SIP-adus: Project Reports, 2014-2018- Automated Driving for Universal Services -2018

Looking Back on Five Years of
SIP-adus
Seigo Kuzumaki
SIP-adus Program Director(Fellow, Advanced R&D and Engineering Company, Toyota Motor Corporation)

There are many issues to solve for the realization of automated driving systems(ADS). Therefore, it was fortunate for us that ADS was picked up in the Cross-ministerial Strategic Innovation Promotion Program (SIP), which aims for industry-government-academia collaboration.
In the beginning, there was a big gap in understanding about ADS among the people
involved, and each OEM has a different stance on ADS development. Through frequent and tense discussions in SIP, a consensus gradually grew to finally reach theconclusion that ADS development needs a “competition and cooperation” strategy.
Two decades of intelligent transport systems history (ITS) development have helped
us collaborate with NPA, MIAC, METI and MLIT. The Cabinet Office has been working with the Cabinet Secretariat to make a roadmap of ADS and ITS. These relationships have made cross-ministerial collaboration smooth.
We have been proactive at presenting our activities at the international congresses.
Ultimately, we made our presence felt with respect to de jure and de facto standardization activities in the areas of dynamic mapping and HMI. Large-scale field operational
tests began in Japan in October 2017. We recruited more than 20 participants fromindustry and academia, not only in Japan but also in Europe. This created an opportunity for open discussion to enhance international cooperation and harmonization.
As a result of our SIP-adus activities, Dynamic Map Platform co Ltd was establishedin 2017. We take pride in having produced such outcome, which is not an end, but a
beginning. We want to keep striving for the realization of Society 5.0 by pursuing the
expansion of big data uses, such as maps and geographical information.
Lastly, I want to convey special thanks to all the members involved in SIP for theirsupport. I would also like to dedicate this report to former Program Director, Dr.
Watanabe, who passed away in the midst of his efforts to make the program a success.

Toward the Realization of
Mobility in Society 5.0
Ryo Kuroda(Deputy Director General for Science, Technology and Innovation, Cabinet Office)The novel Society 5.0 concept was proposed as the ideal Japan should pursue in the
5 Science and Technology Basic Plan formulated by the Council for Science, Technolthogy and Innovation (CSTI) in 2016. Society 5.0 is the human-centered society thatsimultaneously realizes solutions to social problems and economic growth by means of
the integrated ICT technologies in cyberspace and in physical space. CSTI established
the Cross-ministerial Strategic Innovation Promotion Program (SIP) for Society 5.0.
The feature of SIP is to carry out activities ranging from basic research to practicalapplication and commercialization with the cooperation of cross-ministerial government agencies, academia and industry.
SIP-adus (Automated Driving for Universal Services) is one of the eleven SIP projects. It aims to reduce traffic accidents and provide mobility as a necessary service bygathering the information from vehicles in physical space, analyzing it in cyberspace,and feeding the analyzed results back to the vehicles for safe and comfortable control.
SIP-adus precisely embodies Society 5.0 in the mobility domain. Under the leadershipof Program Director Seigo Kuzumaki, members of SIP-adus from industry, academia,
and government have collaborated with each other and solved many of the challenges
presented by advanced automated driving systems. We hope the fruitful outcomesdeveloped in these five-year of the SIP-adus project will be widely shared and utilized
as firm foundation for the future stages.

Contents
Introduction• Looking Back on Five Years of SIP-adus• Toward the Realization of Mobility in Society 5.0

1. Overview• SIP Automated Driving System Outline 

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2. Results of Research and Development[I] Development of Automated Driving Systems① Dynamic Maps
• Overview of SIP Dynamic Map Research and Development for Automated Driving • Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, andUpdating Methods • Strategic Innovation Promotion Program (SIP) Automated Driving Systems/Large-scale Field Operational Test/Dynamic
Map/Utilization of Vehicle Probe Information • A Study of Specifications for Providing Lane-Level Traffic Information in Dynamic Maps • Change Detection and Automated Mapping for Dynamic Maps • Feasibility Study for Multipurpose Use of Dynamic Maps • Study on Considerations for the Making of a Business Model for the Dynamic Map Service Platform • Investigation about Availability of Satellite Positioning to Realize Automated Driving Systems • Report on model Verification for the Enhancement of Driving Support Utilizing Traffic Signal Information (SIP NPA 1) 
• METI 3: Development and Demonstration of Technology for Real-time Utilization of Traffic Signal Information • Construction of Traffic Regulation Information Management System for Realization of Automated Driving • Development of V2V and V2I Communication Technology for Automated Driving Systems • Research into Practical Application of Safe Driving Assistance System Utilizing Vehicle to Vehicle (V2V) Communication
and Research into Required Conditions of Vehicle to Pedestrian (V2P) Communication ② Human Machine Interface (HMI)• Outline of Human Machine Interface (HMI) • Human Factors and HMI • Basic Research into Requirements of HMI to Ensure Safety for Automated Driving Systems ③ Information Security• Outline of Cybersecurity • Formulation of a Comprehensive Threat Model for Automated Driving Systems Including External Vehicular Attacks
such as V2X and the Establishment of an Attack Evaluation Method through Telecommunication • Research and Development Project for Automobile Security for Utilization of Information Obtained by Communicationsuch as V2X 

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[II] Development of Basic Technologies to Reduce Traffic Accident Fatalities andCongestion, and Initiatives to Raise Social Acceptance• Development of Core Technology for Reducing Traffic Accident Fatalities and Traffic Congestion, and Creating Social
Acceptance of Automated Driving ① Initiatives to Reduce Pedestrian Accidents• Pedestrian Safety Support Using V2P Communication Technology ② Development of Basic Technologies to Reduce Traffic Accident Fatalities• Research and Surveys for Enhancing Driving Safety Support Systems (DSSS) Utilizing Radio Waves, and Research andSurveys for Establishing Technology to Provide Vehicle/Pedestrian Detection Information Toward the Realization ofAutomated Driving • Development of Practical Roadside Sensor Utilizing Millimeter-Wave Radar Technology 

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③ Estimating the Effectiveness of Measures to Reduce Traffic Accident Fatalities• Study of Japanese Traffic Accident Patterns for Estimating Effectiveness of ADAS Based on ITARDA Macro Database 107④ Analysis of Nationwide and In-Depth Accident Data, and Development of Simulation Technology• Development and Substantiation of Simulation Technology for Estimation of Detailed Traffic Accident Reduction Effects
110• Development and Evaluation of Technologies for Constructing Driving Video Database 114⑤ Visualization of Local Traffic CO2 Emissions• Development of Tool for Assessing Impact of Automated Driving Systems on Traffic Flow and CO2 Emissions 118⑥ Analysis of Social and Industrial Aspects of Automated Driving• Analysis of Social and Industrial Aspects of Developing and Popularizing more Advanced Automated Driving Systems  122⑦ Initiatives to Raise Social Acceptance of Automated Driving Systems• Future Needs of Automated Driving Systems, and Analysis and Research of Issues Arising out of such Needs 125[III] Next-Generation Urban Transportation• Next Generation Transport 130① Development of Next-Generation Urban Transportation Systems• Survey about Feasibility of Requirements for Next-Generation Urban Transportation System 132• Development of Sensing and Control Technology for Precise Docking of Advanced Rapid Transit system 135• Survey about Precise Docking of Next-Generation Transportation Systems for Strategic Innovation Program (SIP) –Automated Driving System 140② Sophisticated Local Transportation Management• Examination of Enhancement of Bus Priority Control in Next-Generation Urban Transport Systems 
144• A Study on the Speediness and Safety of Advanced Rapid Transit 147• Development of ART Information Center Function 
152• Investigation of Methods for Forecasting Traffic Congestion and Guiding Congestion Avoidance 157③ Improvement and Popularization of Accessible Transportation (Initiatives for People with Restricted Access to Transportation)• Development of Movement Support System for People with Mobility Constraints 160• Research into Common Pedestrian Support System Platform 163[IV] Field Operational Tests• Outline of Large-scale Field Operational Test ① Large Scale Field Operational Tests• Dynamic Maps • HMI • Information Security • Pedestrian Collision Reduction • Advanced Rapid Transit ② Regional Tests• Feasibility Study of Advanced Automated Driving System for Public Buses in Okinawa • Field Operational Tests for Automated Driving Systems in Rural Areas 

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[V] International Collaboration• Overview of International Cooperation Activities by SIP-adus ① Leading International Debate on the Six Key International Collaborative Themes• Dynamic Maps • Connected Vehicles • Human Factors • Information Security • Impact Assessment 
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• International Cooperation Activities for Next-Generation Transport ② Promotion of International Standardization• Activities toward International Standardization of Dynamic Maps • General Survey of International Trends Regarding International Cooperation with Dynamic Maps • International Standardization for Human Factor Issues in Automated Driving Systems 

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Author list
Introduction
Seigo Kuzumaki

Fellow, Advanced R&D and Engineering Company, Toyota Motor Corporation

Ryo KurodaDeputy Director General for Science,Technology and Innovation, Cabinet Office

1. Overview

Noriaki Minamida Panasonic System Networks R&D Lab. Co., Ltd.
Ken Kumagai
NTT DOCOMO, INC.
Tatsuhiko HirabayashiKDDI Research, Inc.
UTMS Society of Japan② Masafumi KobayashiYoichi Nakagawa
Panasonic Corporation③ Toru Kiuchi, Satoko ItoInstitute for Traffic Accident Research and Data Analysis

2. Results of Research and Development

Japan Automobile Research Institute[I]Pacific Consultants Co., Ltd.

Yasuyuki Koga

Cabinet OfficeNissan Motor Co., Ltd① Masao Fukushima
Norifumi Ogawa
Mazda Motor Corporation
Yoshiaki TsudaMitsubishi Electric Corporation
Naoto Sato, Namie Toyoda
Aisan Technology Co., Ltd.
Hideto Yonezawa, Junya OishiIncrement P Corporation
Tomohiko Masutani, Kiyohiro Yamauchi, Akane TanakaZenrin Co., Ltd.

Shintaro Osaki
Toyota Mapmaster Incorporated
Tomohide Kitagawa
Pasco Corporation
Eiji Muramatsu et al.
Pioneer Corporation
Koichi Miyashita, Suguru Kawajiri, Shintaro Shimizu
Mitsubishi Research Institute, Inc.

Yasunari Goto, Takuma Kadoya

Mitsubishi Electric Corporation

Naoyuki TsuchidaDynamic Map Platform Co., Ltd
Isao Yagasaki
Fujitsu Limited
Akio Kani
Hitachi, Ltd.
Yukikazu KanoyaMitsubishi Electric Corporation
Akinori SatoNEC Corporation
Naoki IsoNTT DATA Corporation
Masaharu Hamaguchi
Oki Electric Industry Co., Ltd.
Takeharu Arakawa
Pioneer Corporation
Mikihiro Hosoi
Aisan Technology Co., Ltd.
Yuichi Takayanagi
UTMS Society of Japan
Masao Fukushima
UTMS Society of Japan
Masayuki Jinno
Sumitomo Electric System Solutions Co., Ltd.
Manabu Sawada
DENSO CORPORATION
Yoshinori HatayamaPanasonic Corporation
Tetsuya Takahashi
Pioneer Corporation
Yasushi Yamao
The University of Electro-Communications
Toshiya Hirose
Shibaura Institute of Technology
Toru Kojima, Ichiro Sakamoto, Toshihiro TakeuchiJapan Automobile Research Institute④ Sou Kitajima
Kazunori Nomoto, Hiroshi Tanigawa, Akito Adachi⑤ Daisuke Oshima, Takashi Kurisu⑥ Yoshihiro Suda, Takashi Oguchi, Koichi Sakai⑦ Akiko Narita

[III]
Masayuki Kawamoto
University of Tsukuba
Advanced Smart Mobility Co., Ltd.
① Tatsuya Hashimoto
Sadahiro Kawahara
JTEKT Corporation
Ryohta Wakai
The Institute of Behavioral Sciences
UTMS Society of Japan② Toru MabuchiDaisuke Oshima, Akira Mitsuyasu, Takane Imagawa


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





Honda R&D Co., Ltd.

University of Tsukuba

National Institute of Advanced Industrial Science and Technology

Tatsuru Daimon
Toru Kojima

Keio University

National Agency for Automobile and Land Transport Technology,National Traffic Safety and Environment Laboratory

President, Toyota InfoTechnology Center Co., Ltd.
③ Takashi ImaiKen Okuyama
PwC Consulting LLC
Atsushi Ohba, Hiroshi Ito Japan Automobile Research Institute[II]
Hiroyuki Kanemitsu
① Yoshiyuki Okubo
IVToyota Motor Corporation
Panasonic Corporation

Satoshi Watanabe, Satoshi Hashimoto

Navitime Japan Co., Ltd.
Zenrin Co., Ltd.

Masato Minakata
Toyota Motor Corporation① Yoshiaki Tsuda, Yukishiro Saito, Yukio Takahashi

National Institute of Advanced Industrial Science and Technology

Makoto Itoh
Toshihisa Sato

Hitachi, Ltd.

Hideki Yamazaki, Hikari Nakazawa, Yayoi Horie[IV]

National Traffic Safety and Environment Laboratory

Pacific Consultants Co., Ltd.

Chiemi Tsuchiya, Kojin Yano, Miyu Tsukamoto,
Junko Ushiyama
Hitachi, Ltd.
Tsutomu Yabe, Go Hagihara, Noriyuki Izumi,
Yuichi Mohri
Institute of Behavioral Sciences
Yoshihiko Watanabe, Jun Yamazaki, Shunsuke Minami,
Rieko Otsuka
Hitachi, Ltd.
UTMS Society of Japan③ Yukiko Hatazaki
Junko Ushiyama, Masao Yamamoto, Haru Ando

National Agency for Automobile and Land Transport Technology,② Kiyozumi Unoura
Satoshi Kitazaki

The University of Tokyo
SC-ABeam Automotive Consulting

Mitsubishi Electric Corporation

Shinya Muroyama, Kazuki Hirao Aisan Technology Co., Ltd.
Sosuke Hirano, Junichi Kamichika Increment P Corporation
Kiyohiro Yamauchi, Yumi TakehanaZenrin Co., Ltd.
Shintaro Osaki
Toyota Map Master Incorporated
Ryota Kataoka, Miho Shikata
Pasco Corporation
Keita Hosaka
Tokyoto Business Service Co., Ltd.
Makoto Itoh
University of Tsukuba
Toshihisa Sato
National Institute of Advanced Industrial Science and Technology

Satoshi Kitazaki

National Institute of Advanced Industrial Science and Technology

Ken Okuyama
Kosuke WATABEPwC Consulting LLC
Nippon Koei Co., Ltd.

Daisuke Oshima, Akira Mitsuyasu, Takane Imagawa

Pacific Consultants Co., Ltd.

Junko Ushiyama, Chiemi Tsuchiya, Masao Yamamoto

Hitachi, Ltd.

Advanced Smart Mobility Co., Ltd.
② Takayuki AndoMinistry of Land, Infrastructure, Transport and Tourism
Road Bureau ITS Policy and Program Office

[V]Hajime Amano① Satoru Nakajo
Norifumi OgawaSatoshi Kitazaki

ITS Japan
The University of Tokyo
Mazda Motor Corporation

National Institute of Industrial Science and Technology

Makoto Ito
University of Tsukuba
Tatsuru Daimon
Keio University
Kiyozumi Unoura
Honda R&D Co., Ltd
Hiroki Mori
Toyota Motor Corporation
Takashi Sunda
Nissan Motor Co. Ltd
Takashi Imai
President, Toyota InfoTechnology Center Co., Ltd.
Nobuyuki Uchida
Japan Automobile Research InstituteHiroaki Miyoshi
Doshisha University
Masayuki Kawamoto
University of Tsukuba
Mitsubishi Research Institute, Inc.
② Hiroki Sakai
Sumio Nishiyama
Kiwi-W Consortium
Sumio Nishiyama
Shobunsha Publications, Inc.
Masahiro KoibuchiMitsubishi Research Institute, Inc.
Motoyuki Akamatsu

National Institute of Advanced Science and Technology

Satoshi Kitazaki

National Institute of Advanced Industrial Science and Technology

VOverview

SIP Automated Driving System
OutlineYasuyuki Koga (Cabinet Office)
ABSTRACT: SIP-adus(Automated Driving for Universal Service) started in June 2014 as a national research and development
project for innovation. It has a social significance, providing a fundamental solution to suchissues as the reduction of traffic
fatalities, the reduction of the environmental burden by easing traffic congestions, travel support for elderly people and othervulnerable road users, and the revitalization of rural areas. The improvement of the competitivenessof the automobile industry and the expansion of related markets are significant from an industrial point of view. Under the Steering Committee, weestablished the System Implementation WG, the International Cooperation WG, and the Next-Generation TransportWG. In
2016, we identified five important area of focus (1) dynamic maps, (2) human-machine interface(HMI), (3) cyber-security,(4) pedestrian accident reduction, and (5), next-generation transport. We also conducted a large-scale FOTs on these areafrom 2017and delivered the achievement of the research and development.

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Significance and Targets

1.1. Background, Domestic and International Situation
Automated driving systems have been promoted throughindustry-academia-government collaboration in crossfield efforts made in cooperation with government departments under the framework of the Strategy Innovation
Creation Program (SIP) started in June 2014. In addition
to conducting comprehensive R&D starting from thedevelopment of core technologies and aiming for practicalapplication and commercialization, the results obtained
through field operational tests (FOTs) have also contributed to reviews of regulations and systems. Since automobiles are international products and also one of Japan’s key
industries, the projects has always been promoted withinternational standardization in mind.
The promotion of R&D, practical application and commercialization of automated driving has a two-sided significance for the Japanese government. It has a social
significance, providing a fundamental solution to such
issues as the reduction of traffic fatalities, the reduction of
the environmental burden by easing traffic congestion,
travel support for elderly people and other vulnerable road
users, and the revitalization of rural areas. The improvement of the competitiveness of the automobile industry
and the expansion of related markets are significant froman industrial point of view.
In addition to various projects promoted under the EU
R&D program Horizon2020, Germany, the UK and other
European countries have been working on formulating astrategy and developing technologies. In the United States,
the Federal Department of Transportation (USDOT) formulated a national strategy on automated driving systems

and ITS in December 2014. Since July 2015, the Universityof Michigan has been conducting a large-scale public road
FOT near Detroit involving car manufacturers from Japan,
the United States and Europe, imitating an urban area
based on the M City test course.
Moreover, the joint statement between Japan and Germany on the promotion of R&D in the field of automateddriving technology in January 2017 marked the beginningof international collaboration.
The number of traffic accident fatalities in our country
has been decreasing for more than 10 years as a result of
long-standing efforts by national and regional public organizations, related private organizations and citizens. The
Tenth Fundamental Traffic Safety Program, approved in
March 2016, established a national target of reducing thenumber of traffic accident fatalities to 2500 or less by 2020,
and realizing the safest road traffic in the world. However,while the number of fatalities declined for two consecutive
years, dropping to 3,694 in 2017, considerable efforts arestill required to reach the national target. In particular,intersection, pedestrian, bicycle, and motorcycle accidents
are major problems. An integrated approach including not
only vehicles, but also improvements to the traffic environment, and the education of people, is required.
On a different note, vehicle driving functions are comprised of three elements: recognition, decision and operation. There are technologies that recognize the roadenvironment through radars installed in vehicles (automated systems), and technologies that recognize the roadenvironment by relying on communication between vehicles, and between vehicles and external devices (cooperative systems). To realize automated driving systems,
advances in the three elements mentioned above must be
achieved by integrating both types of technologies. AutoSIP-adus: Project Reports, 2014-2018

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SIP Automated Driving System Outline

mated systems alone cannot solve the problem of reducing
the traffic fatalities, and they must be complemented bycooperative systems.
1.2. Significance and Politic ImportanceAccording to the Declaration to be the World’s Most
Advanced IT Nation: Basic Plan for the Advancement of
Public and Private Sector Data Utilization approved by the
Cabinet in May 2017, “the relevant ministries and private
enterprises shall work together to promote the development and practical application of safe driving support andautomated driving systems, as well as the utilization of
traffic data, based on the Public-Private ITS Initiative/
Roadmaps 2016. In particular, they will promote efforts,
including the development of institutions and infrastructure, aimed at realizing the possibility of automated drivingon expressways and unmanned automatic driving in limited areas, including the necessary FOTs, by 2017.”
In addition, the Comprehensive Strategy on Science andTechnology Innovation 2017 formulated based on the 5th
Science and Technology Basic Plan shows that it is necessary to actively promote R&D for automated driving systems to realize Society 5.0, core efforts and positioning
towards the realization of full-scale cyber-physical systems,
the development of dynamic maps, and so on.
Over the past 20 years, Japan has developed and introduced the world’s most advanced ITS system, and the automobile industry is currently its largest export industry. Asvarious Western countries accelerate their R&D on automated driving systems as part of their national policy, it isimportant for Japan to push forward with the development, practical application, and popularization of thesesystems. Reaching the aforementioned national target andrealizing the world’s best road traffic society will produce
both great social and industrial value for the people.
On the other hand, many ministries are involved in thefield of ITS and automated driving systems, and it must be
considered not only from a technical point of view, but also
based on other considerations, including social acceptanceand institutional aspects. Government agencies, as well as
public and private organizations, must work together topromote the development of the field.
At the beginning of the SIP automated driving systemproject, we were seeking collaboration in the absence of
mutual public and private understanding. However, in theprocess of establishing the Public-Private ITS Initiative/
Roadmaps with the Strategic Headquarters for the Promotion of an Advanced Information and Telecommunications
Network Society (IT Strategic Headquarters) in June 2014,
the SIP Automated Driving System Steering Committee
and the Headquarters were able to promote cooperative
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studies, as well as advance the understanding of the parties
involved and foster communication. Through these discussions, institutional development was also promoted by various government departments based on automated driving
measures specific to each department and in accordancewith the Outline of Development of Institutions for Automated Driving (April 2018, Strategic Headquarters for the
Promotion of an Advanced Information and Telecommunications Network Society, Strategic Conference for the
Advancement of Public and Private Sector Data Utilization).
Regarding industry-academia cooperation, which cannot
be said to always have been strong in the automotive field,
SIP has created opportunities for cooperation by promoting interaction allowing the different parties to meet with
one another. The industry-academia cooperation in theautomotive field must now be strengthened even further.
International standardization is also important for vehicles, which are global products. It is essential for the SIP
efforts aimed at maximizing effectiveness through FOTs
based on a hands-on approach to generate international
cooperation and gain citizen understanding and acceptance. With respect to international collaboration, we wereable to demonstrate initiatives for the standardization andde-facto standardization of dynamic maps and HMI
through the use of ITS Japan networks in EU (ERTICO)
and US (ITS America), as well as active popularization
efforts in cooperative areas of automatic operation fromJapan.
1.3. Targets and Objectives
1.3.1. Social Targets
We seek to achieve national targets such as the reductionof traffic accident fatalities (2,500 people / year) and easingof traffic congestion. The timeline for these targets will beclarified by following up on the Public-Private ITS Initiative/Roadmaps 2018 and developing technologies enabling
the effectiveness of traffic accident fatality reduction safetymeasures to be predicted. These approaches will be integrated in a PDCA cycle.
1.3.2. Technological Targets
We decided to revise the automated driving levels anddefinitions of the automated driving systems and driving
support systems implementing these levels, taking into
consideration international trends on the definition of
automated vehicles including those in Europe and theUnited States. To avoid confusion due to differences in definitions, and to ensure international consistency, we will
adopt the definitions in SAE J 3016, which was approvedby SAE International in September 2016 in the US.

SIP Automated Driving System Outline

However, at any level, the driver can take control of thesystem at any time.
• Regarding the expected commercialization time, we plan
to establish the technology required to implement a system that utilizes infrastructure information such as signaling information and congestion information (SAElevel 2) by 2017, and a high-end system (SAE level 2) thatwill be a step towards SAE level 3 by 2020. We will promote R&D in cooperative areas to ensure the commercial
availability of SAE level 3 systems in 2020, and SAE level4 systems in 2025.
• We will create an internationally open R&D environment
and establish a new international collaboration system tosolve global problems.
1.3.3. Industrial Targets(1) Industry creation
There is a wide range of new industry fields that can be
related to automated driving systems. It is expected thatthe market will expand drastically not only for vehicle sensors (cameras, radars, etc.), but also for vehicle communication equipment, roadside communication equipment,
portable communication equipment and other information
communication equipment and digital infrastructure.
Moreover, as new platform technologies related to highprecision 3D mapping technology, basic dynamic map
technology, and probe information-based map update
technology, evolve after automated driving systems are put
to practical use and popularized, new industries will becreated in various fields beside automated driving, such as
information development, operation, or services utilizing
high precision positioning information. In addition, thecombination of HMI, security technology and advanced
automotive control technology will bring a new value inthe context of a cyber-physical system where machines
adapt to, and support, people. New technologies and businesses, such as next-generation transportation systems,
support systems for vulnerable road users and walking
movement, and package-based export of regional traffic
management services and infrastructure, will be created.
(2) World Share
We will lead the standardization of automated driving
systems in terms of international cooperation, and establish our position as the top global player by building upon
the work of our predecessors in the area of cooperative
systems.
To establish concrete numerical targets (KPI), we aim tobuild a collaborative organization towards the further
advancement of automated driving systems, which willconsist of representatives from industry, government andacademia and will remain even after the SIP project ends.

A wide range of university experts will analyze social andindustrial impacts, and determine these targets in cooperation with the IT Strategic Headquarters, the Investigation
Committee for New Strategy Promotion and the Road
Transportation Subcommittee.

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Deployment Milestones

2.1. Reaching the National Target for the Reduction of
Traffic Accident Fatalities
We will establish a technical foundation and execution
organization to implement traffic accident countermeasures adapted to vehicles, people and infrastructure as awhole to reach the national target described in the Tenth
Fundamental Traffic Safety Program.
In addition to development of driving support systems
and automated driving systems, and the promotion of thepractical application and popularization thereof, we willdevelop technology to advance the data analysis and simulation of traffic fatalities and predict and verify the effectiveness of safety measures. We will analyze the execution
organization integrating multiple related parties, and use
the results to establish a mechanism to progress towards
the national target.
2.2. Implementation and Popularization of Automated Driving Systems
Regarding the expected commercialization time, we plan
to establish the technology required to implement a systemthat utilizes infrastructure information such as signaling
information and congestion information (SAE level 2) by
2017, and a high-end system (SAE level 2) that will be a
step towards SAE level 3 by 2020. We will promote R&D incooperative areas to ensure the commercial availability of
SAE level 3 systems in 2020, and SAE level 4 systems in
2025. In the area of dynamic maps, Dynamic Map Platform Co., Ltd. was established in June 2017, and there areplans for that company to prepare high-precision maps ofall expressways and all motorways by the end of 2018. This
will represent a big step towards practical application.
2.3. Development in Collaboration with Tokyo
Metropolis with the Tokyo Olympics and Paralympic
Games in 2020 as a Milestone
Defining the Tokyo Olympics and Paralympic Games in2020 as a milestone, work is progressing on the practicalapplication of next-generation transportation systems that
contribute to the future generations in Japan, improveaccessibility (measures for vulnerable road users), and propose solutions to social acceptance and institutional issues
SIP-adus: Project Reports, 2014-2018

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SIP Automated Driving System Outline

for social implementation of the aforementioned measuresin anticipation of the development of Tokyo and the aging
of society. In particular, we have signed a memorandum of
understanding with the Cabinet Office, the Tokyo Metropolitan Government and related parties to incorporate
ART technology into the waterfront city area BRT which isunder consideration by the Tokyo Metropolitan Government. Technical development, demonstration experiments
and other efforts are promoted in close cooperation by theparties involved.

