresearch and innovation was finished in 2020, and a newframework program for research and innovation, Horizon
Europe was launched in 2021. The Horizon Europe program
follows the Horizon 2020 approach of encouraging international
cooperation with Japan and other countries outside Europe,and since June 2022, various research and innovation programsin the field of automated driving have been launched under theframework of Horizon Europe. New cooperation activities with
the Horizon Europe projects will be expected through therelationship built between Japan and Europe.
Although the current cooperation structure will once
terminate with the completion of the second phase of SIP-adus
activities, it is expected that a new cooperation framework willbe established to continue the activities of the Japanese-German
and Japanese-EU cooperation.

[Contacts]
Mobility Innovation Collaborative Research Organization (UTmobI), the Universityof Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, 03-5452-6801 Contact:
Manabu Umeda

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Promoting International Cooperation

3) Dynamic Maps
Satoru Nakajo (The University of Tokyo)(Abstract) International cooperation activities for dynamic maps are based on the results of SIP-adus (Cross-ministerial
Strategic Innovation Promotion Program (SIP) Automated Driving for Universal Services) research and development
in Japan. Their main purpose is to make the activities and R&D results related to SIP-adus , especially dynamic maps,
known to the world, to obtain feedback on them, and to ensure consistency with international standards.Our activities
can be broadly broken down into presenting our work at international conferences etc., international standardization
activities, industy-wide alignment activities, and Japan-USA-Europe trilateral cooperation activities. Our activities
during the second phase of SIP-adus had included presenting our work at several international conferences, including
the ITS World Congress; establishing four international standards, ISO17572-1, 4 (location reference methods) andISO20524-1, 2 (geographic data file); formally participating as a steering member of the OADF (Open AutoDrive
Forum), an industry standardization activity related to digital maps; and achieving results in the implementation of
SIP-adus FOTs (Field Operational Tests) based on specifications from ADASIS (Advanced Driver Assistance Systems
Interface Specification Forum), another organization participating in the OADF.
Keywords: Road traffic environment data, location referencing methods, digital map, international standards, OADF

1

Overall undertakings

The main purpose of the international cooperation activities
relating to dynamic maps is to make the activities and R&D
results of SIP-adus, especially dynamic maps, known to the restof the world, and to obtain feedback on them.
Specifically, four activities have been conducted: (1)presentations at various international conferences, (2)international standardization activities, (3) industry-widealignment activities, and (4) Japan-USA-Europe trilateral
cooperation.
In regard to presentations at various international
conferences, SIP-adus has made presentations at numerous
such conferences, including the ITS World Congress. In regard
to international standardization activities, several items
supported by SIP-adus have now become international
standards. Specifically, four of them; ISO17572-1, 4 (locationreference methods) and ISO20524-1, 2 (geographic data file).
In addition, TS22726-1, 2 (semi-dynamic information and map
database specifications for applications in automated driving
systems) has been proposed as a new item for standardization.
In regard to industry-wide alignment activities, we havecontinuously participated in the OADF events that are held
several times a year, explaining the progress of SIP-adus to theother participants, and have also participated in the OADF
Steering Committee, which meets around once a month todiscuss the direction of OADF management. Furthermore, wehave concluded a memorandum of understanding withADASIS, one of the other organizations participating in the
OADF, to disclose specifications for use in the SIP-adus FOTs,
and then conducted FOTs using the ADASIS specifications andprovided feedback on the results. In regard to Japan-USAEurope trilateral collaboration activities, we regularly exchanged
information with contacts in Europe and the USA in the
Physical and Digital Infrastructure SWG, which is itself one of
the activities in this arena.

2

Presentations at international conferences

During the first half of the second phase of SIP-adus, wemade presentations at the following international conferences.
In each presentation, we explained the positioning of dynamic
maps in SIP-adus, the results of the research and development
to date, and an overview of the activities in the second phase.
In particular, we highlighted how dynamic maps have been
focusing on dynamic information coordination during thesecond phase of activities. Our explanations emphasized thatvarious FOTs are being conducted based on the idea of integrally

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handling not only static maps but also dynamic information.
• 2018 ITS World Congress, Copenhagen• 2019 AVS, San Francisco• 2019 ITS World Congress, Singapore• 2020 International Conference on HD Maps for Autonomous
Vehicles, Taipei (video participation)• 2021 ITS World Congress, Hamburg (video participation)• 2022 ITS World Congress, Los Angeles

3International standardization activities

3.1. Summary of activities for the second phase of
SIP-adusIn regard to international cooperation activities for dynamic
maps, we have had an ongoing involvement in the ISO/TC204/
WG3 (ITS geographic information) since the first phase of SIPadus. ISO/TC204/WG3, which is a TC204 committee thatconsiders ITS international standards, mainly considers
international standards for digital maps and spatial information.
SIP-adus activities have resulted in four international standards,
ISO17572-1, 4 (location reference methods) and ISO20524-1, 2(geographic data files). Furthermore, TS22726-1, 2 (semidynamic information and map database specifications forapplications in automated driving systems) was proposed as a
new item for standardization. The following is an overview of
each international standard.

3.2. ISO 17572-1, 4 (location referencing methods)
A location referencing method is a standard for representing
locations when exchanging information between different
applications and map databases. When exchanging traffic
information between different systems, the objective is to beable to know where the location is even if different map
databases are used.
Pre-coded profile (ISO17572-2) and Dynamic profile(ISO17572-3) standards have been established by past activities.
The pre-coded profile uses an ID (road link, etc.) defined inadvance for shared use by all users, and the location is referenced
based on this ID. It is used in such systems as the VICS (Vehicle
Information and Communication System) and the RDS-TMC(Radio Data System, Traffic Message Channel) used in Europe.
The dynamic profile references locations by transmitting
latitude, longitude, and additional information.
In addition to the above, a fourth profile, "Precise Relative
Location Referencing for Geographic Databases" was approvedin April 2016 to enable high-precision location referencing forcooperative/automated driving systems, and work started on
this as NP 17572-4. This proposal was made by Japan based on
the discussions as part of SIP-adus. After a process of standard

review and deliberation, an ISO (ISO 17572-4) was issued in
April 2020.
Two methods are specified in the Precise Relative Location
Referencing Method, as shown in Fig.1. Method 1 is lane
number counting, which is applied to road sections where lanes
exist and is used to identify the lanes. Method 2 is a difference
measurement from a reference point and is applied to roadsections with unclear lane definition (such as withinintersections and before or after toll booths). In order torepresent an exact location, these are always applied withinaround 200 meters of the reference point. ISO17572-4 wasissued as an ISO in April 2020. Furthermore, based on theestablishment of this new standard, ISO17572-1, which defines
the basic concept of location referencing methods and any
documentation to be referenced, was revised to explicitly
position the Precise Relative Location Referencing Method as
one of the location referencing methods specified byinternational standards. The revised version of ISO17572-1 wasissued in July 2022.

Referencepoint 01
Area using method 2:[Concept image]Distance Δx, Δy, Δh fromreference point 01

Area using method 1:[Concept image]
Righthand lane 1 from 2
lanes

Referencepoint 02

Two methods are available, and can be selected depending on the road site and application.
Method 1: Lane number countApplicable to road sections where lanes are present
Utilized to identify lanes
Method 2: Difference measurement from a reference point
Applicable to road sections with unclear lane definition (such as at intersections
and before/after toll booths)
Applicable in the area within 200m from the reference point
Utilized as a relative positional representation with respect to the road (<25cm
positional accuracy)

Fig.1: Basic concept of the Precise Relative Location Referencing Method(1)3.3. ISO20524-1, 2 (geographic data file)The geographic data file (GDF) has mainly been used for car
navigation systems. With the emergence of further applications
for it, such as cooperative ITS, multimodal navigation, andautomated driving systems, the need to revise it has increased.
Taking this into account, PWI 20524 was approved in October
2014, and work to revise GDF 5.0 has begun. Japan has taken
the lead in discussions on automated driving systems, based on
input from Japan, Europe, and the USA. Finally, the ISO forpart 1 was issued in April 2020, and the ISO for part 2 wasissued in October 2020.

3.4. TS22726-1, 2 (quasi-dynamic information andmap database specifications for applications in
automated driving systems)
TS22726 details specifications for handling static and quasi-

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dynamic geographic data in a unified manner for use inautomated driving. They have been proposed by Japan based onsuch data as the results of the FOTs. As of September 2022, bothare still at the WD (working draft) stage.

4

Industry-wide alignment activities
Open AutoDrive Forum

SIP-adus has continuously participated in OADF, a crossdomain platform for standardization for digital maps related toautomated driving. OADF is a forum started in 2015 for the
sharing of information among standards organizations. SIPadus participated in the 5th conference (Beijing) in 2016, where
it had individual meetings with the chair, and has beenpresenting continuously since the 6th conference (Brussels). In
2017, SIP-adus hosted the 8th meeting in Tokyo together with
the SIP-adus Workshop. After these activities, Japan became amember of the Steering Committee in 2019.
Currently, the OADF has the following six organizations as
official members, as shown in Fig.2.
• ADASIS: Advanced Driver Assist Systems Interface
Specification• NDS: Navigation Data Standard• SENSORIS: Sensor Interface Specification• SIP-adus• TISA: Traveler Information Services Association• TN-ITS: Transport Network Intelligent Transport Systems
In addition, there were ongoing discussions concerning the
OpenDRIVE® and OpenSCENARIO® simulation standards led
by ASAM (Association for Standardization of Automation andMeasuring Systems).
The OADF itself does not set standards, and its main purpose
is to exchange information among the participatingorganizations. SIP-adus is not a standardization body, but has
participated in order to provide reference results for various
industry standards.
Based on the ongoing OADF discussions, SIP-adus signed amemorandum of understanding with ADASIS in 2019 toreceive disclosure of ADASIS specifications (version 3) to study
their feasibility for use in the SIP-adus FOTs. SIP-adus thenproceeded to utilize ADASIS specifications in the SIP-adus
FOTs, while exchanging opinions with ADASIS as appropriate,and conducted evaluation and feedback of the evaluation results
to ADASIS.

5OADF generates input for standardization and aligns the results towards
industry wide acceptance and state of the art solutions

Japan-USA-Europe trilateral cooperation activities

Discussion Thread: Eco system architecture
Discussion Thread: HD maps and functional safety

Fig.2: Positioning of SIP-adus in OADF(2)memorandum of understanding signed by the European
Commission and the U.S. Department of Transportation in
2009, and by the Japanese Ministry of Land, Infrastructure,
Transport and Tourism in 2010 and 2011 regarding cooperationin the field of ITS between Japan, the USA, and Europe.
SIP-adus has been participating in the initiatives related toautomated driving and exchanging opinions regularly withcontacts in Europe and the USA. One of the initiatives related
to automated driving is the Physical and Digital Infrastructure
SWG. As part of this activity, SIP-adus regularly reports tocontacts in Europe and the USA on the dynamic map-related
trends in SIP-adus, and receives information on the status ofactivities in Europe and the USA.
In previous years, meetings had been held in conjunction
with the ITS World Congress, as well as various international
conferences in the triad; from 2019 onwards, meetings were
held online several times a year.

6

Conclusion

Through the international cooperation activities of SIPadus, we have continuously lobbied international and industry
standardization organizations, publicized Japan's activities andachievements, and as a result, our achievements have been
incorporated into several international standards. This marks avery significant achievement for the future development ofJapan's automated driving industry.
Although the activities of SIP-adus will be coming to an end,
it would be desirable to utilize the relationships established insubsequent programs as required. In the future, automateddriving, as one of the future mobility services, is expected to befurther linked to other modes of transportation and various
urban activities. We hope that the results of SIP-adus will befurther utilized in a wide range of fields that make use of
automated driving.

The Japan-USA-Europe Trilateral Cooperation Activities aregovernment-led international cooperation based on a

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﻿3) Dynamic Maps

[References]
(1) Society of Automotive Engineers of Japan: Standardization of ITS 2021, (2021),
https://digitalbook. jsae.or.jp/its_2021_jp/book/index.html/, (accessed
2022.09.02)(2) OADF website, https://www.openautodrive.org/, (accessed 2022.09.02)[Contacts]
Center for Spatial Information Science, The University of Tokyo, Ce-508, 4-6-1
Komaba, Meguro-ku, Tokyo 153-8505, 03-5452-6408, Contact: Satoru Nakajo(snakajo@csis.u-tokyo.ac.jp)

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6

Promoting International Cooperation

4) Human Factors
Satoshi Kitazaki (National Institute of Advanced Industrial Science and Technology (AIST))(Abstract) Human factors in automated driving are important factors related to public acceptance and safety of
automated driving. Understanding humans can be positioned as foundational research in the cooperative area. During
the first and the second phases of SIP-adus (Cross-ministerial strategic Innnovation Promotion Program (SIP)
Automated Driving for Universal Services), Research questions, methods and results were actively shared anddiscussed through the international collaborations. The results were also brought to international standardization.
This article introduces such activities.
Keywords: Human factors, HMI (human machine interface), international cooperation, trilateral cooperation, Japan-EU
cooperation, Japan-Germany cooperation, international standardization

1

2

Background
Human factors in automated driving are important factors

related to public acceptance and safety. In Level 2 and 3
automated driving, the driver still has a certain role even during
automated driving. If the drivers is unable to fulfill this role, anunsafe situation can occur. Also, when intents of an automatedvehicle with Level 3 or higher system are not understood bysurrounding drivers or pedestrians in the mixed traffic, safety

International cooperation

2.1. Japan-US-Europe trilateral cooperation
Activities were operated on the Human Factors subWorking Group (HF-subWG) under the Automated Road
Transport Working Group (ART-WG) in the trilateral
cooperation framework agreed on by the governments of Japan,
Table 1: Trilateral cooperation Human factors sub-WG members

and efficiency of traffic can be degraded. This may also impede

United States

fostering public acceptance of the automated vehicles.

Stacy Balk, co-chair

NHTSA

Brian Philips

FHWA

Bobbie Seppelt

Ford

Dan McGehee

University of Iowa

Johan Engström

Waymo

Chuck GreenMITJohn Lee

University of Wisconsin

Human factors research in automated driving can bepositioned as foundational research in the cooperative area. The
research was conducted through frequent communication with
the SIP-adus members from the academia, industry and thegovernment. Research questions, methods and results wereshared and discussed at international conferences and meetings
to verify them. The results also contributed to international
standardization.
Thia

chapter

introduces

activitiesininternational

cooperations and standardization in the field of human factors
in automated driving.

European Union
Natasha Merat, co-chair

University of Leeds

Emma Johansson

Volvo trucks

Andreas Keinath

BMW

Anna Schieben

DLR

Klaus Bengler

TU MunichLudgrer RoggeEC, DG R&I

Japan
Satoshi Kitazaki, co-chair

AIST

Makoto Itoh

University of Tsukuba

Tatsuru Daimon

Keio University

Keisuke Ishii

Honda, JAMA

Observers
Joanne Harbluk

Transport CanadaPeter Burns

Transport Canada

Mike Regan

University of New South Wales, Australia

Mandi Mees

National Transport Commission, Australia

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6.Promoting International Cooperation﻿4) Human Factors

US, and European comission . The HF-subWG is jointly chaired

human factors. Also, in order to appeal to cooperation activities,

by Stacy Balk (NHTSA) for the US, Natasha Merat (University

we communicated research results through organizedof Leeds) for Europe, and Satoshi Kitazaki (AIST) for Japan. It

discussions in the International Ergonomics Association (IEA,

is comprised of several expert members from industry,on-line) in June 2021.

academia, and government of each country. (Table 1)
The purpose of this cooperation is to (1) share the knowledge
and information related to human factors in automated driving,(2) extract new human factor issues, (3) generate opportunities
for research cooperation, and (4) joint authorship of research

Table 2: Japan-Germany cooperation research themes and member institutes
Research theme
One-road
communicationand eHMI
1

papers. The sub-working group meetings are held around once
every three months, where members exchange information anddiscuss items (1), (2), (3) mentioned above. Also, at majorinternational conferences in Japan, the US, and Europe, themembers planned workshops and organized sessions, inviting
more experts, to exchange informtion and discuss various
issues. Regarding (4), the group published a paper about the23

writing a paper related to mental models of automated driving.

Members in Germany
Chemnitz University ofTechnology
Dresden University ofTechnology
Ulm University
Technical University of MunichDLR
AISTTechnical University of Munich
The University of Tokyo Ulm University
University Tsukuba
Dresden University of
Kumamoto University Technology
Technical University of Munich

2.3. Other international cooperations

Out-of-the-Loop concept for automated driving as a result of
this cooperation in 2019.(1) Currently, the group is jointly

Driver-systeminteractions
Driver educationand training

Members in Japan
Keio University

The SIP-adus human factors research project had been also
cooperating with two projects of EU Horizon 2020. MEDIATOR
is an R&D project for new and safer interactions between thedriver and the automated driving system. Satoshi Kitazaki, the2.2. Japan-Germany Cooperation
The Japanese-German cooperation related to automateddriving based on an agreement of the governments of Japan andGermany started for human factors in 2019. Five research
institutions in Japan, National Institute of Advanced Industrial
Science and Technology (AIST), University of Tsukuba, Keio
University, the University of Tokyo, and Kumamoto University,
which were the consortium members for the SIP-adus Human
Factors research project cooperated with five research
institutions in Germany, Technical University of Munich,Chemnitz University of Technology, Ulm University, Dresden
University of Technology, and German Aerospace Center

project leader of the SIP-adus human factors research project,served as the advisory board member and provided advice
based on the results of the SIP-adus project.
HADRIAN is an R&D project for Human Machine Interface(HMI) of automated driving that fluidly changes for safety
depending on road environment and driver states. There had
been regular meetings of the two project leaders. A workshop
with members of the two projects was held in September 2022.

3

International standardization(DLR). The cooperation coordinators were Satoshi Kitazaki(AIST) for Japan and Klaus Bengler (Technical University ofStandardization activities for human factors and HMI of

Munich) for Germany. Three research themes set in the SIP-

vehicles are being conducted in ISO/TC22/SC39/WG8. The

adus human factors project (on-road communication andJapanese representative organization to the WG8 is Human

external HMI, driver-system interactions, and driver education

Interface (HI) Working Group of Japanse Society of Automotive

and training) were also set in the German side (Table 2).

Engineers (JSAE). Therefore, main members of the SIP-adus

Japan and Germany alternately hosted two workshops every
year between the first workshop in Tokyo in November 2019

human factors research project participated in the WG8 asmembers of the HI Working Group.

and the last one in Tokyo in October 2022. Through the

For driver-automated system interactions, TR (Technicalworkshops, progress of the three themes was shared andReport) 21959-1(2) and TR21959-2(3) published in 2018 anddiscussed to brush up the research plans and to deepen

2020 focused on safe transitions from automated driving tounderstandings of obtained results for both sides. The theme 1

manual driving. TR21959-1 defines the transition process

and theme 2 also included joint experiments. Scientific papers

models for system and driver (Fig.1) and summarizes thewith co-authorship were planned to be published.

concepts related to human factors in automated driving.

In addition to research cooperation, we held webinars in

TR21959-2, considerations in designing experiments toboth Japan and Germany. The webinars spread the scope toinvestigate transition processes, includes information aboutareas beyond the cooperation research themes and invited

human factors and system parameters influencing takeover

students and young researchers for the aim of educating about

performance of the driver, construction of experimental

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﻿

DriverDriver state
accepted byautomation

Driver state transition

SystemAutomated Driving

Take-over mode
Take-over timeC
du ritice t alo s evys ent
tem
lim
Ve
it
fu hiclly lesta cobil ntrize oldSof ystesy mste inm ter
lim nait l de
tec
tio
Re
n
qu
es
tt
oI
nt
er
ve
ne
(R
tl)

S
to ignire ficasu n
m td
e m rian verua intl c eron ve
tro nti
l on
Co
m m
an pl
oe etiuv on
er of(if dr
re ivi
qu ng
ire
d)

4) Human Factors

Post transition (manual) control
Driverintervention

Control stabilization

Intervention
time

Remaining action
time

Total time budget
Control stabilization time
Fig.1: Transition process model (system-initiated transition) defined in ISO TR21959-1

scenarios, measurement indicators for performance evaluationand selection of experimental setups. Japanese delegates served
as co-leaders with US delegates for both TR21959-1 and4Conclusion

TR21959-2. The results from the human factors project of the
SIP-adus Phase 1 were included in the two TR documents.

During the human factors projects in SIP-adus Phase 1 andA Japanese delegate served as a co-leader of TS (Technical

Phase 2, the themes, methods, and results were repeatedlySpecifications) 5283 project with a UK delegate. TS5283,

discussed and verified throuh the international cooperations.

ergonomic aspects of driver monitoring in automated driving,The

started in August 2020 as a 24 months project. The projectcontributed to international standardization. By doing so,members did not reach agreements on specifications and,presence of Japan was enhanced interntionally in the field of

therefore, category of the project was changed and the newhuman factors in automated driving.

achievements

werepresented

internationallyandproject, TR5283-1,(4) started as a 24 months project in May
2022.
For on-road communication and external HMI of
automated vehicles, the concept was published under US
leadership in 2018 in TR23049.(5) Currently, PAS (Publicly
Available Specifications) 23735,(6) which is related to eHMI
design, is being conducted with a US leader. Japanese delegatesare proposing to embed knowledge and results from the humanfactors projects in SIP-adus Phase 1 and Phase 2 into the
document.
A new project on human factors in remote operations ofautomated vehicles(7) started in 2022 with leaders from US andCanada.

[References]
(1) Natasha Merat, Bobbie Seppelt, Tyron Louw, Johan Engström, John D. Lee,
Emma Johansson, Charles A. Green, Satoshi Kitazaki, Chris Monk, Makoto Itoh,
Daniel McGehee, Takashi Sunda, Kiyozumi Unoura, Trent Victor, Anna
Schieben, and Andreas Keinath: The "Out-of-the-Loop" concept in automateddriving: proposed definition, measures and implications. Cognition, Technology& Work 21: pp.87-98 , 2019.
(2) International Organization for Standardization: ISO TR21959-1, Road vehiclesHuman Performance and State in the Context of Automated Driving: Part
1─Common underlying concepts, 2018.
(3) International Organization for Standardization: ISO TR21959-2, Road vehiclesHuman Performance and State in the Context of Automated Driving:
Considerations in designing experiments to investigate transition processes,
2020.
(4) International Organization for Standardization: ISO TS5283, Road Vehicles:
Ergonomic aspects of driver monitoring and system interventions in the context
of automated driving, （under development）.
(5) International Organization for Standardization: ISO TR23049, Road Vehicles:
Ergonomic aspects of external visual communication from automated vehicles
to other road users, 2018.
(6) International Organization for Standardization: ISO PAS23735, Road vehicles—
Ergonomic design guidance for external visual communication from automatedvehicles to other road users, (under development.)(7) International Organization for Standardization: ISO TS17691, Road vehicles—
Principles for human remote support of automated systems (underdevelopment)

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6.Promoting International Cooperation﻿4) Human Factors[Contacts]
International Research and Development Corporation, National Institute ofAdvanced Industrial Science and Technology (AIST), Human Mobility ResearchCenter, 1-1-1 Chuo No. 6, Tsukuba, Ibaraki 305-8566, 029-861-3924, Contact:
Satoshi Kitazaki

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6

Promoting International Cooperation

5) Safety Assurance
Hideaki Sato (TOYOTA MOTOR CORPORATION)(Abstract) As automated vehicles are expected to be put into practical use and be deployed in order to realize a safer,more efficient, and freer mobility society, there is an urgent need to develop safety assurance standards that ask whatkind of concepts should be employed to make safety judgments that are acceptable to the public, as well as safetyassurance methods that seek to comprehensively assure safety in diverse traffic situations. In Japan, DIVPⓇ,(1) which
is supported by the Cabinet Office in its efforts to build a safety assurance virtual environment, and SAKURA,(2) which
is supported by the Ministry of Economy, Trade and Industry in its efforts to build a safety assurance scenario
database, have launched, and the Japan Automobile Manufacturers Association, Inc. (JAMA), while presenting atechnical strategy across these projects, is collaborating with industry, government, and academia to build safetyassurance foundational technologies to support safety assurance standards and methods. Many research projects
on safety assurance have been launched in various countries, and it is important to establish and operate a cooperative
system that provides a lever for establishing international standards and criteria and common foundational technologies
through close cooperation among such projects, both domestic and international.
Keywords: safety assurance, scenario-based, UN-R157, ISO 21448, ISO 34502

1

Issues in Safety Assurance Methodologies

Amid numerous dangerous situations faced when automatedvehicles take over driving from people, safety guarantees, whichguarantee a certain level of probabilistic reliability by conducting
safety assurances through long-distance and long-term test
evaluations in real traffic environments and by repeating
improvements in a black box-like manner, are processes thatare not transparent to the market because the evaluation results
are not disclosed by the developing company or project. There
are issues in the sufficiency and transparency of the evaluation
scope in the evaluation process because, even when reviewing
authorities perform audits, it is difficult for them to makeabsolute determinations of the sufficiency of safety guarantees.
In addition, it is difficult to comprehensively cover a large
number of driving situations and conduct sufficient safetyassurances if the tests are conducted on a controlled test course
under test conditions that have already been determined as partof the approval tests. To address the issues of safety assurance
technology for automated vehicles, the following two projects
have been launched in Japan to establish a common foundational
elemental technology. (1) A project to study scenario-based
testing, in which the conditions for safety assurance are logicallystructured and incorporated into evaluation scenarios, which
are maintained and improved as a scenario database. (2) A

project to construct a virtual environment technology to coverevaluation conditions that are too dangerous to be performedin the real world and innumerable evaluation patterns thatcannot be completed in a realistic period of time in terms ofefficiency.