3

Outline of R&D

3.1. System Development and Research Subjects
Since the beginning of the SIP project in 2014, we havebeen advancing R&D on individual topics while promoting the establishment of teams such as the Steering Committee, the System Implementation WG, the International
Cooperation WG, and the Next-Generation Transport
WG. In 2016, we identified five important areas of focus:(1) dynamic maps, (2) human–machine interface (HMI),(3) cybersecurity, (4) pedestrian accident reduction, and(5) next-generation transport.
The R&D in the competitive field of automated systems
for vehicles is carried out by the automobile industry itself,while SIP has mainly promoted the development and practical application of fundamental technologies and of cooperative areas (related to cooperative systems) which require
collaborative public and private efforts.

Fig. 1 Process Chart

3.2. Large-Scale FOTs
Conducting large-scale FOTs in cooperation with government departments is important to promote collaboration
between industry, academia and government. The purposeof the experiments is to speed up the realization of the

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deployment milestones by identifying institutional, business and other problems from the R&D results obtained sofar, providing feedback for development, and applying the
PDCA cycle consisting of: (a) the activation of technology
development, promotion of infrastructure development,(b) the prompt identification and solving of problems forpractical application, and (c) a sequence of large-scale
FOTs mainly focused on the five important topics started
in October 2017 on 300 km of road (600 km round-trip)
covering the Joban, Shuto, Tomei, and Shin Tomei expressways as well as general public roads around the Tokyo
waterfront city area. The main purposes of these FOTs
were the intensification of international collaboration by
providing a forum for open study and discussion for participants, including overseas manufacturers, and the intensification of collaboration between industry, academia andgovernment, transmission of the state of social demands
and R&D results. Twenty-two domestic and overseas automobile manufacturers, automotive part manufacturers,
universities, and other organizations participated in longterm experiments and development conducted until theend of December 2018.

4

acknowledgment

Two distinguished program directors devoted theirenergy to SIP-adus program to make automated driving
technologies in reality. Counsellors and expert staffs has
supported PD’s effort with their full capacity. I would liketo thank you all of them and all contributors to the program.
<Program Directors>
Hiroyuki Watanabe (2014.6-2016.3)
Seigo Kuzumaki (2016.4-2019.3)<Counsellors>
Tomoyuki Tanuma (2013.10-2015.7)
Shin Morishita (2015.8-2017.7)
Yoshiyuki Izawa (2017.7-2018.7)
Yasuyuki Koga (2018.8-2019.3)<Expert Staffs>
Mamoru Hyodo (2013.10-2015.7)
Takeshi Yamamoto (2014.9-2015.6)
Shinya Tsuchida (2015.3-2017.2)
Motoatsu Yoshikawa (2015.7-2016.5)
Takeshi Yamamoto (2015.9-2017.9)
Masaki Chikuma (2016.6-2019.3)
Kaoru Sugie (2017.3-2019.3)
Toji Okayama (2017.11-2018.4)
Yukiko Hatazaki (2018.10-2019.3)

Results of Researchand Development[ I ] Development of Automated Driving Systems[ II ] 	Development of Basic Technologies to Reduce Traffic
Accident Fatalities and Congestion, and Initiatives to Raise
Social Acceptance[ III ] Next-Generation Urban Transportation[ IV ] Field Operational Tests[ V ] International Collaboration

I

Development of Automated Driving Systems① Dynamic Maps

Overview of SIP Dynamic Map Research and Development for Automated Driving
Masao Fukushima (Nissan Motor Co., Ltd)
Norifumi Ogawa (Mazda Motor Corporation)
ABSTRACT: Dynamic maps are three-dimensional high precision maps on which various kinds of semi-static (scheduled
phenomena), semi-dynamic (current phenomena), and dynamic information (real-time changes), are combined with position
data for smart automated driving (AD) and advanced driver assistance systems (ADAS).
Many SIP R&D measures are carried out to achieve the early realization, wide deployment of dynamic maps, as well as reduce
their cost.
Dynamic map use will not be limited to vehicles featuring AD/ADAS, but also address social needs such as the prevention or
mitigation of the effects of natural disasters, advanced agriculture, or infrastructure management and maintenance. These elements are therefore also considered in SIP-adus.
The final stage of SIP R&D involves a large scale Field Operational Test (FOT) carried out by many of the stakeholders
involved.

1

About Dynamic Maps

The dynamic map is one of the most important technologies for the realization of high level automated vehicles.
AD system must have recognition, decision, and operation
functions. Sophisticated recognition requires map and ITS
information to achieve high level self-position estimation,
as well as on-board sensors such as video cameras, radars,
lidars, or laser scanners to perceive the vehicle surroundings. A lot of various high level research and development
is carried out as part of SIP activities to create high performance and low cost dynamic maps.
Though AD systems fundamentally use high precisionmaps and on-board sensors, automated vehicles will beable to run more intelligently with ITS information.

2

current phenomena such as traffic accidents, traffic congestion occurring within the preceding 30 minutes, or the
local weather forecast. The top layer consists of dynamic
information that shows real-time changes from ITS anticipative information such as vehicle-to-infrastructure communication (V2I), vehicle-to-vehicle communication(V2V), vehicle-to-pedestrian communication (V2P), or
traffic signal rotation cycles.
This information includes position data with timestamps,
and is transmitted from support infrastructures to automated vehicles via V2X or mobile communication such as
LTE, 4G, or 5G. These are plotted on the base layer map in
the AD system, providing it with knowledge of surrounding conditions on the traveled route.

The Structure of Dynamic Maps

Dynamic maps are maps consisting of both high-precision digital maps and ITS anticipative information. They
have four layers, as shown in Fig. 1. The bottom layer is athree-dimensional digital map which contains a high precision lane level road map with road shape and topological
data, as well as road objects such as road sign, road paints,
traffic signal poles, and stop lines.
Above that, Layer 1 is a semi-static information layer
which shows scheduled phenomenon such as traffic control plans, road construction plans, or weather forecasts.
Layer 2 is a semi-dynamic information layer that shows

Fig. 1 Structure of a Dynamic Map

3

Research Items for Dynamic Maps

SIP research on dynamic maps cover the aspects listed
SIP-adus: Project Reports, 2014-20189[ I ] Development of Automated Driving Systems① Dynamic Maps

Overview of SIP Dynamic Map Research and Development for Automated Driving

below. These results are shown in item by item reports.
(1) Estimation of current SIP map specification (laneinformation, road objects accuracy, and so on)(2) Development of methods for three-dimensional highprecision static map creation and maintenance, including automatic mapping systems.
(3) Consideration of the semi-static, semi-dynamic,
dynamic information specifications and service methods.
(4) Dynamic map application considerations and research(5) Communication specification research on V2X(6) ITS information service research at intersections.
3.1. Estimation of Current SIP Map Specification
Three-dimensional high precision map data for a longdistance expressway route, ordinary roads in the 2020
Tokyo Olympic and Paralympic Game stadium area, and aproving ground isolated from open roads, covering
approximately 760 km, were prepared. Many of the SIP
large scale FOT participants, OEMs (car manufacturers),
suppliers, academic institutions, and other private firms
are validating the map specification, accuracy, and otherfactors.

Fig. 3 Example of map database update

3.3. Consideration of Semi-Static, Semi-Dynamic, andDynamic Information Specifications and Service Method
Road operators must have a road repair plan. Road level
public traffic information services and means of data transmission such as ETC 2.0 are already available. However
automated vehicles and advanced ADAS cars need lanerather than road level information as shown in Fig. 4. Waysto create or transfer lane level information from infrastructures to automated vehicles were therefore considered.

Fig. 2 Dynamic map estimation area

3.2. Development of Method for Three-Dimensional
High Precision Static Map Creation and MaintenanceIncluding an Automatic Mapping System
In order to serve accurate and low cost maps, effective
map creation and maintenance methods were considered,with a particular focus automatic difference updates using
AI technology and partial point cloud data gathering byroad operators and contracted cargo trucks, or from ordinary vehicle probe data.

10

Fig. 4 Example of lane level traffic condition information

3.4. Dynamic Map Application Consideration andResearch
Using dynamic maps only for automated vehicles haspoor cost benefit. Therefore, other applications of dynamic
maps are being considered for businesses providing mapbased information service or to address social needs suchas the prevention or mitigation of the effects of natural
disasters, advanced agriculture, or infrastructure management and maintenance.

[ I ] Development of Automated Driving Systems① Dynamic Maps

Overview of SIP Dynamic Map Research and Development for Automated Driving

Fig. 5 Dynamic map information service platform

3.5. Communication Specification Research on V2X
Smart automated vehicles require connected car technologies that make use of V2X (V2I, V2V, V2P, 5G, probe
data, so on), including existing infrastructure like secondgeneration electronic toll collection (ETC 2.0) on highways, advanced infrared beacons on ordinary roads, andLTE and 4G mobile communications.
Communication between automated vehicles—machine
to X communication (M2X)—is needed. The results ofthese considerations are shown in item by item reports.

3.6. ITS Information Service Research at Intersections
At intersections, automated vehicles need the following
information to ensure traffic safety: real-time traffic signal
information and signal rotation cycle, information onpedestrians crossing the road, oncoming vehicles, andmore.
On-board sensors will not be able to acquire that information. Therefore, road-side sensors will detect pedestrians or oncoming cars, while the traffic signal rotation cycle
will be obtained from the traffic management system, andthe information will be sent to approaching automatedvehicles via dedicated short range communications(DSRC)

Fig. 9 ITS information services at an intersection

Fig. 6 Merging scenario supported by machine-to-machine communication

Fig. 7 Vehicle-to-pedestrian communication using DSRC

3.7. Communication Assets for Dynamic Maps
Various technologies, including mobile communication
capable of mass data distribution and DSRC capable ofhandling real time information are being considered ascommunication methods to provide semi-dynamic andsemi-static information for dynamic maps. In Japan, anumber of ITS communication systems have been put into
practical use since before 2000. The ETC 2.0 system for tollcollection also provides information for safe driving. In
addition, the advanced infrared beacons and radio communication road side units at intersections provide traffic
signal information and traffic control, as well as traffic congestion information. These systems have already spreadthroughout Japan. Systems spread on such a large-scale area valuable social asset. Therefore the utilization of thesesystems is also being considered preferentially for the distribution of dynamic map data.

4

Fig. 8 Wide area communication via mobile network

Summary

Dynamic maps are absolutely necessary for smart automated vehicles, and SIP has put a great deal of effort intheir development. We are also taking care to avoid making maps with unnecessarily high quality or maps that will
not be accepted as either international or de facto standards.
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[ I ] Development of Automated Driving Systems① Dynamic Maps

Overview of SIP Dynamic Map Research and Development for Automated Driving

Map cost reduction is also very important to ensure thewidespread propagation of automated vehicles. We havebeen developing low-cost map maintenance methods andconsidering other uses such as businesses offering mapbased information services or social needs such as the prevention or mitigation of the effects of natural disasters,
advanced agriculture, or infrastructure management andmaintenance.

12

I

Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution
Functions, and Updating Methods
Yoshiaki Tsuda (Mitsubishi Electric Corporation)
Naoto Sato, Namie Toyoda (Aisan Technology Co., Ltd.)
Hideto Yonezawa, Junya Oishi (Increment P Corporation)
Tomohiko Masutani, Kiyohiro Yamauchi, Akane Tanaka (Zenrin Co., Ltd.)
Shintaro Osaki (Toyota Mapmaster Incorporated)
Tomohide Kitagawa (Pasco Corporation)
ABSTRACT: Over a five year period, beginning in 2014, we deliberated the approaches to use in relation to dynamic map static information
requirements, data specifications, preparation guidelines, and encoding specifications, as well as the location referencing methods used to linkstatic information and dynamic information in the dynamic maps necessary for automated driving systems. Through the Large-Scale Field
Operational Test (Dynamic Map), we conducted static information evaluation with the collaboration of test participants. Based on the evaluation results, we concluded that the essential features that make up static information are sufficient for use by automated driving systems. We
also defined features for which there were numerous requests from test participants as quasi-essential features. Through this test, we foundthat road signs, road markings, and the like change over the course of three to four months after dynamic maps are created, and we identified
a need to organize approaches to updating static information.

1 Introduction
In automated driving systems, vehicle-mounted sensors(cameras, LiDAR, etc.) serve as a vehicle’s “eyes,” enablingsafe and comfortable vehicle control. The detection capabilities of these vehicle-mounted sensor technologies have
improved in recent years, but there are limits to what they
can detect. Furthermore, there is a demand for the ability
to use inexpensive sensors in regular vehicles. Dynamic
maps can be used as a supplementary technology for vehicle-mounted sensors. These dynamic maps can be used tosupplement important automated driving functions, suchas vehicle localization and path planning.
Dynamic maps are high-accuracy 3D maps overlaid withconstantly changing information regarding the positions of
nearby vehicles, the status of traffic signals, and so on.
Dynamic maps are composed of four layers, as shown in
Fig. 1: static information, semi-static information, semidynamic information, and dynamic information. Static
information is the high-accuracy 3D map itself. It is composed of actual features (such as carriageway lines, street
signs, and traffic signals) and virtual features (such as lanelinks, carriageway links, and intersection areas). Semistatic information consists of future road transport information (such as traffic congestion forecast information,
restriction plan information, and weather forecast infor-

mation). Semi-dynamic information is composed of roadtraffic information such as accident information and traffic
congestion information. Dynamic information consists oftraffic signal information and information regarding physical objects on or near roads, such as the positions of nearby
vehicles and pedestrians. Location referencing methods
have been defined for linking these four layers, from static
information to dynamic information.

Fig. 1 Dynamic map four layer structure

In 2014, the Study of Advancement of Map Information(Information Assembly and Structuring) for Use in Studies
and Investigations of the Approaches to Use in Resolving
Issues Affecting the Realization of Automated Driving Systems was carried out in preparation for the creation of thehigh-accuracy 3D maps that comprise dynamic maps.
Mobile mapping systems (MMS) were used to measure
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[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
Tokyo’s Odaiba area (approx. 65.3 km of roads), the acquisition of features was verified, and issues were identified
and organized.
Based on the results of these activities, in 2015, the Studyof Prototyping and Evaluation Aimed at the Creation of
Dynamic Maps for Use in Studies and Deliberations on the

Approaches to Use in Resolving Issues Affecting the Realization of Automated Driving Systems was carried out.
Automated driving system use cases were used to create arequirement specifications proposal, an automated driving
system map data specifications proposal, and a map datapreparation guideline proposal.

Table 1 Overview and results of past activities

In 2016, a Study of Prototyping and Evaluation Aimed at
the Creation of Dynamic Maps for Use in Studies andDeliberations on the Approaches to Use in Resolving Issues
Affecting the Realization of Automated Driving Systems
was carried out. It measured 50 km of ordinary roads and250 km of highways, and, based on map data encoding
specifications proposal for automated driving systems(prototype data encoding specifications), created a highaccuracy 3D map covering approximately 300 km.
Furthermore, in order to verify dynamic maps throughthe large-scale field operational test, a 758 km test area wascreated in 2017, with 20 Japanese and overseas motor vehicle manufacturers, electrical component manufacturers,
universities, and other organizations participating in the
project and conducting a dynamic map evaluation. In carrying out this test, test participants’ feedback and results
were collected and organized through dynamic map field
operational test working group sessions in order to develop
a de facto standard and contribute on an international scale.
In 2016, Dynamic Map Platform Planning Co., Ltd. wasestablished, and in the following year, 2017, it became
Dynamic Map Platform Co., Ltd. in preparation for commercialization. Table 1 shows an overview of these past
activities.
14

2 Dynamic Maps
2.1. Overview of Dynamic Maps
Table 2 shows map use cases for the automated driving
systems discussed by SIP-adus. The static, semi-dynamic,
and dynamic information used by automated driving systems was identified based on these use cases. The location
accuracy of static information was set to the equivalent of
map information level 500, the location accuracy of semidynamic information was set to lane level accuracy, andthe location accuracy of dynamic information was set toseveral meters. These are defined in Requirement Specifications (Proposal) Ver. 1.0.
Table 2 Map use cases

[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods

2.2. Static Information Data Model
The Automated Driving System Map Data Specification
Proposal Ver.1.1 (the data specifications) document was
created based on the requirement specifications in 2.1.
Within the static information defined in these data specifications, carriageway lines, pedestrian crossings, traffic
signals, road signs, and the like are defined as actual features, while lane links, carriageway links, intersection
areas, and the like are defined as virtual features. The data
specifications also define the methods used to acquire
actual features. Figures 2 to 6 show the acquisition definitions for road signs, traffic signals, carriageway links, andlane links. Figure 7 is an illustrative depiction of static
information.

Fig. 4 Shoulder acquisition position example

Fig. 5 Road marking acquisition position example

Fig. 2 Road sign acquisition position example
Fig. 6 Lane link and carriageway link acquisition position example

Fig. 3 Traffic signal acquisition position example

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Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
These data specifications define attribute information andrelated information. The data structure is indicated in Fig.
8. The data specifications define collaborative area featuresand competitive area features separately (Table 3). During

deliberations regarding the collaborative area, opinions
were exchanged with two major overseas map suppliers,and features used globally were specified as essential features.

Fig. 7 Depiction of static information

Fig. 8 Examples of definitions of feature attributes and related information
16

[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
Table 3 List of essential/extended features

Fig. 9 Example of static information (feature record)

2.4. Static Information Generation Method
A mobile mapping system (MMS) is used to satisfy static
information location accuracy requirements. Table 5 shows
the static information creation procedure.
Table 5 Static information creation procedure

2.3. Static Information Encoding Specifications
The Automated Driving System Map Data EncodingSpecification Proposal (Prototype Data Encoding Specifications) Ver.1.0 was created based on this static information data model.
Static information is divided into four types of records, as
shown in Table 4: metadata records, feature records, attribute records, and feature relationship records. Figure 9shows an example of static information (feature record).
Table 4 Data structure of static information

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[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
Types 1 and 2 express location information in terms of
distance from common reference points (CRPs). CRPs are
virtual features defined for intersection areas. They are used
as reference points for linking dynamic or other information to static information. CRPs are defined for the centersof ordinary road intersection areas (see Fig. 11) and on lanelinks in intersection areas of highways (see Fig. 12).
CRP specifications are stipulated in the Dynamic Map
Semi-dynamic/Semi-static Information Data Specifications(Proposal).

Fig. 11 Example of CRP defined for an ordinary roadFig. 10 Example of MMS point group data

2.5. Location Referencing Method
The location referencing method defines how static information (high-accuracy 3D maps) is linked to dynamic
information, semi-dynamic information, and semi-static
information.
There are four location referencing methods that can beused with dynamic maps (see Table 6). Types 1 and 2 wereused for the dynamic maps in the large-scale field operational test.
Table 6 Types of location referencing methods

Fig. 12 Example of CRP defined for a highway

Location information expression types 1 and 2, used in
the large-scale field operational test, are defined below.
1) Location information expression type 1
Location information expression type 1 is primarily used
to indicate the locations of vehicles and pedestrians, etc.,
inside intersection areas. As shown in Fig. 13, distance differences from CRPs defined for intersection areas are
expressed using rectangular coordinate systems with thestructure shown in Table 7.

Fig. 13 Conceptual image of location information expression type 1
18

[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
Table 7 Location information expression type 1 data elements

pair of CRPs, one for the start point and one for the end
point, defined for intersection areas. The position on the
carriageway link is expressed in terms of road distance,
and the offset distance from the carriageway link is used toexpress the carriageway lateral position and lane location,with the structure shown in Table 8.
2.6. Linking Semi-Dynamic and Static Information
Linear semi-dynamic information such as traffic congestion information and restriction information is expressedusing expression types 1 and 2 and start and endpoints (seeFig. 15).

2) Location information expression type 2
Location information expression type 2 is primarily used
to indicate the locations of phenomena outside of intersection areas. Figure 14 shows, carriageway links connect a
Fig. 15 Semi-dynamic information concept

Figure 16 and 17 show examples of linking semi-dynamic
information and static information.

Fig. 14 Conceptual image of location information expression type 2
Table 8 Location information expression type 2 data elements

Fig. 16 Traffic congestion information

Fig. 17 Traffic restriction information

2.7. Linking Dynamic and Static Information
Point dynamic information such as traffic signal information and crosswalk pedestrian information is expressed
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[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
using expression type 1 and intersection centers (see Fig. 18).

Fig. 18 Dynamic information concept

Figure 19 shows an example of linking dynamic and static
information.

Fig. 21 Static information evaluation area (ordinary roads: 50 km)
Table 11 List of essential/extended features

Fig. 19 Vehicle, pedestrian, and traffic signal information

3 Evaluation of Static Information
3.1. Method of Evaluating Static Information
Figure 20 and 21 show the Large-Scale Field Operational
Test (Dynamic Map) test area. The blue lines in the figure
are the 300 km area created in 2016, and the red lines are
the test areas added in 2017 (the total length or red andblue lines is 758 km).
Table 11 shows the static information features distributed
during the verification test.
3.3. Evaluation Results
Feature usage status information and requests for
improvements to static information were collected fromtest participants, and evaluated.

Fig. 20 Static information evaluation area (total length: 758 km)
20

3.3.1. Feature Usage Status
Table 12 shows the status of feature usage by test participants and the evaluation results. Some test participants
requested the addition of features other than essential features. Data specifications were reviewed based on requests
for additional features and the Japan Automobile Manufacturers Association’s feature recommendations. Table 13

[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
shows the results of the review.
Based on the above, the essential features in the data

specifications were found to be sufficient for use by automated driving systems.

Table 12 Feature usage conditions and evaluations

Table 13 Requests for addition of features other than essential features

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[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
20,991 prepared features and 134 improvement requests.
Table 14 shows the types and numbers of improvement
requests.
Tables 15 and 16 show typical examples of improvement
requests. Table 15 shows an example in which a gantry andsigns (three signs each for outbound and inbound lanes)
were present when the data was generated, but had been
removed by the time the field operational test was performed. Table 16 shows a road marking (bus stop) that wasadded after the data was generated.

Table 15 Improvement request example (data contains non-existent sign)

Essential feature: Collaborative area features envisioned as being used by many
automotive manufacturers
Quasi-essential feature: Features requested by test participants
Extended feature: Other features* Features for which consensus has been reached with two major overseas map
suppliers3.3.2. Requests for Improvements from Test Participants
Test participants pointed out some discrepancies in thepositions and existence of road markings, signs, traffic signals, and the like. These were caused by construction orother activities after the data was prepared. There wereTable 14 Types and numbers of improvement requests

22

Table 16 Improvement request example (road marking missing from data)[ I ] Development of Automated Driving Systems① Dynamic Maps

Dynamic Map Prototyping and Design, and Establishment of Data Linking Functions, Distribution Functions, and Updating
Methods
Platform Co., Ltd. and Mitsubishi Research Institute, Inc.,
which were involved in the creation of the dynamic maps.

3.3.3. Issues
As Table 14 shows, the main improvement requests wereto address issues in which features existed when the data
was generated but no longer exist, and issues in which features didn’t exist when the data was generated, but currently do exist.
These were caused by changes to roads in the three tofour months after MMS measurement. When there arechanges to roads, static information must be updated.
Updating static information involves a sizeable cost, so theapproach used to update static map information must be
given consideration, taking into account the timing of feature and road updates, update frequency, and other factors.

4 SummaryThis article discussed the approach used in dynamic map
static information requirements and data specifications,preparation guidelines, encoding specifications, and thelike, and described the location referencing method thatuses CRPs to link static information with dynamic information and semi-dynamic information.
Based on the results of evaluations by Large-Scale Field
Operational Test (Dynamic Map) participants, we concluded
that the essential features that make up static information(14 features) are sufficient for use by automated driving systems. Test participants also agreed that features for which
there were numerous requests would be defined as quasiessential features so that they could be developed as needed
by individual companies during practical implementation.
We found that, within the category of static information,
there were examples of road signs and road markingschanging over the course of three to four months after
MMS measurement was performed, and we identified aneed to organize approaches to updating static information. We will consider these items during the 2018 field
operational test.
This research and development work was carried out withthe collaboration of Program Director Seigo Kuzumaki,
members of SIP-adus, and Large-Scale Field Operational
Test (Dynamic Map) participants, as well as Dynamic Map

References(1) Strategic Innovation Promotion (SIP) Program for
Automated Driving Systems, Study of advancement of
map information (information assembly and structuring) for use in studies and investigations of theapproaches to use in resolving issues affecting the realization of automated driving systems (2014)(2) Strategic Innovation Promotion (SIP) Program for
Automated Driving Systems, Study of measures andevaluations aimed at the creation of dynamic maps foruse in studies and investigations of the approaches touse in resolving issues affecting the realization of automated driving systems (2015)(3) Strategic Innovation Promotion (SIP) Program for
Automated Driving Systems, Study of measures andevaluations aimed at the creation of dynamic maps foruse in studies and investigations of the approaches touse in resolving issues affecting the realization of automated driving systems (2016)(4) Implementation and Management of Strategic Innovation Promotion Program (SIP) for Automated Driving
Systems/Large-scale Field Operational Test//Dynamic
Map Prototyping and Design, Establishment of Center
Functions and Updating Methods, etc.
(5) Requirement Specifications (Proposal) Ver. 1.0 (2015)(6) Automated Driving System Map Data Specification
Proposal Ver.1.1 (2016)(7) Map Data Preparation Guidelines (Proposal) Ver. 1.0(2015)(8) Automated Driving System Map Data Encoding Specification Proposal (Test Data Encoding Specifications)Ver.1.0 (2016)(9) Dynamic Map Semi-dynamic/Semi-static Information
Data Specifications (Proposal) Ver.1.0 (2016)(10) Recommended Specifications for High-accuracy Maps
Used for Automated Driving (November 2016, Japan
Automobile Manufacturers Association, Inc.)(11) Materials Related to New Advanced Digital Road Surface Information for Advanced Driving Support (Japan
Digital Road Map Association)
About the author
1) Yoshiaki Tsuda is the chief engineer of the Information
Technology Systems Department of Mitsubishi Electric
Corporation’s Kamakura Works. He is engaged inresearch and development of social infrastructure systems such as ITS, DSRC, and high-accuracy 3D maps.

SIP-adus: Project Reports, 2014-2018

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I

Development of Automated Driving Systems① Dynamic Maps

Strategic Innovation Promotion Program (SIP) Automated Driving Systems/Large-scale Field Operational
Test/Dynamic Map/Utilization of Vehicle Probe
Information
Eiji Muramatsu et al. (Pioneer Corporation)
ABSTRACT: Vehicle probe information is getting more important than ever. It is useful to get data on road situations such as traffic flow,
objects, as well as weather conditions, in near real-time. It is expected that this kind of information will be shared among industries to realizea more efficient and safer society especially when automated vehicles become a reality.
The purpose of this project is to evaluate the feasibility of the vehicle probe information exchange interface between cloud services throughthe large-scale field operational test conducted by SIP. The interface specifications are currently under development by the Dynamic Vehicle
Information Sharing Working Group in Japan Automotive Software Platform and Architecture (JASPAR).

1 Background
JASPAR established the Dynamic Vehicle Information
Sharing Working Group in 2017. They started to create thevehicle information exchange interface specifications
referred to as the Common Vehicle Information Specification.
The purpose of this specification is to share vehicle information among industries through the dynamic map platform.
The scope of the JASPAR Common Vehicle Information
Specification is depicted in Fig. 1.

2 Project Overview
2.1. Purpose
The main purpose of this project is to evaluate the feasibility of the JASPAR Common Vehicle Information Specification using real vehicle information. The experiment isdone by joining the SIP Large-scale Field Operational Test.
2.2. Period
This project started October 30, 2017 and will end in February 28th, 2019. This report shows the status of the project from October 30th, 2017 through September 2018.
2.3. Content to evaluateThis project makes use of traffic flow content that indicates vehicle average speed on a certain section of road.
Traffic flow is one of seven items that the JASPAR Common Vehicle Information specification defines.
We decided to evaluate both road- and lane-level trafficflow.

Fig. 1 Scope of Common Vehicle Information Specification

The JASPAR Common Vehicle Information Specificationconsists of three parts, the concept specification, the data
set specification and the API specification. The concept
specification defines use-cases for the vehicle informationand its necessary data. The data set specification defines
the exchange data format between different cloud services.
The API specification defines the API to send, delete orreference data from the OEM side in the dynamic map
platform presented in Fig. 1.

24

2.4. Evaluation scheme
Fig. 2 shows the experiment scheme of this project.

Fig. 2 Evaluation scheme

The road-level traffic flow is created by utilizing the vehicle trajectory data from a commercial service (called[ I ] Development of Automated Driving Systems① Dynamic Maps

Strategic Innovation Promotion Program (SIP) Automated Driving Systems/Large-scale Field Operational Test/Dynamic
Map/Utilization of Vehicle Probe Information“Smart loop”) offered by Pioneer Corporation.
The data is derived from vehicles on the road equippedwith a car navigation system. The vehicle trajectory information is matched to the road links of the Japan Digital
Road Map Association (DRM). The average speed of vehicles is then calculated based on DRM road links.
In contrast, the lane-level traffic flow is created using
experiment vehicles equipped with modified car navigation systems. Currently, the lane-level position cannot be
determined by GNSS, so the experiment vehicle drives in a
pre-defined lane and generates the traffic flow for that lane.
The vehicle trajectory information is matched to lane links
in the SIP dynamic map to calculate the average speed ofvehicles for a section length based on SIP lane links.
The next step is to convert traffic flow data to the JASPAR
message data format specified by the Common Vehicle
Information Data Set Specification.
Fig. 3 shows an overview of the traffic flow message data
format defined by the JASPAR specification.

Fig. 4 shows an example image of road-level traffic flowon the SIP Dynamic Map viewer. As you can see, the traffic
flow data can contain altitude information to distinguishoverpasses or road underneath.

Connecting to the Dynamic Map plat-

4 formThe dynamic map platform defined in the JASPAR specification is operated by Dynamic Map FOT (Field Operational Test) Consortium, which is the management body of
the SIP Dynamic Map Large-scale Field Operational Test.
The Field Operational Test starts in October 2018 and ourtraffic flow data will be treated as transient dynamic data
for the dynamic map in their project.
We needed to confirm that the traffic flow data was transmitted to their server without problems.
4.1. Implementation specification
The JASPAR specification does not specify implementation specific matters. We created implementation specifications to connect and communicate appropriately with eachserver. Table 1 shows example items from the implementation specification that we defined.
Table 1 An example item of implementation spec

Fig. 3 Traffic flow message structure of JASPAR specification

Finally, the message is transmitted to the dynamic map
platform using the REST API specified by the JASPAR API
specification.

3 Confirmation of the Traffic Flow Data
To confirm the traffic data visually, we utilized the
dynamic map viewer provided by SIP for the Large-scale
Field Operational Test.

Fig. 4 Visualized image of road-level traffic flow data

4.2. Logical connection test
We also created a test specification for the logical connection test between the servers of each party. The test items
are classified as follows:· FormatCheck if the data format complies with the JASPAR
specification.
· DataCheck if the system works for various data ranges.
· Performance
Check that the system is not vulnerable to high loads.

Fig. 5 Connection test with Dynamic Map FOT Consortium
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[ I ] Development of Automated Driving Systems① Dynamic Maps

Strategic Innovation Promotion Program (SIP) Automated Driving Systems/Large-scale Field Operational Test/Dynamic
Map/Utilization of Vehicle Probe Information
4.3. System test
To confirm that all systems work as we expected, we
started a systems test that utilized the final environment of
the field operational test.
For lane-level traffic flow data, we prepared some experiment vehicles and gathered real-world vehicle information.
Figure 6 shows the driving course for the lane-level traffic
flow data.

PAR specifications during the implementation/evaluation.
Table 2 shows the example items that we reported to the
JASPAR Working Group.

6 Measurement of Calculation Delay
We measured the calculation delay of our server (between
when probe data was received and when the JASPAR traffic
flow message data was sent to the dynamic map platform)
to understand our server performance and possible transient dynamic data latency.
In doing so, we found that the map matching function
takes a relatively longer time than the other functional
modules. We plan to continue investigating the overall
delay in cooperation with the Dynamic Map FOT Consortium.

7 Future scheduleFig. 6 Driving route for lane-level traffic flow

Figure 7 shows an image of the results of the lane-leveltraffic flow collected from experiment vehicles and transmitted to the dynamic map platform using the JASPAR
Common Vehicle Information Specification.

The Large-scale Field Operational Test will start in October 2018. The road-level traffic flow data will be deliveredfrom October 1st to December 27th, except for the periodof November 26th to November 30th. The lane-level traffic
flow will be delivered from November 26th to November
30th.

Fig. 7 Lane-level traffic flow field check using SIP Dynamic Map Viewer

5 Feedback to JASPAR Working GroupWe found some problems to be pointed out in the JASTable 2 Example feedback items for JASPAR Working Group

Fig. 8 Deliver area of road-level traffic flow data (Green area)

8 Conclusion
The progress of this project is on schedule. We are continuing to proceed with the experiment and evaluations incooperation with the Dynamic Map FOT Consortium. The
final report is planned for submission in February 2019.
References(1) JASPAR, Common Vehicle Information Concept Specifications Draft ver. 1.0 (February 1, 2018)

26

I

Development of Automated Driving Systems① Dynamic Maps

A Study of Specifications for Providing Lane-Level
Traffic Information in Dynamic Maps
Koichi Miyashita, Suguru Kawajiri and Shintaro Shimizu (Mitsubishi Research Institute,
Inc.)
ABSTRACT: For automated driving to be able to use lane-level traffic information, it will be necessary to verify whether or not there areproblems with regard to the following issues: 1. errors due to data conversion in the multiple conversion processes conducted when providing
traffic information, 2. differences in map accuracy, updating frequency and so on between the maps used for human drivers to provide routenavigation and the maps used by systems, including automated driving, and 3. time lag at each stage in the generation, transmission andutilization of traffic regulatory information by the vehicle. A proposed testing plan has been prepared to conduct tests to verify these issues aspart of a different project during the next fiscal year. In this study, an equipment configuration to receive the regulatory information distributed by road operators and overlay this information onto the dynamic maps was proposed.

1

PurposeThe purpose of this study was to gather information
through a survey of the traffic information currently being
provided, as well as an exchange of views with the relevant
entities, for using semi-static and semi-dynamic information in the dynamic maps expected to be used by automated driving systems (ADS). A proposed testing plan has
been prepared for conducting tests to verify the use oflane-level traffic information for automated driving.

Study of Providing Lane-Level Traffic

2 Information in Japan

2.1. Specifications for semi-static and semi-dynamic
lane-level traffic information, as well as technical trends
and specifications for methods of provision.
2.1.1. Process for Providing Traffic Information
Fig.1 shows the process for providing traffic information.
Traffic information in Japan passes through three stages
before use: information collection and consolidation,
information processing and editing, and information provision.

2.1.2. Specifications for Semi-Static and Semi-Dynamic Lane-Level Information
Common data formats and specifications have been
issued to interchange road traffic information smoothly
between traffic administrators, road administrators, theJapan Road Traffic Information Center (JARTIC), and the
Vehicle Information and Communication (VICS) centeron the process for providing traffic information (see Fig. 1).
The VICS center, UTMS society of Japan, and Highway
Industry Development Organization (HIDO) jointly own
and manage the intellectual property concerning these
specifications. In Japan, traffic information has been provided as VICS links, which are segments created by dividing up t he road network into inter va ls b etween
intersections or other road sections.
2.2. Domestic Projects and Mechanisms for HandlingSemi-Static and Semi-Dynamic Lane-Level Traffic Information
The data format used to transmit semi-static and semidynamic traffic information via FM multiplex broadcasts isdefined in ARIB STD-B3 ARIB Standard for Operation of
The FM Multiplex Broadcasting System. Of the types ofroad traffic information, a format has been defined for regulatory information, making it possible to provide information on regulatory events at the individual lane level.
This format is almost the same as that for both infraredbeacons and radio wave (RF) beacons.
2.3. Points to Verify when Using Road Traffic Information for ADS
For ADS to be able to use lane-level traffic information, it
will be necessary to verify whether or not there are problems with regard to the issues described below.

Fig. 1 Process for providing traffic information (1).

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[ I ] Development of Automated Driving Systems① Dynamic Maps

A Study of Specifications for Providing Lane-Level Traffic Information in Dynamic Maps

2.3.1. Data Conversion Errors
In providing traffic information, errors may occur duringmultiple data conversion processes as shown in Fig. 2,
therefore verification is needed to ensure that there are nodiscrepancies in positional recognition.
Data processing, editing
and provision

Information use

3
Fig. 2 Process for road traffic data format conversion.

2.3.2. Differences between Dynamic Maps and Digital
Road Maps
When the map used by the ADS (dynamic map) and themap used to provide traffic information (digital road map)
are different (see Table 1), it must be verified that the traffic
information can be correctly correlated with the dynamic
map.
Table 1 Comparison of dynamic map and digital road map.

2.3.3. Time Lag at Each Stage in the Generation,
Transmission and Use of Information by the Vehicle
Time lags as shown in Table 2 may occur during the provision of traffic regulatory information.
Table 2 Types of time lag and examples.

28

Study of Domestic Efforts Relating to theProvision of Lane-Level Traffic Information

3.1. Exchange of Information with Relevant Entities inJapan Regarding the Provision of Lane-Level Traffic
Information
We held opinion exchange conferences to share information about the provision of lane-level traffic information
with relevant entities in Japan. The attendees were the
National Police Agency Traffic Bureau, the Ministry ofInternal Affairs and Communications Telecommunications
Bureau, and the Ministry of Land, Infrastructure, Transport and Tourism Road Bureau, while the observers were
the VICS center, HIDO, the Japan Traffic Management
Technology Association, the Japan Automobile Manufacturers Association (JAMA) , the Japan Digital Road Map
Association (DRM) , JARTIC, the UTMS society of Japan,
East Nippon Expressway Company (NEXCO East), Central Nippon Expressway Company (NEXCO Central) andthe Dynamic Map Large-Scale Field Operational Test Consortium.
3.2. Study of the Specifications for Lane-Level Traffic
Information Needed to Implement Automated Driving
We investigated formats and specifications for the lanelevel traffic information needed to implement automateddriving, as well as use case scenarios for connected andautomated vehicles (CAVs) utilizing this information.
On the highway, lane-level traffic regulatory information
defined in common data formats and specifications wasprovided through FM multiplex broadcasting and ETC 2.0.
Lane-level traffic regulatory information also may be presumed to be used in automated driving.
JAMA has organized four use case scenarios for CAVs, as
shown in Table 3. These common use cases are not only
applied by JAMA, but also shared with other related organizations(2).

[ I ] Development of Automated Driving Systems① Dynamic Maps

A Study of Specifications for Providing Lane-Level Traffic Information in Dynamic Maps

Table 3 Prioritized use case scenarios for CAVs (2)Table 4 Proposed test schedule and locations.

3.3. Preliminary Draft Plan for Test of the Provision of
Lane-Level Traffic Restriction Information
Study of test plans in anticipation of the proving tests tobe conducted during the next fiscal year by the Cross-Ministerial Strategic Innovation Promotion Program (SIP), in
order to confirm the following two points:
1. Technical confirmation that lane-level traffic restriction information can be used in dynamic maps.
2. Confirmation by test participants in the dynamic map
proving tests that lane-level traffic restriction information can be used.

Fig. 4 Test locations and provision status of lane-level traffic restriction
information.

3.3.1. Proposed Configuration of Equipment to Use
for Technical Verification
For the “dynamic map” proving tests in the SIP Automated driving system / Large-scale Field Operational Tests,
the test functions used by dynamic maps on the vehicle
side are expected to be employed for the lane-level traffic
restriction information that provided by the ETC 2.0 roadside unit (see Fig. 3).
Verification of the test functions (on the vehicle side) thathave been constructed in the dynamic map proving tests.

Fig. 3 Anticipated equipment configuration.

3.3.2. Proposed Test Schedule and Locations
We proposed test schedule and locations as shown in
Table 4. Fig.4 shows the test locations and provision statusof the lane-level traffic restriction information.

4 Conclusion
We confirmed that lane-level traffic restriction information (restrictions on driving in driving lanes, passing lanes,etc.) is currently being provided on some expressways
using FM multiplex broadcast and the ETC 2.0 system. The
points of contention regarding the use of lane-level traffic
restriction information on ADS were identified. During
the next fiscal year, proving tests to verify that lane-leveltraffic restriction information can be used in dynamic
maps are expected to be conducted through the SIP Program, and preliminary draft plans for the proving tests are
currently being studied.
References(1) VICS Center website (http://www.vics.or.jp/en/vics/
pdf/vics_pamph.pdf).
(2) Norifumi Ogawa: “Status of Connected Vehicle Technology Development for Automated Driving in Japan”,
SIP-adus Workshop 2017, http://en.sip-adus.go.jp/evt/
workshop2017/file/evt_ws2017_s3_NorifumiOgawa.
pdf, (2017).

SIP-adus: Project Reports, 2014-2018

29

I

Development of Automated Driving Systems① Dynamic Maps

Change Detection and Automated Mapping for
Dynamic Maps
Yasunari Goto, Takuma Kadoya (Mitsubishi Electric Corporation)
ABSTRACT: Static high-precision 3D maps, which form the basis of dynamic maps promoted and maintained for use as automatic driving
maps on highways, are required to remain up to date and maintain a high level of quality. We proposed an automation technology for map
generation to reduce map creation costs and realize quick updates. We confirmed the effectiveness of improving the map generation cost bycomparing and verifying the automation technology and existing map generation method. As improvement measures included not only map
generation but also data measurement, we implemented real-time measurements and diagrams. We will proceed with a verification of thetechnology for practical application.

1

Introduction

Static high-precision 3D maps, which form the basis ofdynamic maps promoted and maintained for use as automatic driving maps on highways, are being developed.
Static high-precision 3D maps are required to remain up todate and maintain a high level of quality. If we can alleviate
the cost burden on users while satisfying the update frequency and quality requirements, their use can be expectedto spread. We examined the time and cost improvement
effectiveness of creating and updating maps using automation technology.

Practical Verification of Automation

2 Technology

2.1. Static High-Precision 3D Map Data Generation
Process
An example of the process leading up to the generation of
static high precision 3D map data is shown in Fig. 1. The
map generation process is classified into measurement andmapping. We considered the application of automation
technology for this mapping process. For verification purposes, an intercity highway (the Tomei Expressway aroundthe Ebina junction) and an urban expressway (the Metropolitan Expressway, C1) were taken as typical examples ofhighways.

Fig. 1 An example of the flow until static high precision 3D map data is
generated

30

2.2. Practical Verification of Automated Mapping
2.2.1. Performance Evaluation of Automated Mapping
The following indicators are used as evaluation indices forautomated mapping. These definitions are shown in Fig. 2.
(1) Correct ratio: (C) / (B) in Fig. 2
Ratio of the correct features in the automated mapping
results.
(2) Detection ratio: (C) / (A) in Fig. 2
The ratio of all correct features detected by automatedmapping.
(3) False detection ratio: (3) in Fig. 2
Ratio of items that do not actually exist found in the automated mapping result.
(4) Undetected ratio: (4) in Fig. 2
The ratio of all correct features not detected by automatic
mapping.

Fig. 2 Definition of evaluation index

Table 1 shows the evaluation results on the expressway.
Table 1 Evaluation results of automated mapping[ I ] Development of Automated Driving Systems① Dynamic Maps

Change Detection and Automated Mapping for Dynamic Maps

Figure 3 shows an example of the result of automated mapping. For the shoulder edge and road lines, the results ofautomated mapping were compared with the correct data.

Shoulder edge: About 75% reduction
Lane division line: About 30% to 40% reduction

Fig. 5 Confirmation of the effectiveness of improvement by comparing
map creation time

Fig. 3 A result example of the automated mapping

As shown in Fig. 4, the error factor of the shoulder edge
was dominant in the case where the point cloud data of theshoulder was not acquired due to obstruction by plants ora vehicle running parallel to the measurement vehicle.
Automated mapping has a function to generating theshoulder edge by interpolation if a vehicle running in parallel blocks a certain section. However, there are cases
where interpolation is impossible, so there is room for
improvement.
For the lane division line, there were cases where roadmarkings such as paint blurring and arrows were erroneously detected as lane division lines. In particular, theinfluence of road markings is observed more frequently incurves than in straight lines, and we will continue toimprove and refine the detection algorithm for automatedmapping.

2.3. Practical Verification of Change Detection
Two kinds of technologies were examined and evaluatedas change detection technology for map updates. One ispoint cloud change detection, which detects changes inroads and makes it possible to judge whether a modification of the map is necessary. The second is dynamic mapschange detection, which extracts only the features to
update in the static high-precision 3D map. Through these
two techniques, it is easy to generate update data and distribute it.
2.3.1. Point Cloud Change Detection
In order to judge whether there was a change in the road,we extracted the difference between past point cloud dataand newly measured point cloud data using point cloudchange detection technology. The extracted difference
points are shown in Fig. 6. Four items, representing differences in shape (addition and deletion) and reflected luminance (addition and deletion), in the point cloud data aredetected in order to detect changes in road shape, lane
markings and road signs. In Fig. 6, a change in the lane
division line has been extracted. Based on this detection
result, it can be judged that a correction of the lane division line is necessary, and that only the lane division line
should be corrected on this route.

Fig. 4 Examples of error factors

2.2.2. Verification of the Effectiveness of Improvement Achieved with Automated Mapping
As shown in Fig. 5, the time required for automatic
method and for the previous method (manual mapping)
was measured to confirm the effectiveness of the improvement obtained by applying automated mapping. We confirmed the following reductions in map creation time(including automatic processing time and manual confirmation / modification time) when automated mapping isused.

The difference in lane
division line is shown ingreen.

Fig. 6 Example of the point cloud change detection

2.3.2. Dynamic Maps Change Detection
When a change in the road is detected by point cloudchange detection, the static high-precision 3D map is corSIP-adus: Project Reports, 2014-201831

[ I ] Development of Automated Driving Systems① Dynamic Maps

Change Detection and Automated Mapping for Dynamic Maps

rected by the automated mapping technique. We extracted
the difference between the result of automated mapping
and the existing static high-precision 3D map throughdynamic map change detection. The results are shown in
Fig. 7. By comparing the data, it is possible to detect theparts with differences and extract variations on a featurebasis. Although a separate discussion is required for the
distribution method of the update data, it is expected thatthe data update and distribution cost can be reduced byminimizing the amount of data to replace.

Fig. 9 Real-time MMS process with automation technology

In order to cope with the invisible satellite section, inertial navigation system (INS) combined positioning, whichuses the inertial measurement unit (IMU) and the odometer, was adopted, and post-processing analysis of MMS wasrealized in real time. By sequentially applying the point
cloud data generated in real time to the processing of automation technology, it is possible to generate the map in thevehicle.
Fig. 7 Example of the dynamic maps change detection

3

Real-Time Automation Technology

In order to more efficiently generate and update static
high precision 3D maps using automation technology, we
combined automation technology with real-time MMS to
make the automation operate in real time. Since thedynamic map data conversion is performed in the MMS
vehicle, spending the time required for normal MMS measurement such as copying terabytes of data, is unnecessary,and real-time MMS also eliminates the time needed togenerate the point cloud data. Figure 8 visualizes the process from measurement to mapping.

Fig. 8 Process from measurement to mapping using real-time MMS andautomation technology

Figure 9 shows the overall process. For real-time centimeter grade high precision positioning with real-time MMS,
we used the centimeter level augmentation service (CLAS)of the quasi-zenith satellite system (QZSS), which makes it
is possible to generate highly accurate point cloud data inreal time.

32

4 Conclusion
We evaluated the practicality of the automation technology and verified the effectiveness of the improvement withthe aim of improving the efficiency of generating andupdating static high-precision 3D maps serving as thebasis for dynamic maps. It was confirmed that false detection and non-detection by the automation technology is
predominant due to disturbance such as influence of plants
or other vehicles, and that point cloud data can be gatheredcorrectly if it can be measured without disturbance. However, since actual disturbance always occurs in actual measurements, we will promote the practical application of
automation technology and work on making continuous
improvements to the algorithm.
We will also proceed with the verification of further efficiency improvement for future map updates using realtime automation technology.
About the authors
1) Yasunari Goto is the Deputy General Manager of Information Technology Systems Department of Mitsubishi
Electric Corporation Kamakura Works.
2) Takuma Kadoya is the Manager of Spatial Information
Systems Engineering Section Information Technology
Systems Department of Mitsubishi Electric Corporation Kamakura Works.

I

Development of Automated Driving Systems① Dynamic Maps

Feasibility Study for Multipurpose Use of Dynamic
Maps
Naoyuki Tsuchida (Dynamic Map Platform Co., Ltd)ABSTRACT: This study was performed in order to identify the challenges for specifications in dynamic map preparation and clarify the necessary basic data structure requirements and the challenges to be resolved, based on on-site demonstration using specific use cases, for theultimate purpose of employing the dynamic maps and three-dimensional map common platform data acquired during the dynamic map
preparation process in public services.

1 Study Summary
The purpose of this study was to promote the utilizationof dynamic maps for automated driving systems along withthe three-dimensional map common platform dataacquired during the platform development process (3D
map common platform data) in public surveying and multipurpose applications, with the aim of creating new value
and services through future realization of the goals ofJapan’s Society 5.0 vision. We have clarified the basic data
structure requirements that are necessary in terms of application to public surveying, road ledger preparation, supp or t for s now re mov a l, and tele c om mun ic at ion
infrastructure management, along with the challenges tobe resolved, and we have established specific use cases andevaluated the requirements from the standpoints of preparation methods, procedures, quality, and cost. With respect
to using 3D map common platform data, etc. in public
surveying and multipurpose use, the idea is that 3D map
common platform data will be supplied according to theneeds of users, as shown in Fig. 1. In addition, it isexpected that the existing content will be expanded,
depending on the requirements for use in public surveying
and multipurpose use.

2

Study on Use in Map Applications for
Public Use (Study for Application in Public Surveying)

2.1. Identification of 3D map Common Platform Data
Requirements (Accuracy- and System-Related Challenges)
In the development of 3D map common platform data,
which is prepared by private entities for use in public surveying results, the data must have adequate accuracy foruse in public surveying, and must meet the system-related
requirements of public surveying. Therefore, we have prepared a 3D Map Common Platform Data and Dynamic
Map Work Manual (Preliminary Draft) by verifying
whether the level of accuracy is adequate for use in public
surveying and whether the necessary requirements of public surveying are met, while referring to results from past
years. With regard to accuracy requirements, we have concluded that the only solution is for the 3D map common
platform data to satisfy accuracy requirements equivalent
to those of the public surveying work regulation standards(revised March 31, 2016). With regard to accuracy-related
challenges, we have clarified differences between the work
regulation standards and the preparation of 3D map common platform data and studied techniques and operation
methods that are both rational and efficient. We haveplaced the greatest emphasis on how to lower the cost of
Table 1 Measures for use of automated driving dynamic maps in public
surveying

Fig. 1. Diagram of 3D map common platform data when expanding
automated driving dynamic maps for multipurpose use

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[ I ] Development of Automated Driving Systems① Dynamic Maps

Feasibility Study for Multipurpose Use of Dynamic Maps

installation of correct-answer values (ground control point,abbreviated GCP) obtained on-site using evaluation methods of absolute accuracy. Meanwhile, with regard to system-related challenges, it was necessary to study means of
implementing the project of preparing 3D map common
platform data in the context of public surveying. Table 1
below summarizes the (proposed) accuracy-related andsystem-related measures, with reference to advice andguidance from the Geospatial Information Authority of the
Ministry of Land, Infrastructure, Transport and Tourism.

topics as necessary. This will lead us to study the requirements needed for revision of the specifications for the
preparation of dynamic maps for use in automated driving
systems, based on a model of utilization in the following
areas: road ledger preparation and updating, support forsnow removal, and everyday utility pole/power line inspection, maintenance, and management.

2.2. Study of Methods to Assure Accuracy and Quality
Under the existing approach, when differences from GCP
fail to meet the standard, accuracy is improved either by
repeat measurement or by means of adjustment based on acontrol point. However, GCP installation has a considerable impact on cost because of the on-site work that it
involves. Therefore, we have devised ways to make effective
use of existing resources of the public and private sectors,
such as a method of GCP limitation by combined evaluation and analysis of good measurement data as an effective
replacement for GCP installation, and enabling the use of
aerial photography results in verification. We have also
given consideration to new technologies expected to beeffective in 3D map common platform data preparation,accuracy improvement using quasi-zenith satellites, andthe feasibility of automated plotting based on high-density
point group data.

4.1. Positing and Demonstration of Use Cases
Actual field data was obtained and used to evaluate thevalidity of the results of the theoretical study. For application to public surveying, based on the Work Manual (Preliminary Draft), we demonstrated the effects of feasibility
and practicality in the field, and estimated the cost of preparation of 3D map common platform data. The estimate
confirmed that an increase of approximately 16% could be
anticipated with preparation according to the existing standards for public surveying, while the expected increase
would be only about 6% for preparation according to theWork Manual (Preliminary Draft). Based on the study
results, the Work Manual (Preliminary Draft) was compiled with the Work Manual (Draft) of 3D map common
platform data and dynamic maps. In road ledger preparation and updating, when the accuracy and imaging rangeof the 3D map common platform data meet the requirements of the specifications for the preparation of road ledger drawings, this can substitute for basic data collection,
or a portion of the work. In addition, road edges, which are
handled as features of dynamic maps, can be directly usedas sidewalk-road boundary data. (Some processing may beneeded depending on the place of preparation.) Shapes
such as signals and road signs are usable, because they are
similar to the acquisition standards of dynamic maps. With
regard to support for snow removal, location information
on individual features can be extracted, and this can beused as basic data for data preparation. With regard to
everyday utility pole/power line inspection, maintenance,and management, the data acquired through such operations was shown and explained to workers, assuming uses
such as on-site confirmation prior to construction for the
purpose of verifying the increase in efficiency and practicality of using 3D map common platform data. Meanwhile,it has become clear that 3D map common platform data
would have a limited scope of applicability as input data
for the automatic identification of utility pole inclinationand electric line sagging during inspection of equipment.
Since the identification technologies for cable inspection
purposes are highly dependent on point group density, the3 Study for Utilization in Various Fields
Confirming the Usefulness of 3D Map Common Platform
Data Based on the Specifications of Systems for AutomatedDriving Mobile mapping systems (MMS) are promising for
future multipurpose use, because they use high-resolution
lasers and images to supply the information needed byautomated vehicles when detecting their surroundings,
and offer a high level of visibility and affinity. However, thespecifications of MMS equipment are varied, and it is necessary to study the requirements needed to improve theirmultipurpose and practical expandability. These systems
are expected to be used in many ways by relevant government agencies and key infrastructure management companies because of their public nature. In the study, we haveexamined use cases and identified the requirements andchallenges in order to clarify the differences between conventional maintenance methods in each of the use cases
and the maintenance specifications for dynamic maps foruse in automated driving systems. We also held periodic
study sessions with persons involved in other SIP research
34

On-Site Demonstration Based on Specific

4 Use Cases

[ I ] Development of Automated Driving Systems① Dynamic Maps

Feasibility Study for Multipurpose Use of Dynamic Maps

results of automated identification by the conventional
type (27,000 points/second) show low accuracy of detection. In high-density measurement (250,000 to 1,000,000
points/second), the rate of accurate identification increased
to over 90%.
4.2. Results of Verification of Effectiveness for Multipurpose Use (Changes in 3D Map Common Platform
Data)
Figure 2 below shows the points of change in the equipment requirements, acquisition scope requirements, plotting requirements, and freshness requirements as
preparation specifications to increase affinity when thespecifications for the preparation of 3D map common platform data for use in automated driving systems are applied
to the three use cases that were used in this study.
Changes will need to be studied to meet the necessary
requirements for multipurpose use in the future, mainlywith regard to the portions outlined in red in Fig. 2. Also,
since it has become clear that dynamic maps and 3D map
common platform data can be given a high degree of
expandability in multipurpose use, we have learned that it
will be necessary to change the work processes in the stan-Fig. 2 Equipment requirements, acquisition requirements, etc. when
applying the specifications for preparation of 3D map common
platform data, etc. for automated driving systems
Table 2 Changes from the 3D map common platform data for automateddriving

dards and dynamic map preparation specifications, and tochange the 3D map common platform data through additional file provisions to supplement features that were lacking in terms of multipurpose use. The changes and benefits
are summarized in Tables 2 and 3 below.