1.1. 
Joint Domestic Promotion and International
Cooperation on Safety Assurances
The Ministry of Economy, Trade and Industry isimplementing SAKURA (Safety Assurance KUdos for Reliable
Autonomous Vehicles) for scenario database construction for(1), and DIVP has been launched as a study under this SIP-adusfor (2) to study a recognition virtual evaluation environment. In
FY2021, a task force and steering committee were established tojointly promote the two projects in order to accelerate thedevelopment of safety evaluation technologies for automateddriving, to improve the safety and development efficiency ofJapan’s automated driving technology, and to ensure its
international competitiveness. (Fig.1.)
Business Discussions on
Autonomous Driving
Technologies

SIP-adus Program Steering
Committee

Safety Evaluation
Strategy Working Group

Steering Committee for Safety
Evaluation Platform

System Implementation Working GroupScenarios

Safety Evaluation Platform
Study Task Force

Virtual safety
Evaluation

Fig.1: Joint promotion structure for automated driving safety assurances

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6.Promoting International Cooperation
﻿5) Safety Assurance

Safety Assurance Scenario Database

Virtual evaluation environment
Feedback of traffic flow scenarios and sensor failure scenarios

Study of scalable simulationarchitecture for evaluation purposes

ODD definition

AD system verification data
Driver tag

Field monitoring

Certification

Heavy rain

Single and combined
performance evaluation

Hazard analysis & risk assessment

Spatialattenuation

Single and combinedfunctional assessment

Triggering condition analysis

Backlighting

Driver tag

Noise

Evaluation of systems on public roads(FOTs)

Implementation

Fig.2: Aims of the joint promotion of automated driving safety assurances
AD system

Scenario database

Principle
analysis

Scenario systemmodel definition

Annotation
systemMap
High
precision GPS

Scenario management andgeneration
Functional Scenario（Scenario Structure）

Kanazawa University
sensor hardware

Simulator

Logical Scenario（Foreseeable Structure）
Concreate Scenario（Preventable Structure）

Team (1) Scenario
I/F study
Pass/fail judgement

Concrete Scenario（Open SCENARIO）
Road structure(OpenDRIVE)

3DCG/Reflective
assetsScenario
generator

Safety assurance
database/Sim I/F

VehiclesVirtual environment I/FTeam (2) Sim I/F andarchitecture study

Parameter Search Engine
Principle Recognition
Space
PrioritibehindfailureDesignrecognition
zationfactorfailureParameter search

Actual driving
sensor data

Kanazawa
Sensor recognition
AD app (localization
part and sensor
planner/controller)
fusion model

Sim resultsSpace
propagation

Sensorperception
part modelSensorrecognition partmodel

Validation of virtualenvironments

AD app(localization
planner/controller)

Vehiclemodel

Team (3) Scenario
and model study

Fig.3: Overall view of safety assurance infrastructure
Evaluation Target (Scenario)

Evaluation Methods (Validation)

Judgement Criteria (Metrics)■ Geometry scenarios based on
analysis of accident data, etc.

■ HILS■ Actual Vehicle Evaluation■ Determines accident margins
by accurately predicting
vehicle positions

Systemevaluation

Won't it crash?

Team (1) True value
scenario study■ Sensor weakness scenarios
based on expert knowledge
Sensorevaluation

Combination and utilization of
the methods■ Simulations based on virtualenvironment with verified
consistency■ Virtual evaluation of recognition
performance based on a model ofconsistency of measurable
perceptual outputs

Team (3) Sensing
weakness scenario study

Do you see it?

Team (0) Study ofassessment methods basedon an assessment case study

Team (2) Metrics study using
an intersection case study

Fig.4: Issues to be studied in the safety assurance foundation

The aim of this joint promotion system is to improve thesafety and development efficiency of Japan's automated driving
technology and to ensure its international competitiveness byconstructing a common foundation for safety assurances that is
logically comprehensive, feasible, and transparent regarding thesafety performance that automated driving systems should
possess, and applying it to the actual development of automatedvehicles. (Fig.2)
In the task force for joint promotion, teams are organizedacross projects to tackle technical issues necessary to apply the

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﻿5) Safety Assurance

Regulation

Standard

Assessment

SimIF de-fact

BMWi
VV Methods
SetLevel4to5

BMBF
VIVALDI（VIVID）

DE

Existing
tools

Pegasus project family / VIVALDI
Perception
Virtual testing platforms
Simulation
requirement

Scenario structure

Commonstrategyforsafetyassurance

3DCG
reflection

Space
model

Proprietary inOEMs & Tier1s

Sensormodel

AD/ADASapplication

Vehicledynamics

ACT

Proof-of-concept（Simulation validation）
JAMA / DIVPⓇ
METI

CAO
DIVP
（VIVID）

JP

Fig.5: Strengthening Japanese-German cooperation through joint promotion

JT1- Toolchain -JT2- Scenario -JT3- Sensor Model -JT4- Framework & metrics -Linking virtual validation tool
chain to test facilities

Scenario structures, data
quality verification, status
open scenario data bases

Sensor modelling andvalidation, sensor abstraction

Data structures & metrics,
Q-gates for acquired data

VIVALDIDIVP
DIVP proposed
communalization of scenarios
For Validation of SW/HW
Reprocessing/Data-Replay for
Perception Systems

Outcome

JT1
Tool
chain

JT2
Scenarioio Exchange information on
perception/recognition
model output, tested andhardware interfaces
 Provide OSI trace file
output to JT3.x activities

 Activities are in progress to
examine common formats
 Structures for the exchangeof sensing weakness
scenarios
Exchange assets and material
database

JT2
Material

 Discuss material properties
and formats
 Making proposals for
ASAM open material
standardization

JT3.1
CameraVerify the connectivity
between DIVP® data and the
VIVALDI platform for ASAMOSI I/F standardization

JT3.2
Lidar

Mutually
understand VIVALDI/DIVP®
sim. P/F archit. for spatial
scattering and attenuation
model standard study

JT3.3
Radar

Verify simple scenario
selected for first exchange
for ASAM OSI I/F
standardization

JT4
Measur-ementMetrics

For De -facto standardizationof measurement
methodology, consistency
validation of environment,
object and sensor model Compare Process with
Methodology,
 Mutual understandcommonalities andcomplementarities

Fig.6: VIVID, a Japanese-German cooperative system for a safety assurance virtual environment

scenario database of SAKURA and the recognition virtualenvironment of DIVP to the development of actual automatedvehicles. (Fig.3)
In addition, we are promoting research on safety assurance
systems by adding the study of combinations of evaluation
methods and the study of metrics to our research themes.
(Fig.4)Furthermore, the system under which the Japanese-German
cooperation with Pegasus for scenarios and VIVALDI for
virtual environments had been promoted has been integrated
into the joint promotion system to strengthen the international
cooperation between Japan and Germany. (Fig.5)
In particular, in the cooperation with the VIVALDI project(VIVID: Virtual Validation methodology for Intelligent Driving
systems), activities are divided into expert task forces. The task
forces are aiming to strengthen proposals for the standardizationof safety assurance systems and simulation interfaces forautomated driving in ISO, ASAM (Association forStandardization of Automation and Measuring Systems), etc.,
as shown in Fig.6, so that the technology can be easily combinedwith overseas sensor simulation environments to prevent itfrom becoming a technology that can only be used domestically.

The VIVID project has been promoting technicalcollaboration through regular meetings of expert task forces
and Breakout Workshops of the SIP-adus Workshop, and held asymposium in Berlin in June 2022 with standardization
organizations such as the Pegasus(3) family, a Japanese-German
governmental organization, and ASAM, a non-profit
organization promoting standardization of tool chains forvehicle development and evaluation. At the symposium, there
was a high level of interest from non-VIVID experts in VIVID’s
activities, and it was a good opportunity to further strengthen
Japanese-German cooperation. Of particular note, the proposal
for ASAM OSI4.0 on DIVP's ray tracing interface wasimmediately approved and resulted in a successful outcome.

2

International Cooperation in Safety
Assurance Standards

2.1. Background of International Standards Harmonization
Safety assurance standards are being actively discussed,
including by ethics and legal experts. In the development of

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﻿5) Safety AssuranceStandards

Mar/ʼ20
Proposal

UNR
draftALKS

Partiallyreferred

Scheduledtobe adopted

JP law

Jan/ʼ21

Formally adapted

AD Safety Assurance Guidelines publishedVer 1.0 (’20 Oct), Ver 2.0 (’21 Dec)

Methods

Jun/ʼ22

April/ʼ20

UNR15701 series

UNR157

ISO 34502 (IS codified in
November 2022)

ISO 3450X Convener:
China (CATARC)

Automated Driving
Framework Safety
Assurance Ver 1.0
Japan Automobile
Manufacturers
Association, AutomatedDriving Safety Assurance
Working Group,
September 2022

https://www.jama.or.jp/english/reports/framework.html

Fig.7: Japan's international standardization and international criteria initiatives

international standards for automated vehicles, an expert group(GRVA/VMAD) under the United Nations World Forum for
Harmonization of Vehicle Regulations (WP.29) was establishedin 2018, and in 2020, the first UN regulation for type approvalof Level 3 automated vehicles, UN-R157,(1) which covers lowspeed lane maintenance on motorways, was agreed upon.As aframework for judging safety that has been discussed in thecontext of international standards harmonization, the guidelineof whether the risk level is below the socially acceptable risk
level is a proven criterion for judgment for the social
implementation of technological innovations, including thosein industries other than the automotive industry, anddiscussions continue on realizing acceptable risk levels in thefield of automated driving. In the Framework Document on
Automated Driving(5) adopted in June 2019 after discussions
under WP.29, the concept that it is necessary to ensure that noreasonably foreseeable and preventable accidents involving
injury caused by automated driving systems occur within the
set ODD was set forth. In this discussion, the Ministry of Land,
Infrastructure, Transport and Tourism, the National Traffic
Safety and Environment Laboratory, and JAMA made proposals
under JASIC, one of which was to interpret the "reasonably
foreseeable preventable level" as "whether the system
performance exceeds a human driver's capability," using a"competent and careful human driver" as a reference for ahuman driver’s capability.(6)(7) This was codified in UN-R157
5.2.5, and incorporated into UN157 Annex 4 - Appendix 3
Guidance on Traffic Disturbance Critical Scenarios for ALKS as
safety assurance standards for measurable and comprehensive
safety assurance scenarios.

2.2. International Cooperation to Support Harmonization of International Standards
As a background to this discussion, a project on safetyassurance scenarios was launched as ISO TC22/SC33/WG9 by
the convenor of CATARC, China, and the Japanese VMAD
chairman expressed his hopes for technical input from the ISO
side to the SC33/WG9 convenor. In response to this, JAMA

provided support for the promotion of harmonization between
international standards and international criteria by proposing
the Safety Assurance Guidelines(8) to both ISO and VMAD.
Furthermore, the promotion of ISO 34502, which defines a
scenario-based safety assurance framework under theinternational standard SC33/WG9, progressed smoothly
through close collaboration between the Japanese leader andGerman co-leader, leading to the issuing of an international
standard in November 2022. This is the fruit of the relationshipof trust that has existed between Japan and Germany since thefirst phase of the Pegasus project, which has continued throughclose technical cooperation. As described above, UN-R157
reached a certain level of technical maturity after about one
year of discussion and led to the world's first international
harmonization of standards for Level 3 automated vehicles,
which is the result of strategic international collaborative efforts
supported by close cooperation among industry, government
and academia in Japan. These achievements will serve as thefoundation for Japan's continued international contribution to
the international standards harmonization discussion in the
future. (Fig.7)

2.3. New International Cooperation Efforts
While past collaborations between Germany, France, China,
and the U.S. have been aimed at accelerating technologicalstudies through individual projects, going forward it will benecessary to integrate various types of collaboration in order toharmonize the international standards activities that haveflooded the automated driving space. As one of the measures toachieve this, the EU and Japan launched a joint initiative tocompile a white paper(9) that brings together the state of the artof safety assurance frameworks in cooperation with theEuropean automated driving project HEADSTART,(10) andpublished the paper in December 2021.

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3

Conclusion

While automated driving technology is expected tosignificantly reduce the number of traffic accidents caused bydrivers, and to enable freer and more convenient mobility insociety, there are still significant issues to be addressed regarding
the sufficiency and transparency of its safety performance. It is
important to realize a healthy competitive environment for therealization of a safe transportation society by continuing tobuild common foundational technologies through collaboration
among industry, government, and academia and to develop
standards and criteria through international cooperation.

[References]
(1) DIVP PROJECT homepage, https://divp.net/, (accessed 2022.09.01)(2) SAKURA PROJECT homepage, https://www.sakura-prj.go.jp/, (accessed
2022.09.01)(3) PEGASUS RESEARCH PROJECT homepage, https://www.pegasusprojekt.de/en/home, (accessed 2022.09.01)(4) UN-R157, https://unece.org/sites/default/files/2021-03/R157e.pdf, (accessed
2022.09.01)(5) WP.29: Framework document on automated/autonomous vehicles, https://
unece.org/fileadmin/DAM/trans/doc/2019/wp29/WP29-177-19e.pdf, (accessed
2022.09.01)(6) Ministry of Land, Infrastructure, Transport and Tourism: Safety Technology
Guidelines for Automated Vehicles, https://www.mlit.go.jp/common/001253665.
pdf, (accessed 2022.09.01)(7) SIP cafe - Automated Driving: What is the degree of safety required forautomated vehicles? , https://sip-cafe.media/column/1210/, (accessed 2022.
09.01)(8) JAMA: Safety Assurance Framework for Automated Driving, https://www.jama.
or.jp/english/reports/docs/Automated_Driving_Safety_Evaluation_Framework_
Ver2.0.pdf, (accessed 2022.09.01)(9) SAKURA, SIP-adus, HEADSTART Joint White Paper, https://www.sakura-prj.
go.jp/Portals/0/images/Publications/HEADSTARTSAKURA_WP.pdf, (accessed
2022.09.01)(10) HEADSTART PROJECT homepage, https://www.headstart-project.eu/,(accessed 2022.09.01)[Contacts]
Toyota Motor Corporation, Automated Driving and Advanced Safety Development
Department, 1 Toyota-cho, Toyota City, Aichi 471-8572, Contact: Hideaki Sato

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6) Connected Vehicles
Masanori Misumi (Mazda Motor Corporation)(Abstract) Participation in SIP-adus workshops and related international conferences in the context of connected andcooperative automated driving technologies not only promoted overseas trend survey but also contributed to outreachactivities from SIP- adus and building networks with overseas experts. In particular, the SIP-adus Workshop introduced
SIP-adus activities to overseas experts to deepen understanding and about the status of overseas activities from theexperts themselves. At international conferences held in Europe and the United States, a wide range of information
on government initiatives and business activities in each country has been obtained. This article introduces an
overview of these efforts.
Keywords: Cooperative mobility, ITS, V2X, DSRC, C-V2X

1Outline of Activities

Concerning the connected vehicle, the status in using radio
communication technology in Japan was publicized to foreigncountries. Several surveys on trends in Europe and the UnitedStates were conducted, the results of which were disseminatedto related parties in Japan.

2

SIP-adus Workshop

2.1. FY2018 SIP-adus Workshop
1) Plenary Session
The latest pieces of information, outlined below, wereexplained by experts from the U.S. and Europe on the"connected vehicle":
Kevin Dopart (United States Department of Transportation):
Introduced an overview of Automated Vehicle 3.0 (AV 3.0)
by the USDOT. V2X deployment programs including Vehicle
Safety Communication Support pilot projects and their field
operational tests at 70 sites in the United States were also
explained.
John Kenney (TOYOTA IT Center):Presented on topics related to FCC (Federal Communications
Commission) technological requirements, the status of the US
market (5,315 roadside units), DSRC (dedicated short range
communication), cellular V2X (C-V2X) for 5.9GHz band
DSRC, along with WiFi bandwidth.

Christian Rousseau (RENAULT):
Introduced topics such as EU projects, SCOOP, an overview
of V2X-related tests in C-ROADS, sharing of 50MHz in 5.9
GHz frequency band allocated in EU (road traffic 30MHz,
railroad 20MHz), etc.
Maxime Flament (5G Automotive Association):
Introduced network slicing and edge computing as newest
communications technology.
From Japan, the followings were introduced as SIP-adus
activities in Japan including the verification of capabilities for
applying V2X application through ITS communication, an
overview of FOTs (Field Operational Tests) and installation ofcommunications infrastructure in Tokyo Waterfront City area.
2) Breakout Workshop
15 participants (2 from US, 3 from Europe, 10 from Japan)
shared trends in each area.
The US speakers introduced issues to study they hold , suchas neutrality of technology related to use of 5.9GHz andcommunications compatibility with each device/state.
The European speakers introduced EU projects
HEADSTART, ICT4CART, 5G CROCO, 5G MOBIX, and 5G
CARMEN, and gave an overview of Delegated act of V2X.
The Japanese speakers introduced studies of technologicalrequirements related to delivering traffic signal informationand expressway merging.

2.2. FY2019 SIP-adus Workshop
1) Plenary Session
Three presenters, including a Japanese speaker, took the
podium.

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Kevin Dopart (United States Department of Transportation):
Explained details of CARMA (Cooperative Automation
Research Mobility Application), a cooperative automateddriving development platform.
Kodo Shu (Huawei):
Introduced an overview of the inter-operability test using
C-V2X at Wuxi, Jiangsu.
From Japan, the main points of communications utilizationplans in the first phase of SIP-adus and plans for the FOTs in theTokyo waterfront area in the second phase were explained.
2) Breakout Workshop
Ten experts (US:1, CN:2, JP:7) joined in the workshop andshared information related to connected vehicles.
The US expert explained CARMA as described above andintroduced SAE J3216 "Taxonomy and Definitions for Terms
Related to Cooperative Driving Automation for On-Road
Motor Vehicles."The Chinese experts gave complementary explanations ofpractical application in buses and commercial cars, which
would be preceded than others, among those tested in Wuxi.
The Japanese experts explained initiatives by the National
Police Agency and Ministry of Internal Affairs andCommunications, SIP FOTs (Field Operational Tests), activities
by automobile manufacturers and actitivies in ISO/TC204/
WG14 (Intelligent Transport Systems / Vehicle - roadway

warning and control systems) as its chair country.

2.3. FY2020 SIP-adus Workshop
In FY2020, the workshop was held online due to the impact
of COVID-19, and only the Plenary Session was held.
Kevin Dopart (United States Department of Transportation):
Introduced the categories of cooperative automated driving
based on SAE J3216 which was shown at the previous year's
session and CARMA as well as truck platooning.
John Kenney (TOYOTA IT Center):Reallocation of spectrum for ITS by FCC (Federal
Communications Commission) and an overview of nextgeneration DSRC-IEEE802.11bd.
Christian Rousseau (RENAULT):
Introduced the PACV2X and InDID projects for C-ITS in
France.
Japanese speakers introduced action plans related tofrequency allocation by the Ministry of Internal Affairs andCommunications, studies on SPaT (Signal Phase and Timing)
information provision through V2I and V2N system, and use
cases for cooperative automated driving by SIP-adus.

2.4. FY2021 SIP-adus Workshop
In FY2021, the workshop was also held online due to
COVID-19.

1) Plenary Session
The following overseas experts participated in the session.
John Kenney (TOYOTA IT Center)
Tom Schaffnit (USDOT)
Niels Peter Skov Andersen (Car2Car-CC)
Martin Böhm (Austria Tech)
In the US, there is a proposal by the FCC for the spectrum of
5.9GHz bands for ITS to be reduced from current 75MHz to
30MHz, allocating remaining 45MHz to WiFi, in conjunction
with transition of the communications channels from DSRC to
C-V2X.
An overview of planned and operational projects in each US
state were introduced. Approximately 6,000 infrastructures and18,000 vehicles are in operation in 143 locations. A majority use
the DSRC system, but there is some movement to shift to
C-V2X.
In Europe, hybrid communication systems, ITS-G5 (DSRC)
for short range communication and cellular network for longrange, have begun to be introduced in the market in 2019. At
least 500,000 vehicles are on roads with the system andinfrastructures are also deployed, as 2,000 km of DSRC and100,000 km of cellular networks.
Two Japanese speakers introduced FOTs using
communication networks at the Tokyo waterfront area andstatus of studies on communication method roadmap discussed
through the Task Force (TF) on V2X communication for
Cooperative Driving Automation.
2) Breakout Workshop
In addition to the speakers of the Plenary Session, thefollowing experts joined in a panel discussion and Q&A.
Host:
Satoshi Oyama (Association of Radio Industries andBusinesses)
Panelists:
Justin McNew (JMC Rota Inc.)
Bettina Erdem (Continental AG)
Shinji Ide (Ministry of Internal Affairs andCommunications)
Koichi Sakai (Ministry of Land, Infrastructure, Transport
and Tourism)
Despite the fact that the session was held from 11:00 pm
JST, there were 134 audiences including 32 overseas
participants.
US speakers spoke about development of C-V2X and its
standardization and European speakers introduced the status ofprivate-public cooperation for the C-Roads Project.
Japanese speakers introduced status of studies on frequency
for ITS by the Ministry of Internal Affairs and Communications,
utilization of ETC 2.0 from the Ministry of Land, Infrastructure,
Transport and Tourism, the FOTs in the Tokyo waterfront area
by SIP-adus, and activities for the TF on V2X communication

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for Cooperative Driving Automation.

are to be employed to verify interconnectivity.

2.5. FY2022 SIP-adus Workshop

3.3. ITS World Conference Copenhagen (9/17-21,
2018)

This was the first in-person workshop in three years.
1) Plenary Session
The same four speakers as the FY2021 session provided the
new information related to the US and Europe. The Japanese
speakers reported the results of SIP-adus FOTs using
communication network.
2) Breakout Workshop
In FY2022, as a non-public workshop, experts engaged in a
deep discussion on issues to spread connected automateddriving.

3

Information Gathering: Participation
in international conferences

3.1. ITS America Detroit / CV-Pilot / Smart Columbus (06/2018)
ITS America Annual Meeting:
GM announced introduction of ITS radio in the next
generation Cadillac. The USDOT showed its position oftechnological neutrality related to the conflict between DSRC
and C-V2X.
CV (connected vehicle) pilot:
An SIP expert visited traffic control center in New York andinvestigated the status of connected vehicle pilot programs,
such as implementation in roadside units.
Smart Columbus:
The SIP expert observed smart city feasibility tests in
Columbus, Ohio. Planning was proceeding to combine public
transportation and personal traffic.

3.2. Automated Vehicle Symposium in San Francisco (7.9-12, 2018)
U.S.:
Promotion groups for DSRC and C-V2X emphasized thebenefits in terms of technology and operation of theircommunications method.
FHWA (Federal Highway Administration) placed roadsideunits in 15 intersections in Texas and 13 intersections in
Michigan. Trials to optimize traffic flow by green light optimal
speed advisory and priority of CACC (Cooperative Adaptive
Cruise Control).
Europe:The EU Project ICT4CART aims for level 4 automateddriving by using C-ITS and C-V2X. Test sites in Austria,
Germany, and Italian and cross-border Italy-Austria courses

The overview, certification method, and privacy issues withregard to the Delegated Act related to V2X proposed by theEuropean Commission were reported.
In the US, CV pilot deployment programs began. Challenges
in the system implementation were explained, including vehicle
localization accuracy in downtown New York and datacollection issues. It was also reported that the number of DSRC
roadside units, the purpose of which is still testing, has exceeded
5,000 in the U.S.