5 SummaryIn this study, we have examined 3D map common platform data for use in automated driving systems with regard
to its application to public surveying, and organized therequirements and issues related to multipurpose use. We
have drafted a Work Manual for more efficient and economical preparation for application to public surveying. To
prepare 3D map common platform data that is highly public in nature and more flexibly allows multipurpose use, it
is important to unify the location standards. It is also necessary to study ways to achieve uniform accuracy, while
giving consideration to the implementation techniques andquality evaluation methods indicated in the work regulation standards. Meanwhile, it is also necessary to study
ways to ensure that the dynamic map preparation projectwill be of a public nature in the future. In multipurpose
use, we have conducted evaluation in terms of data quality
and the cost of preparation. We have determined that utilization will become feasible in the three use cases studied
by making changes in work processes at the time of datapreparation and providing additional files at the time ofdata provision, and we have learned that the cost of preparation will increase by about 6% compared to existing
costs. Meanwhile, expanding the scope of application to
include everyday utility pole/power line inspection, maintenance, and management, and making changes to satisfy
all of the studied use cases, including use as input data inmaintenance and inspection, would require changes inmeasurement equipment and incur additional data management costs due to increased data capacity, in addition
to the above cost increase of around 6%. The reduction in
total work hours due to changes in work methods andimproved accuracy compared to data preparation using
existing methods can be expected in both cases. In addition, we have determined that it is possible to achieve sharing and reduction of preparation costs through data
sharing, as well as ensure the safety of workers. Further
benefits are also anticipated, including more efficient measurement work due to changes in measurement equipment,
and more accurate automatic plotting and improved detection rates due to higher density 3D map common platform
data (point group data). Data utilization based on multipurpose use is also expected to have both direct effects and
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[ I ] Development of Automated Driving Systems① Dynamic Maps

Feasibility Study for Multipurpose Use of Dynamic Maps

secondary benefits.
As a final note, this study was implemented with cooperation from NTT Data Corporation, MRI Research Associates, Inc., Mitsubishi Electric Corporation, and PASCO
Corporation.
Table 3 Summary of verification of multipurpose use benefits

36

I

Development of Automated Driving Systems① Dynamic Maps

Study on Considerations for the Making of a Business
Model for the Dynamic Map Service Platform
Isao Yagasaki (Fujitsu Limited)
Akio Kani(Hitachi, Ltd.)
Yukikazu Kanoya(Mitsubishi Electric Corporation)
Akinori Sato(NEC Corporation)
Naoki Iso(NTT DATA Corporation)
Masaharu Hamaguchi(Oki Electric Industry Co., Ltd.)
Takeharu Arakawa(Pioneer Corporation)
ABSTRACT: The concept of Society 5.0, a super smart society aiming to balance economic development and the resolution of social issues,states that intelligent transport systems, one of the 11 types of system identified in the 2015 Comprehensive Strategy, will be developed as coresystems. The construction of a platform enabling various systems to utilize dynamic maps is needed to create new values and services. For thatreason, it is important to realize the Dynamic Map Service Platform, a base system for the use of dynamic maps that is planned to be constructed in the context of the SIP-adus project and encompass various fields. The object of this project is to develop the concept of the
Dynamic Map Service Platform, which will be utilized in various fields in the future, as a part of government measures involving cutting-edge
ICT technologies and extensive collaboration between wide-range of parties.

Concept of the Dynamic Map Service

1 Platform

1.1. Definition of the Dynamic Map
In this project, we defined the components of the
Dynamic Map as shown in Fig. 1.

Fig. 2. Outline of the SPF

Fig. 1 Definition of the Dynamic Map

The dynamic map consists of both map and geographic
information. Map information includes the 3D maps madeby SIP, and the 2D maps provided by map vendors. Geographic information is the generic name for information
linked with map information, such as longitude, latitude,
or load ID.
1.2. Outline of the Dynamic Map Service Platform
The Dynamic Map Service Platform (SPF) has a mechanism which enables the geographic information, stored atvarious fields, to be utilized in various fields (see Fig. 2).

It is assumed that the types of geographic information
handled by the SPF will gradually expand. For that reason,
scalability should be considered for the SPF. Accessibility,
including the preparation of interfaces for various systems,
should also be considered because the SPF sends andreceives a variety of information through connections withmany systems. In addition, it is necessary to consider security measures to use the information in a safe and securemanner.

2 Considerations for the SPF
2.1. Study of Use Cases for the SPF
We considered SPF service models in various fields, andstudied the needs and feasibility of those models. We also
considered the information processing required in those

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Study on Considerations for the Making of a Business Model for the Dynamic Map Service Platform

models, and sorted out the requirements needed to create
the SPF architecture.
2.2. Architecture of the SPF
Based on the results of the above study, we consideredsystem functions that enable the SPF to distribute geographic and map information to various fields (see Fig. 3).

Fig. 3 Architecture of the SPF

3 Demonstration of the SPF
Based on the results of considering the use cases andarchitecture of the SPF described in Section 2, Considerations for the SPF, we made a prototype to demonstrate its
value.
3.1. Dynamic Map Information
Based on the results of considering the use cases of the
SPF, we chose the dynamic map information that should
be handled by the SPF as listed in Table 1.

3.2. Service Model
Through the study of the use cases for the SPF, we clarified the issues in actual businesses in each field. We
selected issues that match the following conditions.
- Issues that can demonstrate solutions in collecting the
dynamic map information during the project activity
period.
- Issues that can be resolved by capitalizing on thestrong points of the SPF, such as gathering and collating the various types of dynamic map information.
We prepared service models for each field that resolve theabove issues, and created prototypes.
3.2.1. Logistics Field(1) Outline of the Service Model
This service model provides a safe route for truck drivers
by analyzing the latest traffic information based on the
dynamic map information such as vehicle probe information, road width, and the weather.

Table 1 Selection list of information that should be linked to dynamic map

Fig. 4 Service Model for Logistics(2) Effectiveness of the Service Model
It was clarified that this service model is effective forcommercial vehicle drivers and truck operation managers.
If commercial vehicle drivers lack the knowledge necessary to drive safely in a certain area (e.g., traffic information, or information near-miss spots), this service model isuseful for them. Truck operation managers can utilize thisservice model when they plan the delivery route, and sendalerts to truck drivers. At the same time, providing value38

[ I ] Development of Automated Driving Systems① Dynamic Maps

Study on Considerations for the Making of a Business Model for the Dynamic Map Service Platform

added information, such as traffic jam prediction, traffic
regulation and the travel history of different vehicle types,
is required to ensure this service model is actively utilized.
This service model is useful not only for safe driving, butalso for increasing the revenue of transport companies. If
the driving time for commercial vehicle can be calculatedwith high accuracy, the number of pick-up and shipping
destinations in a day can be predicted correctly. This thenmakes it possible to deliver a lot of goods with fewer
trucks.
3.2.2. Local Government Field(1) Outline of the Service Model
This service model is utilized to shorten the time ofarrival of emergency vehicles and to support appropriate
instructions such as the order of emergency vehicle dispatch and decision-making by superimposing a variety of
information such as vehicle probe data, or weather information on a 2D map using commercial GIS software, anddisplaying information and relevant directives on thescreen of the vehicle device.

Fig. 5 Service Model for Local Government(2) Effectiveness of the Service Model
This service model supports the firefighting and disasterprevention management tasks by local governments by, forexample, shortening the arrival time of emergency vehicles
to certain sites, or determining the order of the dispatch of
emergency vehicles. Traffic jam information is not currently fully utilized in firefighting and disaster prevention
management, and there are cases where it takes time forthe emergency vehicles to reach its destination. Details of
traffic conditions can be ascertained using this service
model, and arrival time to the site can be shortened. As aresult, this service model contributes to minimizing damage in the event of a disaster, as described below.
For humans: minimizing critical injuries, increasing thesurvival rate, and so on.
For the economy: minimizing the spread of fire, flooding
damage, and so on.
3.2.3. Construction Field(1) Outline of the Service Model

This service model provides various information, such as
road regulations, spots with frequent occurrences of rapid
deceleration, operation routes that take prohibited roads
into account, or entrances to construction sites, on themap screen. The information is shared between construction sites and construction vehicle drivers, allowing the useof the wrong entrance to be avoided, thereby reducing
vehicle accidents. Furthermore it make it possible to ascertain the driving conditions of construction vehicles andshare them among the people involved at the construction
site based on real-time vehicle probe data.

Fig. 6 Service Model for Construction(2) Effectiveness of the Service Model
It is effective to confirm road regulations and spots withfrequent occurrences of rapid deceleration on a map in
advance when designing construction vehicle routes. This
information, as well as information on prohibited roads,
which are decided by neighborhood agreement, are displayed on the map and can be shared between drivers andsites using this service model. This is effective at preventing
drivers from using the wrong route. In this demonstration,
we confirmed that no driver entered the prohibited roads
while using construction vehicle position data in real time.
This service model can reduce the management tasks ofthe construction site supervisor. If the position and statusof construction vehicles can be ascertained with a terminal
device in real time, it is possible for the site supervisor to
make work plans based on the construction vehicle arrival
time. Also, it is expected to reduce the task of making contact for supervisors and construction vehicle drivers.
3.2.4. Personal Navigation Field(1) Outline of the Service Model
This service model supports travel using personal navigation by displaying destinations such as communal facilities
around the station in a list, allowing users to easily search
for their destination while providing guidance and support
for user travel by highlighting feature information such as
public restrooms and crosswalks.

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Based on predictions of human movement flow andbehavior, information concerning congestion, such as theresident population, precipitation, and messages on social
media, is displayed on a map and visualized. This service
model provides services which contribute to enhancing theefficient management, attractiveness, and area safety fortransportation companies, public agencies, area management companies, and town planning councils.
Fig. 7 Service Model for Personal Navigation(2) Effectiveness of the Service ModelIn this project, we confirmed that providing information,such as the location of the doorways of facilities and thedetails on public restrooms, is effective at expanding services for a wide range of users including the elderly andpersons with disabilities. In light of the aging of society, it
is mandatory to improve services by taking user characteristics into account to maintain the number of personalnavigation system users.
3.2.5. Vehicle Service Field(1) Outline of the Service Model
This service model makes it possible to provide detailedroute guidance for individual road lanes by transmitting
information to the vehicle navigation system (road lane
information and per-lane traffic information), allowing
drivers to reach their destination faster and more safely.

Fig. 8 Service Model for Vehicles(2) Effectiveness of the Service Model
For general drivers, the service is expected to effectively
enable smooth driving through higher route guidance
accuracy than current vehicle navigation, and the visualization of traffic conditions in individual road lanes. For
the society overall, the effectiveness transport efficiency
can be expected to improve. Also, this service model also
has the potential to be utilized not only to acquire traffic
jam information, but also for automated cruising such as
maintaining road lanes and signal control, to estimate thenumber of participants at an event, and to detect vehicle
accidents.
3.2.6. Infrastructure and Area Management Field(1) Outline of the Service Model
40

Fig. 9 Service Model for Infrastructure and Area Management(2) Effectiveness of the Service Model
For the general users (residents, commuters, students,
visitors) in the area, the service is effective at resolving
issues related to travel support/congestion avoidance toenhance the attractiveness of the area. For transportationor security companies, this service model is effective at
resolving issues involving crime prevention/security (suchas understanding congestion situation in areas supervisedby the company as well as surrounding areas, improving
arrangements for security guards and attendants, or suitably increasing the number of temporary buses or trains)when unexpected congestion or a disaster occurs. For
transportation companies, public agencies and infrastructure companies, this service model is effective at achieving
efficient management when planning a special schedule,
vehicle guidance to parking lots, park maintenance, orother activities by providing useful data, such as congestion information in the surrounding areas, ahead of time.
3.2.7. SPF(1) Functions of the SPF
The SPF has functions to retrieve, provide, and catalog
the information it offers. Meta-information is displayed on
the web portal by the cataloging function. The SPF also has
a function to visualize a variety of information (it canchange the order of overlapping information), and changes
in that information due to the passage of time.

Fig. 10 Function image of the SPF

[ I ] Development of Automated Driving Systems① Dynamic Maps

Study on Considerations for the Making of a Business Model for the Dynamic Map Service Platformthe SPF.
(2) Effectiveness of the SPF
We could confirm the effectiveness of the SPF in the demonstration, as outlined below.
(a) Effectiveness of dynamic map information
It was confirmed that it is possible to use dynamic map
information collected in various fields and combined
across various fields. In particular, it was confirmed that vehicle probe information was effective as follows.
- If the amount of vehicle probe information increases,
the accuracy of traffic density estimation improves.
- Multiple combinations of large vehicle probe information make it possible to judge whether it is possible totravel on a road.
(b) Effectiveness of common interface
It was confirmed that developing a common interface
makes it possible to reduce development work hours to agreater extent than if interfaces were developed separately.
(For the service models studied in this project, three models utilizing the same information would have required thedevelopment of nine interfaces. However, using the SPF
reduced the number of interfaces from nine to three.)(c) Effectiveness of 3D maps
The effectiveness of the road lane, road width and pedestrian crossing information extracted from the 3D map was
confirmed. Road width information is needed in multiple
fields. Advances in the maintenance of maps for generalroads make those maps more effective. It was confirmedthat geographic information, such as road lanes, pedestriancrossings, signals for pedestrians, and sidewalks and steps,
can be used effectively after they are extracted from a 3Dmap.
(d) Effectiveness of prototype functions
It was confirmed that the meta-information listed in the
catalog portal can combine the seeds and needs of dynamic
map information. Visualizing the function of dynamic map
information is useful to obtain an intuitive understandingof granularity, of the provided areas, and the update frequency of dynamic map information. It was confirmedthat dynamic map information could be used on multiple
2D maps.

4 Future Tasks
The study conducted and considerations examined in thisproject identified the following issues in terms of finalizing

4.1. Establishing an Environment to Promote the Useof SPF
With support from governments and private industry
groups from each field, it is necessary to create an environment that promotes the provision of geographic and map
information from their owners to the SPF and fosters anincrease in the number of companies and corporations
accessing the SPF.
4.2. Formulation of Interfaces to Connect with Various Systems
It is necessary to define the interfaces (e.g., API, authentication methods) by checking the specifications of otherrelevant system to enable the exchange of information withthose systems.
4.3. Provision of Valuable and Unique SPF Information
To expand the business scale of the Dynamic Map Service
Platform, it is very important to gather and analyze a variety of information to produce valuable data that can beused cooperatively in specific business fields.
4.4. Providing Information in Real Time
It is necessary to consider an architecture that allows ashorter lead time (from getting the dynamic map information to analyzing and providing it). For example, it isexpected that the lead time between acquiring vehicle
probe information, analyzing it, and generating the traffic
information would be 5 to 10 minutes.
4.5. Utilization of Social Information
Public information, such as the regulation of traffic andaccident information, which is stored in local government
databases, is expected to be provided in real time in the
future. It is necessary to consider how the system willobtain this information.

5 Conclusion
We made prototypes of six service models and the SPF,
and interviewed companies representing potential users of
those service models. We confirmed the effectiveness ofthe six service models and the effectiveness of the service
platform through the above approaches.
5.1. For Dynamic Map Information
It was confirmed that it is possible to use dynamic map
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information collected in various fields and combined
across various fields. It was also confirmed that utilizing thevehicle probe information, which combines multiple kindsof data from vehicles and real time traffic flow information
based on that data is essential for many services.
Also it was confirmed that 3D maps would be easy to use
and that it is possible to expand their field of application by
providing features extracted in advance to service providers.
5.2. Service Models utilizing the SPF
In this program, after studying the use cases for the SPF
and its architecture, we made prototypes of six services andverified whether they could offer the expected value totheir users. The results confirmed that each service model
was marketable. At the same time, we deepened ourknowledge of how to use dynamic map information and of
the needs related to dynamic map information throughinterviewing various companies.
5.3. Effectiveness of the SPF
It was proven that it is possible to support the understanding of the content of individual dynamic map information items and the value generated by combining
dynamic map information to provide cataloging and visualization functions. It was also proven that it is possible toconnect to each service model by making prototypes of theinformation acquisition and provision functions, as well as
the geographic information management function, whichwere needed to distribute dynamic map information. The
benefit of dealing with different types of dynamic information in a common interface was also confirmed. It was possible to obtain the requirements of additional functions to
the interface from service providers, and to consider theinterface more concretely.

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Development of Automated Driving Systems① Dynamic Maps

Investigation about Availability of Satellite Positioning to Realize Automated Driving Systems
Mikihiro Hosoi (Aisan Technology Co., Ltd.)ABSTRACT: Recently, new satellite positioning methods with multi-GNSS or Quasi-Zenith Satellite System (QZSS) are becoming available.
In this survey, we installed several GNSS devices in a measurement vehicle, conducted experiments and evaluated the performance of eachsatellite positioning method. In addition, the tasks for using satellite positioning in automated driving systems were summarized. From thissurvey, it was confirmed that the new satellite positioning method makes driving lane recognition possible with no other positioning sensors.
However, it has also been shown that there are some restrictions, such as satellite signals shielded by buildings or slopes. To utilize the map
information, it is necessary to acquire the position obtained by satellite positioning, and the next step will be to effectively tackle improving
the availability and reliability of satellite positioning to achieve practical application.

1 Background
In automated driving systems, advanced sensors for deadreckoning and precise dynamic map information are themain methods used to determine vehicle position. Studies
to complement these local positioning methods utilizing
satellite positioning, which can obtain global positions
without being affected by weather or time, have been conducted. International discussion about automated driving
systems, such as the standardization of inter-vehicle communication, is expected to intensify in the future. Meanwhile, the various satellite positioning system providers areactively updating and deploying their systems. In that
respect, QZSS is aiming to start full practical operation in
2018, and the shift from experimental to practical service
has begun. It is necessary to verify the availability of satellite positioning in automated driving systems to contributeto the discussion of international standardization.

sis (MADOCA) precise point positioning (PPP).

Fig. 1 Measuring vehicle (MMS)

2.1. Evaluation of Satellite Positioning Accuracy
The positioning ratio of single-frequency positioning was
100%, and the RMS of lateral error was within 1.5 m. Even
with single-frequency positioning, it can recognize thedriving lane if the sky visibility is good.
Table 1 Positioning accuracy on expressway

2 Contents of Investigation and Review
We gathered satellite positioning information in urban
areas and an expressway between cities using a measurement vehicle equipped with various GNSS devices. We
used the mobile mapping system (MMS), which can obtainthe correct position where a vehicle has driven, in the measurement vehicle. We evaluated performance of each satellite positioning method in reference to the correct position
obtained from MMS. The target satellite positioning systems in this experiment were GPS, QZSS, GLONASS, Galileo, and BeiDou. The positioning methods examined are
single-frequency, multiple-frequencies, the sub-meter levelaugmentation service (SLAS) and centimeter level augmentation service (CLAS) in QZSS, RTK, and Multi-GNSS
Advanced Demonstration tool for Orbit and Clock Analy-

In positioning using QZSS CLAS, the fix ratio was as low
as 63.59%. This is mainly because L6 at the time of ourexperiment was only available for GPS and QZSS. We
expect this to improve as it is adapted to Galileo and
GLONASS in the future. The value of RMS is within 10 cm,
which can be said to represent sufficient performance. The
CLAS method, which does not depend on a base station, isa positioning method suitable for moving platforms. QZSSL1S, which is an augmentation signal for single-frequency
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Investigation about Availability of Satellite Positioning to Realize Automated Driving Systems

positioning, was evaluated by post-processing. The value of
RMS was 0.55 m, and the lateral error within 1.5 m was
90%.

Fig. 2 Heat map of positioning accuracy

Figure 2 shows a heat map of lateral error during expressway driving. The left is the result for single-frequency andthe right is the result of augmentation using the QZSSSLAS method. This positioning result is calculated fromobservation data of the same receiver by post processing. It
was confirmed that the positioning performance isimproved by the augmentation signal of QZSS.
2.2. Multipath Mitigation
Multipath is a major factor that degrades the performanceof satellite positioning. We studied the correlation withmultipath using data around the Ginza area where multipath occurs frequently due to the presence of many tallbuildings.
2.2.1. Measures Using Signals from Satellites
The possibility of improving positioning accuracy bychecking and removing the signal from the satellite when
signal strength is low or no carrier phase is observed wasconfirmed as a viable method of removing a signal including multipath in single frequency measurement. However,the positioning ratio falls as the number of satellites
decreases. It was confirmed that using RTK with Doppler
frequency coupling improved the positioning ratio from51.54% to 92.41%.
2.2.2. Simulation of Radio Wave Propagation
We confirmed that positioning performance can be
improved by removing satellite signals that generate multipath from the results of radio wave propagation simulation using a 3D map. However, we judged that utilizationin real time is difficult because calculating the simulation
takes a long time. Instead, we showed that by creating asight mask of satellite signals by simulation, it is possible toeliminate satellites in which multipath occurs.
2.3. Dynamic Map and Satellite Positioning
We examined methods of arranging the difference
between the dynamic map and the position information of
the satellite positioning and achieving consistency. We
examined methods to consistently determine the differ44

ence in position information between dynamic maps andsatellite positioning. The differences in the coordinate systems between dynamic maps and satellite positioning wereclarified, and a conversion method was demonstrated.
Figure 3 visualizes the crustal movement of Japan from
JGD2011 based on the semi-dynamic correction parameter
provided by the Geospatial Information Authority of Japan(GSI). It shows that a horizontal movement of about 1 m
has occurred in the Tohoku region or remote islands.
Moreover, it became clear that complicated distortion(deviation of direction) has occurred.

Fig. 3 Variation from 2011 to 2016

We showed that it is possible to reduce the error of thecoordinate value in the Tohoku region down to several
centimeters by applying grid parameters calculated fromthe movement amount estimated from the GNSS Earth
Observation Network System (GEONET) coordinate values.
2.4. Consideration of Integrity
Integrity is important to use the results of satellite positioning with confidence.
2.4.1. Integrity Evaluation of Satellite Positioning
We carried out static observation to verify the reliabilityof satellite positioning. We defined the error variance ofsatellite positioning solutions as the reliability of the satellite positioning and used error ellipse as an indicator toevaluate that reliability. As the non-line-of-sight (NLOS)
multipath does not follow a normal distribution, the rate at
which the positioning solution occurs outside the error
ellipse increases, which allows the positioning reliability
can be evaluated.

Fig. 4 Sky plot and satellite signal status

[ I ] Development of Automated Driving Systems① Dynamic Maps

Investigation about Availability of Satellite Positioning to Realize Automated Driving Systems

Figure 4 shows the influence of shielding objects on thesatellite signal. It can be confirmed that signal deterioration and cycle slip occur due to the influence of the shield.
We showed that positioning precision and reliability are
improved by rejecting NLOS through the use of multi
GNSS and an SNR mask. We propose a method to compare the error covariance of the observed value with theestimated prior error covariance from inertial navigation
sensors (INS) or other sensors as a method of detecting
missed fixes in a moving platform.
2.4.2. Investigating the Security of the Positioning
SignalAttack methods on positioning signals include jammingand spoofing. We clarified the attack method and investigated what kind of influence it has on the actual attack. We
investigated the methods of detecting and suppressing
attacks on the current positioning signal and evaluatedeach method. If the attack direction is known, it was foundthat suppression in the spatial domain using an array
antenna is the most suitable method. In addition, we evaluated the robustness against attack of specific receivers by
simultaneously sending the correct signal and a spoofed
signal at the same time to simulate a spoofing state. Figure
5 shows the experimental results of the strength of the synchronized spoofing signal. This result indicates it is possible to spoof when the spoofing signal becomes stronger
than the true GNSS signal.

cle equipped with many GNSS devices. We also showed amethod of evaluating satellite positioning in a moving
platform. It was confirmed that using the augmentation
signal, allows position information by satellite positioning
to be obtained at a higher level than before, and is fully
usable for precision around the level of lane recognition.
Using a high-accuracy augmentation service, we canobtain positioning results with an accuracy of about 10 cm.
Meanwhile, multipath and attacks on signals have become
serious issues. To use satellite positioning with confidence,
its results must have high integrity. Utilizing the position
information on the map with high accuracy, it is necessary
to deal with the difference between the satellite positioning
and the position of the map. Integrity and availability are
critical issues for satellite positioning. It is necessary tosolve these problems by combining satellite positioning
with other sensors. In the future, as satellite systems are
upgraded, the convenience of satellite positioning is
expected to further improve. The utilization of satellite
positioning in a moving platform requires ongoing information gathering.

Fig. 5 Synchronized simplistic spoofing attack

Furthermore, we showed that attacks can be detected bymobile combinations such as INS. Since the influence on
the positioning result when receiving an attack differs
according to the suppression technique used by thereceiver, filtering settings, and other factors, an evaluation
system using a simulator is indispensable in evaluating the
behavior of a specific receiver.

3 Conclusion
We have evaluated the accuracy and availability of satellite positioning in various cases using a measurement vehiSIP-adus: Project Reports, 2014-2018

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Development of Automated Driving Systems① Dynamic Maps

Report on model Verification for the Enhancement ofDriving Support Utilizing Traffic Signal Information(SIP NPA 1)
Yuichi Takayanagi (UTMS Society of Japan)
ABSTRACT: The effectiveness of introducing ITS wireless roadside units (the complement rate with infrared beacons and improvement in
the percentage of drivers who decelerate in reaction to information from the beacons) was evaluated using a model route (a route equippedwith ITS wireless roadside units and infrared beacons) installed in Aichi Prefecture.
The results of the evaluation concluded that an optimal combination of introducing infrared beacons and ITS wireless roadside units will
have a positive effect on intersections, particularly important intersections whose traffic signal cycle lengths and other parameters change inreal time.
In the future, the accuracy of signal information required for controlling automated vehicles will be examined. Cost reductions for roadsideunits must also be considered.

1

PurposeIn the field of ITS, vehicle-infrastructure cooperative systems that help to prevent traffic accidents are an essential
technology for connected and automated vehicles, thedevelopment of which is progressing rapidly around theworld.
In particular, to realize automated driving, a mechanismthat allows a vehicle to recognize traffic signal informationin real time and perform control is indispensable.
Therefore, surveys and research were carried out with theaim of enhancing driving support through cooperation
between signal information provided from roadside systems to vehicles and the automated systems installed inthese vehicles. In addition, existing systems were also studied, and methods of improving the accuracy of information provision, and operational management were
examined.

communication on the 700 MHz band is effective in consideration of security and actual performance.
2.2 Fiscal 2016
Selection of model points (Aichi Prefecture - Refer to Fig.
1)
An on-road driving survey was conducted at points in
key prefectures where advanced infrared beacon services
are not functioning effectively for distance-related reasons.
It was decided to install 700 MHz band wireless roadsideunits at five important intersections on the Seto Obu TokaiRoute in Aichi Prefecture where eight advanced infraredbeacons are installed.

Details of Implementation over Three-

2 year Period from Fiscal 2015 to 2017

2.1 Fiscal 2015
The Japanese Police have developed and introduced Traffic Signal Prediction Systems (TSPS) as vehicle-infrastructure cooperative systems that provide traffic signal
information. However, because TSPS is inadequate to provide signal information at important intersections where
the traffic signal cycle length varies according to traffic volume, various types of communication media including
Wi-Fi were evaluated and examined in a comparative verification of communication media to complement advanced
infrared beacons. The results concluded that ITS radio
46

Fig. 1 Model points in Aichi Prefecture

2.3 Fiscal 2017
Utilizing the multi-adapter (including a hybrid receiving
function for receiving information via 700 MHz and information via advanced infrared beacons), which was used in
SIP projects led by the Ministry of Internal Affairs andCommunication, a function to sort the data describedabove when receiving and storing data from the adapter

[ I ] Development of Automated Driving Systems① Dynamic Maps

Report on model Verification for the Enhancement of Driving Support Utilizing Traffic Signal Information (SIP NPA 1)

was developed, and an application was built for displaying
TSPS information on a PC monitor.
Data was obtained in 514 road tests using a demonstration system that displays traffic signal colors and theremaining seconds until the signal color changes.
From the data obtained, the number of times that effective support was provided by infrared beacons, and thecomplement rate of the ITS wireless roadside units to theinfrared beacons were derived.
Further, the number of sudden decelerations and energy
consumption were tallied and examined based on theacceleration data of the test vehicles.
In addition, the difference between the signal colors at thefive important intersections and the remaining seconds
shown by the system was measured using a high-precision
video camera (to an accuracy of 10 ms).

3 Evaluation Details
An example of the evaluation system screen is shown in
Fig. 2.

Fig. 2 Evaluation system screen (example)

In fiscal 2017, statistical data about the following eightitems were collected and evaluated.
(1) Verification of complement rate (effective support rate)
for information at important intersections [effectiveness verification](2) Confirmation of Received Signal Strength Indicator(RSSI) information relating to ITS radio communication [verification of basic function](3) Comparison of fuel consumption [effectiveness verification](4) Comparison of stop time ratios and stop time lengths[effectiveness verification](5) Evaluation of longitudinal acceleration [effectivenessverification](6) Verification of origin-destination travel times (southbound and northbound travel times) [effectivenessverification](7) Confirmation of the accuracy of signal information
provision [verification of basic function](8) Compilation of the results of questionnaire surveys
from participants (drivers) about the acceptable rangeof difference in the remaining signal time, and at how
many meters from the intersection it was helpful toreceive the information.

4 Evaluation Results
Among the items described in Section 3, the results ofmain items (1), (5), (7), and (8) are shown from Table 1 toTable 3.
Table 1 Complement rate of service

After confirming the status of information complementation with ITS wireless roadside units at important intersections where the service cannot be provided by infraredbeacons alone (due to reasons such as the range of traffic
signal cycle length variations and expiration of the validity
period), it was found that the complement rate at all fiveimportant intersections reached 50% or higher.
Table 2 Percentage of sudden deceleration

Improvement in the smoothness of driving was confirmed by comparing and evaluating longitudinal acceleration. It was also confirmed that the frequency of sudden
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Report on model Verification for the Enhancement of Driving Support Utilizing Traffic Signal Information (SIP NPA 1)

decelerations decreased when the remaining seconds of agreen signal were provided.
Table 3 Accuracy of signal color information

Accuracy of within 100 ms was obtained at the importantintersections after verifying the difference in accuracy bymeasuring the variation between the information provided
via the ITS radio communication and the actual traffic signal phases.
The variation in the results is probably due to fluctuationsin the output time required to switch from the signal controller to the signal lamp device.
The questionnaire survey of drivers examined the effects
of introducing the system. Responses stated that the system is significant because, if it allows drivers to receive
information about the intersection through which thevehicle is going to pass at a point 300 to 500 m from theintersection, as well as information about the next intersection, then drivers can adjust their speed, which is an effective way of helping to alleviate congestion.

5 Conclusion and Discussion
The research project found that the introduction of 700
MHz ITS wireless roadside units helps to complement thefunction of advanced infrared beacons.
However, research needs to be continued to investigate
the following five issues.
(1) The reliability of the system in cases where differences occur in the timing of signal phase changes
obtained from multiple information sources (infraredbeacons and ITS radio communication)(2) Statistical study on driver behavior when informed
about a situation in which traffic signal phase changes
are predictable.
(3) Optimization of HMI, such as the timing of information provision to drivers(4) Correlation of the situation observed in this research48

project in which the influence on the driver’s psychology cannot be eliminated to match the current statusof automated driving.
(5) The effects of traffic signal information provision on
automated driving algorithms.
(*) Reference
1. The 15th Symposium on ITS 2017 (in Japanese)[Analysis of the impact of the percentage of drivers on arterial roads who responded to signal information on thepassage through traffic lights] (in Japanese)
K. Nishio*1, Y. Matsumoto*2, M. Sugita*3
Meijo University, Graduate School of Science and Technology, Civil Engineering Major*1
Meijo University, Graduate School of Science and Technology, Department of Civil Engineering*2
Japan Road Traffic Information Center*3
2. 24th World Congress on ITS presentation materials
Yuichi Takayanagi (2017). Strategy of Practical Implement V-I Cooperative Systems for Traffic Accident
Avoidance (24th World Congress on ITS, Montreal)
3. 25th World Congress on ITS presentation materials
Yuichi Takayanagi, Shunichi Kawabe (2018). Advanced
Traffic Signal Prediction Systems(TSPS)I

Development of Automated Driving Systems① Dynamic Maps

METI 3: Development and Demonstration of Technology
for Real-time Utilization of Traffic Signal Information
Masao Fukushima (UTMS Society of Japan)
ABSTRACT: This project is aiming to develop driving support systems that are effective at preventing traffic accidents at intersections, as well
as reducing traffic congestion and environmental burden, while assessing and examining the cost effectiveness and driver acceptance of such
systems. The systems covered by this project utilize traffic signal information (a collection of traffic signal phase and timing information fromup to 16 downstream intersections) provided by advanced infrared beacons, which are scheduled to be installed in the future. The project will
be implemented under the auspices of the Ministry of Economy, Trade and Industry in consideration of commercialization, and involves theparticipation of the National Police Agency from the standpoint of traffic safety.

1 Commissioning Framework
UTMS Japan will receive the order for the project, andSubaru Corporation, Nissan Motor Co., Ltd., and Mitsubishi Motors Corporation will be re-commissioned todevelop an on-board system that allows vehicles smoothpassage through signalized intersections, safe deceleration,and smart idling stop using traffic signal information fromadvanced infrared beacons, for which installation or
replacement has already started. The project will also verify
the effectiveness and acceptance of the traffic signal information, as well as its impact on traffic flow.

3 Services that Utilize Signal Information
The following five services that utilize route traffic signal
information were tested.

2 Route Traffic Signal Information
As shown in Fig. 1, the traffic control center determines
the optimal traffic signal parameters (cycle, split, and offset) based on traffic volume data obtained from traffic
detectors, and provides traffic signal information to vehicles via advanced infrared beacons. However, as the traffic
control center periodically updates these traffic signal
parameters, traffic signal information may lose its freshness while vehicles are driving along a set route. This may
cause a time gap between actual signal indications and predicted indications on vehicle displays.
Furthermore, if there is an actuated traffic signal along aroute whose indication is affected by nearby traffic, a time
gap similar to the above will also occur.

Fig. 1 Traffic Control Center signal control system
Fig. 2 Five services that utilize signal information
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METI 3: Development and Demonstration of Technology for Real-time Utilization of Traffic Signal Information

4 Development of On-board unit
An on-board unit for realizing and testing the services
described above was developed by the car manufacturers
participating in the project. The on-board unit is equippedwith an antenna for advanced infrared beacons, a processing unit, forward and backward cameras, vehicle CAN
data, a display for providing information to drivers, and anaudio and voice function. The on-board unit can record
how the test vehicle was driven using signal information.
Due to differences in the configuration of the driving support systems used by the car manufacturers, an on-board
system was provided by each of the three manufacturers.

each experiment area in such a way to eliminate genderand age bias. In addition, the signal passing support system, signal stopping support system, signal change starting
support system, and idling stop support system describedabove were evaluated using passenger vehicles from eachmanufacturer. In the vehicle-signal cooperative traffic signal control system experiment, the on-board unit was
installed in shuttle buses operating between a company
office and railway station to statistically identify the effects
and impacts of the system on travel times and traffic flow.

Fig. 5 Experiment route example (Gunma area)

6 Experiment Results and Conclusion
Fig. 3 On-board system configuration example 1

Fig. 4 On-board system configuration example 2

5 Demonstration Experiments
Based on a policy of conducting demonstrations in different traffic environments, demonstration experiments werecarried out in three areas where (1) the traffic volume ishigh and chronic traffic congestion occurs (Kanagawaarea), (2) the traffic volume varies greatly throughout theday (Gunma area), and (3) driving speeds tend to increase(Aichi area). The routes for the demonstration experiments
were selected after a survey of the installation status ofadvanced infrared beacons, traffic light control systems,
and the like in each area.
Around 50 experiment participants were recruited for50

6.1. Effects on travel times and fuel consumption
For the signal passing support system, signal stopping
support system, and idling stop support system, the resultsof the experiments carried out in each area found no statistically significant difference in travel times and fuel consumption with or without the systems. Therefore, no
noticeable effects were observed. Although various factors
may have contributed to these results, the following can beconsidered as the main causes:(1) The experiments were conducted on public roads usedby ordinary vehicles, which resulted in less frequent occurrence of the situations expected by the systems in which
effective support can be provided. Even in situations inwhich effective support was provided, there were cases inwhich the vehicles were not able to utilize the system support because of the preceding and following vehicles. For
example, in the case of the signal stopping support system,some participants did not follow the deceleration advice as
they were concerned about the possible negative impact ofdeceleration on following vehicles.
(2) Some time ranges in route traffic signal information
provided by infrared beacons contained differences
between the signal information and the actual signal phasetiming, and cases occurred in which the validity of route
traffic signal information expired due to its short validity
period.
In contrast, in the signal change starting support experi-[ I ] Development of Automated Driving Systems① Dynamic Maps

METI 3: Development and Demonstration of Technology for Real-time Utilization of Traffic Signal Information

ment, the time until the brake is released after the traffic
signal turned green was compared with and without support. The mean value of the time with support decreased inboth the Kanagawa and Aichi areas. However, although
there were no significant statistical differences in
Kanagawa, significant differences were apparent in Aichi.
This result is probably due to differences in the traffic environment between the two areas.

Fig. 6 Example of effect of signal change starting support system(Aichi area)

In the vehicle-signal cooperative traffic signal control
experiment, the travel time of the experimental route wascompared with and without route traffic signal information. The results found an average decrease of 15 to 16%,
i.e., about 30 seconds, in travel times, which confirmed theeffectiveness of the system in this experimental environment.

Fig. 7 Example of vehicle-signal cooperative traffic signal control(Kanagawa area)

6.2. Acceptance evaluation results
There were no quantitative differences in the results withand without the systems in the signal passing support system, signal stopping support system, and signal change
starting support system experiments. However, the questionnaire survey found that about 60 to 80% of respondents evaluated the systems positively, although the
responses varied slightly in each area. Feedback about thevehicle-signal cooperative traffic signal control system also
found that many bus drivers who participated in the experiment felt that travel times were reduced by one to two
minutes. Thus, the evaluation results demonstrated user
expectations for these systems.
In contrast, some respondents expressed that they could
not feel the benefits of the support service provided by theidling stop support system.

6.3. Future issues (accuracy of route traffic signal
information)
Route traffic signal information provided at critical intersections indicates the start time of a green signal and thecycle length in the form of maximum and minimum values. However, this generates relatively large time ranges,
leading to gaps in timings between the signal color predicted by the route traffic signal information and actual
signal color. Also, the validity of data may expire while thesystem’s service is being provided because of its shortvalidity period, as seen in the data from the critical intersections, where the validity period is equal to only one
cycle length.
In the experiment, when the route traffic signal information was provided within a certain time range, the median
was used to obtain an estimate. However, last year’s preliminary survey confirmed that the data was effective even
if its validity had expired. Therefore, expired data was also
included in statistical processing to ensure a sufficientnumber of samples.
To address these issues, it is desirable to examine ways toprovide information in the most manageable way for the
on-board unit, such as narrowing the range of fluctuationsin the information on the timing of traffic signal colors andadding information on the timing of red and yellow signals, with consideration also given to traffic control systems that performs dynamic control while detecting thedegree of traffic flow congestion. If the range of time fluctuation in traffic signal information is relatively small, themedian between the maximum and minimum values may
be used for the time being as timing information, as was
the case in this experiment.
It should also be effective to provide information byrefreshing traffic signal information at regular distances on
the route using infrared beacons or other communication
media to reduce the occurrence of information validity
expiration.

Fig. 8 Example of questionnaire results for signal passing support system(Aichi area)

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Development of Automated Driving Systems① Dynamic Maps

Construction of Traffic Regulation Information Management System for Realization of Automated Driving
Masayuki Jinno (Sumitomo Electric System Solutions Co., Ltd.)ABSTRACT: To realize automated driving, use of the latest traffic regulation information is an important aspect of vehicle control. This traffic
regulation information is managed by the police in each Japanese prefecture. It is necessary to build a structure capable of gathering andproviding traffic regulation information from the whole country. To develop this structure, we carried out the following steps: (1) standardization of the traffic regulation information data format, (2) construction of a traffic regulation information management model system.

1Standardization of Traffic Regulation
Information Data Format

1.1. Investigation of traffic regulation information data
items
First, in the 2014 fiscal year, we investigated the traffic
regulation information data items used by each prefectural
police.
1.2. Design of standard data format
As shown in Fig. 1, in the 2015 fiscal year, we designed astandard data format by selecting the necessary data items
for realizing automated driving from all the data items.

data from the standpoint of using for automated driving,even though the required traffic regulation information
data for automated driving are not yet clear.

2 Construction of Model System
2.1. Design policy of model system
When designing the functions of the model system,
adopting only traffic regulation information database editing functions through input, correction, and deletion is notenough. As shown in Fig. 3, the model system functions
must support the whole workflow of traffic regulation
information and facility management. For example, thedata application function shown in Fig. 4 and a road sign
construction management function are needed to maintainthe traffic regulation information database in the latest
state.

Fig. 1 Design of the standard format

Specifically, first, as shown in Fig. 2, we counted the number of prefectures using each data item and selected thefrequently used items for the standard format. Data items
used only by specific prefectural police were excluded.
Fig. 3 Work flow of traffic regulation information and facility
management

Fig. 2 Number of prefectures using each data item

In addition, we added some items to the standard format
52

Fig. 4 List and sheet of regulation data application
function[ I ] Development of Automated Driving Systems① Dynamic Maps

Construction of Traffic Regulation Information Management System for Realization of Automated Driving

Some prefectural police manage traffic regulation information using a paper account book and some prefectural
police use electronic data via an original prefectural computer system. The model system is not a substitute for these
original prefectural computer systems. Instead, the model
system is introduced when a prefectural police officewishes to introduce a new computer system. However, it isnecessary to unify the data to the designed standard format. Therefore, the model system must have a functionthat can output data in a standard format. If a prefectural
police office wishes to continue managing traffic regulation
information using an original prefectural computer system,a conversion tool to generate standard format data will benecessary. By the 2017 fiscal year, the standard format data
was adopted by four prefectures. It is planned to provide aconversion tool to generate standard format data to otherprefectures in the future.
2.2. Connection function of traffic regulation and roadsign information
From the standpoint of automated driving, the model
system requires a function to display the traffic regulation
and road sign information on maps, as shown in Fig. 5.
The model system should also have a function of mutuallyconnecting this information.

4) It is hard to identify the relationship between traffic regulation and road sign information.
5) Correct positional information is necessary.
6) Operability (the speed of operation response) is not
good. Work efficiency is not good.
7) It requires twice the labor costs to register information
on both the original prefectural system and the National
Police Agency system.
It was necessary to consider measures for these problems
when designing the specifications of the model system.
Table 1 on the next page shows the results of the questionnaire survey and the measures for the identified problems.
We designed draft model system specifications that include
these measures in the 2015 fiscal year.
2.4. Construction of model system
In the 2016 fiscal year, we introduced a model system
based on these draft specifications to the Kyoto police
headquarters, which had conventionally managed traffic
regulation information using a paper account book. As aresult, the traffic regulation information was computerized.
We investigated road signs at the same time, and joined all
the road sign information with the traffic regulation information using a connection function.

3 Conclusion

Fig. 5 Connection function of traffic regulation and road sign
information

We designed a standard traffic regulation information
format and made a conversion tool to generate standard
format traffic regulation information data. We also
designed specifications for a model system and constructeda system based on these specifications. This initiative hascreated a roadmap for implementing national uniform
management of traffic regulation information (103 kinds).
It is believed that the national uniform management oftraffic regulation information should help to realize automated driving.

2.3. Creation of draft specifications for traffic regulation information management system
To examine the model system, a questionnaire survey
about the problems of each original prefectural system wascarried out.
A summary of the results of that questionnaire survey is
as follows.
1) Management of road sign information is not possible.
2) Data management with maps is not possible. (The system has no geographic information system (GIS)).
3) Map searches are not possible. Traffic regulation androad sign information cannot be searched using maps.
Screen captures and print outs are not possible.
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Construction of Traffic Regulation Information Management System for Realization of Automated Driving

Table 1 Problems of each prefectural system and measures adopted in model system

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Development of Automated Driving Systems① Dynamic Maps

Development of V2V and V2I Communication Technology for Automated Driving Systems
Manabu Sawada (DENSO CORPORATION)
Yoshinori Hatayama (Panasonic Corporation)
Tetsuya Takahashi (Pioneer Corporation)
Yasushi Yamao (The University of Electro-Communications)
ABSTRACT: The development of automated driving systems is mainly based on the use of in-vehicle sensors, but adding vehicle-to-vehicle(V2V) and vehicle-to-infrastructure (V2I) communication has the potential to realize advanced automated driving systems capable of cooperating with other vehicles and the surrounding infrastructure. The purpose of this project is to develop the necessary technologies for realizing such cooperative automated driving systems. We are confirming the feasibility of communication to support automated driving systems,
and developing technologies to improve the characteristics and processing efficiency of this communication.

1 Summary
The purpose of this research and development is todevelop the necessary technologies for realizing cooperative automated driving systems that utilize vehicle-to-vehicle(V2V) and vehicle-to-infrastructure (V2I) communication. We evaluated the feasibility of two V2X use cases:
merging support on the freeway and emergency vehicle
recognition support on ordinary roads. To research thesecases, we developed communication technologies toimprove communication quality, as well as nd methods toimprove the efficiency of data processing. This study isbased on the 700 MHz band ITS communication (ARIB
STD-T109) deployed for safe driving support in Japan.

V2X communication Use Cases for Auto-

2 mated Driving Systems

V2V and V2I communication enables the acquisition of
information from areas beyond the sensing range of invehicle sensors, which can provide safety margins to theoperation of automated driving systems. Two cases were
examined. One is merging support on a freeway, which has
the potential for early deployment. Merging vehicles on theslip road build a consensus with vehicles on the freeway
using V2V or V2I communication, enabling the vehicles
on the freeway to decelerate to maintain their following
distance and realizing smooth merging. The other case isrecognition support, in which an automated vehicle
encounters emergency or public vehicles on an ordinary
road. Smooth automated driving can be realized by using
V2V communication to recognize the presence and behavior of ambulances, buses, and trams at longer distances
than in-vehicle sensors.

2.1. Feasibility evaluation of merging support on freeway
First, we conducted V2V communication experiments on
several freeways to evaluate. As a result, although therequirements were satisfied in many places, we found thatthe requirements could not be satisfied at the junctions oftunnel roads seen on urban freeways. In response, we also
considered a support model (message formats andexchange sequence) using V2I communication (Fig. 1). To
verify the validity of the support model, we conducted
experiments on a test course (Fig. 2). As a result, it wasdemonstrated that V2V communication built a consensus
between the merging vehicles on the slip road and thevehicles on the freeway, enabling the vehicles on the freeway to decelerate to maintain their following distance formerging. We confirmed that V2I communication can beused to maintain following distances or adjust acceleration
timings by providing information about detected vehicles
merging on a slip road and vehicles on the freeway fromroadside infrastructure.

Fig. 1 Merging support model using V2V and V2I communication

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[ I ] Development of Automated Driving Systems① Dynamic Maps

Development of V2V and V2I Communication Technology for Automated Driving Systems

Fig. 2 Verification experiment on test course

2.2. Evaluation the usefulness of emergency vehicle
recognition support
We conducted experiments to compare methods of recognizing ambulances by installing V2V equipment in
actual ambulances. We confirmed that V2V communication can recognize the existence of ambulances from further away than camera images or siren sounds. We createda deceleration model for yielding automated vehicles when
encountering an ambulance entering an intersection on a
red traffic signal. Using the model, we confirmed that thedifference in the success rate of yielding in an intersection
depended on whether V2V communication was used (Fig.
3). It was found that the success rate of yielding wasincreased by V2V communication, and that cases of maximum deceleration can be reduced. In addition, we conducted V2V experiments on route buses and taxis, andfound that the behavior of these vehicles can be recognizedfrom further away in low visibility situations.

Fig. 3 Emergency vehicle recognition support model using V2V
communication

3 Technology Development for Use Cases
One of the important issues for practical application ofthese use cases is maintaining performance and functions
even the spread of V2X systems. We developed technology
to maintain and improve communication performance(Section 3.1.) and to improve data processing efficiency(3.2.) without making major changes to current specifications. We also developed new communication technology(3.3.) under the consideration that higher communication
performance will be required in the future.

56

3.1. Development of V2V communication technology
for merging support
As V2X communication systems become widespread andcommunication traffic increases, packet collisions will
become more likely and communication quality may
decrease. It is important to maintain communication quality for merging support, because it is necessary to establish
communication in a short period of time. In addition, it isnecessary to add messages for merging support while
maintaining compatibility with current specifications. To
resolve these issues, we examined a variable transmission
period function, a reservation transmission timing function, and a hierarchical modulation technique. Simulation
results confirmed that the amount of transmission data canbe expanded and that high communication quality can be
maintained compared to a case without the developed
technology (Fig. 4).

Fig. 4 Communication quality of V2V communication for
merging support

3.2. Development of method to improve V2V dataprocessing efficiency
When performing V2V communication with many surrounding vehicles, it is necessary to filter the data of thetarget vehicles to be monitored by the driver’s vehicle fromthe high volume of received data. Especially in complicatedroad shape environments (like freeway junctions), filtering
is difficult with the current message format. We examined
including “road-specifying information,” which specifies
the road on which the driver’s vehicle is traveling, in transmitted messages. We then clarified that this allows efficientand appropriate filtering even with complicated roadshapes (Fig. 5). We also showed that this road-specifying
information can be added without changing the current
V2V communication message format (ITS FORUM
RC-013).

(a) Selected road link at position #16(b) Comparison of exclusion rates

Fig. 5 Simulation example of target selection

[ I ] Development of Automated Driving Systems① Dynamic Maps

Development of V2V and V2I Communication Technology for Automated Driving Systems

3.3. Development of V2V and V2I communication
technology for future use cases
3.3.1. Study of high-reliability V2V communication
with multi-dimensional distributed cooperative technology
To realize highly reliable V2V communication, a transmission method applying multi-vehicle cooperative multihop communication using space-time block codes wasstudied. By adaptively selecting relay vehicles with a highcontribution to packet transmission based on radio wave
propagation characteristics and vehicle position, unnecessary relay transmission can be reduced while enhancing
communication reliability through the cooperative diversity effect. We conducted a computer simulation assuming
use on a freeway, and confirmed that the proposed method
can reduce the average number of relay transmission vehicles while attaining the same communication success rate
as the conventional method. We also constructed a communication reliability database based on the experimental
V2V communication data, and confirmed that the packet
error rate improves when the relay vehicles are selectedusing this database (Fig. 6).

Fig. 7 Improvement effect of V2V communication quality using
sectorized relay stations

3.3.3. Study of high-reliability V2V and V2I communication with error correction code technology
We focused on repeat-accumulate (RA) code, which has
small coding computation volumes and strong error correction capabilities, as an error correction coding technique for improving the quality of V2V communication.
We designed a lattice code that exploits the properties ofRA code. Computer simulations clarified that the RA signal code exhibits the best characteristics at mid-range code
lengths, including the convolutional code adopted in current V2V communication standards. In addition, we studied multi-dimensional spatially-coupled RA coding
coordination as a technique to realize highly reliable communication from a group of vehicles to a base station. By
optimizing the encoding method, it was clarified thatdeveloped codes can obtain high-reliability in various
communication channels (Fig. 8).

Fig. 6 Multi-vehicle cooperative relay transmission with communication
reliability database

3.3.2. Study of high-reliability V2V communication
with sectorized relay stations
We investigated a system using roadside relay stations
with a sectorized antenna system as a measure against the
shadowing loss and hidden node issues of intersections,which severely deteriorate the quality of V2V communication. By using sectorized receiving antennas and individual
receivers for each direction, the relay station can maximize
the relay performance and compensate for both the hidden
terminal issue and shadowing loss caused by corner buildings. We clarified that the area averaged packet delivery
success rate can be greatly improved by assuming a case inwhich multiple roadside relay stations are installed on an
urban road consisting of multiple intersections, and thenassigning the relay area between adjacent relay stations(Fig. 7).

Fig. 8 Improvement effect of V2I communication quality using
multi-dimensional spatially coupled RA codes

4 Conclusion
This research and development verified use cases anddeveloped technologies to help realize cooperative automated driving systems. Since these research results coverwide technical fields and a wide range of assumed application timings, they have the potential to contribute to therealization of cooperative automated driving systems.

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Development of Automated Driving Systems① Dynamic Maps

Research into Practical Application of Safe Driving
Assistance System Utilizing Vehicle to Vehicle (V2V)
Communication and Research into Required Conditions of Vehicle to Pedestrian (V2P) Communication
Toshiya Hirose (Shibaura Institute of Technology)
Toru Kojima, Ichiro Sakamoto, Toshihiro Takeuchi (National Agency for Automobile andLand Transport Technology, National Traffic Safety and Environment Laboratory)
ABSTRACT: These two research cases were carried out as a three-year project. In the first year, the accuracy of vehicle and pedestrian positions of V2V and V2P communication systems, communication delay, and the like were investigated in tests on public roads. In the second
year, driver acceptance of these kinds of safe driving assistance systems that utilize V2V and V2P communication was investigated in tests on
a test track. In the final year, driver acceptance of automated driving systems that utilize V2V and V2P communication was investigated in
tests using a driving simulator.

The following main items were carried out in these two
research cases to help make a draft of technical guidelines
for automated and safe driving assistance systems that utilize V2V and V2P communication.
(1) Investigations of the positional accuracy, communication delay, and the like of V2V and V2P communication in tests on a test track and public roads(2) Investigations of driver and pedestrian acceptance ofsafe driving assistance systems that utilize V2V andV2P communication in tests on a test track(3) Investigations of driver acceptance of automated driving systems that utilize V2V and V2P communicationin tests using a driving simulator

Fig. 1 Validation tests in Nagoya

Number of data

1 Purpose of the Researchwere respectively provided with mobile terminals. The
communication delay was within 200ms.

Data was measured in the driverʼs vehicle

Validation of V2V and V2P Communica-

2 tion2.1. Locations of validation tests
V2V and V2P communication were validated in three cities, Yokosuka, Kobe, and Nagoya. Figure 1 shows photographs of the validation tests in Nagoya.
2.2. Examples of data from validation tests
Figure 2 shows measured data of the differences in the
distance between high-accuracy and normal GPS installedin the test vehicles. The data was measured in Nagoya. The
average error was 6.1m, with a minimum error of 2.1m
and a maximum error of 11.5m.
Figure 3 shows measured data of the communication
delay of mobile terminals. Fifty vehicles and pedestrians
58

Fig. 2 Difference of distance between high-accuracy and normal GPS
installed in vehicles

The V2V and V2P communication system validation tests
confirmed that current positioning accuracy technology
can only be utilized for V2V and V2P communication systems that supply information to drivers or pedestrians.
Further improvement of positioning accuracy is necessary
to utilize V2V and V2P communication systems that supply cautions or warnings to drivers or pedestrians.