3.4. 
Transportation Research Board, Washington
D.C. (1/13-17, 2019)
The USDOT introduced the CARMA platform and its
application development.
AUTOCITS (2016-2019) was introduced from Europe.
In order to improve safety and to apply C-ITS to automateddriving, Verification tests were being conducted on roads thatcrossed from Portugal to Spain (10km expressway) and France.
The 5GCroCo, 5G-Carmen, and 5G-MOBIX started as
5G-related projects. The cross-border projects were being
conducted over 1,000km of road across eight countries.
The European Commission passed the C-ITS Delegated Act
and planned to finalize after inviting public comments.

3.5. Automated driving symposium Orlando, Florida (7/15-18, 2019)
FHWA director declared that he would protect the 5.9GHz
for traffic safety in response to the FFC director’s comments
about reallocation of frequencies, including releasing some
5.9GHz bands to WiFi.

3.6. ITS World Congress Singapore (10/21-25, 2019)
In Europe, the C-ITS Delegated Act was rejected by theEuropean Parliament. The C-ITS deployment had, therefore,
become dependent on initiatives in the private sector.
In the United States, 5GAA requested allocation of upper 20
MHz in 5.9GHz bands (75MHz) to cellular V2X.

3.7. 
Transport Research Board Washington D.C.
(1/12-16, 2020)
In the CV-Pilot session, in addition to progress reports inthree zones (New York, Tampa and Wyoming), there were some
negative opinions about equipment renovation expenses andproject delays related to repurposing 5.9GHz bands by the FCC.

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3.8. Automated driving symposium Garden Grove,
California (7/19-21, 2022)
This symposium was held in person for the first time in three
years. Because accidents of pedestrians looking at theirsmartphones is increasing in the US, there is increased attention
on "Cooperative Perception" by FWHA (Federal Highway
Administration), the recognition of objects outside of view by
sharing onboard/roadside sensor data using pedestrian/cyclist
protection technology and communications through V2X.
Also, in Europe, the roadmap of the CCAM (Connected,
Cooperative & Automated Mobility) Partnership, which close
to 200 organizations participate in, was presented. Large scale
tests are planned at 30 places across Europe in 2030 and it canbe seen that full-scale implementation is envisioned after 2030.
In addition to the above, information gathering is planned in
FY2022 at the ITS World Congress 2022 Los Angeles (September2022), Transportation Research Arena 2022 Lisbon (November2022) and Transportation Research Board Washington DC(January 2023).

4

in Japan, the US, and Europe.
The International Cooperation Working Group activities
end this year. However, the knowledge built in these five years
will be a precious asset to Japan’s activities related to automateddriving in years to come.

[Contacts]
Product Strategy Div., Mazda Corporation 3-1, Fuchu-cho, Aki-gun, Hiroshima
730-8670, 070-7575-0653, Contact: Masanori Misumi

Information transmission

The following is information transmission besides the SIPadus Workshop.

4.1. 
World Forum for Harmonization of Vehicle
Regulations (WP.29 ITS IWG) held online (11/6,
2020)
There were chances to introduce SIP-adus activities at WP.29
ITS informal groups, including the status of deliberations of use
cases for Cooperative Driving Automation and activities for
SIP-adus Task Force (TF) on V2X communication for
Cooperative Driving Automation.

4.2. Future Network Car Web held (3/25, 2021)
Requested from the WP.29 participants above, an SIP expert
participated in a conference hosted by ITU (International
Telecommunication Union) along with approximately 150
participants. The status of studies and the use cases related to
SIP-adus cooperative automated driving communication
methods were presented.

5Conclusion

Direct exchange with overseas experts through international
cooperation activities contributed to the improvement ofmutual understanding and awareness of the status of activities

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7) Cybersecurity
Yasumasa Hirai (TOYOTA MOTOR CORPORATION)(Abstract) We can assume that the advanced map information that forms the basis of automated driving systems andthe information on vehicles, people, infrastructure facilities, etc. that are mapped on those maps, will be obtained
mainly from external networks.
This information is sent to the control/information devices of the vehicle for use in vehicle control by the automateddriving system, but this also causes cybersecurity problems that have not existed in conventional vehicles. In addition,with the agreement of UN-R155/R156 at UNECE1 WP.29, it is necessary to take measures against cyberattacks fromthe legal point of view as well.
In order to solve such problems, the second phase of SIP-adus (Cross-ministerial Strategic Innovation Promotion
Program (SIP) Automated Driving for Universal Services) focused on intrusion detecting systems (IDS) as acountermeasure technology against cyberattacks post-shipment. The evaluation viewpoints and testing methods forselecting IDS were studied for off-the-shelf development that utilizes existing products, and verification using wholevehicle test bench was conducted and summarized as IDS evaluation guidelines.
In addition, to speed up the initial response to new threats when they emerge, we are studying a mechanism forquickly providing threat information on connected cars and a method for proactively collecting potential threat
information. We are analyzing threat intelligence activities in the leading IT industry to establish a mechanism andverify its feasibility, as well as examining honeypots and conducting observation experiments to verify theireffectiveness. Furthermore, through a Japanese-German cooperative workshop, experts from Japanese and German
universities have joined to share and utilize their knowledge on these efforts.
Keywords: cybersecurity, IDS (Intrusion Detection Systems), threat information, honeypots, playgrounds

1International Legal Trends

The World Forum for the Harmonization of Vehicle
Regulations (WP.29) has established international standards forautomated driving, namely UN-R155 (cybersecurity) and UNR156 (software updates). Japan, as a co-chair of WP.29 GRVA,
has been promoting cybersecurity, and has been ahead of othercountries in enacting legislation, especially for Level 3
automated vehicles.
In line with this, automakers must comply with processcertification under UN-R155 for all new passenger andcommercial vehicles released to the market after July 2022 inorder to obtain type certification (continued production
vehicles must be compliant from July 2024 onwards). UN-R155
requires having a cybersecurity management system andprocesses that adequately respond to new and evolving cyberthreats and vulnerabilities, and these processes are subject totriennial audits.
OEMs and suppliers are building on the aforementioned
process with reference to the international standard ISO/SAE
21434, which was officially issued on August 31, 2021 and isreferenced as an example of meeting the requirements of UNR155.
Based on this background, in this project we conducted
researches on detection technologies for cyberattacks emerging
especially after vehicle production and on collection andsharing system of cyber threats such as vulnerability, with theaim of transferring and utilizing the results in industry
organizations.

1 UNECE: United Nations Economic Commission for Europe

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2

Current Issues and Research Status

2.1. Development of IDS Evaluation Guidelines
From a legal standpoint, WP.29 UN-R155 requires thatcyberattacks be detected and handled appropriately, and it isnecessary to explain that the vehicle itself is capable of detecting
and handling cyberattacks. However, existing regulations andguidelines do not clearly indicate what kind of attacks should be
detected and to what extent, so it is necessary for each company
to make its own determinations. With this research theme, we
aim to contribute to post-shipment security measures, with thegoal of developing IDS Evaluation Guidelines and transferring
the results to industry organizations so that OEMs can use
them as a baseline when selecting, verifying, and operating onboard IDS.
The guidelines are meant to improve the quality of postshipment vehicle cybersecurity throughout the industry, andare especially intended for OEMs that have just begun
considering the introduction of on-board IDS.
In these activities, we derive cybersecurity events that IDS
should detect based on actual attack cases. In order to derive
this information, we conducted a survey of conferences heldand vulnerability information released from 2017 to 2020.
From these results, among cases that directly related tovehicles, we further narrowed down the cases that affectedvehicle control and analyzed them in detail to extract events
that could be generated and observed in the network as
cybersecurity events. (Table 1)
With this research theme, based on the results of previous
research, from known specific attack cases we developed amethod for creating scenarios of attack cases and a method forderiving functional requirements to detect similar attacks. We
also derived test items for these functional requirements for
detection, verified the feasibility and validity of the test items
through experiments on actual benches with the cooperation ofOEMs and IDS vendors, and implemented feedback to theTable 1: Cybersecurity events extracted from the survey cases
EventsInvalid message ID
Data range error

Overview

Message ID does not exist on (bus)Invalid range of signal values for a single message
transmission cycle for messages with the same
Invalid transmission cycle Invalid
message ID
Invalid message volume
More/fewer messages on on-board NW than specified
Invalid transmission order Invalid reception sequence for multiple messages
Amount of change in
Invalid difference from the data value of the message
invalid data
received immediately before
Context violation data
Invalid correlation of data values for multiple messages
Invalid message source (determined by CAN using
Invalid source/destination ECU feature volumes, MAC, etc.)
Message destination is invalid (for TCP/IP)
UDS Protocol violation
Violation of UDS protocol specifications
OBD protocol violation
Violation of OBD protocol specifications
DoCAN protocol violation Violation of DoCAN protocol specifications
Error response of diagnos- Receipt of error responses for diagnostic messages intic msg in excess of thespecified number of times excess of the specified number of times

guidelines from these basic test cases.
In addition, in the interests of social implementation,
discussions were held at regular study meetings with thestakeholders to ensure that the system is useful for the intended
users, and the results were built upon.
The results of these investigations and experiments havebeen compiled into a single achievement, namely the "IDS
Evaluation Guidelines," and the results therein have beentransferred to industry organizations.

2.2. Investigative Research on Threat Information
and Initial Response Support for ConnectedCars
We have developed a method for collecting and storing
threat information on connected cars as well as a basic system
specification for initial response support using threat
intelligence, and are working toward the goal of transferring
these to industry organizations for operations in 2023.
Threat intelligence is information collected, analyzed, andaccumulated to support responses to cyberattacks and otherthreats. In some industries, cross-company intelligence sharing
activities are underway.(1)By sharing threat intelligence, companies can expect toprevent successive damage from similar cyberattacks, but thethreat intelligence that is currently shared is mainly from the IT
domain. In IT systems, a common platform may be used amongorganizations and users, such as a general-purpose OS, while inautomobiles, each vehicle model has a different architecture.
Therefore, there is a risk that the form of threat intelligence thatis shared in the IT domain cannot be used for countermeasures.
This is because automobiles are OEM-dependent in terms ofhardware, software, and communication protocols for vehicle
control, which is a major difference from IT.
On the other hand, the sequence of attacks leading up to
vehicle control may use OEM-independent methods, and as acommon threat among OEMs, we can expect that thisinformation is worth sharing.
In addition, the key to responding to new threats that emerge
after shipment is not only the sharing of threat information, butalso the ability to proactively ascertain threat information to
speed up initial response.
Honeypots are one such proactive threat informationcollection method. For example, as an observation result of
current threat information collection experiments using
honeypots, we observed activity that matches the characteristics
of IoT malware (e.g., Mirai), which sends IDs and weakpasswords to telnet as it would to IoT products. (Fig.1) This isconsidered to be an automated attack from a device infectedwith the malware, and not an attack based on the recognition of
the honeypot as an on-board device.

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4

Fig.1: Example of observed packets

Our current research theme is part of a plan that extends to
February 2023. As of September 2022, we are conducting
experiments to collect threat information and studying methods
to share the collected information for use in incident response
within the industry. In the future, we plan to utilize description
methods for threat information, build mechanisms for systems
to share the assumed threat information, and verify theirfeasibility through proof of concept.
For proactive information collection, we plan to set up
honeypots and conduct experiments to observe threat
information, as well as hold playgrounds where white hat
hackers, vendors, and others can try out attacks, and observe
the activity during these events.

Conclusion

It is no exaggeration to say that ensuring automotive
cybersecurity is directly linked to the safety and security of
automobiles, and it is appropriate to make the minimum
security standards to be met and common threats in theindustry a cooperative area for the entire industry, and toactively share them. This will also improve the efficiency of thedevelopment and operation of connected services and help
Japanesecompanies
maintain
theirinternational
competitiveness.
It is also important to strategically approach standardization
organizations so that security measures and information
sharing mechanisms can be used to the advantage of Japanesecompanies by, for example, proposing them as international
standards for automotive cybersecurity development, rather
than limiting them to sharing within the domestic industry.
Cybersecurity is essential for automated driving systems,
including connected cars, which are still in the development
stage. Cybersecurity threats to them are expected to increase in
the future. Going forward, it is important to continuously
evaluate and improve cybersecurity as social implementation
progresses and actual threats become more apparent.

[References]

3Japanese-German Cooperation

In Germany, the Federal Ministry of Education and Research(BMBF) is leading an initiative to support research anddevelopment of security for the connected and automated car,and four projects are currently underway. Of these, we arecollaborating with the SecForCARs project,(2) which entails
four research topics (#1: Hardware and on-board network
cybersecurity, including ECUs and their constituent LSIs; #2:
Requirements and verification methods for the overall securityof a vehicle system consisting of secure subsystems; #3:
Methodologies for observing, capturing, and analyzing
cyberattacks; and #4: Methods for using actual vehicles andvirtual systems [honeypots] for assumed attack vectors to
observe cyberattacks). The project has participation by experts
from Japanese and German universities to share and utilize
their knowledge from both sides. The research status and results
are being shared through workshops (five workshops areplanned by the end of 2022, the first in July 2021, the second in
December 2021, and the third in April 2022.)(1) Cross-ministerial Strategic Innovation Promotion Program, Ensuring
Cybersecurity in Critical Infrastructure (b2) Information Sharing Platform
Technology, "Information Sharing Design Guide - Construction Volume,"
Hitachi, Ltd.https://www.nedo.go.jp/content/100904081.pdf(2) SecForCARs, https://www.secforcars.de/, (accessed 2022.08.12)[Contacts]
Control Electronics Platform Development Department, Toyota Motor Corporation,
1 Toyota-cho, Toyota-shi, Aichi 471-8572, Japan, Contact: HIRAI Yasumasa

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8) Socioeconomic Impacts
Takashi Oguchi (The University of Tokyo)(Abstract) International cooperation efforts on socio-economic impact have been promoted mainly within theframework of Japanese-German cooperation. Specifically, the German research project CADIA, led by researchers
from the German Aerospace Center (DLR) and Karlsruhe Institute of Technology (KIT), and the two projects
commissioned to the University of Tokyo and Doshisha University under the second phase of SIP-adus (Crossministerial Strategic Innovation Promotion Program (SIP) Automated Driving for Universal Services): "Study ofsocioeconomic impacts of automated driving including traffic accident reduction" and "Development of assessment
methodology for socioeconomic impacts of automated driving including traffic accident reduction." This collaboration
was recognized by the Japanese-German Steering Committee for Bilateral Cooperation in January 2019. Frequent
online meetings are held between the two countries, with experts from both sides also meeting in person in Germanyin October 2019 and Japan in May 2022 to discuss such issues as the diffusion models for automated driving andpublic acceptance of new transportation services by automated driving, in light of shared international understanding
and national and cultural differences. The results will be published in early 2023 as a book jointly authored by Japan
and Germany. In addition, during the annual SIP-adus Workshop and the Trilateral Conference among Japan, the U.
S., and Europe, there was an exchange of information and opinions on socio-economic impact not only between
Germany and Japan, but also among a wide range of international stakeholders.
Keywords: automated driving diffusion model, public acceptance, international shared understanding, national and culturaldifferences, international joint publication

1

Background and outline of activities

Regarding the collaboration between Japan and Germany on
automated driving, activities were carried out based on "Joint
Declaration of Intent on Japanese-German Cooperation on the
Promotion of Research and Development on AutomatedDriving Technologies" between the Cabinet Office and theGerman Federal Ministry of Education and Research (BMBF)
on January 12, 2017.
Japanese and German sides exchanged opinions on the R&D
themes that should be promoted in cooperation. In regard to
the social impact assessment, the Japanese second phase of SIPadus conducted the "Study of socioeconomic impacts ofautomated driving including traffic accident reduction" and"Development of assessment methodology for socioeconomic
impacts of automated driving including traffic accident
reduction," while in Germany a consortium led by the German
Aerospace Center (DLR) adopted similar research as a BMBFsupported project and began collaborative activities in the 2019fiscal year. From the Japanese side, the research project members
led by Professor Ayako Taniguchi of the University of Tsukuba
also volunteered to cooperate. The final product is scheduled
for publication in early 2023 as a joint publication.
Meanwhile, in the ITS trilateral meetings among Japan, theU.S., and Europe, impact assessment (IA) has been set as a subtheme for the trilateral collaboration, alongside digital maps
and human factors, with the aim of realizing a high level frame
for evaluating the impact of automated driving on the overall
transportation society in a mutually harmonized manner in thethree regions. Provision and sharing of information and debate
are ongoing in order to achieve these ends.
Here, we introduce these two instances of international
cooperation relating to socioeconomic impact.

2

Undertakings in Japanese-German
cooperation

2.1. Project formation background
Regarding the Japanese-German cooperation on the social

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﻿8) Socioeconomic Impacts

impact of automated driving, first, the research members of the
SIP-adus "Study of the Impact of Automated Driving on
Reducing Traffic Accidents and on Others" visited the Institutefor Technology Assessment and Systems Analysis (ITAS) of the
Karlsruhe Institute of Technology (KIT) in the fall of 2018 toexchange views on the possibility of a Japanese-German
cooperation. Subsequently, in FY2019, experts from bothcountries held discussions and agreed that the following two
points are of merit and interest to both sides as matters forcollaboration in both countries.
1) Diffusion of Connected and Automated Driving in a Future
Vehicle Stock: The aim of this theme would be to construct amodel to quantitatively simulate the spread of automateddriving by identifying the factors that affect this spread andanalyzing their correlation. Through this initiative, feasible
popularization scenarios for the next several decades could
be drawn up.
2) Social Acceptance of Automated Driving Explored: In
addition to defining what is meant by social or public
acceptance of automated driving, the aim of this theme
would be to carry out an advanced research and study
program to analyze cross-border similarities and differences
attributable to separate social and cultural environmentswith respect to public acceptance of automated driving. In
addition, this program would also identify discussion points
related to national innovation strategies related to automateddriving, as well as issues for international cooperation related
to automated driving and its related technologies (including
standardization).
In January 2019, BMBF in Germany and SIP-adus in Japan
convened a meeting of the dual Japan-Germany Collaborative
Program Steering Committee, and decided to launch a joint
Japan-Germany research program that combined these twothemes into a single project. It was also agreed to set outcomesof the collaboration as holding joint-symposium to deliver the
fruits of the collaboration and a publishing book jointly, after
having regular research meetings.

2.2. Summary of progress in Japanese-German cooperative research
In the 2019 fiscal year, after a series of online advance
preparation meetings, Japanese experts visited Germany on
October 7 and 8, and held the first meeting of the joint projectat the Berlin office of the DLR.
The main objective of the first meeting was to outline theprojects being carried out by both sides, to describe the points
of interest of each country, and to determine the direction of thecollaborative project. After discussion through the meeting, thediscussion point for the next meeting was determined to be theissues on car ownership and sharing. The second meeting wasscheduled to be held in March 2020 in Japan, but due to theworldwide COVID-19 pandemic, the schedule was drasticallychanged, and it was decided to promote collaborative activities
mainly through online meetings.
At the 4th bilateral expert workshop for the JapaneseGerman research cooperation, held on November 25, 2020, andthe 5th bilateral expert workshop for the Japanese-German
research cooperation, held on December 6, 2021, Japanese andGerman experts on social impact assessment gathered online toreport on the progress of their activities to the German BMBF,
the Japanese Cabinet Office, and the SIP-adus program director(PD).
We have been holding frequent online meetings since April
2021 to discuss the policy, content, and structure of the
publication of a book as a result of our joint efforts. During thefirst half of 2022, we worked on actually writing the book. The
second meeting was held in May 2022 in Kyoto, Japan, during a
break in the COVID-19 pandemic. The content of themanuscripts prepared by both Japan and Germany was mutuallyconfirmed, and then reconciled and adjusted, with a session
held with reference to this discussion in order to deepen ashared understanding of points of commonalty and difference
between the two countries. Based on the discussions in thissession, we decided to draft the final section of the manuscript
for joint publication, and from July onward, we worked together
to prepare the manuscript for publication in early 2023 throughmutual readings and peer review and proofreading by external
parties.

2.3. Activities in the SIP-adus Workshop
As part of the SIP-adus Workshop 2020 held on November
10-12, 2020, an online symposium was held on November 9with experts in Japanese-German cooperation activities on
impact assessment. The content on this topic was widely
disseminated by distributing a recording of the symposium.
Speakers were Torsten Fleischer (Karlsruhe Institute ofTechnology), Ayako Taniguchi (University of Tsukuba), Satoshi
Nakao (Kyoto University), Kosuke Tanaka (Tokyo University of
Science), Hiroaki Miyoshi, Shoji Watanabe (Doshisha
University), Masanobu Kii (Kagawa University), Christine
Eisenmann (German Aerospace Center), and Bart van Arem
(Delft University of Technology).
Although the session was held online, it was an excellent
opportunity to hear four reports, and online discussions
continued voluntarily after the session ended. Opinions were
exchanged actively about the following discussion points: thediffusion of automated vehicles, predictions of remaining
human-driven vehicles, the public acceptance of transportation
services using automated driving and their impact on daily
behaviors and habits, the difficulty of securing lanes exclusively
for automated driving vehicles, and other topics.
On November 9-10, 2021, the Plenary Session of the SIPadus Workshop 2021 was fully recorded and broadcasted. In

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the session with the theme of "Impact Assessment," Christian
Winkler (German Aerospace Center), Hiroaki Miyoshi, Shoji
Watanabe (Doshisha University), Masanobu Kii (Kagawa
University), Torsten Fleischer (Karlsruhe Institute ofTechnology), Ayako Taniguchi, Kasumi Miyadai (University ofTsukuba), and Scott Smith (Volpe Center, US Department ofTransportation) gave presentations. A hybrid-style Breakout
Workshop was also held in person onsite and online, and in
addition to the plenary speakers, Bart van Arem (Delft
University of Technology) and people involved in the JapaneseGerman cooperation project participated in a 3-hour session
for information exchange and discussion on automated driving
diffusion models and their social impact.

2.4. Joint publication by Japan and Germany
The contents of the book entitled "Japanese-German
research cooperation on connected and automated driving:
Socio-economic impact assessment" was started to be discussed
from 2021 and written by the members of the Japanese-German
cooperation in the first half of 2022. The book is composed asfollows:
1. Introduction
2. 
Characteristics and trends of the Japanese and German
mobility systems
3. Policy process with respect to CAD in Japan
4. Industry analyses and prognoses regarding CAD ride-hailingmarket5. Detailed evaluations of the empirical material on acceptanceof CAD
6. Transportation effects of CAD in Germany
7. Transportation effects of CAD in Japan
8. Overall Comparison between Germany and JapanThe discussions at the session held in May 2022 are organizedand described in section eight of this book. More specifically,
the following nine points are discussed: Social expectations
affecting CAD diffusion; common expectations of groups andindividuals in Germany and Japan; attitude of the car industry;
mobility services expected to be realized; differences in formsof residence; decision-making toward diffusion; expectations
for the type of CAD initially introduced; forming a correctunderstanding of CAD by citizens; and risks that may affectexpectations.

3

to Impact Assessment of automated vehicles is organized. This
sub-group is run by the following representatives: Dr. Scott
Smith from the Volpe National Transportation Systems Centerrun by the United States Department of Transportation (DOT)
for the U.S., Dr. Satu Innamaa from the VTT Technical ResearchCentre of Finland for Europe, and, currently, Takashi Oguchi
from the University of Tokyo (the author of this paper) forJapan. This sub-group meets two or three times a year to
exchange information about initiatives being carried out ineach region, and the free exchange of opinions has continued
throughout. In Europe, research activities funded by the
Horizon 2020 and various other government supported projects
in each country are underway, which form the background of
the research reported in these meetings. In contrast, the Volpe
Center in the U.S. is currently building a system dynamics
model for establishing the framework of the Impact Assessment.
These progresses are reported at the meetings. Japan has also
been reporting as appropriate on the activities in our "Study ofsocioeconomic impacts of automated driving including traffic
accident reduction" and "Research on assessment of the impactof automated driving on society and the economy and on
measures to promote deployment."[Contacts]
Department of Human and Social Systems, Institute of Industrial Science, The
University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, 03-5452-6419,
Contact: Takashi Oguchi

Trilateral cooperation among Japan,
the U.S., and Europe

Regular trilateral meetings among Japan, the U.S., andEurope have been held to exchange information and opinions
related to general ITS topics, including automated driving.
Under the scheme of this trilateral meeting, a sub-group related

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Promoting International Cooperation

9) Service and Business Implementation
Yurie Toyama (Mitsubishi Research Institute, Inc.)(Abstract) Discussions on the social implementation of services which utilize automated driving are important for thecommercialization of automated driving.Although pilot programs of services which utilize automated driving havebeen conducted in Japan and overseas, the issues that arise when stepping up from tests to implementation andmeasures to deal with them are still the subject of various studies and discussions around the world.Therefore, in ourinternational cooperation activities, we have been exchanging information on the implementation of automated driving
in other countries from the perspective of promoting service and business implementation.This paper introduces thestatus of these efforts.
Keywords: social implementation, business model, service and business implementation, service cars, mobility service

1

Introduction

The implementation of service vehicles (Note: in this paper,'service vehicles' refers to vehicles used for passenger andlogistics transportation services, as opposed to private vehicles)that utilize automated driving has been promoted since the firstphase of SIP-adus (Cross-ministerial Strategic Innovation
Promotion Program (SIP) Automated Driving for Universal
Services) under the activities of the Next Generation Transport
Working Group, and also in international cooperation as "NextGeneration Transport" under the leadership of (then) Professor
Masayuki Kawamoto of Tsukuba University.
In the second phase of SIP-adus, with a greater awareness ofthe perspective of implementation, the Next Generation
Transport Working Group became the Service and Business
Implementation Working Group, and in international
cooperation activities as well, activities were conducted under
the new area name of "Service and Business Implementation"(SBI) from the 2020 SIP-adus Workshop. In international
cooperation activities, the topic of FOTs (Field Operational
Tests) was also worked in cooperation with SBI.

when stepping up from FOTs to implementation and measures
to deal with them are still being discussed both in Japan andoverseas. Therefore, SBI conducted discussions on service andbusiness implementation with domestic and foreign experts
through workshops.