[ I ] Development of Automated Driving Systems① Dynamic Maps

Research into Practical Application of Safe Driving Assistance System Utilizing Vehicle to Vehicle (V2V) Communication andResearch into Required Conditions of Vehicle to Pedestrian (V2P) Communication

Tests to Investigate Driver and Pedestrian

3 Acceptance of Safety Assistance Systems

V2P scenario 1

V2P scenario 2

Fig. 4 Experimental test scenario for V2P communication

3.3. Test results
Figure 7 shows an example of the test results regarding
V2P scenario 1

V2V scenario 2

Fig. 5 Test scenario for V2V communication
Fig. 3 Example of data of communication delay

3.1. Test preconditions
Tests were carried out under ideal conditions enabling a
positioning accuracy of within 10cm by utilizing highaccuracy GPS at a test track. Driver and pedestrian acceptance of a safe driving assistance system that supplies
information, cautions, and warnings utilizing V2V andV2P communication was evaluated.
3.2. Scenarios and test conditions
The test scenarios were chosen based on accident data inJapan. We carried out tests involving two scenarios of V2Pcommunication and two scenarios of V2V communication. Figure 4 shows the V2P scenarios, and Fig. 5 shows
the V2V scenarios.
To decide the test parameters (for example, the timing of
information supply) before the test using actual V2P and
V2V communication systems on a test track, a pre-test was
carried out using twenty people (ages: from 20s to 60s) in adriving simulator.
The test using the actual V2P and V2V communication
systems was carried out on a test track at the Japan Automobile Research Institute that simulates city roads. The
prototype V2P and V2V communication systems realized
positioning accuracy within 4 cm. Ten people (ages: from
20s to 50s) who are frequent drivers participated in thetest. Figure 6 shows photographs from the test.

V2P scenario 1

V2V scenario 1

Fig. 6 Test on test trackdriver acceptance of the V2P communication system. In
this case, “before 4.0s” is considered to be an appropriate
timing for drivers.
V2P scenario 1（driver support）

Fig. 7 Example of data of driver acceptanceAccording to the test results, effective operation timings
for drivers and pedestrians were confirmed as follows.
· V2P communication system (drive support)
Information supply: from 5.1s to 6.5s in advanceCaution: from 3.2s to 4.0s in advance
Warning: more than 2.0s in advance· V2P communication system (pedestrian support)
Information supply: from 5.1s to 6.5s in advance,
Caution: from 3.2s in advance
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Research into Practical Application of Safe Driving Assistance System Utilizing Vehicle to Vehicle (V2V) Communication andResearch into Required Conditions of Vehicle to Pedestrian (V2P) Communication
Warning: more than 2.0s in advance· V2V communication system
Information supply: from 5.1s to 6.5s in advanceCaution: from 3.2s to 4.0s in advance
Warning: more than 2.0s in advance4

Driving Simulator Tests to Investigate
Driver Acceptance of Automated Driving
Systems

4.1. Test preconditions
The automated driving system utilizing V2V and V2Pcommunication in this test was provided with functions toboth supply information and warnings to the driver andcontrol the speed of the vehicle to avoid a collision. Whenthe system receives information of an object around thevehicle, it can reduce the vehicle speed before the driversees the object if it judges that the risk of collision is high.
It was necessary to investigate whether normal drivers
would accept such a system.
4.2. Scenarios and test conditions
In this research, the following two traffic scenarios wereused.
Scenario A: When a normal driver would not usually beaware of a potential collision with an object thatcannot be seen directly
Scenario B: When a normal driver would usually beaware of a potential collision with an object thatcannot be seen directly
Table 1 shows the combination of the test cases for theV2P system, and Fig. 8 shows the traffic scenarios. Table 2
shows the combination of the test cases for the V2V system, and Fig. 9 shows the traffic scenarios.
Table 1 Combination of test cases (V2P)
Test
numberTraffic
scenario

Automatic
emergency braking (foravoiding a collision)

Traffic scenario : A

Traffic scenario : B

Fig. 8 Traffic scenario for test cases (V2P)

Table 2 Combination of test cases (V2V)
Test
numberAutomatic
emergency braking (foravoiding a collision)

Traffic
scenario・As the experiment condition of test number ⑦, ⑧, ⑩ and ⑪, the driver could not see the object
vehicle at the timing of start of smaller deceleration braking.

Seven non-elderly people (ages: 30s to 50s) and sevenelderly people (ages: from 65s to 80s) who are frequent
drivers participated in the test. The participants weredirected to operate the OFF button of the automated driving system if they felt that they could not accept the behavior of system.
Traffic scenario : A

Traffic scenario : B

Fig. 9 Traffic scenario for test cases (V2V)

4.3. Test results (example)
Figures 10 and 11 show the rate of OFF button operationby the drivers after the system reduced the vehicle speed in
scenarios A and B, respectively. The rate of OFF button
operation in scenario B by the non-elderly drivers was
lower than that in scenario A. This suggests that driver
acceptance of reductions in vehicle speed by the system inscenario B was higher than that in scenario A. In contrast,
the rates for both scenarios A and B were lower for elderly
drivers than for non-elderly drivers.
Pedestrian-to-vehicle, test number ②

Vehicle-to-vehicle, test number ⑧

・As the experiment condition of test number ①,②,④ and ⑤, the driver could not see the object
pedestrian at the timing of start of smaller deceleration braking.

Fig. 10 Rate of OFF button operation (traffic scenario A)

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Research into Practical Application of Safe Driving Assistance System Utilizing Vehicle to Vehicle (V2V) Communication andResearch into Required Conditions of Vehicle to Pedestrian (V2P) Communication
Pedestrian-to-vehicle, test number ⑤

Vehicle-to-vehicle, test number ⑪

Fig. 11 Rate of OFF button operation (traffic scenario B)

5 Conclusion
Draft technical guidelines for automated driving and safe
driving assistance systems that utilize V2V and V2P communication systems were formulated based on the resultsof the research described above.
References(1) Ministry of Land, Infrastructure, Transport and Tourism:
Guidelines for Communication-based driver assistancesystems, pp.12-13 (2011).

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Development of Automated Driving Systems② Human Machine Interface (HMI)

Outline of Human Machine Interface (HMI)

Kiyozumi Unoura (Honda R&D Co., Ltd.)With regard to the human machine interface (HMI) usedby automated driving systems, SIP is formulating guidelines and aiming to achieve international standardization.
Although HMI technology is in the competitive domain ofeach company, minimal arrangements must be agreed toprevent user confusion, facilitate understanding of thefunctions, states, and actions of the automated driving system, establish the driver’s condition and appropriate
handover time, and the like. Guidelines have been formulated for these items. Each company is considering how torealize these guidelines, and to develop competitive andhigh-quality products.
We describe three issues to be resolved to help realize alevel 3 automatic driving system in “Considerations on
automated driving HMI” (JAMA) and proposed guidelines
for the readiness index and measurement method (international standard ISO/TC22/SC39).
For a level 3 automated driving system, it is desirable thatthe results of prior knowledge be understood and utilized
by vehicle sales companies, driving education sites, and thelike so that drivers and all traffic participants can be madeaware of this information. However, to accept and utilize
automated cars as safe vehicles, we believe that it is important for each stakeholder to obtain common awarenessthrough, for example, the development of specific teachingmaterials, and to enhance the quality of understanding.

1

Task A (issues related to driver understanding of the system)

For level 2 and 3 automated driving systems, it was possible to clarify the influence of the HMI on driver behavior
and the influence of driver behavior on the system functions through driving simulator and test course experiments that examined the handover from automated tomanual operation. Based on these results, we derived basic
knowledge about HMI and knowledge about what information to provide in advance.

2

Task B (issues related to identifying
driver readiness)

As an index to evaluate the state of the driver during
automatic driving, the conventional techniques of observing driver inattention and drowsiness were supplemented
by technology to observe driver consciousness and thestate of the driver based on these indices. We developed aprototype monitoring system, demonstrated its effectiveness and feasibility, and derived the relationship between
the time required for an appropriate transition whenreturning from automated to manual operation and thedriver condition. Using these results, we proposed a readiness level to the International Standards Conference, which
represents the degree of driver preparation when switchingfrom automated to manual operation.
At the same time, we developed an HMI concept anddevice that helps to restore and maintain driver preparedness, and obtained results suggesting that this HMI iseffective.

3

Task C (issues related to interaction withpedestrians and cars other than automated vehicles)

The intention of an automated vehicle to alter its path canbe communicated through inter-driver communication,
vehicle behavior, and external HMI (mainly related todeceleration behavior). In situations where deceleration
behavior or the like cannot be sufficiently understood bydrivers and pedestrians at an early timing, the potential62[ I ] Development of Automated Driving Systems② Human Machine Interface (HMI)

Outline of Human Machine Interface (HMI)

effectiveness of external HMI to indicate behavior wasdemonstrated.
In contrast, if the intention of an automated vehicle cannot be clearly predicted, thereby affecting other drivers andpedestrians, it was suggested that standardization and educational learning for drivers and pedestrians will be necessary to enable utilization of external HMI. However, since
it was not possible to identify the specific effects of external
HMI on the current traffic environment, efforts were limited to consolidating basic knowledge and concrete device
development has yet to take place.

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Development of Automated Driving Systems② Human Machine Interface (HMI)

Human Factors and HMI
Satoshi Kitazaki (National Institute of Advanced Industrial Science and Technology)
Makoto Itoh (University of Tsukuba)
Toshihisa Sato (National Institute of Advanced Industrial Science and Technology)
Tatsuru Daimon (Keio University)
ABSTRACT: The Human Factors and HMI research project consisted of three tasks: A, B, and C, and started in FY2016 as a three-year project. Task A investigated the effects of system information on driver takeover performance. The provision of well-defined knowledge about thesystem before driving and experience of using the system were found to have a positive effect. The provision of some dynamic information
about the system state was also shown to be effective. Task B investigated the effects of the driver state (readiness) on takeover performanceand metrics of readiness. It was found that different driver states influenced takeover performance in different ways. Some metrics for states(readiness) influencing takeover performance were found. Task C investigated effective ways of functionalizing automated vehicles to communicate with nearby road users. An external HMI was found to be an effective additional cue for pedestrians to make a decision to cross aroad when an approaching automated vehicle intended to yield but did not decelerate largely enough to clearly signal this intention. It wasobserved that the external HMI also induced unsafe behavior in some pedestrians. This report was produced in the middle of the third yearof the project and does not include all the studies conducted in the project.

1

Defining Tasks for Research

The SIP-adus HMI Taskforce was established in 2015 todefine the research tasks related to human factors for safe
and socially acceptable automated driving. The taskforce
used a framework to extract potential problems related tohuman factors. The framework consisted of three interactions: interactions between the driver and the system,
between the system and nearby road users, and between
the system and society (Fig. 1).

Fig. 1 The framework used to extract potential problems related to humanfactors in automated driving.

Extracted potential problems were classified between
those in cooperative fields and those in competitive fields
from the standpoint of developers in the industry. Issues incooperative fields were prioritized. Three issues with thehighest priority in the cooperative fields were set as thethree tasks A, B, and C for the SIP-adus human factors and
HMI research project.
◆ Task A investigated the effects of system information(knowledge and dynamic state) on drivers’ takeover per64

formance for level 2 and 3 automated driving systems.
◆ Task B investigated the effects of the driver state (readiness) on takeover performance for level 2 and 3 automated driving systems, and extracted metrics ofreadiness for driver monitoring.
◆ Task C investigated effective ways to functionalize automated vehicles to communicate to nearby road users forlevel 2 and higher automated driving systems.

2 Task A
2.1. Overview of the task
In the case of a level 2 automated driving system, thedriver must constantly acquire information about the traffic and the environment outside the vehicle (object andevent detection and response: OEDR), and must acquirethat information within the designed transition time for alevel 3 system. The driver must also have or acquire information about the system at the same time to enable potential safe transitions in the near future. Such system
information is classified as knowledge (i.e., static information) and dynamic information (Fig. 2). Knowledge
includes information about the system functions, limitations, and the driver’s role. Dynamic information includes
the system state (i.e., its operating mode, level, occurrenceof malfunctions, and the like) and actions that the system
is planning and executing.
Aim 1 of this task was to investigate the effects of systemknowledge given to the driver before driving and experience of using the system on takeover performance. The
effects of age were included. By achieving aim 1, we[ I ] Development of Automated Driving Systems② Human Machine Interface (HMI)

Human Factors and HMI

expected to learn requirements for pre-driving educationand training for safe use of these systems. Aim 2 was to
identify effective dynamic information for enabling successful takeovers. This aim also included fundamental
studies for HMI that effectively display dynamic information in the cockpit.

Fig. 2 Information the driver needs to have or acquire for level 2 and 3
automated driving systems.

2.2. Aim 1
2.2.1. Methods
The experiment used the fixed-base driving simulator
located at the University of Tsukuba (Fig. 3). Subjects in
two groups, a younger subject group (below 60 years old)and an older subject group (60 years and above) participated in the experiments. The subjects were given knowledge about the functions and limitations of the level 3
system before driving with the system using the driving
simulator. The knowledge information was categorized
into five levels of quantity raging from condition 1 for no
information to condition 5 for full information (Table 1).
The subject was instructed to perform a surrogate reference task (SuRT, ISO/TS14198) with both hands off thesteering wheel until the onset of the takeover request(TOR).

The experiment scenario included several objects/events
for which the system issued a TOR using a HMI with aflashing visual icon and an auditory alert. When the TOR
was issued, the subject was expected to take over control ofthe vehicle and continue driving. The subject’s responsetime before initiating a steering operation was measured as
the takeover performance metric. The first event of the scenario was exiting a motorway, which was used to analyze
the effects of knowledge information on takeover performance. The subsequent objects and events were used toinvestigate the effect of experiencing takeover situations.
2.2.2. Some results
The response time from the onset of the TOR to initiationof the steering operation of each subject group was classified into three (Fig. 4) for each condition: within 10 seconds (green), between 10 and 15 seconds (orange), andlonger than 15 seconds or failure to implement takeover(red). It was found that information about takeover situations was important for implementing successful takeovers(conditions 1 to 3 vs. conditions 4 and 5). However, toomuch information about takeover situations degraded thesubjects’ performance, especially in the case of the older
subjects (condition 4 vs. condition 5).

Fig. 4 Response time from TOR to initiation of steering operation.

Fig. 3 The driving simulator at the University of Tsukuba used for the Task
A experiments.

Changes in the takeover success rate (success was defined
as a response time within 10 seconds) over a number of
takeover situations were calculated. The success rates weremean values across all the conditions from 1 to 5 for each

Table 1 Experimental conditions

Fig. 5.1 Change in success rate of takeover of younger subjects overnumber of takeover situations.
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Human Factors and HMI

Fig. 5.2 Change in success rate of takeover of older subjects over number of
takeover situations.

subject group. Experiencing takeover situations improved
the success rate of the younger subjects (Fig. 5.1), while theeffect was limited for the older subjects (Fig. 5.2).
2.3. Aim 2
2.3.1. Methods
A level 2 system may stop functioning due to a failure
without notifying the driver (i.e. a silent failure). The system may also fail to alert the driver when an object orevent that is not detected by the system is encountered due
to a functional limit of the system. The driver is expected
to monitor the environment (OEDR) and the system all
the time so that the takeover action can be initiated whennecessary. The study investigated what dynamic system
information can help the driver to initiate a takeover action
when an object or event that is not detected by the systemis encountered.
The experiment compared driver-initiated takeover performance when information about objects detected by thesystem was provided, when information about plannedsystem actions was provided, when a combination of these
two information types was provided, and when no information was provided (i.e. the baseline). The information
was visually displayed with icons (Fig. 6) on a monitor
placed in the center stack of the cockpit mounted in the
fixed-base driving simulator. A total of 60 subjects aged 60
years and older participated in the experiment. The subjects were given knowledge prior to driving that the system
may fail to detect objects or events due to a functional limitation. The subjects were instructed to perform the OEDR
task with both hands off the steering wheel.
The scenario included several objects and events forwhich the system issued a TOR, avoided automatically, or
failed to detect. The object detection failures (4 times) were

Fig. 6 (a) Information of detected objects (b) Combination of detected
objects and planned action.
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designed to cause crashes unless the subject initiated atakeover. The number of crashes was counted as the measure to compare the four conditions.
2.3.2. Some results
The number of crashes with undetected objects is shownin Fig. 7. The crashes were caused when the subject failed
to initiate or delayed initiation of a takeover. Subjects who
did not use the HMI throughout the scenario were
excluded. The combination of information about detected
objects and planned actions largely lowered the number ofcrashes from the baseline condition (no information) while
the number of crashes did not decrease when only information about detected objects or planned actions was provided.

Fig. 7 Number of crashes with objects undetected by the system with
provision of various system information.

2.4. Some conclusions◆ Knowledge required for successful takeover was clarified.
◆ Experiencing takeover situations improved the takeover
performance of younger subjects, but had a limited effectfor older subjects.
◆ The results will be verified in test track experiments.
◆ The provision of a combination of visual information
about detected objects and planned system actions
improved driver-initiated takeover performance in thecase of objects undetected by the system.
◆ Other types of dynamic information are also being studied.

3 Task B
3.1. Overview of the task and aims
With a level 2 system, the driver is expected to performthe OEDR task at all times and take over control immediately after a system request. With a level 3 system, thedriver is allowed to perform non-driving related activities
but is expected to take over control within a designed transition time after a system request. When the driver state is
not ideal, the takeover action may be delayed or its quality
may decline when a system request occurs.
A driver monitoring system (DMS) constantly monitors

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Human Factors and HMI

the readiness of the driver to take over control, detects
decreases in readiness, and informs the system of this state.
The main system then executes an intervention through analert and/or termination of the automated mode with theaim of improving the reduced driver readiness, or stops thevehicle when continuing driving is determined to have ahigh risk (Fig. 8).

Fig. 8 Research aims of driver monitoring system and interventions.

This task had two aims. Aim 1 was to investigate theeffects of various driver states on takeover performanceand to define readiness as a state that affects takeover performance. Aim 2 was to extract readiness metrics for a
DMS. Readiness was considered to include both the physical and visual-cognitive state of the driver. Although physical states include the position of hands, posture, seating
position, and the like, these were not included in this task
because their effects and metrics are both relatively apparent. The visual-cognitive state was classified as cognitively
loaded, visually loaded, and low alertness, and their effects
on takeover performance and the metrics were investigated. Takeover performance included both the speed andthe quality of the action to manually cope with an object orevent, and to stabilize the vehicle after the takeover to normal manual driving.
3.2. Methods
Experiments 1 and 2 below were carried out to accomplish aims 1 and 2. A total of 80 subjects participated in theexperiments. The experiment used the motion-base driving simulator located at National Institute of Advanced
Industrial Science and Technology (Fig. 9).
3.2.1. Experiment 1
The subjects drove a simulated level 2 automated driving
system while both performing and not performing nondriving related tasks (NDRTs). The NDRTs were cognitive
N-back tasks (low and high loads) and visual SuRTs (lowand high loads). The subjects were instructed to place theirhands off the steering wheel, and monitor the environment
as well as the system while performing the NDRT. Various
physiological indices of the subjects were measured whiledriving (Table 2).
The driving scenarios included several events with low
criticality for which the system requested takeover to thesubject. The following event was used for measuring takeover performance: The subject vehicle was following aleading vehicle with the system activated, a TOR was
issued due to a system malfunction causing the system toshut down. The leading vehicle then changed lanes 1 second after the TOR and exposed a stationary broken-down
vehicle in the same lane. The TOR was issued when the
TTC to the broken-down vehicle was 6 seconds. The subject was expected to takeover in response to the TOR,
detect the stationary vehicle, change the lane manually, andfinally stabilize the vehicle in the next lane. Various performance measures were collected in terms of response to the
TOR, maneuvering to change lanes and avoid a crash, andstabilization of the vehicle after changing lanes.
3.2.2. Experiment 2
To investigate the effects of arousal, the subjects drove alevel 3 system through another scenario. The subjects were
not given any tasks, including monitoring the environmentor the system, with the expectation that monotony would
induce drowsiness with enough inter-subject variability.
The physiological indices shown in Table 2 were also measured in experiment 2. The scenario was a monotonous 20
minutes motorway drive and included a motorway exit
event after issuing a TOR at the end of the scenario. The
time to initiate a steering maneuver in response to the
TOR was used as the performance measure.

Fig. 9 The driving simulator at AIST used for the Task B experiments.

3.3. Some results
The level of cognitive load applied to the subjects while
the system was operational correlated with the minimum
distance to the stationary vehicle while changing lanes after
the TOR (Fig. 10.1). The results implied that the effect of
the cognitive load remained after the TOR and that thecognitive process remained slow, resulting in degradationof the driver’s ability to avoid a collision with the object. In
contrast, the level of visual load correlated with the variability in the steering angle in the 5 seconds after changing
lanes (Fig. 10.2). The results implied that the visual load
degraded situational awareness, resulting in abrupt steering and unstable steering operations to stabilize the vehicle
after changing lanes. There was no difference found in the
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Human Factors and HMI

response times for steering initiation between the twoloading conditions. A schematic expression of the vehicle
trajectories under the two loading conditions is shown in
Fig. 11.
Table 2 Physiological measurements

Fig. 11 Schematic expression of trajectories of the subject’s vehicle after the
TOR.

Fig. 12.1 Cognitively loaded driver state and biometrics.

Fig. 10.1 Cognitive load applied to the driver before the TOR and takeover
performance after the TOR.

Fig. 10.2 Visual load applied to the driver before the TOR and takeover
performance after the TOR.

Multiple biometrics of the cognitive and visual state ofthe driver (readiness) were extracted. Considering in-vehicle real-time monitoring of readiness by the DMS, theblinking frequency and the frequency of saccadic eye
movement were candidate metrics for the cognitively
loaded state (Fig. 12.1), while the percentage of time looking forward and the frequency of saccadic eye movement
were candidate metrics for the visually loaded state (Fig.
12.2). For the arousal level, “Perclos” (percent eye closure,
Dingus and Grace, 1998) was found to correlate with thetime to initiate a steering operation to exit the motorway
after the TOR (Fig. 13).

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Fig. 12.2 Visually loaded driver state and biometrics.

3.4. Some conclusions◆ Low arousal, cognitive load, and visual load degraded
the driver’s take-over performance in different ways.
They were found to be components of readiness.
◆ The frequency of saccadic eye movement, blinking frequency, percentage of time looking forward, and Perclos
were extracted as metrics of readiness for driver monitoring.
◆ The results were tested in test track experiments andsimilar results were obtained. The results will be tested
in a FOT.
◆ A prototype DMS is being prepared based on these findings to examine applicability with an on-board system.
◆ An HMI to maintain an appropriate level of driver alertness is being investigated.

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Human Factors and HMI

in communication.

Fig. 14 Approaches to Task C
Fig. 13 “Perclos” measured before the TOR and takeover performance after
the TOR.

4 Task C
4.1. Overview of the task
Drivers exchange their intentions with other drivers orvulnerable road users (e.g. pedestrians and cyclists) using
various non-verbal communication cues in situations
where traffic regulations are uncertain. These cues include
signaling devices, vehicle behavior, and driver behavior
such as eye contact and hand gestures. A large part of onroad communication is informal and likely to be influenced by individual attributes and social norms.
When considering mixed traffic flows of automated (level2 or above) and non-automated vehicles, automated vehicles are also expected to non-verbally communicate withother road users for safety, security, and traffic efficiency,
all of which are considered to be components of social
acceptance. Task C firstly investigated current on-road
communication between drivers and between drivers andpedestrians (Aim 1). Secondly, measures to functionalize
automated vehicles to enable communication were investigated to identify design requirements and recommendations (Aim 2). The effects of the attributes of road users
and social norms were also considered (Fig. 14).
4.2. Aim 1
To understand the current situation, on-road communication between drivers and between drivers and pedestrians was observed and measured using multiple methods,
including fixed point observation, in-vehicle observation(using an instrumented vehicle), test track experiments,
and web-surveys. It was found that vehicle behavior was
the primary communication cue when yielding to otherroad users. Rather than deceleration alone, flashing theheadlights was sometimes used as an additional cue toexpress that the vehicle planned to yield and let anotherroad user go first. It was also found that the driving experience of pedestrians influenced the type of cues they used

4.3. Aim 2
4.3.1. Method
Test track experiments were conducted using a simulated
automated vehicle with a simulated external HMI. The
simulated external HMI was a board with a written message, which was displayed in the car facing towards otherroad users in front of the vehicle (Fig. 15). Several different
written messages were used to investigate the effects of thetype of message separated from the effects of the external
HMI design. The written messages were large enough forother road users to read.

Fig. 15 Simulated external HMI.

One of the experiments investigated communication
between the automated vehicle and a pedestrian. The subject stood by an unsignalized crosswalk waiting to crossthe road while the simulated automated vehicle wasapproaching. The simulated automated vehicle sent communication cues that it planned to yield. These cues werecombinations of different written messages (no message,“After you” and “Automated driving”) and different deceleration profiles (large deceleration: 2510 km/h and small
deceleration: 2515 km/h). A total of 14 adults with driving experience (licensed subjects) and 13 adults without
driving experience (non-licensed subjects) participated in
the experiment. The subject was instructed to press ahandheld button when the subject believed the vehicle
planned to yield and when the decision was made to startcrossing. The subject rated the level of confidence about
the decision after pressing the button. In one of the conditions, an additional ordinary (manual) vehicle approached
the same crosswalk from the other side of the crosswalk(Fig. 16). The subject’s head turning behavior to check theapproaching vehicles on both sides was observed (after
reading the message and before crossing).
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Human Factors and HMI

decreased confidence.

Fig. 16 Experiment with an additional ordinary vehicle.

4.3.2. Some results(1) Traffic efficiency
The ratio of the subjects who believed that the vehicle
planned to yield is shown for each written message in Fig.
17.1 for the large deceleration condition and in Fig. 17.2
for the small deceleration condition. It was found that large
deceleration was a clear sign that the vehicle planned toyield. However, whereas small deceleration was not a clear
sign that the vehicle planned to yield, the “After you” message compensated for this situation. The “Automated driving” message resulted in lower or no increase in the ratethat the subjects believed the vehicle planned to yield. The
responses were similar for both subject groups.

Fig. 17.1 Ratio of the subjects who believed the vehicle planned to yieldfor each of the written messages under the large deceleration
condition.

Fig. 17.2 Ratio of the subjects who believed the vehicle planned to yieldfor each of the written messages under the small deceleration
condition.

(2) Sense of security
The subjects’ confidence scores are shown in Fig. 18 foreach message and small deceleration. It was found thatboth the “After you” and “Automated driving” messages
increased the confidence of the licensed subjects compared
to a condition with deceleration only. For the non-licensed
subjects, deceleration alone gave more confidence than forthe licensed subjects, and the effect of the “After you” message was small. The “Automated driving” message largely
70

Fig. 18 Scores for the subjects’ confidence in their decisions under thesmall deceleration condition.

(3) Safety
Observation of the subjects’ head motions for checking
approaching vehicles after reading the message indicatedthat two of the 27 subjects did not check the other side atall, and that three subjects checked both sides but fewer
times than the baseline situation without the automatedvehicle. The results implied that the messages might havedrawn too much attention from some subjects anddegraded their alertness, resulting in unsafe behavior. The
behavioral changes did not show any correlation to thesubjects’ driving experience or the message type. Moredata were needed to find criteria to minimize negative
effects.
4.3.3. Some conclusions◆ Vehicle behavior is the primary communication cue tosurrounding pedestrians.
◆ An external HMI can be an additional cue for pedestrians to clarify the intention of an automated vehicle toyield when the vehicle behavior is not clear enough.
◆ The meaning of an external HMI signal needs to be
selected carefully to magnify the positive effects.
◆ Some external HMI might cause negative safety effects
for some pedestrians.
◆ The driving experience of pedestrians may be a contributor to different responses to the external HMI. The
design of an external HMI must be universal.
◆ Communication between automated vehicles and nearby
driver is being investigated in parallel.

5 General Conclusions
The three tasks A, B, and C have been tackled. The results
indicated that human factors are major issues, but also suggested some possible solutions to these issues for the development of safe and socially acceptable automotive vehicles.
This document only shows a part of the experiments andresults that were carried out and omits some studies for allof the three tasks.