2.1. FY2021 SIP-adus Workshop
At the FY2021 SIP-adus Workshop,(1) the Plenary Session
was held as a joint session with Human Factors.With thecommercialization of automated driving approaching, there aremany areas which are related to human factors, so we jointly
planned sessions, selected participants, and set the presentation
topics to enable integrated discussions on how to connect"technology" with "urban activities and people," how to design
services, and how to think about business models.
Because it is essential to consider human factors, service
design, and business models for the social implementation of
automated driving, the session was planned to focus not on thetechnology itself but on the areas that connect the technology
Scope of the Session*The scope of the session is to discuss how to implement automated service
shuttles/buses/trucks into local from the points of human factor, service design, and businessmodel.
Vision for local area/local cities
Local Economy(Industry, People's
life etc.)2

Activities in the SIP-adus Workshop

Local Policy (Culture,
Housing, Health etc.)

Vision for local mobility

BusinessmodelsService design
Implementation to local
Human factors

Although the study use cases and business models of services
which utilize automated driving is important for thecommercialization of automated driving, the issues that arise...Scope of the session

Technology

C

Connected

A

Automated

S

Sharing

E

Electric

Fig.1: Agenda for the 2021 Plenary Session

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﻿9) Service and Business Implementationand local cities/areas as the scope, as shown in Fig.1.
The Plenary Session was jointly organized with Human
Factors and a total of eight speakers were invited. Experts in awide range of fields from the U.S. and Europe appeared,
including some who were invited to the SIP-adus Workshop for
the first time in FY2021.
The speakers presented on the technological development
and FOTs of automated driving in various regions and cities
around the world, and provided topics that focused on
discussions on implementation from the initiatives in theirrespective countries. SIP-adus is also aware of "social
implementation" in its initiatives, and in order to lead to theintroduction of services in real areas, discussions were held
regarding what automated driving services should be
introduced, based on the viewpoint of which business models
and service design and taking into account the vision of eachregion and city.
Also, a Breakout Workshop was conducted in an online
format at a later date. In the Breakout Workshop, the following
points were discussed with the speakers from the Plenary
Session who were invited from the SBI area.
• What are the benefits of automated driving (from therespective perspectives of citizens, operators, and local
government)• Ideas for the realization of economically self-sustainable
automated driving services• How should local areas accept and manage automated driving(both in terms of policy and service design)
As it was conducted online and included several first-time
invitees to the SIP-adus Workshop, it was a difficult environment
for cross-discussions among the speakers, but it was anopportunity for the Plenary Session speakers to meet each otherand mutually exchange opinions and answer questions.

2.2. FY2022 SIP-adus Workshop
The FY2022 SIP-adus Workshop,(2) which was held in person
for the first time in three years, was conducted solely by SBI.
Since this was the last SIP-adus workshop, in addition to an
SIP-adus topic on the testing of automated driving services inrural areas as a case study of service and business implementation,
three speakers were invited from the U.S. and one speaker from
Europe.
In order to implement automated driving services, there arevarious issues in non-technical areas, such as service design,
cooperation with government, and compliance with legal
systems. These issues were discussed based on the status ofinitiatives in the U.S. and Europe.
In addition, the Breakout Workshop was also held in person
in FY2022. In addition to the Plenary Session speakers,

3

Information Gathering at International Conferences

3.1. ITS European Congress 2022
(1) Overview of the Congress
The event was held in Toulouse, France from Monday, May
30th to Wednesday, June 1st 2022. AIRBUS and EasyMile haveoperations in Toulouse.
The theme was "Smart and Sustainable Mobility for all," andas all of the discussions throughout the conference started froman awareness of environmental issues, the author strongly felt
the interest in and sense of urgency about climate change inEurope.
The author attended the Plenary Session and individual
sessions such as the Special Interest Session (SIS), and the
keywords that were often seen and heard during the three days
were 'Sustainable', 'Multimodal', 'Digitalization' ('Data'),'Connected', 'Acceptance', and 'Ecosystem'. The author'sinterpretation, in the context of these keywords combined withthe overall theme, is that the aim is to break away from excessive
dependence on private cars as a countermeasure against climatechange ('Sustainable'), and a 'Multimodal' response is necessary
to achieve this because the alternative means of transportationneed to be convenient; to realize this, the 'Digitalization' and'Connection' of 'Data' is important, and furthermore, the'Acceptance' of this approach by citizens is necessary; it is also
necessary to consider the transportation modes or city as an'Ecosystem'.
(2) Summary of the Plenary Session Discussion on Service andBusiness Implementation
At the ITS European Congress, information was collected
mainly from sessions that discussed the use of "automateddriving" for service uses (i.e. not for private use) such as public
transportation. The following is a summary of the main points
discussed in the Plenary Session and other sessions.
1) Sustainability
As the theme of the ITS European Congress suggests, of all
the social issues in Europe today, sustainability receives themost interest. In the Plenary Session, a speech by a representativeof the European Parliament declared that digitalization andsustainability must be proceeded urgently and the mobility
sector must also provide smarter solutions.
2) Utilization of urban space
The conference also discussed the utilization of urban space.
For example, based on the experiences under the COVID-19
pandemic over the past several years, how people maintaindistance from each other, as well as their opportunities to use
and stance towards public transportation have changed
significantly. Also, new personal mobility devices such ase-scooters have recently appeared and how to adapt them to258

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urban spaces is an issue. In addition, discussions have begun inEurope regarding the use of urban air space with an eye toward
the commercialization of drones and other flying mobility
services.
3) Mobility as a Service (MaaS)
MaaS, which is a cross between ITS and digitalization,
expands the possibilities of multimodal services and is also
highly anticipated in Europe. The Plenary Session discussed
how multimodality is indispensable for sustainability and is the
only way to achieve it. Harmonization and collaboration werementioned as keywords for the development of MaaS, and in
addition, the transparency of the framework and accessibility
that considers all people, including women and people withdisabilities, were also mentioned.

3.2. ARTS (Automated Road Transportation Symposium) 2022
(1) Overview of the Symposium
ARTS2022 was held from July 18 to 21, 2022, in Garden
Grove, California, U.S.A. The overall size of the conference was
reduced from the previous conference held as the Automated
Vehicles Symposium (AVS) three years ago. Specifically, thenumber of sessions was reduced, each session was made longer(with an emphasis on interactive discussion), anddemonstrations and the poster space were eliminated.

accessible boarding and alighting technologies (lifts and ramps).

4

Conclusion

As the movement toward the social implementation of
automated driving accelerates, as a hint for service and business
implementation and business models, it is necessary not only tofocus on automated driving technology and automated driving
services alone, but also to clarify the social issues that require
automated driving services, such as sustainability and traffic
safety, as well as the needs related to the necessity of automateddriving, and each region demands the creation of models thatare aware of the linkage with other mobility services and various
policies (environment, welfare, and other urban planning)
within the city.

[References]
(1) SIP-adus Workshop 2021 Website, https://en.sip-adus.go.jp/evt/workshop2021/,(accessed on 2nd November 2022)(2) SIP-adus Workshop 2022 Website, https://en.sip-adus.go.jp/evt/workshop2022/,(accessed on 2nd November 2022)[Contacts]
Mitsubishi Research Institute, Inc., Smart Region Division, 10-3, Nagatacho
2-chome, Chiyoda-ku, Tokyo 100-8141, 03-6858-0241, Contact: Yurie TOYAMA(2) 
Summary of the Discussion on Service and Business
Implementation
The theme of the ITS Europe Congress was "Sustainable,"and most of the discussions started with an awareness of theissue of the environmental impact of mobility, and there wasactive discussion on using multimodal in order to reduce theenvironmental impact, reducing the dependence on private caruse, and promoting the use of data to make these possible. On
the other hand, at this year's ARTS held in the U.S., the sense ofcrisis over environmental issues was not so much in theforeground, and it was symbolic that discussions on automateddriving were conducted from the aspect of other social issues
that the U.S. is facing, such as the increase in the number oftraffic deaths, international competitiveness, and addressing
equity.
Future investment and policy making in the transportation
sector based on the Infrastructure Investment and Jobs Act(IIJA) enacted by the Biden administration were also mentioned
in various discussions.
In the U.S., various tests of automated driving services arebeing conducted.
Accessibility has also become an important perspective
during development, as the USDOT Federal Transit Authorityemphasizes accessibility and has declared the application of theAmerican Disabilities Act to automated driving services, andlocal government initiatives have also included wheelchairSIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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7

Other Achievements and Activities

Other Achievements andActivities
Takahiro Tanaka (New Energy and Industrial Technology Development Organization (NEDO))

1Other Implemented Projects

This section introduces the projects implemented in thesecond phase of SIP-adus (Cross-ministerial Strategic
Innovation Promotion Program (SIP) Automated Driving for
Universal Services) which are not introduced in the Mid-Term
Results Report or in Section 7: "Other Achievements andActivities," nor in Sections 2 to 6 of this report but conductedduring the second phase of SIP-adus, for reference in future
research and development of automated driving.

1.1. C ommissioned Project on Survey on Environmental Improvement for Practical Use of Transport Services by Automated Driving in the New Town Area
In 2019, a survey project was conducted in the new town
areas of Tama City in Tokyo and Sanda City in Hyogo Prefecture,in cooperation with the Ministry of Land, Infrastructure,
Transport and Tourism, local governments, and local
transportation service providers, in order to organize the issues
for the introduction of public transportation services which
utilize automated driving.(1) The reason for selecting the newtowns was that many of the new town areas were developed in
the period from the mid 1960s to the mid 1980s, many of theresidents are aging, and they are located in hilly areas, making
the provision of public mobility services an urgent issue.
Although they were only short-term tests of about one week,
the residents of the new town areas could try actually riding avehicle, and their raw opinions could be gathered throughquestionnaires and interviews, including their needs,
expectations, and concerns about automated driving. On theother hand, issues in urban space design, business models, andpublic acceptance for social implementation were identified
through study meetings with stakeholders.

1.2. Survey on Utilization Method of V2X (vehicle-to-everything) Information for Ensuring Traffic Safety
under Coexisting Traffic – Simulation of Evaluationof Impact of Autonomous Vehicles on Traffic Flow –A study was conducted on the impact of automated vehicles
on general vehicles and traffic flow, with the aim of utilizing theresults in the planning of traffic safety measures and traffic
management operations when general vehicles and automatedvehicles are running together.
Fixed-point cameras installed at observation points within
the FOTs (Field Operational Tests) in the Tokyo waterfront areaacquired data on current traffic flow and the traffic volume andvehicle behavior when automated vehicles were running, theimpact of automated vehicles in mixed traffic was collated via
traffic flow simulations based on this data, and a study of how it
could be used in planning traffic safety measures and traffic
management operations was conducted.

1.3. S urvey for Introducing Precise Docking Technology
by Automatic Driving to BRT (Bus Rapid Transit)
The importance of the accurate arrival of buses at bus stops
without any gap is recognized as an element of barrier-freetransportation, and technology for precision docking at bus
stops using magnetic markers was studied in the Haneda area of
the FOTs in the Tokyo waterfront area in the automated drivingof the second phase of SIP-adus. On the other hand, in the firstphase of SIP-adus, due to the development schedule the guiding
line method, which uses a camera to read a guiding wire andhas been successfully introduced overseas, was adopted for the260

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﻿
Other Achievements and Activities

buses that were to be used in the Tokyo 2020 Olympic andParalympic Games, and technical verification was required forthe system usage conditions of this guiding wire system, such as
weather and illumination, as well as the durability of the guiding
wire. This project also incorporated testing of the effects on
barrier-free access, the reduction of riding and getting off time,
and the prevention of falls when riding and getting off andwhen the bus stopped and started.

1.4. Study of Overseas Trends, etc., in Preparation
for International Collaboration Regarding Traffic Environment Information
The SIP-adus has worked on the construction of dynamic
maps since the first phase, and the construction of a mechanism
to utilize dynamic road traffic environment data linked withhigh-precision three-dimensional map information in thesecond phase. To promote the international standardization ofroad traffic environment data, researches of domestic andinternational standardization trends have been conducted andinternational standardization activities have been supported.
In particular, study meetings with various Japaneseorganizations and experts have been conducted, focused on
exchanges with overseas organizations that promote industry
standardization of high-precision 3D map information androad traffic environment data (e.g. the Open Auto Drive
Forum (OADF)).(2)In addition, the creation of mechanisms for the distribution
and management of mobility data (e.g., the European Commission's NAPCORE,(3) which coordinates National Access Points,
and the German Federal Ministry of Transport and Digital Infrastructure's Mobilithek)(4) has been active in Europe in recent
years, and an additional investigation of these mobility data distribution and management mechanisms was conducted, andthey were used as a reference for promoting the development of
the MD communet®(5), a portal site for road traffic environment
data.

2

Handling of SIP-adus Results and Assets

In the final fiscal year, the Intellectual Property Committee
discussed the handling of intellectual property obtained
through the second phase of SIP-adus and organized it asfollows. Also, how the website, research and development
facilities, test data, etc. will be stored and accessible after theend of the second phase of SIP-adus is described.

2.1. Handling of Intellectual Property
In the terms and conditions of the operation consignment
contracts between the contractors and NEDO, intellectual

property rights are defined as the right to receive industrial
property rights (patent rights, utility model rights, design
rights, circuit layout-design exploitation rights, and copyrights),
industrial property rights, copyrights, and know-how. Thissection will explain the handling of patents and copyrights.
(1) Patents
The Japanese version of the Bayh-Dole Act (Article 17 of theIndustrial Technology Enhancement Act,(6) hereinafter referred
to as the "Bayh-Dole Act") states that the intellectual property
rights relating to all consigned research and development(including that conducted through national research anddevelopment corporations, etc.) to which ministries andagencies provide government funding may belong entirely tothe contractor (e.g., a private company) if the contractor agrees
to certain conditions. Therefore, the rights to the patents
obtained as a result of the SIP-adus shall belong to thecontractors based on the Bayh-Dole Act.
(2) Copyrights
Copyright to the results reports for each project registered in
NEDO's Result Report Database(7) belongs to NEDO. The
copyright to other materials, such as progress report materials
of projects presented to working groups and taskforces, andpresentation materials used at various events, belong to therespective authors, and NEDO will not assert copyright nor
exercise any moral rights. However, when the results of thesecond phase of SIP-adus are cited in other materials, credit is
to be given that the results are from the SIP-adus project.
Example of credit:
This [paper/report/presentation, etc.] contains the resultsof "The Second Phase of Cross-ministerial Strategic
Innovation Promotion Program (SIP) Automated Drivingfor Universal Services" (NEDO management number: JPNP18012) which was implemented by the Cabinet Office andserved by the New Energy and Industrial Technology
Development Organization (NEDO) as a secretariat.

2.2. Types of Websites and Actions Following theEnd of the Second Phase of SIP-adus
Websites have been used for proactive information
distribution in the second phase of SIP-adus. The following
describes each website's contents and how it will be handled
following the end of the second phase of SIP-adus.
(1) SIP-adus Website(8)The SIP-adus website is managed and operated by the
Cabinet Office and NEDO, and contains reports on the results

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﻿
Other Achievements and Activitiesof each project, presentation materials for events such as the
SIP-adus Workshop, and the driving plan for the FOTs in theTokyo waterfront area. It is planned for the SIP-adus website toremain accessible for several years after the end of the second
phase of SIP-adus.
(2) SIP-café(9)SIP-café is an online community for sharing ideas about the
automated driving society, and is a website operated andmanaged by a contractor in accordance with NEDO's
consignment of research as part of the projects to foster public
acceptance. SIP-café is aimed at the general public and its
contents include automated driving-related news, interview
articles, columns by experts, and announcements and reports
on events related to fostering public acceptance. The successorof SIP-café is still under discussion at the time of writing thisfinal report, so please refer to the SIP-adus website for the latest
information.
(3) Websites Managed and Operated for Respective Projects
The websites operated and managed by contractors inaccordance with the research and development work consigned
by NEDO (e.g. Jidosapo(10) for regional FOTs and the roadtraffic environment data portal site MD communet®) belong to
the respective contractors, and are planned to be managed andoperated by them after the completion of the second phase of
SIP-adus.

2.3. Research and Development Facilities, Experimental Data, etc.
Fixed assets and cost items acquired during the research anddevelopment will be owned by the contractor in accordancewith the terms and conditions of the operation consignment
contract between the contractor and NEDO, on the premisethat they will continue to be used or disposed of after thecontract ends. The facilities and equipment used in the FOTs in
the Tokyo waterfront area are under discussion for transfer to a
subsequent project.
The data obtained from the FOTs belongs to the implementerof the tests, who will manage it appropriately based on a data
management plan. Special data use agreements were established
for the experimental data obtained in the FOTs in the Tokyo
waterfront area and the data was provided to those whorequested it. Specifically, the provision of experimental datafrom the V2I (Vehicle-to-Infrastructure) FOTs, which usednarrowband wireless communications and roadside
infrastructure, began on May 21, 2021, and the provision ofdata obtained from the V2N (Vehicle-to-Network) FOTs, whichused a wide-area public network, began on May 31, 2022.(11)
Regarding V2N, in addition to video data etc. from dashcam

recorders, data from the equipment installed in the test vehicles
used in four FOTs (lane level road traffic data, rainfall
information, predictive SPaT (Signal Phase and Timing)
information, and simulated emergency vehicle location
information) was provided. The method of applying for thedata involved applying to NEDO's contact point for the FOTs in
the Tokyo waterfront area after consenting to the terms of use
for the FOTs in the Tokyo waterfront area data. The data usage
period will be until the end of the second phase of SIP-adus forboth V2I and V2N data.
Software and programs created in the course of research anddevelopment activities are copyrighted by the creator based on
the Bayh-Dole Act.

3

Report of Results for Each projectIn the second phase of SIP-adus, the results reports for eachproject are available from the following two repositories. Please
use them if you want to obtain more detailed information after
reading the mid-term results reports or this final results report.

3.1. SIP-adus Website
The reports by project and by fiscal year can be downloaded
from the page linked from the "Research and Development" tabof the SIP-adus website. This page contains detailed reports in
Word format and summary reports in presentation format. The
English versions of the summary reports are available on the
English site of the SIP-adus website. Those who wish to read thereports can freely download the PDF files.
Also, this Final Report and the Mid-term Report, the PDF
version of the SIP-adus Use Cases for Cooperative Driving
Automation(12) and the PDF version of the Roadmap for
Communication Methods for Cooperative AutomatedDriving(13) are also registered on this site and can be downloadedin whole or in parts.

3.2. NEDO's Results Reports Database
The final results reports for each project on the SIP-adus
website are also registered in NEDO's results reports database.
This database only has the detailed versions. NEDO's results
reports database can be accessed from the "Publications" tab on
the NEDO website, but consent to the database's terms of use
and user registration are required. This database contains
reports of all NEDO projects, so it is necessary to filter the
search by specifying keywords. If you specify "P18012" as the
project number, it will filter the results to the final reports of thesecond phase of SIP-adus. Although only the detailed versions
are registered in NEDO's results reports database, they will beaccessible for a longer period of time.

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7 Other Achievements and Activities
﻿
Other Achievements and Activities

4

Information Distribution ThroughLectures and Paper Presentations

The SIP-adus research results continue to be presented andlectured at conferences and events outside of the SIP-adus,
mainly at universities and research institutes. Table 1 shows the
major research presentations and lectures.
Table 1: Presentations at Other Conferences by project (excerpt)
Project Name

Major Conference or Publication

Approach development for improving an automated driving
validation environment in virtual
space

Safety Engineering Symposium 2021
Gunma University Next Generation Open
Innovation Council
FAST-zero '21
Automotive Functional Safety Conference
2021/2022
Science Trends February 2022
CAE Forum 2022 Invited LectureIEEE ICCVE 2022
Society of Automotive Engineers of Japan,
Autumn Congress 2022

Research on the recognition technology required for automateddriving technology (levels 3 and4)

Journal of TRB
IEEE ITSC2021
IEEE IV2021
FAST-zero '21
ICAM2021
IEICE journal

Research and study on HMI andsafety education methods for advanced automated driving

ICICT2020/2021
HCI International 2020
TRANSLOG2020/2021
IEEE TALE2020
ITS Symposium
JSET 2021
IEA2021IEEE IV2021/2022
FAST-zero '21, HFES2021

Research on assessment of the
impact of automated driving onsociety and the economy and on
measures to promote deployment

Science Council of Japan 2019Japan ITS Promotion Forum
Automotive Software Frontier
CICV 2022

Strategic planning for fosteringsocial acceptance and survey regarding its evaluation

Lectures throughout Japan in cooperation
with Kyodo News and regional newspapers
on the theme of "Considering Mobility inthe 100-year Life Era: The Spread of Automated Driving and Consumer Awareness”

5[References]
(1) Ministry of Land, Infrastructure, Transport and Tourism press release, https://www.mlit.go.jp/report/press/toshi08_hh_000043.html, (accessed on 22nd
October 2022)(2) Open Auto Drive Forum (OADF), https://www.openautodrive.org, (accessed on
22nd October 2022)(3) National Access Point Coordination Organization for Europe (NAPCORE),
https://napcore.eu, (accessed on 22nd October 2022)(4) Mobilithek, https://mobilithek.info/ABOUT, (accessed on 22nd October 2022)(5) MD communet®, https://info.adus-arch.com, (accessed on 22nd October 2022)(6) Japanese version of the Bayh-Dole Act (Article 17 of the Industrial Technology
Enhancement Act), https://www.meti.go.jp/policy/economy/gijutsu_kakushin/innovation_policy/bayh_dole_act.html, (accessed on 22nd October 2022)(7) NEDO Results Report Database (user registration required), https://www.nedo.
go.jp/library/database_index.html, (accessed on 22nd October 2022)(8) SIP-adus Results Report, https://en.sip-adus.go.jp/rd/, (accessed on 22nd
October 2022)(9) SIP-café, a community for considering an automated driving society, https://
sip-cafe.media/, (accessed on 22nd October 2022)(10) Jidosapo, https://www.hido.or.jp/jidosapo/, (accessed on 22nd October 2022)(11) Regarding the provision of data acquired in the FOTs in the Tokyo waterfrontarea, https://www.sip-adus.go.jp/fot/fot01_05.php#f05_03, (accessed on 22nd
October 2022)(12) SIP Use Cases for Cooperative Driving Automation, https: //en.sip-adus.go.jp/
rd/rddata/usecase.pdf, (accessed on 22nd October 2022)(13) SIP Roadmap of V2X Communication Methods for Cooperative Driving
Automation - Activity Report of Task Force on V2X Communication for
Cooperative Driving Automation in FY2021, https://www.en.sip-adus.go.jp/rd/
rddata/roadmap.pdf, (accessed on 6th December 2022)(14) The Future of a Digitalized Transportation Society 2022, https://www.digi tal.
go.jp/news/22791050-006d-48fd-914d-e374c240a0bd/, (accessed on 22nd
October 2022)[Contacts]
Robot and Artificial Intelligence Technology Department, New Energy andIndustrial Technology Development Organization (NEDO), 15th Floor, Muza
Kawasaki Central Tower, 1310 Omiya-cho, Saiwai-ku, Kawasaki, Kanagawa 2128554, 044-520-5241 (direct line) Contact: Takahiro TANAKA (Project Manager)

Conclusion

Various research and development activities have been
conducted in the second phase of SIP-adus. At the same time,
there has also been a focus on the distribution of informationon the results both domestically and internationally. The results
are summarized in the Mid-term Result Report and this report,
as well as in detailed results reports for each project. We hopethat they are referred to and are useful for future research anddevelopment related to automated driving, and that they help
realize the automated driving society that is aimed for in "The
Future of a Digitalized Transportation Society 2022"(14).