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Development of Automated Driving Systems② Human Machine Interface (HMI)

Basic Research into Requirements of HMI to Ensure
Safety for Automated Driving Systems
Toru Kojima (National Agency for Automobile and Land Transport Technology, National
Traffic Safety and Environment Laboratory)
ABSTRACT: While a driver is driving on the highway using a level 2 automated driving system and a system malfunction occurs, control ofdriving operations are handed over from the system to the driver. This research examines the necessary time margin for the driver to take overmanual operation based on experiments conducted with normal drivers using a driving simulator.

1

Purpose of the Research

If a malfunction occurs while a driver is using an SAE
level 2 automated driving system on a highway, and it
becomes difficult to continue automated driving, the driver
has to take over manual driving safely and smoothly. In
this case, if communication via the human machine interface (HMI) between the system and driver is not executed
rapidly and appropriately, the driver may become confused
and unable to drive safely. This research investigated thetechnical requirements for HMI and the like to ensure thesafety of automated driving systems (level 2) based on
experiments using a driving simulator (DS) capable ofsimulating typical driving transitions from the system tothe driver.

2 Experimental Method
2.1. Functions of the automated driving system
This research defined a combination of an automaticallycommanded steering function (ACSF) and adaptive cruise
control (ACC) as a level 2 automated driving system foruse on a highway under the premise of driver monitoringof the surrounding traffic situation. ACSF is capable ofkeeping the vehicle in its lane and changing lanes automatically, and ACC is capable of keeping a constant distance
between cars. While the system was operating normally, it
was not necessary for the driver to operate the steering
wheel, accelerator pedal, or brake pedal manually.

Figure 2 shows the methods for reducing steering torquewhen a malfunction occurs. In case 1, steering torque isreduced to 0 Nm suddenly, and in case 2, steering torque isreduced to 0 Nm gradually. Table 1 shows the time fromthe start of the malfunction warning until the system stops.
Table 1 also shows the condition of button operation atcertain time intervals. Button operation was carried out asa task to investigate how effectively drivers can maintain
concentration while using the level 2 automated driving
system.

Fig. 1 Outline of experimental scenario (driving on curve)

Fig. 2 Methods of reducing steering torque
Table 1 Combinations of experimental conditions

2.2. Experimental scenarios
In this research, the following two experimental scenarios
were carried out.
2.2.1. Occurrence of malfunction on curve
Figure 1 shows the outline of the experimental scenario.
An ACSF malfunction occurs while the car is driving on a
curve, requiring the driver to take over steering operation.

2.2.2. Occurrence of malfunction while changing lanes
Figure 3 shows the outline of the experimental scenario.
An ACSF malfunction occurs while the car is changing
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lanes, requiring the driver to take over steering operation.
Figure 4 shows the definition of “lane change time andTable 2 shows the combinations of the lane change time
and the time from the start of the malfunction warning
until the system stops.

Fig. 3 Outline of experimental scenario (changing lanes)

quently drive participated in the experiments. When using
the automated driving system, they kept their hands off thesteering wheel until they recognized a warning about a system malfunction. The participants were also asked to lookforward and confirm the behavior of the car.
Each person participated in all the experimental conditions shown in Tables 1 and 2.
Figure 5 shows photographs of the DS that was used in
the experiment. This DS uses an actual passenger car body,and has several motion devices that simulate the motionsof the car. The steering wheel of the DS turns automaticallyfollowing the vehicle course.

Fig.4 Definition of lane change time
Table 2 Combinations of experimental conditions
Fig. 5 External view and driver’s seat of DS

3 Experiment Results
2.3. Other experimental conditions
Tables 3 and 4 show the running times of the scenarios in
the DS, as well as the number of times of driving on the
curve and the number of overtaking maneuvers that were
performed before a malfunction occurs. In this experiment, several different running times were applied to avoid
creating expectation of the event by the experimental participants.
Table 3 Scenario running times and number of operations (2.2.1.)

Table 4 Scenario running times and number of operations (2.2.2.)

In this experiment, visual information on a display andan acoustic signal from a speaker were used as the HMI.
Twenty-five non-elderly persons (ages: from 20s to 50s)
and five elderly persons (ages: from 65s to 70s) who fre72

3.1. Occurrence of malfunction while driving on curve
3.1.1. Condition: no button operation at certain intervals
Figure 6 shows the response time in each experimental
condition until the driver held the steering wheel after amalfunction warning was shown. The average reaction
time including the standard deviation was from 1.0 to 1.3
seconds, and no statistical difference was found. Figure 7
shows the maximum deviation of the right front wheel ineach experimental condition (a positive value indicatesdeparture from the lane). For case 1 (0[s] later stop), a
maximum deviation of 3.2 meters, including the standard
deviation, was observed, and a statistical difference wasfound compared to the other three experimental conditions. No statistical difference was found in the other three
conditions.
These experimental results suggest that the system should
maintain the steering torque for driving around the curve
for at least 2 seconds after the system shows a malfunction
warning to the driver. In contrast, the experimental resultsof case 2 (0[s] later stop) suggest that reducing steering torquegradually is an effective, way of maintaining the time margin.

Fig. 6 Comparison of steering wheel response time[ I ] Development of Automated Driving Systems② Human Machine Interface (HMI)

Basic Research into Requirements of HMI to Ensure Safety for Automated Driving Systems

Fig. 7 Comparison of maximum deviation of right front wheel

3.1.2. Condition of button operation at certain intervals
Figure 8 shows the rates of button misoperation after
showing a malfunction warning. When the interval condition was set to every 1 minute, 13 drivers recognized thewarning as a button operation signal by mistake. Under the
5-minute interval condition, 3 drivers recognized thewarning as a button operation signal by mistake. Figure 9
compares the response time until the driver held the steering wheel after the malfunction warning was shown in thecase of 13 drivers. Under the 1-minute interval condition,
the average reaction time, including the standard deviation
increased by around 1 second compared to when there wasno button operation task. This is a statistically significant
difference. Figure 10 compares the maximum deviation of
the right front wheel for the same 13 drivers. Under the
1-minute interval, the maximum deviation, including thestandard deviation, increased by around 3 meters compared to when there was no button operation task, whichis also a statistically significant difference.

Fig. 10 Comparison of maximum deviation of right front wheel

3.2. Occurrence of malfunction while changing lanes
Figure 11 shows the response time under each experimental condition until the driver held the steering wheel
after the malfunction warning was shown. When the lanechange time was 6[s] (2[s] later stop), the reaction time
was longer than in the other two cases, which is a statistically significant difference. Figure 12 shows the maximum
deviation of the right front wheel in each experimental
condition. When the lane change time was 3[s] (0[s] laterstop), a maximum deviation of around 1 meter, including
the standard deviation, was observed. This is a statisticallysignificant difference compared to the other three experimental conditions. In contrast, no departure from the lane
was observed under the other experimental conditions(lane change time: 6s).
These experimental results suggest that the system should
continue steering control for at least 2 seconds after thesystem shows a malfunction warning to the driver, thesame as the result described in Section of 3.1. above. However, the experimental results with a lane change time of
6[s] (0[s] later stop) suggest that taking a slightly longer
time to change lanes under normal conditions is an effective way of maintaining the time margin.

Fig. 8 Comparison of number of button misoperations
Fig. 11 Comparison of steering wheel response time

Fig. 9 Comparison of steering wheel response time

Fig. 12 Comparison of maximum deviation of right front wheel

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Basic Research into Requirements of HMI to Ensure Safety for Automated Driving Systems

4 Conclusion
According to the results of the experiments describedabove, when a system malfunction occurs and the driver
takes over manual driving, around 2 seconds may be
regarded as the necessary time margin for a safe takeover
operation in the case of a level 2 automated driving system.
In addition, gradually stopping the steering control after
the start of the warning, and carrying out lane changes
over around 6 seconds under normal conditions were
found to be effective ways of maintaining the time margin.
References(1) SAE: Taxonomy and Definitions for Terms Related to
On-Road Motor Vehicle Automated Driving Systems,
J3016-201401(2014)(2) Cabinet Office, Government of Japan: SIP AutomatedDriving System’s Research & Development plan(2016)

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Outline of Cybersecurity

Takashi Imai (President, Toyota InfoTechnology Center Co., Ltd.)

1

Trends in Vehicle Cybersecurity

1.1. Advances in vehicles
As vehicles become increasingly equipped with multiple
electronic control units (ECUs), these ECUs—which havespecific features and purposes—are connected via a controller area network (CAN). It is necessary for advances invehicles to provide safe and comfortable mobility, as wellas support for basic car functions (e.g. accelerating, steering and braking), and this is realized by having communication links with each ECU. Recently, it is common forvehicles to communicate with the outside world throughwireless communications; mobile networks, dedicatedshort range communications (DSRC), Wi-Fi, Bluetooth orother meas. As a result, vehicle cooperative functions using
external communications have dramatically improved.
1.2. Trends in Vehicle Cybersecurity
As wireless communications are coming to play a more
important role in vehicles with advances in automateddriving technologies, the risks presented by cracking andblack-hat hacking have to be considered more carefully
from the perspective of vehicle cybersecurity. In fact, hacking has become increasingly sophisticated year by year. In
2013, there was a report that certain vehicles were controlled by connecting a wire to the vehicle network to
inject vehicle control commands. In 2015, the first remotehacking via cellular communications was reported in
America. Exploiting vulnerabilities in the internal vehicle
network and devices, the hackers sent unauthorized commands and thus remotely controlled its steering, brakes,
and transmission, all from a laptop. Thereafter, hacking
cases have been reported every year in both wired andwireless networks. Hacking over wireless networks, especially, leaves a lot of cars widely vulnerable to the threat ofa remote attack. With the spread of connected vehicles andtools that access vehicle networks, it is becoming easier tomake attacks. Therefore, it is necessary to ensure that vehicles are protected from cracking and black-hat hacking,and clear cybersecurity is required. In addition, significant
concern and cost is required in developing the safety andsecurity of vehicles with automated driving technologies.

Furthermore, there is high demand for a cybersecurity
evaluation and secure design process.

2

Trends in the Automotive Industry

2.1. Strategies of the Automotive Industry
The difficulties of developing cybersecurity for vehicles
are: 1) to address security challenges for customers andpassengers, a concern that is different from those of the IT
industry, 2) to deal with malicious intent (i.e. cybersecurity), which is a change for automakers that used to focus
on accidental failure from the viewpoint of function safety,3) the long lifecycle of vehicles. These tasks cannot be
achieved through competition. The automotive industry as
whole has to closely collaborate on these issues as much as
possible. Currently, the following organizations have a specific role:• JAMA – Industry Principles (Planning and Operation)• JSAE – Standardization (Requirements)• JasPar – Standard Technologies (Design)Moreover, in terms of international relationships:• JAMA cooperates with United Nations WP.29• JSAE cooperates with the ISO and SAE• Jaspar cooperates with Autosar
2.2. Trend in Legislation
The United Nations WP.29 has decided that the guidelines on cybersecurity and data protection for vehicles with
automatic driving technologies will come into effect in2020. They require warning drivers and a safety controller
for the vehicle when the vehicle detects a cyberattack. The
ISO and SAE have been working together to define a structured process to ensure cybersecurity and ISO/SAE 21434,
a world-first joint standard by the ISO and SAE, will be
released in 2020.
2.3. Auto-ISAC
Meanwhile, in America, Alliance of Automobile Manufactures (AAM) established Auto-ISAC (Automotive InforSIP-adus: Project Reports, 2014-201875

[ I ] Development of Automated Driving Systems③ Information Security

Outline of Cybersecurity

mation Sharing and Analysis Center) in 2016, in responseto the above mentioned cases (cf. Section 1.2). Auto-ISAC
is to share the most up-to-date information on vehicle
cyberthreats for vehicle devices and networks among theautomotive industry. One year later, Auto-ISAC was alsoestablished in Japan under JAMA.

3

Study of Cybersecurity by SIP-adus

3.1. Scope of Study for Research and Development Scenarios by Sub-Working Group
Prior to the start of the study, we, all entities involved in
vehicle cybersecurity in Japan, reached an agreement withrespect to the basic on-board system architecture for vehicles as part of a consensus with the automotive industry inorder to clarify the scope of the study. Thereafter, ourobjective was determined as the study of cybersecurity
inside vehicles to achieve industry and international standards. Meanwhile, data center security is being assessed by
SIP- Cybersecurity for Critical Infrastructure.
3.2.

Overview of 4-Year Plan for Sub-Working Group• Construct a common type of system modeling for automotive driving technologies• Develop security requirements via threat analysis• Develop evaluation environments and standardize evaluation methods• Study the simplification of signatures for V2X communications and standardize the relevant methods

Fig. 1 Objective Establish a Cyber Security Evaluation Guideline

About the author
1) Takashi Imai, President, Toyota InfoTechnology Center
Co., Ltd.

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Formulation of a Comprehensive Threat Model for
Automated Driving Systems Including External
Vehicular Attacks such as V2X and the Establishmentof an Attack Evaluation Method through Telecommunication
Ken Okuyama (PwC Consulting LLC)
ABSTRACT: Information used as the foundation for automated driving systems is expected to be obtained from external networks, which
could cause cybersecurity issues that did not exist in conventional cars. In this project, the activities related to automated driving/connectedcars were researched, categorized and organized on a factual basis to derive a common model for automated driving systems and formulate acomprehensive threat model for automated driving systems. In addition, an information security evaluation method was defined and issued
as a draft to serve as a guideline for the overall assessment of V shape vehicle development models.

1

Project Overview

The basis of automated driving systems, information suchas high definition map data and data on vehicles, pedestrians, road infrastructure and so on, is expected to beobtained primarily from external networks. Such information will be transferred to vehicle control/information
devices used for vehicle control in the automated driving
system. This could lead to cybersecurity issues that did notexist in conventional cars. In order to resolve these issues,
the Cross-ministerial Strategic Innovation Promotion Program (SIP), Automated Driving System/Large Scale Field
Operational Test, Information Security Field Operational
Test conducted threat research/analysis on cybersecurity
threats related to automated driving, established cybersecurity evaluation methods/protocols at the vehicular level
aimed towards international standardization, and planned
to conduct technical research through black box testing of
the vehicle systems provided from participants in the field
operational test.

Formulation of Comprehensive Threat

2 Model

2.1. Objectives and Scope
The objective of the project is to formulate a comprehensive threat model of attacks that could come from sources
external to the vehicle, such as V2X, and to establish publicconsensus on the cybersecurity of automated driving vehicles.
The formulation of the comprehensive threat model was
conducted in two phases. The first phase consisted ofresearch on a common model of automated driving sys-tems. The activities by automotive manufacturers, suppliers, and IT companies related to automated driving/connected cars were researched, categorized and organizedon a factual basis. The second phase listed threats fromsources outside the vehicle, such as V2X, against the common model, evaluated the impact of each threat item, andconducted research on countermeasures for highly criticalthreats, reflecting any changes to the evaluation guidelines
developed as necessary.
2.2. Research Approach
Threat analysis research was conducted in two phases:
Research on a common model for automated driving systems, and Research on a comprehensive thread model.
2.3. Research on Common Model for Automated Driving Systems
In this phase, services and features related to automateddriving systems were listed and, for each feature, anassumed system architecture was developed and taken into
consideration to identify a common model for automateddriving systems.
2.3.1. Listing Services and Features Related to Automated Driving
As a first step, all services and features related to automated driving systems were listed. Research targeting 16
automotive manufacturers, 4 automotive component manufacturers and 24 IT companies that develop automatedvehicles was conducted on services related to automateddriving as well as the features required to enable such services.
The list of services and features identified through theresearch is as follows:
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[ I ] Development of Automated Driving Systems③ Information Security

Formulation of a Comprehensive Threat Model for Automated Driving Systems Including External Vehicular Attacks such as
V2X and the Establishment of an Attack Evaluation Method through Telecommunication
Driving/parking assistance: distant control, lane control,lane control (ITS cooperative), platooning, automateddriving, automated driving (ITS cooperative), parking
environment display, automated parking, automated parking (smartphone cooperative)
Safe driving assistance: emergency braking, pedestrian
detection (V2P), danger alert (ITC cooperative)
Fuel saving driving assistance
Remote vehicle control: door locking/unlocking, charging
control, charging control (AI voice recognition), air conditioner control, air conditioner control (AI voice recognition), restart engine/unable steering lock release
Failure detection: failure detection
Navigation: route search, operator service
Entertainment: calendar/email syncing, social media,
Wi-Fi spots, other applications
2.3.2. Assumed System architecture for Each Feature
Along with the list of services and features related toautomated driving presented in Subsection 2.3.1, assumed
system architectures for each features were developed. In
this process, leading companies in development of various
automated driving vehicle models were selected, and adesk study was conducted based on information madepublic by each companies. A series of expert interviews
was then conducted to review the developed assumed system architecture.
2.3.3. Definition of a Common Model for AutomatedDriving Systems
In this project, all assumed system architectures for eachfeature were considered and integrated to identify a common model for automated driving system for threat analysis/research. Expert interviews were conducted for thisstep as well to review the common model.
The following Figure 1 depicts the common model for
automated driving system identified through the threat
analysis research.

Fig. 178

2.4. Research on Complete Picture of Threats
In this research, threat was defined as “a potential elementthat causes harm (damage) to the common model of automated driving system”. This definition was refined from thedefinition of Threat in ISO/IEC 27000:2009 to match the
purpose of the research.
2.4.1. Listing of Threats against Common Model ofAutomated Driving Systems
Based on the above definition, the common model andthe threat matrix by the World Forum for Harmonizationof Vehicle Regulations (WP.29) were compared to extract
the target of attack and threat against the common model,
which were then categorized based on the CAPEC attack
type, and common weakness enumeration (CWE) wasused to compare and verify the result of categorizationfrom the viewpoint of defects to create a list of threatsagainst the common model of automated driving systems.
The list created is as follows:(1) Threats related to vehicle system
Leakage of OEM assets (information), leakage of vehicle
owner’s personal data, leakage of encryption keys, falsified
vehicle control software, unauthorized vehicle ID change,
ID spoofing, falsification of driving data, unauthorized
vehicle diagnostic data falsification, deletion of log data,
falsification of control feature parameters, falsification of
charging feature parameters, service disruption due to data
flooding, introduction of malware, circumvention of monitoring systems.
(2) Threats related to vehicle physical external interfaces
Sensor spoofing, communication data route falsification,
virus infection from external media, intrusion from physical external interfaces (USB etc.) sending unauthorized
diagnostic message (OBD II etc.)(3) Threats related to vehicle internal communication
channels
Communication interception, unauthorized data accessfrom communication channel, falsification of communication data, falsification of communication feature (e.g.
remote keys), data falsification of short range communication/sensors, unintended feature execution due to command injection, falsification/overwriting/deletion/additionof data/code, virus infection from communication channels, sending unauthorized CAN messages, sending unauthorized special messages (e.g. messages only allowed to besent from OEMs), data input from unreliable source, service disruption due to data flooding, sender spoofing, civil
attacks, replay attacks.
(4) Threats related to vehicle external communication
channels
Communication channel interception, unauthorized[ I ] Development of Automated Driving Systems③ Information Security

Formulation of a Comprehensive Threat Model for Automated Driving Systems Including External Vehicular Attacks such as
V2X and the Establishment of an Attack Evaluation Method through Telecommunication
access to data through communication channels, MITM
attacks, data/code falsification/overwrite/deletion/addition, data input from unreliable source, sending unauthorized V2X messages, virus infection from communication
channel, falsified third party application, disruption of service due to data flooding, black hole attack in V2V communication, civil attack, command injection, replay attack,root compromise.
(5) Threats related to external server
Information leakage due to server intrusion, information
leakage due to inappropriate data sharing, server takeover
due to server intrusion, DoS attack on server, server destruction due to server intrusion, unauthorized usage of facility(6)Threats related to update service
Leakage of encryption key for updates, disruption of
update/falsification of update program (server/local),injection of unauthorized update data, disruption of authorized update(7) Threats related to attacks from vehicle (secondary damage)Transmission of unreliable V2V data, timing attack, sending false emergency information, DoS, attacks from vehicle
to other system, transferring unreliable data to infrastructure, DoS against infrastructure, botnet vehicle, DoS
against network(8) Threats related to physical factors
Data loss due to crash or other accident, data loss due tofailure in DRM management, data loss due to malfunctionof IT component, data leakage due to resale/purchase ofvehicle by the owner, OEM data falsification
2.4.2. Identification of a Comprehensive Threat Modelfor the Automated Driving System Common Model
A framework was developed to evaluate the criticality ofthe threats extracted. The framework combines the evaluation criteria developed by WP.29 and JSAE to semi-quantify the Threat Impact and the Attack Difficulty to estimate
the Threat Criticality.
*Threat Criticality calculation formula:Threat Impact × Attack Difficulty × Impact of Incident ×
Information Asset Criticality = Threat Criticality

Using the framework, threat criticality was calculated foreach feature that enables automated driving systems.
Of the inherent threats, threats with a criticality score of
Level II or higher were extracted and shown in the following table. Countermeasures and responsible parties foreach countermeasure were also determined, taking into
consideration the countermeasures recommended in
WP.29 etc.

Table 1For threats classified as Level II or above, the necessary
vehicle-side countermeasures were included in the Information Security Evaluation Guidelines developed during
this project. However, threats described in red text cannot
be adequately countered with vehicle-side measures alone
and therefore require separate consideration for countermeasures by the responsible party identified in the table.
Refer to Cybersecurity for Critical Infrastructure, separately studied in SIP, for countermeasures by IT service
providers.

Table 2

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[ I ] Development of Automated Driving Systems③ Information Security

Formulation of a Comprehensive Threat Model for Automated Driving Systems Including External Vehicular Attacks such as
V2X and the Establishment of an Attack Evaluation Method through Telecommunication

The following Figure 2 depicts the threats with a score of
6.0 or above along with a visualization of the common
model of automated driving system.

Fig. 3Fig. 22.5. Summary of the Approach towards Research on
Complete Picture of Threats
In the research, all possible threats were derived by taking
into consideration the system architectures related to automated driving system. Threats to be responded with priority were identified using the Criticality Evaluation
Framework. Then, responsible actors for the countermeasures were identified and the necessary vehicle-side countermeasures were reflected in the Information Security
Evaluation Guidelines.
In the analytic approach in the research, 40 assumed system architectures were identified from 35 features thatcomprise 12 services. For each system architecture, 72
threats were listed, and the WP.29, CWE and CAPEC categories were combined to identify 3,040 threats in total.
Of the 579 threats with a possibility of occurrence thatwere derived, considering the system architecture andapplying the Criticality Evaluation Framework identified
560 threats classified as Caution, 17 as Warning and 2 asUrgent. The responsible actors were identified and includedin the guidelines for the 17 threats classified as Warningand the 2 threats classified as Urgent.

Establishment of Information Security

3 Evaluation Method

3.1. Scope of the Evaluation Guideline
The following diagram depicts the function and positioning of the evaluation method established in the guidelines
in the V-model vehicle development process.

80

3.2. Principles of Evaluation Guideline Development
The evaluation items in the guidelines were developed
using the following three principles.
The first principle was to identify threats that could
potentially materialize with clear evidence in real-world
conditions.
Issues identified through attacks (evaluation) from actualattack cases, which are issues that could arise, were used as
final evidence for judging whether remediation is necessary.
The second principle was to construct a practical work
procedure by focusing on discovering the critical path thatcan lead to serious issues.
The work procedure for discovering issues was optimized
to enable adoption in the vehicle development process by
setting the evaluation goal at taking over the feature ofprivilege over vehicle control, which could inflict the mostserious vehicle security issues.
The third principle was to identify items to be added tothe evaluation method from the viewpoint of developers,
as well as included in the guidelines.
Discovering actual issues from the viewpoint of developers clarified the security measures missing in the evaluation method from that viewpoint, which was also applied
to a review of the evaluation guidelines. This will in turnimprove the quality of future development activities
3.2.1. Previous Incidents Covered by the Evaluation
Items in the Guidelines
The guideline aims to evaluate cybersecurity robustness
against cyberattacks performed by actual attackers (hackers). Therefore, the evaluation methods described in theguidelines were used to confirm that past cybersecurity
incidents can be prevented.
The list of cybersecurity attack cases targeting automotive
manufacturers included in the guidelines is presented
below. In actual attacks on the vehicle, techniques such as
hardware analysis, firmware extraction, and reverse engineering were used to steal information, and those techniques are included in the evaluation items in theguidelines.

[ I ] Development of Automated Driving Systems③ Information Security

Formulation of a Comprehensive Threat Model for Automated Driving Systems Including External Vehicular Attacks such as
V2X and the Establishment of an Attack Evaluation Method through Telecommunication(1) Vulnerability in vehicle infotainment system, Automaker A
A vulnerability that can be exploited by a third party toremotely identify vehicle location or control the vehicle.
The vehicle can be remotely controlled by intruding into
the vehicle embedded system from an exploited port on
cellular network and falsifying the CAN controller firmware.
(2) Vulnerability in vehicle infotainment system, Automaker B
A vulnerability that can be exploited to enable a third
party to remotely control the vehicle. The vehicle doors canbe unlocked by sending a command from a telematics
server set up by the researcher.
(3) Vulnerability in wireless LAN, Automaker C
A vulnerability that can be exploited to enable a third
party to remotely control the vehicle. The researcher presented a method to direct the user to the attacking siteusing a fake Wi-Fi spot. The attack can also be madethrough the cellular network. In this case, a fake email will
be used to direct the user to the attacking site.
(4) Vulnerability in mobile application, Automaker DA
vulnerability that can be exploited to enable a third
party to remotely control the settings of the air conditioner
and other devices in the vehicle. The air conditioner or
security alarm can be remotely controlled by accessing the
Wi-Fi spot in the vehicle.
(5) Vulnerability in vehicle infotainment system, Automaker E
Confidential information such as user ID, or password
may be leaked due to a leftover development setting thatgeneral users do not use.
(6) Vulnerability in connected service authentication,
Automaker E
Authentication between smartphones and the server API
was not implemented, allowing attackers to control othervehicles if the last five digits of the VIN were identified.
*While this was a vulnerability in the smartphone application, it was used to confirm that the same issue does notexist between the server and vehicles or between smartphones and vehicles.
(7) Vulnerability in connected service, Automaker F
No expiration period was set for the security token used
to authenticate the smartphone devices, so in case thetoken was stolen, the door can be unlocked by an attacker.
*While this was a vulnerability in the smartphone application, confirm that the same issue does not exist between
server and vehicle as well as between smartphone andvehicle.
(8) Vulnerability in Telematics Control Unit, Automaker G

A vulnerability that can be exploited to enable a third
party to remotely control the vehicle TCU.
(9) Vulnerability in connected service, Automaker H
A vulnerability that can be exploited to enable the execution of unintended code from a USB port inside the vehicle. The vulnerability was used for AVN customization.
*While this was a local attack, it was taken into consideration as an evaluation criterion for anti-reverse engineering performance.
(10) Vulnerability in connected service, Automaker I
A vulnerability that may enable the execution of unintended code from a USB port inside the vehicle. The vulnerability was used for AVN customization.
*While this was a local attack, it was taken into consideration as an evaluation criterion for anti-reverse engineering performance.
3.2.2. Assumed Level of Evaluation Method
Based on the principles in Subsection 3.2, attacker profiles were analyzed in order to ensure the effectiveness andpracticality of the evaluation, and to set its scope to cover
up to the level of attacker capable of combining existing
attack methods to attack new targets and of acquiring andutilizing all commercially available hacking devices.
In terms of attack probability, attacks that require
extremely high level of technical expertise, such as inventing new attacks methods by spending multiple years on
research and development or utilizing analytics devices
that only have a few of their kind in existence, are excluded
from the scope.
3.3. Evaluation Method Established3.3.1. Evaluation Method Overview
The vehicle evaluation was conducted in four phases:
Reconnaissance, Intrusion, Escalation of privilege, Actions
on Objectives.
3.3.2. Evaluation Method in Reconnaissance Phase
In the Reconnaissance phase, in order to avoid inefficiency of random attacks, prior analysis on system architecture, functional conditions of the target vehicle isconducted to identify the conditions necessary for a successful attack. The analysis involves physical contact withthe target vehicle.
This phase has the two following aspects:(1) Hardware investigation
In the hardware investigation, data extraction is tested
against all available interfaces of the target embedded
hardware (vehicle, devices, chips) used for data input/output. Once an attempt was successful, the binary file isreverse engineered and the system is analyzed.
(2) Software investigation
In the software investigation, communication intercepSIP-adus: Project Reports, 2014-2018

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V2X and the Establishment of an Attack Evaluation Method through Telecommunication

82

tion is attempted against wireless communications (TCU
(3G/4G), Wi-Fi, Bluetooth) and related components withthe aim of obtaining information necessary for intrusion
and/or spoofing.
3.3.3. Evaluation Method in Intrusion Phase
In the Intrusion phase, the information obtained in theprevious phase was used to attempt an intrusion throughthe target wireless interface. From this phase onwards, allattacks are attempted through wireless interfaces.
The attack through a wireless interface is attempted until
control of the system console is achieved. Also, the attack
patterns are categorized based on the conditions affecting
the attack method, such as vehicle network access conditions and driver involvement.
This phase has the four following aspects:(1) Passive attack with user intervention(2) Passive attack without user intervention(3) Active attack exploiting a vulnerability(4) Active attack using information obtained through communication interception
3.3.4. Evaluation Method in Escalation of Privilege
Phase
After a successful intrusion, the necessary privilege is
obtained through root compromise, jailbreaking or similarhacks. If the necessary privilege is obtained immediately
after intrusion, this process becomes unnecessary. Depending on the error conditions at the time of the arbitrarycode execution, a workaround for the cause may be
attempted.
This phase has two aspects:(1) Removal of protection(2) Escalation of privilege
3.3.5. Evaluation Method in Actions on Objectives
Phase
After being successful in the previous phases, simulated
attacks are performed to specifically damage the system/
user by compromising security characteristics (confidentiality, integrity, availability) and confirm the impact causedby the discovered intrusion path (vulnerability). DoS(Denial of Service) attacks from the external network arealso tested even if the intrusion fails.
This phase has four aspects:(1) Information leakage(2) Denial of service(3) Unauthorized operation (related to control)(4) Unauthorized operation (not related to control)

lytics work and the clarifying of criteria for the evaluations
as well as the evaluators will be added to the evaluation
guidelines.