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8Conclusion and Outcomes through SIP-adus to be Inherited

1) Looking Back upon SIP-adus History

Seigo Kuzumaki (SIP-adus Program Director, TOYOTA MOTOR CORPORATION)

1Introduction

The purpose of publishing these "Final Results Reports" is tosummarize the activities and results of the SIP-adus (Crossministerial Strategic Innovation Promotion Program
Automated Driving for Universal Services) and to serve as areference for future technological development and thepromotion of industry-academia-government collaboration
projects. For this purpose, it was concluded that not only
technical details but also management innovations should berecorded, which are summarized in this section. In addition,
many of the management innovations in the second phase of
SIP-adus were based on the experiences in the first phase, so thefirst phase of SIP-adus will also be reviewed.

2

Looking Back on the First Phase of
SIP-adus

SIP-adus started in June 2014, but before that a Program
Director (PD) was selected for each theme at the end of 2013,
with then-Senior Technical Executive Watanabe of Toyota
Motor Corporation being selected as the PD of the "AutomatedDriving Systems" program, and a preparatory committee wasestablished in early 2014 to launch the program. Thispreparatory committee consisted of the Steering Committee
and three working groups (WGs), which met up to seven times
a month. SIP-adus itself was an unprecedented experiment inwhich a PD from the private sector was the leader, and the
Cabinet Office coordinated and promoted ministry-agency
collaboration, and to be honest, the response from eachministry at the time was close to "jumping at shadows".
However, thanks to the leadership of PD Watanabe, a counselorlevel person attended every meeting.
On the other hand, a mirror meeting called a "Private Sector
Opinion Exchange Meeting" that corresponded to each group
was created and weekly meetings were held. Almost all motor
vehicle OEMs members participated in these meetings, and therules were that it was a meeting where "participants can freely
express opinions as an individual, not as a representative of acompany", anyone could participate, and no minutes of theproceedings were recorded. This free discussion played a largerole in fostering consensus as an industry. In this "Private Sector
Opinion Exchange Meeting", ideas for technology development
themes necessary for automated driving, such as dynamic map
and HMI (Human Machine Interface), were proposed and how
to proceed with them was discussed. These details were
proposed at the preparatory committee and approved forincorporation into the Research and Development Plan. These
measures proposed by the private sector started small, totaling
less than 100 million yen out of a total budget of approximately
2.5 billion yen in the first year.
After the official launch of the SIP "Automated Driving
Systems" program, we continued to meet seven times a month,and also had frequent individual meetings with each ministry
and drinking parties for communication with the private sector.
In 2015, PD Watanabe became ill and I (Kuzumaki) became
acting PD. In the fall of that year, I was given the opportunity to
explain the SIP "Automated Driving System" program at theCouncil for Science, Technology and Innovation, where thenPrime Minister Abe stated that large-scale FOTs (Field
Operational Tests) would be conducted in 2017, creating anenvironment conducive to obtaining support from various
ministries.
As we entered the third year of the project, it was consideredthat important themes proposed by the private sector, such as
dynamic map, should be strengthened as soon as possible, andwe worked on the revision and elimination of themes. Naturally,
there was some resistance from the current contractors at thetime, as this would mean changes to the original plan, but wemanaged to gain the understanding of the ministries and from
FY2016 we were able to consolidate the themes and position
dynamic map, cybersecurity, HMI, pedestrian accident
reduction, and Next Generation Transport as the five priority
issues, and allocate the budget to these areas as a priority. In
March 2016, former PD Watanabe passed away in the middle of
his appointment, and I (Kuzumaki) was officially appointed as
PD.

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One of the most notable achievements of the first phase of
SIP-adus was the establishment of Dynamic Map Platform Co.,
Ltd. Advanced automated driving systems require highdefinition 3D maps. For each motor vehicle OEM to have its
own maps would be a great burden for both the OEMs and themapping companies. On the other hand, for mapping
companies, the provision of map data is competitive area itself
and a fundamental business issue. We then discussed what are
the minimum "geographic features" necessary to realize
automated driving, and decided that the "mapping" of these"geographic features" would be a cooperative area. As a result,
Dynamic Map Platform Co., Ltd. was established in 2017,
funded by land surveying equipment manufacturers, map
vendors, land businesses, and motor vehicle OEMs. In 2017,
SIP-adus performed large-scale FOTs on the Tomei Expressway
using Dynamic Map, and as a result of that and the feedback
which was received, commercial distribution of maps for all of
the approximately 30,000 km of exclusive motorway
commenced in 2018. These maps were subsequently used in theworld's first Level 3 automated vehicle released by Honda, as
well as in vehicles equipped with Nissan and Toyota's advanced
safe driving support systems.

3

Looking Back on the Second Phase of
SIP-adus

3.1. Launch and Team Building
As the second phase of SIP-adus was commenced via a
supplementary budget, it overlapped with the first phase of SIPadus for one year in 2018. The importance of realizing
automated driving for the realization of Society 5.0 was
recognized, and it was adopted as one of the 12 themes in thesecond phase also. However, the Governing Board (hereafter,GB) strongly reminded us to "work on new issues, rather thanthe leftovers from the first phase," and the name "jidou unten"
for "automated driving" had also become common, so wechanged the Japanese name of the theme for the second phaseto "Jido unten".
A sense of mutual trust had been developed and the openness
among the private sector and ministries improved through theactivities of the first phase of SIP-adus, and it can be said thatthe launch of the second phase went smoothly. On the other
hand, since the first phase was more of a voluntary activity, therelationship with related organizations was weak. From thesecond phase, the participation of representatives from relatedorganizations in the Steering Committee and each working
group was requested, and in particular, experts from the Japan
Automobile Manufacturers Association (JAMA) actively
participated in discussions with the working groups and project
participants, which led to a rapid strengthening of cooperation.
In the first phase, the GB pointed out that the involvement of

academia was weak because the project was industry-led, so we
asked a wide range of experts from universities and research
institutions to participate as members of WGs, contractors,
participants in experiments, and reviewers for evaluation. With
these changes, I believe that a full-fledged industry-academiagovernment collaboration system was established from thesecond phase.

3.2. Schedule, Budget and Project member Management
As described in Section 2, the R&D themes were revised andeliminated in the middle of the first phase, but this required a
lot of effort, including discussions to correct the course of the
project. Based on this experience, in the second phase, the fiveyear period was considered to be three years plus two years, andmilestones were set at the end of the third year to review thethemes and speed up the development process itself. In
addition, priority budget was allocated to themes with highpotential for practical application and commercialization as ofthe end of the third year.
Another thing we were careful to do was to make sure thatall parties involved were aware of the "objectives" of themeasures. Often, the achievement of goals replaces theobjectives. The objectives should come first, so if the outputs do
not match the objectives, then a change of course or
discontinuation of the project should be considered. For thisreason, we aimed to have a thorough discussion at the beginningof a measure about why we were going to perform that R&D.
Regarding budget management, in the first phase of SIPadus, the agreement of the ministries could sometimes not be
obtained, a management agency was not established at first, andthe Cabinet Office reallocated the budget to the ministries eachyear after the budget was decided, and the Cabinet Office also
handled the administrative work of soliciting proposals fromthe private sector. This management of individual fiscal years
was very inefficient. An agreement for NEDO to become themanagement agency from 2017 was reached, and changing thestyle to one in which multi-year contracts were concluded withproject participants and the budget for the next fiscal year was
fixed at the end of the fiscal year according to progress, etc. wasa significant advantage. This increased the amount of time
available for development and ensured flexibility indevelopment.
On the other hand, the management of "project members" isthe most difficult problem in an industry-academia-government
collaboration project. Excellent engineers are necessary to
produce good outputs, and naturally such personnel are
indispensable within each company. In order to get them toprovide people for a project, it is first necessary to have themanagement of each company understand the significance ofthe project. In addition, after the project is launched, theprogress and output of the project must be made visible to the

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engineers' supervisors, otherwise valuable human resources
may be removed in the middle of the project. The key to thesemeasures is "information dissemination". While it is of course
necessary for the PD to personally explain the project at various
opportunities, it is also essential to proactively share materials
so that those involved can explain the project within their own
companies.
In addition, the tenure of ministry officials was short and thehandover period was also short. As a countermeasure, themonthly meetings of the Working Groups and the Steering
Committee meeting held every few months were effective inmaintaining continuity and momentum.

3.3. How to Proceed with Technological Development in Cooperative Areas
Technological development was conducted in cooperative
areas in the SIP-adus program, but the scope of competitive
areas and cooperative areas was not neatly organized from the
beginning, and the boundary line between them often differed
for each company. Therefore, it was difficult to gain support
simply by saying, "this is a cooperative area so let’s perform thedevelopment together". On the premise that "cooperation isimportant, but competition is also important," it is necessary tounderstand the boundary line of each company that cannot becompromised for any reason, and to show the benefits ofcooperation and explain them to the relevant parties.
When development begins, it is easy to fall into a situation ofagreement in general but disagreement with each point. For
this reason, it is often more successful to not spend a long time
discussing concepts and frameworks, but rather to start smalland show concrete outputs in parallel and ask engineers to
express their opinions based on them. Engineers often give
opinions on concrete outputs precisely and from a technicalpoint of view, so an approach that takes their opinions into
account while repeatedly making improvements is effective.

3.4. Deciding Key Themes and Innovations
Deciding what should be the main theme was very difficultat the start of the second phase of SIP-adus. Having worked on
the construction and distribution of high definition 3D maps in
the first phase, it was natural to work on building the Dynamic
Road Traffic Environment Data Framework on top of that. In
light of this and in order to involve as many stakeholders as
possible, we decided to make use of the Tokyo 2020 Olympic
and Paralympic Games. Although it could not be realized in theend, the fact that we were able to work toward the goal ofholding a large-scale joint test-ride event with JAMA at theTokyo 2020 Olympic and Paralympic Games accelerated
development, including in competitive areas, and also led tosynchronization with the institutional development. Havingsuch a "site" was useful for promoting discussions on
standardization as well as for serving as a venue for information
dissemination.
On the other hand, ensuring safety is an urgent issue towards
the realization of automated driving and we felt the need towork on this issue. As a result of interviews, we learned thateach company had to repeat many public road tests to verify
safety, which had become a bottleneck in development, so we
decided to start development of a simulation that could evaluate
multiple sensors simultaneously. Although there were some
initial setbacks, once the parties involved were able to share animage of the output, results began to emerge and the chain
reaction that only an all-Japan organization can produce
accelerated
development
andultimatelyled
to1
commercialization.
To progress these and other important issues, Task Forces(TFs) were established for specific periods of time as needed todeepen the discussion. These TFs were useful because theybrought together the necessary experts in a flexible manner.

4

An Overall View

I will now describe a few other good points other than thosealready mentioned and some other points that I noticed.
One point is the collaboration with the Digital Agency(formerly the Cabinet Secretariat), which is compiling PublicPrivate ITS Initiative/Roadmaps. The Cabinet Office's researchand development under SIP-adus and the Digital Agency's
roadmaps worked in tandem, enabling synchronized progressin technological and institutional development. In particular,
the "Outline on improvement of legal system and environment
for Automated Driving System" issued by the then Cabinet
Secretariat in April 2018 led to the revision of the Road Traffic
Act and the Road Transport Vehicle Act, and the world's first
Level 3 automated driving vehicle, a product of the collaboration,
went on sale in Japan in March 2021.
Another point is that, at the beginning of the first phase of
SIP-adus, the parties concerned had a thorough discussion on"what is the purpose of developing automated driving?" andwere able to reach an agreement that the purpose was therealization of a safe means of transportation. This led to theestablishment of a firm policy of considering not only therealization of automated driving but also the evolution ofadvanced safe driving support systems as a set, which enabled
us to work consistently throughout the project.

1 In July 2020, BIPROGY Inc. established V-Drive Technologies, Inc. as the sole investor.The company entered into a business alliance with
Mitsubishi Precision Co., Ltd. and began selling DIVP® products in September of the same year.

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In addition, we received advice from the Evaluation Working
Group to strengthen the "fostering of public acceptance," andwe were able to conduct public relations activities from a longterm perspective, which led to "strengthening communication"and greatly contributed to maintaining the project's centripetal
force.
The final point is what I myself paid particular attention to,which was the "sense of speed". It took time to reach a consensus
in the development of technology in the cooperative area. If thebrakes are applied slightly on the planning side, it may cause"sag congestion" that completely stops at the rear. For thisreason, we tried to make decisions quickly and proceed in amanner that allowed us to quickly implement small measuresand revise them as needed. We also requested the creation of aschedule management chart, which is the basis of a project, andthat it be shared thoroughly among the parties involved.

5

Conclusion

My experience will not be all that helpful since each person’s
project management style is different and the surrounding
environment is also different, but I would be happy if just one ofthese examples is of assistance in future projects.

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8Conclusion and Outcomes through SIP-adus to be Inherited

2) SIP-adus and Mission Oriented STI Policy
Tateo Arimoto (National Graduate Institute for Policy Studies)

1

Introduction

Since its inception (2016), the author has participated in
the UN Forum to discuss the role of science and technology
in achieving the goals of the SDGs and to develop guidelines.
The author has also participated as a steering committee
member in the OECD's international comparative study on
mission-oriented innovation policies (MOIP).(1) Based onthese experiences and knowledge, this section will introduce
why the SIP-adus (Cross-ministerial Strategic Innovation
Promotion Program (SIP) Automated Driving for Universal
Services) is attracting attention from the international STI
policy community, and explore lessons learned for future
mobility-related programs and for our country's science,technology and innovation (STI) policy in general.

2

The importance of setting out a vision:
Origin of SIP-adus

SIP-adus is a unique program that has involved politics,
industry, academia, government and local citizens for morethan 10 years, including preparation. Let us visit the origin of
the program. First, the first international workshop was held
in November 2014. The main theme was "Innovation ofAutomated Driving for Universal Services (SIP-adus) Mobility Bringing Everyone a Smile." The purpose of theworkshop was "An inclusive society where diverse people
actively participate in value creation in diverse communities
can achieve both individual happiness and economic
development. Automated driving technology integrated withsocial innovation should provide mobility that allows everyone
to fully demonstrate their abilities and enable the sustainable
development of society," a message that still has an impact
today. Second, at the start of the second phase of SIP-adus(2018), the name of the program was willfully changed from
"Automated Driving Systems" in the first phase to "AutomatedDriving for Universal Services." This was a strong statement of

the vision of not only developing technological systems, butalso utilizing them to solve social issues such as logistics/
mobility, and to develop a variety of services and businesses.
I think it is important that SIP-adus has been able to set
out such a clear vision early on, ahead of SDGs and Society
5.0, to guide its activities over the long term.

3

Importance of historical perspective:
STI policy is shifting to a social change/
mission-oriented approach

Here, we review the evolution of international STI
policies since the end of World War II. The objectives andinstitutional framework have changed to reflect political,
social, and economic demands, the state of development of
new technologies, and international conditions, such as
security, industrial competitiveness, and solutions to social
problems, and they can be roughly divided into four phases.(2)The first, second, and third periods are state-led large-scaletechnology development (since the 1950s), industrial
technology development (since the 1970s), and nationalinnovation system (since the 1980s), respectively. Fromaround 2010, the fourth period began while overlappingwith the third period, resulting in a shift to sociallytransformative/mission-oriented
innovation
policies(abbreviated as MOIP). SIP-adus can be regarded as aprogram in this fourth phase.
Last year, the OECD characterized STI policies in the
post-COVID-19 pandemic era as follows: "Economic
competitiveness has been the main rationale for countries'
STI support over the past 30 years. In the future, sustainability,
inclusiveness, resilience, and security will be key priorities in
addition to this."(4)(5) This reflects the recent rapid expansion
and qualitative changes in the relationship between the
global environment, society, economy, technology, and civic
life, including the climate crisis, SDGs, COVID-19 pandemic,
security crises, the 4th industrial revolution, and the digital
revolution. The UN's SDGs, the OECD's MOIP, the EU's
Horizon Europe, and the recent innovation policies of268

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Germany, UK, and other countries are all moving in the
direction indicated by the OECD.
Among this, changes have begun in Japanese STI policies.
In 2020, the Basic Act on Science and Technology was revisedfor the first time in 25 years, adding "creation of innovation" asan objective in addition to "improvement of the level of scienceand technology," and emphasizing the promotion of thehumanities and social sciences and collaboration with scienceand engineering. The Sixth STI Basic Plan (March 2021),
which was decided upon in response to this, sets forth different
social goals and values from those of the past, namely,transformation into a sustainable and resilient society andrealization of the diverse well-being of each individual. These
have been the vision of SIP-adus for the past 10 years since thebeginning.

4

cooperation with 13 countries including Japan, has beencollecting and analyzing MOIP efforts and good practices ineach country. In the process of this study, some of Japan's
initiatives are also included, and SIP-adus is attracting
attention as an important practical example.
Here are three frameworks of MOIP compiled by theOECD in collaboration with other countries.(1)(2)(3)(1)Definition of MOIP: "A coordinated combination (package)of research and innovation policy instruments to address
societal challenges."(2)Five principles of MOIP program design and implementation:(1) setting specific goals, (2) clarifying the time frame forrealization, (3) coordination among different policy areas(ministries, organizations), (4) utilization of various means(supply-side and demand-side, top-down and bottom-up),and (5) strengthening mechanisms and human resources tolink the path from technology development to demonstrationand market deployment.
(3)Formation of a three-tier structure for effective implementationof the MOIP program: (1) strategic direction, (2) policy
coordination, and (3) policy implementation. Strengthening
the human resources and systems capable of managing thismulti-layered structure.

Methodological framework for sociallytransformative/mission-oriented
innovation policies (MOIP): OECD
international collaborative study

In order to materialize MOIP, which is becoming thetrend of the times, it is necessary to review the entire
conventional STI system, from the setting of a vision and theprocess of policy formation, to the promotion system andmanagement, the financial support system, coordination andcollaboration across disciplines, organizations and industries,
public acceptance and business development, humanresource development and evaluation methods. This will
require a review of the entire conventional STI system.
Although various efforts are currently being made in various
countries, common methodologies and evaluation methods
have not yet been established. Therefore, the OECD, in(2) Policy Coordination(1) Strategic Direction

Governance andhierarchy of SIP-adus

5

SIP-adus practical activities and theOECD's MOIP framework

Here, we have organized the specific practical activities,
governance, and hierarchical structure of SIP-adus in linewith the OECD MOIP framework introduced above, andcreated a bird's eye view diagram. (Fig.1)(1) The following
explanation is based on this diagram.
(1) Layer 1: Strategic Direction Level(3) Policy implementation

CabinetPublic-Private ITS Initiative/Roadmaps
Secretariat•Strategic Headquarters for the Promotion of an Advanced Information and Telecommunications
Network Society / Strategic Council for the Promotion of Public-Private Data Utilization•Coordination of government-wide policies and activities (including SIP-adus) •Set targets and roadmaps•Monitoring and evaluation of activities and progress, reflection of results, and annual updating of roadmap•Guideline for various activities and initiatives of private companies, local governments, etc.
Establishment, monitoring, evaluation, and revision of goals and processes
SIP-adus
Ministries and agencies
Promotion of the
METI automotive industry
Cabinet Office CSTI PD: Program Director
ICT technology, radio frequency•Management by PD; policy Management agency NEDOMIC regulation, cooperation withcoordination and practice lead
local governments•Identification of cooperative areas and establishment of R&D tasks
Road administration,•Support for research and development in cooperative areas
MLIT
The SIP-adus program
FOTs•Conducting FOTs
Projects implemented
Road
Traffic
Act, FOTs
NPA•Coordination of research and development projects and FOTs
<Cooperative area>•Joint R&D proposals
Funding and various policy instruments•FOTs(laws and regulations, licensing, regional policies, etc.)
SIP-adus R&D and FOTs

Automotive
industry
ICT company

Diverse regions, conditions, and stakeholders

Universities

Local governments

Small and medium
enterprises/Start-ups

Local community

Insurance and financecompaniesNGO<Example>✓Dynamic Map✓Human MachineInterface✓Cybersecurity✓Pedestrian accident reduction✓Next Generation Transport✓Safety assurance technology invirtual environments✓Construction of architecture
related to geographic data
✓Fostering public acceptanceFig.1 Bird's eye view of SIP-adus governance and hierarchy

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8.Conclusion and Outcomes through SIP-adus to be Inherited﻿2) SIP-adus and Mission Oriented STI Policy

In addition to reducing traffic accidents, reducing traffic
congestion, and improving safety, the socioeconomic issues
that SIP-adus aims to solve include drastic changes in theinternational competitive environment for mobility, andaddressing social, economic, lifestyle, and mobility difficulties
in local communities due to the declining birthrate, aging
population, and depopulation. Based on the above, the Cabinet
Secretariat has formulated Public-Private ITS Initiative/
Roadmaps, setting a clear timeframe for the realization of
automated driving systems and creating specific goals anditineraries. SIP-adus will share and collaborate with relatedorganizations to promote individual activities, and monitor
and analyze the progress to annually update and revise theactivities in strategic direction. Public-Private ITS Initiative/
Roadmaps are both the strategic direction and the driving
force that binds the various activities together.
(2) Layer 2: Policy Coordination Level
An important role of SIP-adus in policy coordination is
management to ensure that the Cabinet Office, Ministry of
Economy, Trade and Industry (METI), Ministry of Internal
Affairs and Communications (MIC), Ministry of Land,
Infrastructure, Transport and Tourism (MLIT), National
Police Agency (NPA), related companies, local governments,
etc. act in a coordinated and timely manner, combining theirrespective policy/strategic measures, such as research funding,legislation and regulation, licensing, technology, standards,
finance, and human resources to accelerate the developmentof automated driving systems, FOTs (Field Operational Tests),
and social implementation based on Public-Private ITS
Initiative/Roadmaps. Behind the scenes discussions andcoordination are also important for this purpose. The next
important role is to establish cooperative areas and competitive
areas among industry, academia, government, and local
communities, and to effectively promote various activities atthe policy implementation level as described in (3) below.
Furthermore, industry, academia, and government should
work together to strengthen the international intelligence
functionality, promote international cooperation andinternational standardization activities, and gain insight into
the drastically changing international competitiveenvironment.
The role and leadership of the program director tocomprehensively manage and coordinate these diverse
activities and organizations is extremely important.
(3) Layer 3: Policy Implementation Level
When a cooperative area is established in (2) and publicfunds are invested, related organizations can build a promotion
system that transcends organizational and disciplinary
boundaries, and collaborate on research and development,
FOTs, and activities to foster public acceptance. To promote
finances, it is possible to identify and meet diverse needs. In
addition to the SIP framework, local governments and various

industries are working on regional needs, natural and social
conditions, and other factors unique to each region in Japan.
Public-Private ITS Initiative/Roadmaps and SIP-adus are
guideposts for these activities and play a catalytic role insharing case studies and promoting projects.

6

Passing on SIP-adus legacy to the next
generation

SIP-adus ends this fiscal year (2022). New programs such as"RoAD to L4" and the next phase of SIP "Construction of smart
mobility platform" have begun to take over from thisachievement. Across the nation, examples of materializing
socially transformative innovation are beginning, including
achieving carbon neutrality by 2050, and the Vision for a
Digital Garden City Nation. SIP-adus is recognized around theworld as an important example of these prior practices. The
experience and knowledge gained over the past 10 years, aswell as the achievements and methods of governance systems,
cooperation and human networks, trust building, and fostering
public acceptance, are invaluable and should be passed on to
the next generation that creates as solid a narrative as possible.(6)We hope that SIP-adus will provide many suggestions for
future innovation policy and implementation at the national
and local levels, and for solving social issues and developing
business.

[References]
(1)

(2)

(3)

(4)

(5)

(6)

The Design and Implementation of Mission-oriented Innovation
Policies-A New Systemic Policy Approach to Address Societal
Challenges, OECD Science, Technology and Industry, February 2021.
Strategic Proposal for "Promotion of Mission-oriented Science,
Technology and Innovation Policy and Research and Development
Funding in Japan," April 2022.
"Trends and Challenges in Mission-Oriented Science and Technology
Innovation Policies for Solving Social Issues," Research Report, March
2021, Center for Research and Development Strategy, Japan Scienceand Technology Agency.
Science & Technology Policy 2025-Enabling transitions through STI;
Concept and project proposal-a scoping note, Committee for S&T
Policy, OECD, October 2021.
Tateo Arimoto: OECD Science and Technology Policy - Transition to a
Sustainable Socio-Economy, Current Trends in Science andTechnology, Nikkan Kogyo, August 12, 2022.
Kazuo Shimizu, Supervisor: SIP-adus Program - How Innovation
Happened (tentative title), SIP-café, to be published in February 2023

[Contacts]

Program for Science, Technology and Innovation Policy, National Graduate
Institute for Policy Studies, 7-22-1 Roppongi, Minato-ku, Tokyo 106-8677,
03-6439-6339 (main), Contact: Tateo Arimoto (t-arimoto@grips.ac.jp,
arimoto@jst.go.jp)

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8Conclusion and Outcomes through SIP-adus to be Inherited3) S IP-adus Achievement as Heritage and Next Step
RoAD to the L4
Toshio Yokoyama (National Institute of Advanced Industrial Science and Technology (AIST))

14) Fostering public acceptance•P romoting accurate understanding and interest in automateddriving, etc. and encouraging behavioral changes througheasy-to-understand information dissemination from theuser's perspective, provision of opportunities for realistic
experiences, and organization of civil liability

Overview of RoAD to the L4

RoAD to the L4: The Project on Research, Development,
Demonstration and Deployment of Automated Driving toward
the Level 4 and its Enhanced Mobility Services was launched by
the Ministry of Economy, Trade and Industry (METI) and theMinistry of Land, Infrastructure, Transport and Tourism(MLIT) as a part of The Panel on Business Strategies for
Automated Driving. It was launched in fiscal 2021 in line withthe recommendations of the "Action Plan for Realizing
Automated Driving Version 5.0" to study the direction thatshould be taken toward the practical application of MaaS
(Mobility as a Service) in Japan.
(1) SignificanceThis project aims to create a sustainable mobility society
based on major trends in the automotive industry such as CASE
(Connected, Automated, Shared&Service, Electrification) andcarbon neutrality, and is expected to contribute to reducing theenvironmental burden, solving mobility issues, and increasing
the economic value of Japan by realizing and promoting
advanced mobility services such as Level 4.
(2) Goals and KPI (Key Performance Indicator)1) Realization and diffusion of unmanned automateddriving services• Realize automated driving services (Level 4) only by remotemonitoring in limited areas and vehicles by fiscal 2022.
• Expand to a variety of areas and vehicles by fiscal 2025,with a minimum of 40 locations2) Spread of new mobility services (MaaS) using IoT and AI• S ocial implementation of new mobility services using
IoT and AI in various regions of Japan to solve local
social issues and revitalize local communities
3) Securing and developing human resources• Secure human resources in a wide variety of fields,
including engineers in hardware and software, andpeople who can match technology with local issues.

(3) Implementation Policy
The project is not limited to technological development,
research and analysis, and FOTs (Field Operational Tests), butalso aims at social implementation of advanced mobility
services such as level 4, based on the significance, goals, etc., set
forth above.
(4) Status of efforts
RoAD to the L4 is a five-year project from fiscal 2021 to
fiscal 2025, promoting the establishment of use cases,
organization and evaluation of driving environments,
development of vehicles and systems, and study of sustainablebusiness models. For passenger transport, the project isstudying three major types of spaces: limited space (low speed),
limited space (medium speed), and mixed traffic space, while
for freight transport, the project is studying limited space on
expressways.

2

Specific Topics of Efforts

Theme 1: Efforts to realize automated driving services (level 4)
by only remote monitoring
Aiming to realize an unmanned automated driving mobility
service with only remote monitoring (level 4) using low-speed
automated vehicles at Eiheiji Mairodo in Eiheiji-cho, Fukui
Prefecture, by fiscal 2022, we are organizing a business model,
verifying tasks for remote monitors, and so on.
In addition, development of automated driving systems andvehicles for mass production for level 4 mobility services, as well as
remote control systems and communication systems for multiple

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8.Conclusion and Outcomes through SIP-adus to be Inherited﻿3) SIP-adus Achievement as Heritage and Next Step RoAD to the L4

vehicles, and technological verification are under way. (Fig.1, Fig.2)

operating conditions, etc.

Specifically, the project model is studied based on interviews
with major logistics operators, and risks are extracted byexamining the driving environment and operating conditions.
(Fig.4)

Fig.1: Mobility Service Vehicles in Eiheiji-cho

Fig.4 Truck for FOTs Equipped with Various Sensors

Fig.2 Remote Monitoring System in Eiheiji-cho

Theme 2: Initiatives to expand target area and vehicles as well as
improve business feasibility
Aiming to realize an unmanned, automated driving
mobility service on the Hitachi BRT (Bus Rapid Transit) in
Hitachi, Ibaraki, the development and safety assurance of an
automated driving system (vehicle, remote monitoring system,etc.) in accordance with the ODD (Operational Design
Domain) settings has been initiated. Local FOTs are scheduled
to be conducted by the end of fiscal 2022. In addition, a task
force consisting of various operators involved in unmanned
automated driving services has been established and has begun
studying ODD classification, etc., and plans to compile various
guidelines based on ODD classification during fiscal 2022.
(Fig.3)

Theme 4: Initiatives to link vehicle-to-vehicle and pedestrianto-vehicle and cooperative infrastructure to expand level 4 inmixed spaces
In order to realize cooperative automated driving services,
we have started to organize use cases in which cooperative
systems are required, and to study requirements on theinfrastructure side and vehicle side and data coordination
schemes required for cooperative systems.
In order to study the requirements for a cooperative systemthat provides safe mobility not only for level 4 users but also forvarious users such as pedestrians and bicycles, we have begun
performance tests for demonstration purposes in Kashiwanoha,
Chiba. (Fig.5)

Fig.5 Various Sensors Installed on the Infrastructure Side

Fig.3: Hitachi BRT FOTs

Theme 3: Initiatives to implement advanced trucks, including
platooning in expressways
With the aim of realizing automated driving trucks (level 4)
on expressways by around fiscal 2025, the project has begun
studying mainly business models, driving environments,

3

Future Initiatives

RoAD to the L4 aims to socially implement the results ofTheme 1 in society and launch automated driving services infiscal 2023 as a leading example of this project.

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For the other themes, the plan is to conduct necessary
studies and preparations during the first two years, start FOTs
in the third year, and then realize social implementation after
undergoing review for various criteria.
For these plans, we plan to make maximum use of theresults of the projects we have been working on in the second
phase of SIP-adus, such as the Tokyo waterfront area traffic
SPaT (Signal Phase and Timing) information provision FOTs,
the study of communication methods that realize cooperative
automated driving, and the study of public acceptance, with theaim of early social implementation of level 4 and otherautomated driving mobility services.

[Contacts]
Invited Researcher, Digital Architecture Research Center, National Institute ofAdvanced Industrial Science and Technology (AIST), Central 2, 1-1-1 Umezono,
Tsukuba, Ibaraki 305-8568, 029-861-5022 (main), Contact: Toshio Yokoyama(Toshio.yokoyama@aist.go.jp)

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8Conclusion and Outcomes through SIP-adus to be Inherited3) S IP-adus Achievement as Heritage and Next Step   
Next Phase of SIP
Kenji Ueki (Cabinet Office)1

the development of Level 3 automatic driving laws by therelevant ministries and the launch of the world's first Level 3
automated vehicle (Honda "Legend"). It is indicative of SIP’s
contributions to the social implementation of innovativetechnologies.
Therefore, industry, academia, and government continue tohave high expectations for SIP as a research and development
program for social implementation, leading to discussions forthe next phase of SIP starting in FY2023. In the interim
institutional evaluation of the second phase of SIP conducted in
FY2020, it was decided that the next phase of SIP would be
implemented after institutional improvements, such as theestablishment of a preparatory period for the formulation ofresearch and development plans.
In response to this, the "Sixth Science, Technology andInnovation Basic Plan" (Sixth Basic Plan, Cabinet decision on
March 26, 2021) states, "Candidate subjects for the next phaseof SIP will be decided toward the end of 2021 in order tostrengthen the headquarters function of the CSTI. Specifically,

Consideration of the next SIP

1.1. Background and Significance
The Cross-ministerial Strategic Innovation Promotion
Program (SIP) was established by the Cabinet Office in FY2014
as a program to promote research and development from basic
research to social implementation in a comprehensive manner
through collaboration among, agencies, industry, academia,
and government, utilizing the headquarters function of theCouncil for Science, Technology and Innovation (hereinafterreferred to as CSTI).
The first phase of the five-year program was implemented
from FY2014 to FY2018, and the second phase from FY2018 to
FY2022.
For example, the high-precision 3D maps developed in thefirst phase of the Automated Driving System have beencommercialized, and are in use in all expressways. This led to

A society in which the diverse well-being of eachindividual can be realized [Science and Technology
A society with diverse social participation
Innovative Basic Plan, Chapter 1, 3. (1) 2)].
(a society with inclusiveness)
Bu
Data
Data
comilding s
mun martities
A society in which abundant
Inclusive community
A society with individualized
Integrated health carefood is provided sustainably
platform
education and work opportunities
system
Data
Data
L ea
styl rninge af andodter
le foinb
CO workiain chaVID ngats-19
Su upply
n
io
s
Vir
erat
p
o
t
u
al e
co
n
a
con
om
hum ics
Quantumy exn of robot
pan
nsio
a
sion
p
Ex
Advanced quantum technologies
Smartmobility
Data
Platform for ocean Data
platform
development
Safe and secure utilizationof AI and data
A society with increasing healthy life expectancy

Material

Data

Material process innovation

Smart disasterprevention network

Society with efficient
infrastructure management

Data
Resilient, safe and secure society

Cross-disciplinary
approach

Developing an integrated promotion system
for the realization of Society 5.0

Circular economy
system

Data

din
ga

Smart energy
management system

Buil

Smart infrastructure
management system

sm
art

isl

an
d

AI, Data, IoT

Achieving comprehensive security

Building a smart city

Sustainable cities and regions

Bio

Carbon neutral society(2050 target)

Data

Recycling-oriented society
Sustainable and resilient society that ensures the safety and securityof the people [Science and Technology Innovative Basic Plan,
Chapter 1, 3. (1) 1)].

Data coordination between domains(e.g., building smart cities, etc.)

Development of common indicators such as
wellbeing, carbon neutrality, etc.

Utilizing a convergence of knowledge forbuilding social systems

Fig.1: Candidate issues for the next SIP

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8.Conclusion and Outcomes through SIP-adus to be Inherited﻿3) SIP-adus Achievement as Heritage and Next Step Next Phase of SIP

it was decided that [...] CSTI will identify social issues to beaddressed in the medium to long term, and will conduct
research and studies on technological development themes thatshould be addressed across ministries and agencies, while
utilizing "the convergence of knowledge."

1.2. Establishing Candidate Issues
In order to fully consider the issues to be addressed in thenext phase of SIP in advance, the Governing Board of CSTI has
identified 10 areas corresponding to the 10 social visions listed
in the Sixth Basic Plan as "Society 5.0" that Japan is aiming for,and five areas corresponding to the technologies that will form
the fundamental elements of these visions, including AI, data,
quantum computing, etc. Those 15 areas form the subject
candidates going forward. (Fig.1)
Meanwhile, efforts are underway at relevant ministries,
industry, and academia. The next phase of SIP will not be an
exhaustive effort, but will serve as a hub for collaboration
among industry, academia, and government, and will work on
the development of platform technologies for technologicalbottlenecks, on the construction of common systems, and on
the establishment of rules.
In addition, rather than working independently in eacharea, efforts will be made to work toward the realization ofSociety 5.0 through the development of an integrated promotion
system, data coordination among areas, the development ofcommon indicators related to cross-cutting social issues such as
wellbeing and carbon neutrality, and the utilization of theconvergence of knowledge for the formation of social systems.

1.3. 
Request for Informtion for Research subject(RFI)
From January to February 2022, we requested for
information of research theme ideas (RFI) for 15 areas, andreceived a total of 971 ideas from a wide range of parties inindustry, academia, and government, including 384 from
Table 1: RFI results for next SIP subject candidates
Primary domain
Sustainable food supply chain
Integrated health care system
Inclusive community platform
Learning and working style after COVID-19
Platform for ocean development
Smart energy management system
Circular economy system
Smart disaster prevention network
Smart infrastructure management system
Smart mobility platform
Expansion of human-cooperation robotics
Virtual economy expansion
Advanced quantum technologies
Safe and secure utilization of AI and data
Material process innovation
Total

Numberof cases Universities
68
98
16
16
80
67
77
191
217
43
17
12
26
21
22
971

36
45
9
6
11
35
29
56
103
13
9
5
9
8
10
384

Of those
National
research Businesses Organizations
institutes
18
12
2
7
30
16
1
6
0
0
7
3
54
10
5
12
16
4
12
31
5
59
71
5
41
57
16
4
24
2
2
6
0
2
5
0
10
7
0
4
8
1
9
2
1
235
292
60

universities, 235 from national research institutes, 292 fromprivate-sector companies, and 60 from sectoral associations.
Although the number and scope of information varied
depending on each area, ideas from a variety of universities,
national research institutes, and companies were gathered frommultiple perspectives. (Table 1)
Based on the results of the RFI, the overall direction andstructure of sub-subjects for each subject candidate wereclarified. The skills required of program director (PD)candidates who will lead the feasibility studies of each subject
candidate were also clarified.

1.4. Selection of PD Candidates and Feasibility Studies
Based on the results of the RFI, the skills required of PD
candidates were clarified, and an open call for PD candidates
for each subject candidate was conducted from April 1 to 22.
Based on the results of a documents review (primary screening)
and interviews (secondary screening), the Governing Board
selected 15 PD candidates on May 26.
In addition, for each subject candidate, task forces (TFs)
was established under the Governing Board, chaired by PD
candidates, and consisting of members from related academia,
industry, ministries, and research promoting companies.
Currently, feasibility studies (FS) are being conducted under
the TFs looking at the research themes provided in the RFI. The
aim is to select the research themes based on the impact andfeasibility from the technical and business aspects, to clarify thedetails and structure of the subjects, and to prepare draft researchand development plans by the end of 2022.
After deliberation by the governing board and publiccomments, the research and development plans will be decided,and based on the plans, an open call the PD will be conducted.
The next phase of SIP is scheduled to start in FY2023.

2

Feasibility study for the next phase of
SIP subject candidate "Formation ofsmart mobility platform"

2.1. Clarifying the Concepts and RFI Results of the
Subject Candidate
When the Governing Board established the subject
candidates at the end of the 2021 fiscal year, "Formation ofsmart mobility platform" was selected as a candidate because it
was reflective of a social vision. The concept was to "form aplatform that realizes safe, environmentally friendly, andcomprehensive mobility service by dynamically integratingmeans of transportation (small mobility, automated driving,
MaaS, drones, etc.), transportation environment hardware andsoftware from the perspective of people and goods on themove."

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8.Conclusion and Outcomes through SIP-adus to be Inherited﻿3) SIP-adus Achievement as Heritage and Next Step Next Phase of SIP

Based on this, an RFI was conducted from January to
February 2022, and a total of 43 ideas were submitted. These
ranged in scope from evaluation indicators and QoL
improvement through mobility to elemental technologies andservices such as mobility-related data platform, traffic
environment simulators, multi-modal MaaS, dynamic
utilization of road space, and innovative sensing technologies.
(Fig.2) Of these, 13 were from universities, 4 were from national
institutes, 24 were from companies, and 2 were from professional
associations.
Valuation
index
Data
platform
Servicecooperation

QoL improvement through mobility (1 case)
Mobility-related data platform (8 cases)
Traffic environment simulator (3 cases)
Multimodal MaaS (5 cases)

Elemental
technologies
and services

Dynamic
utilization ofroad space(6 cases)

Advancedvehicleelemental
technologies(4 cases)

Development
of diversified
mobility(4 cases)

Platform
technologies

Innovative sensing technology (4 cases)

Development offlying carsand drones(5 cases)

Promotion oflogistics MaaS
(3 cases)Fig.2: Organization of RFI results

Based on the RFI results, the skills required of PD candidates
were defined as "knowledge, experience, and networks thatenable them to look at the entire mobility field, including roads,
vehicles, and logistics, and to develop an overall vision andarchitecture based on the functions and roles of mobility inaccordance with the characteristics of cities and regions."

2.2. Feasibility Study Structure
On May 26, 2022, the Governing Board selected Dr. Haruo
Ishida, Professor Emeritus of the University of Tsukuba andVisiting Professor of the Department of Transportation Systems
Engineering, Nihon University, as a PD candidate, and on June
10, a TF was established with Dr. Haruo Ishida as the chair.
The TF includes Noboru Koshizuka, Professor of theInterfaculty Initiative in Information Studies, University of Tokyo,
Fumihiko Nakamura, Professor of Graduate School of Frontier
Sciences, University of Tokyo, Satoshi Hiyama, Chairman of the
Subcommittee of the Road Traffic Section of the Connected
Committee, Next Generation Mobility Committee, Japan
Automobile Manufacturers Association, Shigetaka Murase,
President, WILLER Inc. Ltd. as Sub PD candidates. Other related

experts, ministries and agencies, and NEDO also join the TF, andthe group at the Cabinet Office in charge of automated driving
work as the secretariat of the TF. (Fig.3)

2.3. Status of Feasibility Study Considerations
On June 27, 2022, the TF prepared the FS Implementation
Policy ver. 1.0. In this policy, it was decided to "study thefollowing four sub-subjects based on the RFI results in order toaddress social issues such as carbon neutrality, safety (zerofatalities in traffic accidents), economic vitality and regional
revitalization, real and cyber integration, pursuit of wellbeing,
and collaboration with overseas countries".
(1) Redefining Mobility Services and Strategies for Social
Implementation(2) Data platoform supporting mobility services (Smart
Mobility Data Platform 2.0)
(3) Infrastructure Strategy to Support Mobility Services(4) Strategies for the Social Implementation of Mobility services
Through basic research and interviews with RFI applicants,
we plan to identify core research themes that should beconsidered individually for each subproject, and conduct atechnical feasibility study for each.
These studies are expected to be advanced efforts toward asmart mobility society that include coordinated and flexible
linkage with various means of transportation and transportation
environments other than automated driving, while taking
advantage of the results of SIP-adus (Cross-ministerial Strategic
Innovation Promotion Program (SIP) Automated Driving for
Universal Services).

[References]
(1) The Sixth Science, Technology and Innovation Basic Plan, approved by the
Cabinet https://www8.cao.go.jp/cstp/kihonkeikaku/6honbun.pdf, (2021.03.26)(2) Determination of candidates for the program director (PD) of the next phase of SIP(chairperson of the task force to study each subject candidate)https://www8.cao.go.jp/cstp/stmain/20220527sip_pd.html, (accessed 2022.05.27)[Contacts]
Secrertariat of Science, Technology and Innovation Policy
Cabinet Office, Government of Japan, Chuo Godo-chosha Bldg. 8, 1-6-1 Nagata-cho,
Chiyoda-ku, Tokyo 100-8914, 03-6257-1336

TF
TF Chairperson(PD candidate)
Haruo Ishida

Experts (SPDcandidates)
NoboruKoshizuka,
Fumihiko
Nakamura,
Satoshi Hiyama,
Shigetaka Murase

Relevant ministries andagencies National Police
Agency Digital Agency
Ministry of Internal
Affairs andCommunications Ministryof Agriculture, Forestry
and Fisheries Ministry of
Economy, Trade andIndustry Ministry of Land,
Infrastructure, Transport
and Tourism

NEDO

Secretariat of automateddriving, SIP, Cabinet Office

Fig.3: Feasibility study structure276

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8Conclusion and Outcomes through SIP-adus to be Inherited4) Final Summary of SIP-adus Program— For the Next Generation of Engineers —Seigo Kuzumaki (SIP-adus Program Director, TOYOTA MOTOR CORPORATION)As the SIP-adus (Cross-ministerial Strategic Innovationindustry, academia, and government toward the goal of

Promotion Program; Automated Driving for Universal

realizing automated driving, and ultimately produced a variety

Services) comes to a close, I would like to summarize it in theof results.

form of a message to the next generation of engineers.

Young engineers are not expected to suddenly take on the

As you know, in a "project," people with the skills androle of promoting national projects, but as technology continues

abilities necessary to accomplish a mission temporarily gather

to develop and become more complex, there is no technologicaltogether across organizational boundaries, and when thedevelopment that can be completed solely within one's own

mission is over, the organization is disbanded and ceases todepartment. All research and development can be considered aexist. Strangers meet, begin by sharing an understanding of the"project." What is important is not to create barriers amongissues, form a team, decide on a goal, stimulate each otherourselves, but to tackle the challenges in front of us with ourthrough research and development, and become a force that iscolleagues with all our might. If you continue to do so, even if

more than the sum of their parts to bring the project to a

you fail a few times, when the next opportunity comes, you will

successful outcome. A "project" is a valuable opportunity tosurely get a lot of support and succeed.

grow both technically and as a person, and a great chance togain the wealth of human networks.

With this, the SIP-adus will be "disbanded," but we hopethat the next generation of engineers will boldly take up the

In the project under the name of SIP-adus, a sense ofsolidarity was gradually developed that transcended the barriers
between government ministries and agencies, and between

challenge of creating Japan’s next innovation.
Last but not least, I would like to reiterate my sincere thanks
to all of you who have supported the SIP-adus!

SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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Modeling The Earth
Our company supports innovation in a variety ofindustries by providing a high-precision 3D data
platform that replicates the real world in digitalspace.

3D DIGITAL WORLD opens
doors to a new world
The 3D DIGITAL WORLD requires high precision andreality in order to serve as the foundation of the digitaltwin, which simulates the future by recreating the realworld in digital space, and the metaverse, which is built
based on the real world, as well as for advanced driving
support systems and automated driving.
The high-precision 3D data developed by Dynamic Map
Platform will support a wide range of industrial fields byproviding a highly accurate and realistic 3D DIGITALWORLD in collaboration with other communication anddevice sensor technologies.

Dynamic Map Platform Co., Ltd.
https://www.dynamic-maps.co.jp/en/

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Applications that utilize high-precision three-dimensional map data (HD maps)
Contributing to advanced "self-location estimation" and "environmental
awareness" of autonomous driving systems and ADAS

Expansion of autonomous
driving
Solving social issues such as the aging of snow removal workers through thedevelopment of a snow removal support system repurposed as an HD map

Skill transfer
Achieving DX of current investigation at traffic accident sites

Safety improvement andcongestion reduction

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Automated driving simulation platform

DIV

Virtual space simulator that accelerates safe and resilient automated driving society.

Deliver the DIVP products and servicesat

Our Product
Implementation of highly consistent simulation platform that enables comparable
assessments to real vehicle tests under various traﬃc environments.

Suppo

Existing simulation
Sensor models

Map data

Traﬃc ﬂow scenarios

CameraRadar

Automated
driving
control
software

LiDAR

Develop and adjust
standard Interfaces
Scenarios

Environments

Scenario creation tool
Scenarios

Automated driving vehicle

Sensors

Space rendering

Perceptions

Recognitions

DIVP® "Environment/Space rendering/Sensor" modelLight/Radio
wave ray tracing

AssetsFusion/Control Vehicle motion

Evaluation subjects

Recognition
algorithm

Sensor model

Example of sensor outputs

Scenario of
OdaibaControl
modelsAssessment
scenario
Sensor weakness
scenario

Camera

SDMG®

Radar

Vehiclemodels

LiDAR

Simulation platform
DIVP productSpecial Feature
Simulation models that highly consistent with physical phenomena.
Our modeling for real-virtual consistent sensor outputs is the digital process of modeling physical phenomena in anelectromagnetic spectrum, predicated upon the detection principle of sensors and verifying in comparison withreal vehicle test results.

Simulation of proving ground
Real view

Sim Camera vs Real Life

Simulation

Real view

Cloud

Build neces
cloud and vie

Simulation of backlight scene
Simulation

Source : Kanagawa Institute of Technology, AD-URBAN, SOKEN, Sony Semiconductor Solutions

https://www.vdrive-tech.com/en/

SIP通し.indb 280

The pr2023/03/01 16:48

rm
DIVP

ety.

ces

arableDriving Intelligence Validation Platform (DIVP® )project launched as research
consortium with 10 partners (at present), industries and academia including sensor
manufacturers and software companies as well as universities from 2018.
We are dedicated to creating a safety validation simulation platform for automateddriving in virtual space focusing on highly consistent sensor models.

at one stop to meet customer's needs.
Way to use
Support the throughout process of sensor units and automated driving systems,
from planning and development to assessment and application.
Elaborate simulation of the real environment in virtual space.
Support the model based development of sensor units and/or automated driving system from early process likeplanning and development to validation and veriﬁcation of systems equivalent to real vehicle test.

omatedngrolware

Development

Planning

Sensor weakness scene

vehicleBacklight

le motionApplicationAssessment

Examples of NCAP assessment
Night

Scenario

Camera

LiDAR

Radar

bjectsehiclemodelsRain

Examples of simulation application in each process.

Structures
Products (Cloud & On-premise)
Cloud computing

On-premise (module)

Build necessary modules in thecloud and view simulation results

Purchase modules and installthem to in-house facilities.

Business formation / Product and services
BIPROGY has established V-Drive Technologies, and makes
business alliance with MITSUBISHI PRECISION to deliver DIVP
products and services at one stop.
V-Drive Technologies are going to add and enhance products
and services sequentially.

Customers
Product and Services（Simulation-Platform, SDMG®）

Act as Toolbox of Simulink®,
a standard platform ofmodel-based development.
Capitalinjection（100%）

IP
Technical
supportBusinessalliance

Product provision（SDMG®）

Connect with other simulation SWs and HilS.

The product predicated upon results achieved in entrusted research undertaken together with the New Energy and Industrial Technology Development Organization (NEDO).

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W h at i s M D c o m mu n e t ® a n d i t s v i s i o n

E x amMatc h ing s ite th at aggregates data related to th e mobility field c entrally andWh

generates s ervic es by p ublic-p rivate c oop eration
MD communet® aggregates diverse road traffic environment data in the mobility field that is dispersed acrossRdata

the world in a central portal site as well as supports matching of private and public entities for creating newuser services by establishing places of communication in various formats.

MaaS

Information
surrounding theroad environment

Automated driving

M

Aggregate catalog data on
portal site

Logistics/exports

Cataloglis ting / s earchInformthe roCommunications

N eeds / s eeds
reg is tration

Matching support

Toky ofor log

New service creation

Provideon sudde
driving r
configur
supportvehiclesVa lu e prop o sit ion of M D commu net
MD communet provides support to solve user issues by delivering data and creating services with the aim togenerate new services and solve social issues by data utilization

MD

Variouwill coprojecExtract and provide publicprivate road traffic
environment data

Promote matching of providers
and users and support service
creation

Support service creation
through technical support, suchas data processing, etc.

Post over 7,000 pieces of road traffic
environment data Presents users withdata they did not realize by searches
outside of keyword searches, such asproviding similar data throughmachine learning and d at arecommendations for each user.

Intro duces companies andorganizations that seem to fit theneeds and seeds held by users.
Supports the creation of services
through connecting companies andorganizations that didn’t know or
weren’t connected to one another.

User needs support for data in sales andservice development, but doesn’t know
where to go. In this situation, MD
communet provides support in servicec r e at i o n w i t h t e c h n i c a l su p p o r t ,including company referrals.

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nd

E x a mp l e s o f u t i l i z i n g M D c o m mu n e t
W h at M D c o m mu n e t c a n r e a l i z e
R o a d t ra f f ic e n v iro n me n t
da t a / s e rv ice ca t a lo g a ggre ga t io nd across

ing new
MaaS

Utilize data to realize
services

S ervice 01

S ervice 02

O p tim iz ed deliver y r outep r op os a ls us ing a ctive da ta

O p timiz ed route p rop os als f or
p la ces tr a veler s wa nt to g oAutomated driving

Vehicles stuck due to snowy roads.
Deliveries will be late if nothing is
done!

Truck probe
data

Information surrounding
the road environment

Logistics/exports

Feedback
services/data toportalRecommend
Instinctiveation
overarching UI

g support

On-board
camera data

Meteorological dataU t i l i z e t h i s dat a t o f o r e c as t s n o w b e f o r e h an d an d gui ded r i v e r s t o a v o i d r o ad s wh i c h h a v e n o t b e e n p l o we d

Delivery to customers without
significant delays!

Tech ni calsnTire sensing
data

Rel ati on

Technical
supportPlace forCompany cooperation
new connections

Provide dynamic routing data basedon sudden disaster information,
driving results, and detailed
configuration of roads in order tosupport safe and secure driving ofvehiclesHigh
precision 3D
map

Register vehicle attributes(size, weight, etc.)

Business considerationPoC

F OTs 02

F OTs 01
To ky o w ater f r o nt ar ea ser v i ces
f o r l o gi sti cs co m p ani es

S er vice develop m ent

Real-time
disasterinformation

Navigation to delivery
destination entrance anddisplay rest spots

Information
for trucks

Notifications to focus
attention based on vehicle
attributes andsurrounding conditions

To ky o w ater f r o nt ar ea
p er so nal nav i gati o nProvide dynamic routing data basedon sudden disaster information,
driving results, and detailed
configuration of roads in order tosupport safe and secure driving ofvehiclesMeteorologicalinformation

Register user attributes

Spotinformation

Traffic
information

Propose travel plansfitting purpose andattributes

Railroad
operation
information

Plan proposal bycapturing active
informatione aim toM D c o m mu n e t m e m b e r s ( n o t e x h au s t i v e )Various companies and organizations have provided support and participate as MD communet members. We
will conduct various promotions to expand the number of companies and organizations participating in thisproject in the future.

nsuch

les andknown, MD
servicepport,
rrals.

Contac ts

SIP通し.indb 283

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Mobility Innovation Alliance Japan
BackgroundThe new association, Mobility Innovation Alliance Japan was established in July 2022 led by academia
researchers. There is a common understanding among relevant ministries, agencies and industry thatthe industry-academia-government collaboration on automated driving that was fostered during the 1st
and 2nd phases of the SIP-adus needs to continue. The establishment of a contact organization in Japanfor international collaboration and cooperation is also required.

MissionAn academia platform to identify interdisciplinary research topics for mobility innovationand initiate activities for social implementation working with industries and government
agencies;• To fully utilize and benefit from innovative mobility services
with shared data platforms.
• To increase productivity and strengthen competitive
advantages of industries, create new businesses andopportunities, and enhance well-being of the people.
• To cope with social challenges such as declining population,
demographic overconcentration, energy security, climatechange and intensifying natural disasters.

Policies
Social demands

Government
Mobility Innovation
Alliance Japan

Academia

Collaboration

Industry
International strategies
Business restructuring

Activities
Formulate cross-sectoral academic activities

Taking over the activities of the “Alliance for
Promoting Mobility Innovation” in SIP-adus

Discuss and advance proposals on cross-ministerial policies
Create collaborations over researchers, industries and public sectors
Promote international joint research through annual workshops in Japan
Launch activities to integrate technologies and societal changes
Hold competitions to foster young researchers and start ups
Mobility Innovation Alliance Japan
Date of establishment : July 1st, 2022
Address : Cw501, IIS, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505 Japan
Representative Director : Hajime Amano (Former ITS Japan President, former visiting professor of
the University of Tokyo)
Vice Representative Director : Prof. Yoshihiro Suda (Director, The University of Tokyo (UTmobI))
https://mobilityinnovationalliance.org/en

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References
About Japanese National Laws Related to Automated Driving

The Act for Partial Amendment of the Road
Traffic Act
Hisaaki Ikeuchi (National Police Agency)

1

Permission Framework for Specified
Automated Operation (SAO)

Through the 208th ordinary session of the Diet, the Act
Partially Amending the Road Traffic Act (Act No. 32 of 2022)(hereinafter referred to as the "Amending Act") was enacted,
which establishes provisions related to the permission
framework for specified automated operation (SAO), and willtake effect within one year from the date of promulgation (April
27, 2022). This paper will outline the regulations pertaining to
the framework. (Fig.1)

1.1. Definition of SAO
SAO refers to an operating on a road a motor vehicle whichis equipped with an automated operation device (AOD), whichis equivalent to SAE Level 4 (excluding cases when a person
drives), under its operating conditions.
When using an AOD equivalent to SAE Level 4 under its
operating conditions, it is possible to operate the vehicle
without violating the driver's obligations during the operation,and because the vehicle will immediately stop automatically ina safe manner when the use of that AOD has become unable tosatisfy the operating conditions, it is possible for the vehicle tooperate without a driver. Based on this, the Road Traffic Act(Law No. 105 of 1960) as amended by the Amending Act(hereinafter referred to as the "Revised Act") defines SAO as aconcept that is different from "driving" that requires a driver.
(Article 2 (1)(172) of the Revised Act)

1.2. Permission for SAO
Although it is possible for a vehicle to operate without adriver when using an AOD equivalent to SAE Level 4, if thevehicle has safely stopped due to the operating conditions not
being satisfied, and measures are not taken to respond to
instructions from police officers or approach of an emergency
vehicle, or to report to the police in the event of a traffic accident,
there is a risk of creating a hazard and obstruction to roadtraffic.

Therefore, under the Amending Act, it is stipulated that
only those who have obtained a permission from the prefectural
public safety commission can perform SAO. (Article 75-12 (1)of the Revised Act)1.2.1. Application for Permission of SAO
A person seeking to conduct a SAO must obtain permission
by submitting a written application which includes a plan for
SAO describing the implementation methods for fulfilling theobligations described below that apply to a SAO permission
holder (hereinafter referred to as a "SAO implementer") to aprefectural public safety commission having jurisdiction overthe place where the SAO is to be conducted. (Article 75-12 (2)of the Revised Act)

1.2.2. Criteria for Permission of SAO
Because the permission framework for SAO is a framework
to ensure that even when a vehicle is operated without a driver,
the safety and smoothness of road traffic is ensured in the samemanner as when a driver is present, the prefectural public safetycommission will examine whether or not the plan for SAO
conforms to the following criteria in order to determine
whether or not the system and measures for fulfilling theobligations of the SAO implementer are in place. (Article 75-13(1) of the Revised Act) (Fig.1)(1) The vehicle to be used for SAO in connection with the planfor SAO (hereinafter referred to as "vehicle for SAO") shallbe capable of conducting a SAO. (Item (1))(2) The SAO conducted according to the plan for SAO satisfies
the operating conditions in connection with the AOD ofthat vehicle for SAO. (Item (2))(3) Smooth and reliable implementation of the measures thatmust be taken by the SAO implementer and the persons
who are employed for the SAO (hereinafter referred to as
the "SAO staff ") in accordance with the provisions of theAct, etc. is expected. (Item (3))(4) The SAO conducted according to the plan for SAO is not
likely to significantly interfere with other traffic. (Item (4))(5) The SAO conducted according to the plan for SAO is for the

SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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References
About Japanese National Laws Related to Automated Driving
The Act for Partial Amendment of the Road Traffic Act

purpose of transporting persons or objects, which also
contributes to the improvement of the convenience or
welfare for local residents. (Item (5))
The prefectural public safety commission shall examine
whether or not the submitted plan for SAO complies with theabove criteria, and shall grant the permission of SAO after
hearing the opinion of the Minister of Land, Infrastructure,
Transport and Tourism and the heads of the municipalities(including special wards) (Article 75-13 (2) of the Revised Act).

(b) measures to place a supervisor of SAO in that vehicle for
SAO (Item (2))(5) When conducting a SAO, the SAO implementer must
indicate on an easily visible place of that vehicle for SAO
that the SAO is running. (Article 75-20 (2) of the RevisedAct)

1.4. Responsibilities of a Supervisor of SAO
Regarding the driver’s obligations related to driving
operation that require specific response on site, it is assumed
that even an AOD with functions and capability equivalent to
SAE Level 4 cannot respond to them. Besides, the obligations
other than those for a driver related to driving operation, forexample, responding to a traffic accident, is not presumed tobe handled by the AOD.
Therefore, the Amending Act separately stipulates thefollowing obligations when conducting SAO, which areequivalent to the obligations that an AOD is assumed notcapable of responding to, and imposes them upon the supervisorof SAO (or the persons responsible for on-site measures (7)).
(1) A supervisor of SAO must monitor the state of the operationof equipment, while that vehicle for SAO is conducting the
SAO. In such a case, that supervisor of SAO must, whenfinding that that equipment is not operating in a normalmanner, take measures to halt that SAO immediately.
(Article 75-21 (1) of the Revised Act)(2) A supervisor of SAO must, when a SAO is halted on a road,
immediately check whether or not to take the measures

1.3. Obligations of SAO Implementers
The following obligations are imposed on SAO
implementers.
(1) SAOs must comply with the plan for SAO and the conditions
attached to the SAO permission. (Article 75-18 (1) of the
Revised Act)(2) The SAO implementer must provide education to the SAO
staff. (Article 75-19 (1) of the Revised Act)(3) The SAO implementer must designate a supervisor of SAO
and persons responsible for on-site measures. (Article 75-19(2) and (3) of the Revised Act)(4) 
The SAO implementer must take one of the following
measures: (Article 75-20 (1) of the Revised Act)(a) measures to equip a place with equipment capable ofconfirming the road and traffic conditions around thatvehicle for SAO and the conditions of that vehicle byimages and sounds and to place a supervisor of SAO atthat place (Item (1))

SAO permission framework
Application (SAO plan)

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6$2LPSOHPHQWHU
Permission

0D\RURIWKH
PXQLFLSDOLW\（Understanding by
local residents）

(1)
(2)

• Compliance with SAO Plan
• Education for SAO staff• Indication on the vehicle that the SAO is running, etc.

Designation

Permission criteria(Summary)Opinion hearing on item (5)<Obligation of SAO implementer>

The vehicle must be capable of SAO.
The SAO must be performed incompliance within the ODD(*).
(3) Smooth and reliable implementation is
expected for the measures which the SAO
implementer or the SAO staff must
implement under the Road Traffic Act.

* In addition, hearing the opinions of the Minister ofLand, Infrastructure, Transport and Tourism, etc.
regarding permission item (1) and (2)6$2VXSHUYLVRU

Administrative disposition

5HPRWH0RQLWRULQJ(TXLSPHQW

in the case of violation of a law or
regulations, etc.

Remote monitoring•

A SAO supervisor may be placed in the SAO vehicle
instead of the remote monitoring..

(4) The SAO conducted according to the SAO
plan is not likely to significantly interferewith other traffic.
(5) The SAO is for the purpose of transporting
people or goods and is recognized ascontributing to the convenience or
welfare of local residents

Automated operation system<Obligations of SAO supervisor>• Monitor the state of the operation of the remote monitoring• In the event of a traffic accident:• Measures to notify the fire department andto send a person responsible for on-site measures to thescene• Measures to report the date, time, etc., to a police officer.

BUS

ＯＤＤ(※

※ ODD: Operational Design Domain (driving environment conditions, conditions of use)
Specific conditions under which an automated driving system is designed to operate (the traveling route, the time of day, weather, etc.)

Fig.1: Image of the Permission Framework for SAO

286

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References
About Japanese National Laws Related to Automated Driving
The Act for Partial Amendment of the Road Traffic Act

which a supervisor of SAO must implement. (Article 75-21(2) of the Revised Act)(3) In a case where a SAO is halted, the supervisor of SAO must,when any measure or order is taken or issued to that vehicle
for SAO or to that supervisor of SAO (e.g., in a case that apolice officer is controlling the traffic with hand signals),
immediately take necessary measures to have that vehicle
for SAO run according to those measures or orders. (Article
75-22 (1) of the Revised Act)(4) In a case where a SAO is halted, the supervisor of SAO must,when emergency motor vehicles are approaching to thatvehicle for SAO or there are emergency motor vehicles inthe vicinity of it, immediately take the necessary measures
so that that vehicle for SAO does not prevent the passage of
those emergency motor vehicles. (Article 75-22 (2) of the
Revised Act)(5) In a case where a SAO is halted, the supervisor of SAO must,when that vehicle for SAO is found to have been parked
illegally, immediately take necessary measures to change
how that vehicle for SAO is parked or move that vehicle
from that place. (Article 75-22 (3) of the Revised Act)(6) In the event of a traffic accident in connection with a vehicle
for SAO (excluding when the supervisor of SAO is placed inthat vehicle), the supervisor of SAO must immediately takemeasures to notify a fire department nearest to the scene ofthat traffic accident and must send a person responsible foron-site measures to the scene of that traffic accident. In sucha case, the supervisor of SAO of that vehicle for SAO must
immediately report the date, time, etc., where the traffic
accident occurred to a police officer of the nearest police
station. (Article 75-23 (1) of the Revised Act)(7) In the case of (6), a person responsible for on-site measures
who arrives at the scene of the traffic accident must takenecessary measures to prevent road hazards at the scene of
the traffic accident. (Article 75-23 (2) of the Revised Act)(3) Provisional suspension of permission by the chief of a police
station (Article 75-28 of the Revised Act)

1.6. Towards Enforcement of the Amending Act
As mentioned at the beginning of this paper, the Amending
Act is to take effect within one year from the date ofpromulgation.
Regarding the provisions related to the permission
framework for SAO, matters such as the contents of education
for those involved in SAO, the aptitude required for those who
perform remote monitoring, and the requirements for remotemonitoring equipment are left to subordinate orders andregulations; considering the need for business operators to
prepare for applications of permission, it is necessary tocomplete the formulation of the subordinate orders andregulations as early as possible and within one year.

[Contacts]
Automated Driving Planning Office, National Police Agency of Japan, 2-1-2
Kasumigaseki, Chiyoda-ku, Tokyo 100-8974, 03-3581-0141

1.5. Administrative Disposition
Because there is a risk that a SAO may cause road hazards or
obstruct traffic if it is not performed in accordance with theapproved plan for SAO, provisions have been put in place sothat a prefectural public safety commission may conduct thefollowing administrative disposition against a SAO implementer
if the SAO implementer or SAO staff violates the Revised Act.
(1) 
Instruction to the SAO implementer to take necessary
measures for the SAO by the prefectural public safetycommission (including not conducting a SAO until
measures are taken) (Article 75-26 of the Revised Act)(2) Revocation of permission or suspension of its validity by theprefectural public safety commission (Article 75-27 of the
Revised Act)SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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References
About Japanese National Laws related to Automated Driving

The Act for Partial Amendment of
the Road Act
Road Bureau, Ministry of Land, Infrastructure, Transport and Tourism

1

Introduction

The Road Act (Act No. 180 of 1952) was enacted andenforced in 1952 as a basic law to provide for the designation
and approval of road routes, management, structure,
maintenance, cost sharing, and other matters concerning
roads in order to improve the road network.
In recent years, in order to meet the needs of increasingly
diverse and sophisticated road use, the following main
revisions were passed in May 20, 2020 in the "Act for Partial
Revision of the Road Act, etc." and thus promulgated in May
27, 2020 (Act No. 31 of 2020):-Acting as an agent for direct control of disaster restoration,etc., of locally managed roads-Establishment of dedicated stopping facilities for businessvehicles-Construction of pedestrian-centered road areas-Development of facilities to assist in automated driving-Creation of a new traffic system for special vehicles
This section introduces the revisions related to automateddriving.

2

perform automated driving will not be those that can operatesafely at any time and place (Level 5), but rather those thatcan operate safely only when used under specific driving
environment conditions (Level 3 or 4).
In addition, the driving environment conditions include
the maintenance and use of "infrastructure such as equipment
and communications installed on the road," in addition tokeeping the speed below a predetermined level, limiting thedriving range to only a predetermined route, limiting theweather and time of day, and establishing the necessarycommunication conditions.
The outline also recommends that "the necessary items forinfrastructure such as equipment and communications(including infrastructure-to-vehicel cooperative technologies)
to be installed on roads to complement the safety of automateddriving should be considered based on the driving mode,technological progress, results of FOTs (Field Operational
Tests), and opinions of users and operators." In order to put
automated driving into practical use, these infrastructures need
to be developed. (Fig.1)
Safety level tobe guaranteed

Overview of revisions↑
SafetyThe practical application of automated driving willcontribute to solving various issues related to roadtransportation, such as reducing traffic accidents involving
the elderly, easing traffic congestion, securing means oftransportation in rural areas, and eliminating driver
shortages in logistics services, etc. Therefore, the "Outline on
improvement of legal system and environment for automateddriving system" (April 30, 2008; IT Comprehensive Strategy
Office) was decided as a government policy.
For the time being, however, it is expected that thedevices that will be put to practical use in vehicles that

Ensuring safety
Fullythrough human Driving environment
automatedconditions for
operation
Driving
rules(speed,route,
time,etc.),
driving
automated driving
condition of equipment on the road, etc.
Human
Ensure safety bycognition andsettings
behaviorgyg technolo
ated drivint of automenemov(No restrictions
Impr
on humanoperation,
SafetyCharacteristics, structure,
conditions, etc.)
and equipped functions ofassurance
the vehicle, etc.
by vehicle
Safety assurance by
driving environment
applicable to ordinary
vehiclesLevel 0-2

Normal driving rules,
road maintenanceconditions, etc.

Level 3-4

Automated driving technology→

Level 5

Fig.1: Image of the step-by-step approach to the practical application of
automated driving (Partially edited from the "Outline on improvementof legal system and environment for automated driving system")

The Ministry of Land, Infrastructure, Transport andTourism (MLIT) has conducted FOTs of automated driving
services based at "Michi-no-Eki" and other locations in 18
locations across Japan since 2009. In these FOTs, it became clear

288

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﻿.References
About Japanese National Laws related to Automated Driving
The Act for Partial Amendment of the Road Act

that stable driving is difficult to achieve with vehicle technology
alone due to weather conditions such as snowfall and terrainsuch as mountainous areas. It was also confirmed that assistancefrom facilities installed on the infrastructure side, such as
magnetic markers, is effective in ensuring stable driving.
Based on the above, in order to respond to the practicalapplication of automated driving, the Road Act defines facilities
to assist the driving of automated vehicles as "supporting
infrastructure for automated driving."-Supporting infrastructure for automated driving installed by theroad administrator are added as attachments to the road.
-Relaxing the strict requirements by adding supporting infrastructure
for automated driving to the occupied property, assuming that they
are installed by someone other than the road administrator.
Based on the above, promote public-private efforts toward
the practical application of automated driving.
In addition, automated driving technology is in the processof rapid progress, and Japan aims to steadily reflect thetechnology related to infrastructure for automatic driving ininternational standards. Therefore, it is necessary to develop
supporting infrastructure for automated driving installed in aninternationally coordinated and unified manner throughout
Japan. The act includes the following provisions regarding
supporting infrastructure for automated driving installed byroad administers:-Infrastructure meets the performance standards specified by
the Ordinance of the Ministry of Land, Infrastructure,
Transport and Tourism-After installation, report to the Minister of Land, Infrastructure,
Transport and Tourism (in the case of a municipality, report tothe prefecture, which in turn reports to the Minister of Land,
Infrastructure, Transport and Tourism).
<Reference texts>○Regulations for Enforcement of the Road Act (Ministryof Construction Ordinance No. 25 of 1952) (Extract)(Criteria for the performance of supporting infrastructure
for automated driving, etc.)
1 Article 4-8-2 The performance standards for supporting
infrastructure for automated driving that are attached
to roads specified by an Ordinance of the Ministry ofLand, Infrastructure, Transport and Tourism under
Article 45-2 paragraph (1) of the Act shall be that the
supporting infrastructure for automated driving apply
to any of the following items.

equipped with automated driving systems (meaning
automated driving systems prescribed in item (xx) ofparagraph (1) of Article 41 of the Road Transport
Vehicle Act (Act No. 185 of 1951)) that travel on roads
on which supporting infrastructure for automateddriving are installed.
(ii) 
Items that emits or displays data that shows the
position of supporting infrastructure for automateddriving in order to be detected by sensors that detect
the status of driving of Automated Vehicles shallconform to the standards specified by the Minister ofLand, Infrastructure, Transport and Tourism.
The purpose of such items is to correct the position ofAutomated Vehicles that drive on roads where
supporting infrastructure for automated driving was
installed, roads that intersect with such roads, androads that connect with such roads.
(iii) Items that emit or display data related to the roadstructure, other vehicles, status of pedestrians,
presence of obstacles, and other data related to theroad to be detected by sensors that complement
sensors that detect the status of the Automated Vehicle
scope shall conform to the standards specified by the
Minister of Land, Infrastructure, Transport andTourism. The purpose of these items is to ensure thesafe passage of Automated Vehicles on roads where
supporting infrastructure for automated driving was
installed, roads that intersect with such roads, androads that connect with such roads.
2 Supporting infrastructure for automated driving shallbe such that they do not interfere with the roadway
structure or traffic.

3

Conclusion

We hope that the establishment of infrastructure to
support automated driving will promote the realization of
automated driving and contribute to safer and smoothertraffic and greater convenience on roads, and we ask for thecontinued understanding and cooperation of those involved
in road management and the public in promoting roadpolicies.

[Contacts]

ITS Policy and Program Office, Road Bureau, Ministry of Land,
Infrastructure, Transport and Tourism, 2-1-3 Kasumigaseki, Chiyoda-ku,
Tokyo 100-8918, 03-5253-8111(i) Items such as magnets or objects that emit waves to be
detected by sensors to detect the status of automatedvehicle driving shall conform to the standards
specified by the Minister of Land, Infrastructure,
Transport and Tourism. The purpose of such items is
to correct the position of an automated vehicle or
motor vehicle that operates by technology related toautomated driving (hereinafter "Automated Vehicle")

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References
About Japanese National Laws related to Automated Driving

The Act for Partial Amendment of the Road
Transport Vehicle Act
Yoshitaka Tada (Road Transport Bureau, Ministry of Land, Infrastructure, Transport and Tourism)

At the Ministry of Land, Infrastructure, Transport andTourism, and based on the Road Transport Vehicle Act, safety
is ensured in an integrated manner from the design andmanufacturing stages of vehicles through to the stage of use on
public roads. Specifically, we are involved with measures suchas formulating safety standards, type certification, servicing &maintenance, inspections, and recalls.
On the other hand, this specific law was created in 1951,
and at that time it was not considered that some non-humansystem would also operate vehicles. For this reason, the law
was revised in May 2019 to accommodate automated vehicles(Level 3 and Level 4).

1

Addition of automated driving systems to
the equipment subject to safety standards"Automated driving systems" were added to the list ofdevices subject to the safety standards, as devices necessary
for automatically operating a vehicle by means of a program.
(Fig.1) In addition, the Minister of Land, Infrastructure,
Transport and Tourism shall attach conditions under which
the automated driving system are to be used (the driving
environment conditions) to each such system.
イメージ
Image

ドラDriverイバ①認知(1) Recognition

自Automated
動運行driving
装置
systemセンサ（①）
Sensor ((1))

カメラ，レーダ，
Cameras, radars, lasers,
レーザ，スキャナ等
scanners, etc.

(2) Prediction,(3)②予測，③判断
Judgment④操作(4) Operation

Computer ((2), (3), (4))
Electronicコンピュータ（②，③，④）
control units, electrical circuits, AI programs, etc.

電子制御装置，電気回路，人工知能等のプログラム

等○Eiheiji-cho, Fukui prefecture
Automated driving system examples(overview)
1. Vehicle is on electromagnetic inductionline
2. Weather is not poor
3. Speed is 12km/h or less
4. Road is not icy
5. No emergency vehicles present

Fig.1: Definition of an automated driving system

Article 41
A motor vehicle shall not be provided for use inoperation unless it conforms to the technical standards
for safety, pollution prevention and other environmental
preservation specified by Ministry of Land, Infrastructure,
Transport and Tourism ordinances with regard to thefollowing devices.
20 Automated driving system
2 "Automated driving system" in item (xx) of the preceding
paragraph refers to a device in which the main components
are sensors that detect the conditions of the vehicle and thesurrounding conditions during operation, and thecomputer and program (commands to a computer(including an input or output device; the same shall apply
hereinafter except for this paragraph and Article 99-3(1)(i)) which are combined to obtain a single result;the same shall apply hereinafter) for processing theinformation sent by the sensors which are necessary forthe automated operation of the vehicle by the program,and has functions that replace all of the abilities related to
prediction, judgment, and operation related to operation
by a person who operates a vehicle when used under theconditions imposed by the Minister of Land, Infrastructure,
Transport and Tourism for each device, and is equippedwith a device to record the information necessary toconfirm the operating state of the relevant functions.
Road Transport Vehicle Safety Standards (Ministry of○Transport Ordinance No. 67 of 1951) (Extract)

Driving environment conditions allowance procedures(1) Applicants are to submit an application that includes
conditions in which automated driving is possible, such asplace, weather, speed, etc. to the Ministry of Land,
Infrastructure, Transport and Tourism(2) Ministry of Land, Infrastructure, Transport and Tourism
grants allowance certificate for these conditions when it
recognizes that automated driving system performance inthese conditions meets security standards.

○ Road Transport Vehicle Act (Act No. 185 of 1951) (Extract)

Article 48
A motor vehicle (excluding motorcycles, motorcycles
with a sidecar, three-wheeled motorcycles, light vehicles
with caterpillars or sleds, large-sized special motor
vehicles, small-sized special motor vehicles, and towed
motor vehicles) may be equipped with an automateddriving system.
2 A motor vehicle equipped with an automated driving
system shall conform to the standards specified inpublic notifications regarding function, performance,
etc., as a vehicle capable of ensuring the safety of
automated operation of the vehicle via a program.

290

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References
About Japanese National Laws related to Automated Driving
The Act for Partial Amendment of the Road Transport Vehicle Act

3An automated driving system whose type is designatedpursuant to the provisions of Article 75-3 (1) of the Act
shall make a vehicle equipped with said system conformto the standards set forth in the preceding paragraph.

2

Ensure system that manages
cybersecurity and software
updates

Conduct risk assessments andrisk response andmanagementConduct testing toverify cybersecurity measures

Summary of the Public Notification
Concerning Automated Driving Systems

The following is a summary of the standards set forth in the
Public Notification Concerning Automated Driving Systems.
(1) There is no risk of interfering with the safety of passengers
and other traffic under the driving environment conditions(2) It does not operate outside of the driving environment
conditions(3) An alarm for taking over the driving operation is issued
before the driving environment conditions are left, safe
operation is continued until the driver takes over, and if
the driver does not take over, the vehicle is safely stopped(4) Driver monitoring is installed to monitor the driver's
condition(5) Measures are taken to ensure cybersecurity to prevent
unauthorized access, etc.

3

including ensuring cybersecurity, is required. (Fig.3)

Establishment of a permit system related tomodifications involving alterations to the programs
incorporated in an automated driving system, etc.

Appropriately
implement cyberattack
countermeasures

Appropriately implement
software updates from the
manufacturer

Fig.3: Overview of Cyber security

4

Expansion of the Scope of Disassemblyand Maintenance

It was necessary to expand the scope of "disassembly andmaintenance" because it did not include maintenance andmodifications related to advanced technologies, which meant
there was a risk that safety may not be ensured.
The definition of "disassembly and maintenance" that
requires certification was expanded by adding "automateddriving systems" and also maintenance and modifications
that may affect the operation of such systems, and the namewas changed to "specified maintenance". (Fig.4) In addition,
there is an obligation for vehicle manufacturers, etc. toprovide the model-specific technical information necessary
for inspections and maintenance to the service providers
who perform specified maintenance.
Equipment
Motor
Power

Maintenance and modifications
which affect operation but do
not involve removal

Maintenance andmodifications that involve
removalConventional "disassembly andmaintenance"

Expansion ofdefinition

Running gear
Control unit
Braking device
Shock absorber
Coupling device
Automated
driving system
Lighting
devices, etc.

Term changed to"Specified maintenance"
Expansion

Expansion(e.g.)
Adjustment of cameras
or radarAddition of covered equipment
Not subject to "disassembly and maintenance"

The vehicle manufacturer, etc. is obligated to provide certified
maintenance service providers, etc. with technical informationnecessary for inspection and maintenance of advanced technologies.

Certification by the Director of the District Unnecessary
Transport Bureau, etc. is required

With the advancement of vehicle technology, it has
become possible for vehicle manufacturers, etc. to alter the
programs incorporated in the electronic control devices ofvehicles and change their performance and add (modify)functions easily and on a large scale while the vehicle is being
used by utilizing communications.
For this reason, a person who intends to make amodification by altering a program incorporated in an
automated driving system, etc. through the use of
telecommunication lines, etc., which may cause the vehicle
to fail to conform to safety standards (a Specified ModificationEtc.) (Fig.2) must obtain permission from the Minister ofLand, Infrastructure, Transport and Tourism in advance.
Since the Road Transport Vehicle Act until now did notenvision the large-scale electronic modification of vehicles using
communications, conformance to the standards specified in the
public notification regarding functionality and performance,

Vehiclemanufacturer

Fig.4: Image of specified maintenance[Contacts]

Vehiclemanufacturer, etc.

Ministry of Land, Infrastructure, Transport and Tourism Road Transport

Telecommunication Line

Bureau Engineering and Environmental Policy Division, 2-1-3
Kasumigaseki, Chiyoda-ku, Tokyo 100-8918, 03-5253-8111

Modification
Fig.2: Image of a Specified Modification Etc.

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Projects List and Publication Status of the Second Phase of SIP Automated Driving for Universal Services
The following is a list of all Second Phase of SIP Automated Driving R&D projects. This also shows whether projects havebeen published in previous reports (Mid-term results report1) and final reports (Final results report2), and page numbers ofeach. " ⃝ " indicates that the project is included in the results report, " ー " indicates that the project is not included in the
results report, and " ＊ " indicates the project of producting and publishing the results report.
For more detailed information on each project, please refer to the "Report of Results for Each project" described in Section 7of this report.
Project No.

[I]

Project title

Project period(As of December 1st, 2022)

Mid-term results reports

Final results reports

Development and validation (FOTs) of automated driving systems

F001

Research on effectiveness and impact on traffic flow for field operational tests in Tokyo
waterfront area in 20202018.07.19~

2019.03.20

F003

Review and evaluation of planning regarding 2020 the field operational tests in Tokyo
waterfront area in 20202018.11.01~

2019.06.28

F005

Infrastructure preparation, preliminary research, and maintenance and management for
FOTs

2019.03.08~

2022.05.31

F006

Implementation of FOTs in the Tokyo waterfront area

2019.04.12~

2023.02.28

F009

Constructing the field operational tests environment to provide signal information via the
public network

2021.08.05~

2023.02.28

F002

Field operational tests for automated driving services in rural areas aiming for the
improvement of the social environment

2018.10.12~

2019.08.30

F007

Improvement of the social environment for practical implementation and horizontal
deployment of automated driving services

2019.09.09~

2020.12.28

F008

Research on the social implementation and permanent adoption of automated driving
transport service in rural areas

2020.10.22~

2023.02.28

F004

Research on environmental improvement for practical use of transport services byautomated driving in the new town area

2018.12.14~

2019.04.30[II]

Availability and page numbers○

Pages 53 - 78
[Section 3(1)]○Pages 41 - 46
[Section 2 (1) 1)]○

Pages 111 - 116[Section 4 (1)]○Pages 154 - 161[Section 4 (1) 1)]ー○

Pages 260 - 263[Section 7]

Pages 38 - 41
Pages 192 - 194[Section 2 (2)][Section 7]○

Pages 85 - 93
[Section 2 (2) 1)]ー○

Pages 94 - 98
[Section 2 (2) 2)]○

Pages 42 - 48
[Section 2(2)]○Pages 99 - 103[Section 2 (2) 3)]○

Pages 15 - 22
[Section 2 (1)]○Pages 15 - 22
[Section 2 (1)]○Pages 47 - 53
[Section 2 (1) 2)]ー○

Pages 260 - 263[Section 7]ー○Pages 73 - 79
[Section 2 (1) 6)]

ー○

Pages 67 - 72
[Section 2 (1) 5)]

Development of core technologies for the practical implementation of automated driving

R001

Study of utilization of new communication technologies including V2X technology toautomated driving system

2018.09.13~

2019.02.28

R016

Study of communication technologies for use by automated driving systems

2019.09.20~

2020.03.19

R027

Study on V2X communication for achieving use cases of cooperative driving automation

2020.11.25~

2022.02.28

R028

Study on V2X communication for achieving use cases of cooperative driving automation:
evaluation of 700 MHz band ITS

2021.04.27~

2022.02.28

R033

Study on V2X communication protocol in 5.9 GHz band to realize the coorperative driving
automation use cases

2022.04.07~

2023.02.28

R017

Research and development on the collection, integration, and delivery of short-range andmedium-range information

2019.08.19~

2021.05.31

R002

Research on the enhancement of technologies to provide traffic signal information toward
the realization of automated driving2018.09.20~

2019.03.20

R009

Research on the enhancement of technologies to provide traffic light information toward therealization of automated vehicles

2019.05.28~

2020.10.30

R003

Research on the provision of traffic signal information through means other than vehicle-toinfrastructure communication via ITS roadside radio units and others

2018.09.20~

2019.03.20

R010

Research and development on the provision of SPaT information through methods otherthan vehicle-to-infrastructure communication via ITS roadside radio units and others

2019.05.22~

2020.02.28

R021

Research and development on the provision of signal phase and timing (SPaT) information
using cloud and other technologies

2020.06.22~

2021.02.26

R029

Research and development on the provision of signal phase and timing (SPaT) information
using cloud and other technologies toward social implementation

2021.07.07~

2022.03.31

R004

Research on measures for the utilization of V2X information for ensuring traffic safety
under mixed traffic conditions

2018.09.13~

2019.03.20

R012

Research on utilization method of V2X information for ensuring traffic safety undercoexisting traffic -simulation of evaluation of impact of automated vehicles on traffic Flow-2019.05.28~

2020.02.28

R023

Research and development on traffic signal control using GNSS (location information) andother technologies

2020.07.02~

2022.02.28

R025

Improvement of data accuracy of traffic regulation information

2020.09.16~

2021.03.12

R030

Research on model system for improvement of data accuracy of traffic regulation
information

2021.07.06~

2023.02.28

R005

Approach development for improving an automated driving validation environment in
virtual space2018.12.14~

2023.03.31○

Pages 89 - 94
[Section 3 (2)]○Pages 108 - 120[Section 3 1)]

R006

Research on the recognition technology required for automated driving technology (levels 3
and 4)

2018.12.06~

2023.02.28○

Pages 79 - 85
[Section 3(1)]○Pages 121 - 129[Section 3 2)]

R008

Update of high-precision three-dimensional map with vehicle probe data

2019.02.15~

2021.02.26○

Pages 30 - 37
[Section 2 (1)]R013

Operations of planning and deliberation council for the creation of systems for generating
and providing Lane-level road traffic information using probe information

2019.04.22~

2023.02.28○

Pages 23 - 29
[Section 2 (1)]○Pages 54 - 60
[Section 2 (1) 3]

R020

Technical study and evaluation of automated driving control using lane-level probes or thelike2020.01.16~

2023.02.28

ー○Pages 218 - 221[Section 5 4)]

○

ー

ー

1 SIP 2nd Phase: Automated Driving for Universal Services—Mid-Term Results Report (2018–2020) (ISBN: 9784904056899) https://en.sip-adus.go.jp/rd/rd_page03.php
https://en.sip-adus.go.jp/rd/rd_page04.php
2 SIP 2nd phase: Automated Driving for Universal Services—Final Results Report (2018-2022) (ISBN: 9784904056936)

292

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Project No.

[II]

Project period(As of December 1st, 2022)

Project title

Availability and page numbers
Mid-term results reports

Final results reports

Development of core technologies for the practical implementation of automated driving (continued from previous page)

R011

Research on application and construction of a transport environment information platform
for tourist attractive city

2019.04.25~

2019.08.30

R018-aResearch for design and development of automated driving and drive assistancearchitectures - to build a geographic data communication portal site and geographic data
for FOTs in the Tokyo waterfront area and urban areas for cooperative mobility and logistics
services2019.08.30~

2023.03.31

R018-bResearch for design and development of automated driving and drive assistancearchitectures - to clarify requirements to construc an operation system for local automateddriving service

2019.08.30

~

R026

Establishing an environment that promotes the utilization of mobility-related data

2020.09.30

R014

Research on new types of cyber-attacks and countermeasures against them

R024

○

Pages 156 - 158[Section 5 (1)]○Pages 207 - 210[Section 5 2)]○

Pages 150 - 155[Section 5 (1)]○Pages 199 - 206[Section 5 1)]

2021.05.31○

Pages 117 - 121[Section4 (1)]○Pages 154 - 161[Section 4 (1) 1)]~

2021.03.12○

Pages 159 - 161[Section 5 (1)]2019.07.16~

2020.03.19○

2020.08.07~

2023.02.28

Pages 95 - 98
[Section 3 (2)]○Research and study on new types of cyber-attacks and countermeasures against themPages 130 - 134[Section 3 3)]

R015

Research and study on HMI and safety education methods for advanced automated driving2019.08.19~

2022.08.31○

Pages 99 - 106[Section 3 (2)]○Pages 135 - 149[Section 3 4) - 6)]

R019

Research and study of common reference point (CRP) in high definition map

2019.08.19~

2021.02.28○

Pages 192 - 194[Section 7]

R022

Study and field operational tests for improving logistics efficiency based on architecture
utilizing vehicle information such as probes

2020.07.02~

2021.04.30

R032

Field operational tests and evaluation for improving logistics efficiency based onarchitecture utilizing vehicle information such as probes

2021.12.03~

2022.12.31

R031

Simulation analysis of verification of merging assistance system

2021.09.15~

2023.02.28

[III]

ー

ー

○

Pages 211 - 217[Section 5 3)]ー○Pages 61 - 66
[Section 2 (1) 4)]

Fostering of public acceptance of automated driving○

Pages 130 - 135[Section 4 (2)]○Pages 136 - 141[Section 4 (2)]○Pages 173 - 179[Section 4 (2) 2)]○

Pages 192 - 194[Section 7]○

Pages 180 - 188[Section 4 (2) 3)]

2023.02.28○

Pages 124 - 129[Section 4 (2)]○Pages 166 - 172[Section 4 (2) 1)]

2021.03.01○

Pages 142 - 147[Section 4 (2)]

ー

ー

○

Pages 260 - 263[Section 7]○

Pages 192 - 194[Section 7]○

Pages 189 - 194[Section 4 (2) 4)]○

Pages 165 - 167[Section 6 (1)]○Pages 227 - 229[Section 6 1)]○

Pages 168 - 170[Section 6 (1)]○Pages 230 - 233[Section 6 2)]ー○Pages 260 - 263[Section 7]

P001

Study on advanced driver assistance system for drivers with visual field defects

2018.12.25~

2021.02.28

P002

Study of socioeconomic impacts of automated driving including traffic accident reduction

2018.12.28~

2021.05.31

P010

Research on assessment of the impact of automated driving on society and the economy andon measures to promote deployment

2021.07.02~

2023.02.28

P003

Survey to foster public acceptance of automated driving technology

2019.04.04~

2020.03.19

P004-aStrategic planning for fostering social acceptance and survey regarding its evaluation - to create
overall strategies and to plan and operate information dissemination through web-based media

2019.09.13~

2023.03.31

P009

Research to measure effect of efforts for fostering social acceptance through events

2020.09.09~

2023.03.31

P004-bStrategic planning for fostering social acceptance and survey regarding its evaluation evaluation of efforts to foster social acceptance2019.09.13

~

P005

Visualize the effects of reducing traffic accidents through automated driving and driving
assistance2019.10.08

~

P006

Research for introducing precise docking technology by automatic driving to BRT (busrapid transit)

2020.02.28~

2021.12.28

P008

Fundamental research for automated buses friendly to mobility-constrained people

2020.06.02~

2021.09.30

[IV]

ー

ー

Enhancement of international cooperation

G001

Study on the strengthening of information dissemination capabilities in preparation for therealization of automated driving systems

2018.07.20~

2019.03.20

G003

Research on the strengthening of information dissemination capabilities in preparation for
the realization of automated driving systems

2019.04.05~

2021.05.31

G007

Research of trend in order to strengthen information dissemination capabilities in
preparation for the realization of automated driving systems

2021.05.24~

2023.03.31

G002

Basic study in promotion of joint research on automated driving with overseas research
institutions

2018.08.24~

2019.02.28

G004

Construction of collaboration structure in promotion of joint research on automateddriving with overseas research institutions

2019.05.27~

2023.02.28

G005

Study of overseas trends, etc., in preparation for international collaboration regarding traffic
environment information

2019.07.04~

2023.02.28

G006

Study on intellectual property management strategy

2020.05.28~

2021.03.12

G008

Review and publication of mid-term results report to effectively promote R&D results

2021.04.19~

2022.02.28

G009

Effective compilation and dissemination of R&D results

2022.05.11~

2023.03.31○

Pages 192 - 194[Section 7]ー＊

Mid-term results
reports
preparation services

ー

ー

＊

Final results reports
preparation services

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Correspondence Chart for the Titles Listed in This Report and Each
Project (Reverse Lookup of the List of Projects)
The correspondence between the contents of this report and the related R&D project numbers is shown below. For the
project title corresponding to the project number, please refer to the second phase of SIP-adus project list and reportpublication status in this report.
SIP final result report chapter title

Corresponding project number

Section 1: The Second Phase of SIP-Automated Driving for Universal Services
Section 2: Establishment and Utilization of Traffic Environment Data
(1) Technological Development Concerning the Generation of Traffic Environment Data
1) The Tokyo Waterfront City Area Field Operational Tests

F001, F003, F005, F006, F009

2) T echnological Development to Provide Traffic Signal Information to Automated Vehicles Connected toR003, R010, R021, R029
Infrastructures (V2N)
3) Technological Development for Lane-specific Road Traffic Information Using Vehicle Probes

R013, R020

4) Technological Development and Establishment of Simulation Environment for Lane Merging Support

R031

5) Improvement of Data Accuracy of Traffic Regulation Information

R025, R030

6) T echnological Development for Traffic Signs Control and Emergency Vehicles Information Using GNSS
R023
(Location Information) and Other Technologies(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use Cases

R001, R016, R027, R028

2) Development of New Technologies, V2X and Others, for Communication

R033

3) R
 esearch and Development Concerning the Collection and Transmission of Medium Range
Information

R017

Section 3: Ensuring the Safety of Automated Driving
1) Development of Driving Intelligence Validation Platform (DIVP®) for Automated Driving Safety Assurance

R0052) Research on the Recognition Technology Required for Automated Driving Technology (Levels 3 and 4)

R0063) Research of New Cyberattack Techniques and Countermeasure Technologies

R014, R024

4) Research of Education Methods for Advanced Automated Driving Systems

R015

5) R
 esearch on Communication between Low-Speed Automated Transportation and Logistics Services Vehicles
and Surrounding Traffic Participants

R0156) Research of HMI for Advanced Automated Driving Systems

R015

Section 4: A Society with Automated Driving(1) Automated Driving Mobility Services in Regional Communities
 stablishing the Environment for the Deployment of Transportation Services Relying on Automated1) E
Driving

F002, F007, F008, R018-b(2) Public Acceptance of Automated Driving
1) Research and Evaluations for Fostering Public AcceptanceP004-b evelopment of Assessment Methodology for Socioeconomic Impacts of Automated Driving Including
2) D
P002, P010
Traffic Accident Reduction
3) Projects to Foster Public Acceptance

P003, P004-a, P009

4) R esearch for Automated Driving Bus Friendly to Persons with Disabilities or Reduced Mobility andOrientation

P008

Section 5: Data Connection and Use to Achieve Society 5.0
1) Design of Geographical Data Architecture — Building and Promoting a Traffic Environment Data Portal Site

R018-a2) Resolving Social Issues in Cities Popular with Tourists

R011, R018-a3) Research to Realize More Effective Logistics System with Probe Vehicle Data

R022, R032

4) Utilization and Application of Probe Data to Road Maintenance and Management

R020

Section 6: Promoting International Cooperation
1) SIP-adus Workshop

G001, G003, G007

2) Japanese-German and Japanese-European Cooperation

G002, G004

3) Dynamic Maps
4) Human Factors5) Safety Assurance6) Connected Vehicles
7) Cybersecurity8) Socioeconomic Impacts
9) Service and Business Implementation
Section 7: Other Achievements and Activities

F004, R004, R012, P006, G005

Section 8: Conclusion and Outcomes through SIP-adus to be Inherited

294

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SIP 2nd Phase:
Automated Driving for Universal Services- Final Results Report (2018-2022)
Plannnig

Cabinet Office, Goverment of Japan

Publisher	New Energy and Industrial Technology Development
Organization
Editor		

SIP-adus Final Results Report Editional Committee		Society of Automotive Engineers of Japan, Inc

Published

March 6, 2023

©2023 New Energy and Industrial Technology Development Organization, Printed in Japan(All rights reserved). Unauthorized reproduction of all or part of this report is prohibited.
ISBN 978-4-904056-93-6

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SIP 2nd Phase : Automated Driving for Universal Service

SIP 2nd Phase :Automated Driving for Universal Services
Final Results Report (2018–2022)

Final Results Report (2018–2022)