3.4. Finalizing the Evaluation Method
The evaluation method will be updated to reflect the outcome of the 2018 field operational test. Specifically, theconducting of threat analysis before the initiation of ana-

References(1) Society of Automotive Engineers of Japan, Inc. (JSAE):JASO TP15002 (JP), (2015)(2) Japan Automobile Research Institute (JARI): Research

4 Conclusion
Lastly, based on the outcome of the research, we willsummarize our suggestions for stakeholders in relation to
the threats and countermeasures each of them should consider.
Automotive manufacturers should implement countermeasures to protect from Service disruption due to data
flooding against ITS cooperative automated driving features.
Critical threats that the automotive manufacturers should
respond to are included as items in the evaluation guidelines, in the hope that countermeasures will implemented
based on the guidelines.
IT service providers should implement countermeasuresfor Disturbing Update against over-the-air (OTA) features.
The countermeasures are required mainly for information
systems, such as servers, which are outside of scope of consideration for this project.
However, the topic is separately studied in Cybersecurity
for Critical Infrastructure within SIP, and cooperative
action will be important in the future.
The government and related organizations should implement countermeasures for Service disruption due to data
flooding against road infrastructure that cooperates withthe vehicles.
Currently there is no consideration on cybersecurity
measures in coordination with automated driving systemand the acceleration of such consideration is required toprepare for the spread automated driving system.
Wearable device and smart device manufacturers should
implement countermeasures for Data input from unreliable sources against V2P devices.
Currently there is no consideration on cybersecurity
measures in coordination with automated driving systemand the acceleration of such consideration is required toprepare for the spread of automated driving systems.

[ I ] Development of Automated Driving Systems③ Information Security

Formulation of a Comprehensive Threat Model for Automated Driving Systems Including External Vehicular Attacks such as
V2X and the Establishment of an Attack Evaluation Method through Telecommunication
and Development Project on Security Technology
Related to Utilization of External Vehicular Information such as V2X, (2016)(3) Hiroshi Hashimoto: JasPar’s Activity towards the Standardization of CyberSecurity, (2017)(4) Yuval Weisglass: PRACTICAL ATTACK ON CANMESSAGE AUTHENTICATION, (2017)(5) Dr. Frederic Stumpf: A Holistic Approach to Automotive Security with Security in the Fast Lane”, (2017)(6) Daisuke Noguchi: Security Risk of Automotive Grade
Linux, (2017)(7) National Institute of Standards and Technology (NIST):NIST SP 800-30 Revision 1, Risk Management Guidefor Information Technology Systems, Recommendations of the National Institute of Standards and Technology, (2012)(8) Information-technology Promotion Agency Japan(IPA): Automotive Information Security Practice
Guide, (2013)(9) International Organization for Standardization (ISO):ISO 26262: Road vehicles. Functional safety, (2011)(10) National Highway Traffic Safety Administration(NHTSA): Cybersecurity Best Practices for Modern
Vehicles, (2016)(11) Shinji Sato: Hardware Security, Towards IoT Era,
(2015)(12) Randy Torrance and Dick James: The State-of-the-Art
in IC Reverse Engineering, (2009)(13) Dr. Sergei Skorobogatov, Physical Attacks on Tamper
Resistance: Progress and Lessons, (2011)(14) Bulent Yener: CSCI4974/6974 Hardware Reverse
Engineering, (2014)

About the author
1) Ken Okuyama, Technology Consulting Manager, Cybersecurity, PwC Consulting LLC

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Development of Automated Driving Systems③ Information Security

Research and Development Project for Automobile
Security for Utilization of Information Obtained by
Communication such as V2X
Atsushi Ohba, Hiroshi Ito (Japan Automobile Research Institute)
ABSTRACT: Communication technologies such as V2X are expected to be used in cars to acquire dynamic maps and the surrounding
environment information necessary for automated driving. Meanwhile, external connectivity through communications makes cybersecurity
as an important issue. To derive the requirements for vehicle cybersecurity, a common model of automated driving systems was constructed
and used to carry out threat analysis. Furthermore, evaluations and verifications ranging from the component to the vehicle levels were carried out. Based on this, the testbed requirements for vehicle security studies were developed. In addition, methods of simplifying certificate
verification were studied.

1 Background
The theft of cars is a major topic in automotive security.
In 2010, there was a report(1) that the electronic systems in
automobiles could be hacked. Since then, cybersecurity in
automobiles has been the object of attention. In 2015, it
was demonstrated(2) that controlling a car remotely via amobile phone network was possible.
In recent years, various connected services, such as
telematics, have become widely available. In addition, connecting smartphones to car infotainment systems is gaining in popularity. Under these circumstances, it is expectedthat security measures will be treated accordingly as animportant issue.

2.1. Basic Concept Underlying Automotive Security
Measures
In an automobile, communication with the outside of the
car is the most likely target entry point for a security
attack. Automobile wireless communication such as 4G /LTE or V2X, or wired communication such as OBD-II(OBD: on-board diagnostics) are used to communicate
with the outside world and can become such an attack vector, as shown in Fig. 1.

2 Abstract of Research and Developments
In FY 2014, we conducted the “Survey on overseas trends
in security technology related to the V2X (Vehicle to X)
system in the Cross-ministerial Strategic Innovation Promotion Program (SIP): Automated Driving for Universal
Services (adus). In FY 2015 and FY 2016, we conducted
Research and development of automobile security for utilization of information obtained by communication such as
V2X.
From FY 2017, we conducted the security part of the
Research and Demonstration Project for Social Deployment of Highly Automated Driving : Automated Valet
Parking Demonstration Experiment toward the Implementation in General Society Project (Development of Evaluation Environment for Safety and Security) , which was
supported by the Ministry of Economy, Trade, and Industry (METI).

84

Fig. 1 Example of the External Communications in Vehicles

As in the IT industry, multi-level defense, or defense indepth, is commonly used to protect in-vehicle systems. In
this project, a four-layer model was used, as shown in Fig. 2.

Fig. 2 Layered Structure for Automotive Cybersecurity

The first layer is composed of the entire mobility society
including the cloud and other services, the second layer isthe whole vehicle, the third layer is composed of the components such as the in-vehicle network below the central

[ I ] Development of Automated Driving Systems③ Information Security

Research and Development Project for Automobile Security for Utilization of Information Obtained by Communication suchas V2X
gateway, and the fourth layer is the components such as
ECUs.
2.2. Research Themes
In this research, the following three themes were set for
security at the second layer and below, as shown in Fig. 2:
Threat Analysis, Evaluation Methods and Criteria for
Cybersecurity Countermeasures, and Omitting Message
Verification in V2X Communication.

3 Threat Analysis
A threat analysis was conducted to clarify what kinds ofthreats exist, determine what countermeasures (security
requirements) are appropriate against the threat, and assess
whether the expected risk was sufficiently reduced.
Threat analyses are generally performed based on the following factors: system architecture, use cases (assumed
usage examples), threat analysis methods, criteria for risk
assessment, and creation of countermeasures (security
requirements).
In this section, we surveyed and organized case studies of
threat analysis in domestic and overseas projects and set
up the in-vehicle system architecture of automobiles usedin this research, as shown in Fig. 3.

analysis.

Study on Evaluation Methods and Crite-

4 ria for Cybersecurity Countermeasures

Attack methods using the evaluation systems for thewhole vehicle, in-vehicle systems, and components weredeveloped to examine the evaluation technology and criteria concerning vehicle cybersecurity, and the evaluation
method was examined.
4.1. Evaluation at the Component and In-Vehicle System Levels
Investigating the evaluation criteria for cybersecurity
measures against automotive components (ECUs), requires
quantifying the possibility of attack. Focusing on the software update function (reprogramming), which is an
important function in the component, a cybersecurity
evaluation system was developed. In this research, in orderto evaluate the effect of the level of cybersecurity countermeasures, multiple levels of entropy for the random number used for security authentication were set in theevaluation board.
In the METI project, CAN message authentication in thein-vehicle system was also evaluated for vulnerability by
setting freshness values (FVs).
Figure 5 shows an example of the system used for theseevaluations. The signal coming out of the ECU is acquired
and processed with the PC.

Fig. 3 Assumed In-Vehicle System Architecture

Fig. 5 Evaluation System for Component Level

Fig. 4 Threat Analysis Platform

In addition, to conduct the threat analysis, we first built
the common platform for threat analysis shown in Fig. 4,
as a tool environment that can integrate the various techniques and methodologies necessary to perform threat

Using this evaluation system, attacks were performed byvarious groups. In both cases weak countermeasures, suchas low entropy in random number in authentication or asmall FV saturation number resulted in successful attacks.
These results show that even if measures are taken,
improper configurations in an implementation turn out tobe vulnerable.

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Research and Development Project for Automobile Security for Utilization of Information Obtained by Communication suchas V2X
4.2. Development of Vehicle Simulation System for
Security Evaluation
In the security evaluation of in-vehicle systems, actual vehicles are sometimes used, but there are some difficulties insharing the results of security research, because of differencesin architectures and interfaces depending on vehicle type.
Therefore, it is considered effective to construct an evaluation environment that can be commonly used by manyorganizations and engineers. Here, the vehicle simulation
system based on the in-vehicle system architecture in Fig.
3 was developed as an open platform security testbed thatcan be used to verify and evaluate security technology.
We developed the prototype of the testbed in the SIP
project, and the testbed shown in Fig. 6 in the METI project.
The test bed is composed of a telematics control unit
(TCU) for communication with the outside world, an invehicle gateway (central gateway) receiving communication data from the outside world, ECUs with models forcontrolling motors, a steering and brakes. These elements
simulate the in-car system of a simple electric vehicle (EV).
In addition to the TCU, a Wi-Fi connection, as well as awired communication function equivalent to the OBD-II
port, are implemented.
A dedicated CAN port to monitor what is happening
inside the evaluation environment is provided, anddetected illegal message is displayed.
These functions make cybersecurity research and evaluation of countermeasures on connected cars possible.

Fig. 6 Security TestbedStudy of Omitting Message Verification

5 in V2X Communication

The V2X communications being put into practical use inEurope and the United States, all messages are signed inorder to prevent spoofing or other tampering. Basically, allmessages must be verified. However, since the signature
verification processing requires a relatively long computation time, the messages can not all be processed in the V2X
86

on-board unit when many cars are around and thus many
messages are received.
In order to deal with this problem, a Verify-on-Demand
scheme (VD scheme) has been proposed as a verification
omitting scheme in the United States. In this article, we proposed a message verification scheme with priority that candeal with advanced DoS attacks better than the VD scheme.

Fig. 7 Message Verification Scheme for V2X in OBU

A virtual environment was constructed to verify the effectiveness of each scheme. As a result of the evaluation, theprioritized message verification scheme showed higher tolerance than the VD scheme against advanced DoS attacks.
Both schemes exhibited low tolerance to DoS attacks
focused on filter conditions and algorithm.

6 Conclusion
In the research project, we assessed the three themes of
threat analysis, security evaluation technology, and omitting signature verification in V2X communication.
For threat analysis, we set up an in-vehicle system architecture to analyze and develop a common platform as atool for efficient threat analysis.
For security evaluation technology, we built componentlevel / in-vehicle system level evaluation systems, and verified the susceptibility to attacks when a vulnerability
existed. Also, we developed a simulated vehicle system forsecurity evaluations.
For the omission of message verification, we proposed a
new message verification scheme with priority, and showed
its effectiveness.
References(1) Koscher, K., et al.: “Experimental Security Analysis of
a Modern Automobile” in Security and Privacy (SP),2010 IEEE Symposium on (pp. 447-462). IEEE (2010)(2) Charlie Miller and Chris Valasek: Remote Exploitationof Unaltered Passenger Vehicle: Presentation at BlackHat (2015).

II

Development of Basic Technologies to Reduce Traffic Accident Fatalities andCongestion, and Initiatives to Raise Social Acceptance

Development of Core Technology for Reducing Traffic Accident Fatalities and Traffic Congestion, andCreating Social Acceptance of Automated Driving
Hiroyuki Kanemitsu (Toyota Motor Corporation)

1

Introduction

This chapter describes mainly the results of studies related
to the reduction of traffic accident fatalities. The numericaltarget of the Japanese government is to reduce the annual
number of traffic accident fatalities to fewer than 2,500 by
2020. The use of automated and connected technologies is
considered a prerequisite for achieving this target. This
article presents an overview of these studies.

2

Traffic Accident Analysis

The study presented here analyzed Japanese traffic accidents over five years. First, it identified common accident
types that cause many fatalities each year. Next, it integrated similar accident types into a pattern. The results ofthis integration defined more than 200 patterns. These patterns can be considered as representative of traffic accidents in Japan. Analyzing these patterns every year canreveal increasing or decreasing trends for the number offatalities of each pattern. As a result, it was revealed that
accident patterns involving collisions between pedestrians
and vehicles are not showing a decrease in fatalities.

3

Vehicle and Pedestrian Communication
Systems

Accident analysis found that the main cause of vehicle
and pedestrian accidents was recognition error. Therefore,
the study presented in this section describes technologies
that use communication systems to assist the recognitionof pedestrians. The main system is a direct communication
system between vehicles and pedestrians. Both pedestrians
and vehicles are equipped with a small device that functions as a communication and positioning system. These
devices exchange their position, speed, and direction to
enable mutual recognition. The key technologies of thisvehicle-to-pedestrian (V2P) system relate to pedestrian
positioning and estimation of pedestrian states. This study
realized these technologies by eliminating GPS multi-path
errors and implementing pedestrian dead-reckoning using
map technology. The effectiveness of these technologies

was verified in field operation tests. Under anotherapproach, a new infrastructure sensor for detecting pedestrians and a system that provides detected informationfrom infrastructure to vehicles were studied. Moreover,another study provided high-definition moving pictures
for the front visual field of the vehicle for the developmentof in-vehicle sensors.

4

Simulations

The presented study describes some simulation technologies. Its efforts focused on the development of a simulation
system capable of estimating traffic accidents using automated and connected technologies. This simulation technology uses a multi-agent system capable of showing many
independent agents as drivers, pedestrians, and other traffic participants. The agent can implement individual recognition, decisions, and operations based on models derived
from experimental data. Here, traffic accidents occur incyberspace by some error in agent behavior. This simulation system was used to calculate the potential reduction in
accidents due to the effects of V2P systems. Moreover, eachvehicle in the cyberspace has its own emissions model. The
emissions model changes in accordance with the behaviorof the vehicle depending on the use of automated and connected technology. The simulation system can observe theamount of CO2 emissions for the various models. Therefore, it can calculate the potential reduction in CO2 emissions achieved by these technologies.

5

Impact Assessment

The presented study reviews the impact of automateddriving technologies on society and industry by drawing afuture vision utilizing several automated driving systems. It
also considers specific issues about future society and public reception.

6

Conclusion

The multi-agent simulation system described in this study
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is capable of calculating the numbers of accidents andobserving the negative effects of automated driving systems on trust and so on by creating appropriate agent
behavior models. This simulator is regarded as a promisingmeans of estimating the effects of various technologies.

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Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance① Initiatives to Reduce Pedestrian Accidents

Pedestrian Safety Support Using V2P Communication Technology
Yoshiyuki Okubo (Panasonic Corporation)
Noriaki Minamida (Panasonic System Networks R&D Lab. Co., Ltd.)Ken Kumagai (NTT DOCOMO, INC.)
Tatsuhiko Hirabayashi (KDDI Research, Inc.)
ABSTRACT: Pedestrians and cyclists account for almost half of traffic accident fatalities. To achieve the national goal of reducing traffic
fatalities to 2,500 or less by the year 2020, it is critical to establish a vehicle-to-pedestrian (V2P) communication system that can help to prevent collisions by exchanging position data between pedestrians and vehicles. This research establishes the fundamental technologies for a
V2P communication system. Furthermore, we have created prototypes of the pedestrian terminal and the in-vehicle terminal for experiments
and verified their effectiveness.

1 Overview
In 2017, the number of traffic accident fatalities was 3,694.
Pedestrians (36.5%) and cyclists (13%) account for almost
half (49.5%) of this number(1). There is an urgent need totake measures to prevent traffic accidents involving pedestrians and cyclists. V2P communication systems are being
research and developed with the aim of preventing collisions by exchanging position data between pedestrians andapproaching vehicles.
In this research, we developed two types of technologies:
direct communication technology using a newly developed
pedestrian terminal capable of 760 MHz communication(the ARIB STD-T109) with an in-vehicle system, and technology using mobile phone networks.
Because technology using conventional mobile phone
networks has a communication delay of up to 10 seconds,
this can be used to notify pedestrians in cases where there
is longer than 10 seconds before a predicted collision. In
contrast, direct communication technology should be
adopted in cases where the response time is less than 10
seconds. The results are presented below.

2 Direct Communication Technology
With direct communication technology, the pedestrian
terminal and in-vehicle terminal communicate directlywith each other using 760 MHz ARIB STD-T109 communication, by broadcasting their own position data (latitude
and longitude), speed, and moving direction. Both terminals estimate the future paths and predict a collision whenthese paths overlap. When a collision is predicted both terminals issue alerts. Figure 1 shows an overview of direct

V2P communication.

Fig. 1 Overview of V2P communication

2.1. Range of Support
This system only supports people (pedestrians and drivers) by issuing alerts via the V2P communication system.
Support for automatic vehicle control systems (e.g., automatic emergency braking (AEB)) is beyond the scope of
this research. Although the main beneficiaries of the system are pedestrians, driver support is also discussed. The
following sections describe the selected scenarios in which
support is required and those in which support is not
required.
2.1.1. Scenarios Requiring Support
To focus on high-priority scenarios, we first analyzed historical data of traffic accident fatalities involving pedestrians, and then selected the top ten scenarios with thehighest fatality rate by referring to an existing research
report(2). By combining patterns occurring at the same
location and under similar situations, the five scenarios
shown in Fig. 2 were finally selected as the scenarios inwhich pedestrians require safety support.

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Fig. 3 Scenarios not requiring support
Fig. 2 Scenarios requiring support

Notifications are known to be more effective when issued
on multiple levels (e.g., by providing information, warnings, and then alerts) depending on the time to collision(TTC).
The timing to start providing pedestrian support wasdetermined by referring to the results of a monitor evaluation described in the survey report on requirements forvehicle-to-pedestrian communication (3). The timing tostart providing driver support was determined by referring
to ASV4(4). However, we determined that cases when the
TTC is less than two seconds should be handled by automatic vehicle control (e.g., AEB), and our system does notprovide support for such cases. The cases in which our system provides support are defined in Table 1, where information provision, warnings, and alerts are adopted as themultiple support levels up to a TTC of two seconds.
Table 1 Support timing

2.1.2. Scenarios not Requiring Support
Support is not required when the pedestrian is in a safe
location. To define such scenarios, we first considered life
patterns showing what people spend most time doing during the day. Referring to a survey report on how Japanesepeople spend their time(5), we selected two scenarios inwhich safety support is not required (inside a building andinside a vehicle).
In addition, we selected three other scenarios in which
support is not required, considering their relationship to
the scenarios defined as requiring support: on a sidewalk,on a pedestrian overpass, and on/under an overpass. In
total, we selected five scenarios not requiring support, asshown in Fig. 3.
90

2.2. Performance Target
We set a performance target for our V2P communication
system considering the scenarios defined as requiring or
not requiring support. Based on the guidelines provided by
the system commercialization working group (WG), thetarget was defined using two values: a correct support
operation rate of 80% or higher for scenarios requiringsupport and a false support operation rate of 20% or lessfor scenarios not requiring support. We conducted ourstudy based on these target values.
2.3. Research Plan
A viable V2P communication system requires three fundamental technologies: (1) accurate pedestrian positioning, (2) advanced risk prediction technology that can
provide appropriate support even if some pedestrian positioning error occurs, and (3) a communication protocolthat launches direct communication only when necessary
to reduce power consumption. We have developed all thesetechnologies as described in Fig. 4. It is planned to conduct
an extensive field test with ordinary users, consisting offive vehicles and twenty pedestrians, during the final year
(FY2018) of the project. To confirm the effectiveness of thesystem, we created and provided prototypes of the in-vehicle and pedestrian terminals with improved communication distance and battery duration performance.

Fig. 4 Research plan

2.4. Pedestrian Positioning
To achieve 80% or higher correct support operation rate,
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Pedestrian Safety Support Using V2P Communication Technology

around 3 m considering the standard width of a road. In
addition, to facilitate stable positioning in business districts, we tested integrated positioning using GNSS andsensors, as well as 3D map positioning using an accurate
3D map.
2.4.1. Integrated Positioning
In addition to satellite positioning with GNSS, we developed the following technologies to improve GNSS: An
integrated positioning system also incorporating direction
and speed detection using Doppler frequency deviations,and automated navigation for relative positioning using
pedestrian dead reckoning (PDR) with motion sensors
such as acceleration sensors. The process flow of the integrated positioning is shown in Fig. 5.

Fig. 5 Integrated positioning

The evaluation result in the Odaiba area, where theplanned extensive experiment will also be carried out, isshown in Fig. 6.
In an open area, an accumulated error of 68% corresponds to a horizontal error of 3.3 m, while in an
obstructed area, the error becomes 5.9 m. From Fig. 6, one
can easily see that the horizontal error is closely related to
the field of view. The 3 m level is achieved in an open area
in which there are no obvious obstructions. In contrast, thepositioning accuracy is worse in the area with obstructions
such as railroads, because the field of view is restricted. As

it was assumed that the field of view would deteriorate further in an area with high-rise buildings, thereby creatingeven more severe positioning conditions, we investigatedan alternate method that is described in the following section.
2.4.2. 3D Map Positioning
We tested a 3D map positioning method with dedicated
3D map data developed by Kamijo Laboratory of the Institute of Industrial Science at the University of Tokyo.
Factors causing positioning errors can be categorized into
two groups: satellite related (e.g., orbit data, clock) andtransmission path related (e.g., ionospheric delay, tropospheric delay, multipath). Particularly, multipath interference significantly affects positioning accuracy. Errors dueto the satellite or delays in the ionosphere or tropospherecan be corrected using multiple frequencies and DGPS.
However, errors caused by multipath interference are difficult to correct with these measures and a different
approach is needed. A technique of improving positioning
accuracy by determining whether the received radio wave
from a satellite is on a line of sight (LOS) or behind an
object (NLOS: non-line of sight) using 3D map data, which
includes height data of structures such as buildings, wasproposed. LOS/NLOS distinction is achieved with ray tracing. Errors can be distinguished between cases due tointerference between the LOS and NLOS signals and cases
due to shadowing as the LOS signal is sufficiently small
compared to the dominant NLOS signal. We tried to
improve positioning accuracy with 3D map positioning forthe latter case, in which the effect is greater.
In addition to Odaiba, Shinjuku, Hitotsubashi, and Shinagawa were added for evaluation as areas containing

Fig. 7 3D map positioning

Fig.6. Results of integrated positioning

Fig. 8 Results of 3D map positioning(Map data source: Google Earth)
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high-rise buildings where the conditions are severe. The
evaluation results are shown in Fig. 8.
The above results show that the use of a 3D map generally
improves accuracy, and that the 3 m error level wasachieved at Hitotsubashi. The reason for this improvement
was the suitable use of reflected waves, which were a major
cause of positioning error previously.
In the future, to achieve a horizontal error of around 3 min the remaining three areas, we will try to improve positioning accuracy by adding the Galileo Satellite and by
applying correction using the reference station data
(DGPS).

pedestrian and the driver respectively. Because errors will
occur in the communicated data in a real-world environment, the collision prediction algorithm also takes into
account the error when predicting a collision. Collisionprediction with errors incorporated is illustrated in Fig. 10.

Fig. 10 Collision prediction with errors incorporated

2.5. Risk Prediction
A risk predication algorithm was developed and applied
to each of the five selected scenarios requiring and not
requiring support. For the former, the correct support
operation rate was examined with respect to the target rateof 80% or higher. For the latter, the false support operation
rate was examined, with respect to the target rate of 20% or
less.

Fig. 9 Risk prediction (support required and not required)2.5.1. Scenarios Requiring Support
For the scenarios of a single-lane road crossing, blindintersection where a collision may occur, and walking on a
roadway, support is provided based on collision prediction.
In detail, terminals communicating each other via V2Pcommunication share position, speed, and moving direction data and then predict the possibility of a collision.
When a collision is predicted, the terminals alert the92

The above collision prediction algorithm is not applicableto the case of a right or left turn at an intersection because
it uses linear extrapolation. Large errors in predicted position may delay the provision of correct support.
Hence, a new function to notify the presence of a pedestrian was added for the case of a right/left turn at an intersection. When both the pedestrian and the vehicle arepresent within an intersection, the prediction range on thevehicle side is expanded to include possible collisions during a right/left turn. Whether a vehicle or a pedestrian iswithin an intersection is decided based on map data containing the intersection information.
2.5.2. Scenarios not Requiring Support
For the five selected scenarios (inside a vehicle, inside abuilding, on a pedestrian overpass, on a sidewalk, and on/under an overpass) an algorithm was developed to confirm
the safety status and suppress the provision of support
information to pedestrians and drivers.
A pedestrian is determined to be inside a vehicle in thecase of two patterns. If the moving speed and duration arehigh and long enough and no step movements aredetected, the pedestrian is assumed to be inside a moving
vehicle. When the carrier to noise (CN) value of the radio
signal from a high elevation angle satellite is poor while no
step movements are detected, the pedestrian is assumed to
be inside a stopped vehicle. The pedestrian is determined
to be inside a building when CN values below a specified
threshold continue for a certain duration. The pedestrian isdetermined to be on or under a pedestrian overpass oroverpass based on map data of the sidewalk and roadway
networks with the necessary attribute values, and pressure
data from pressure sensors.
The pedestrian is determined to be on a sidewalk by first
determining whether a sidewalk exists based on map dataof the sidewalk and roadway networks. Even if a sidewalk
exists, the system also determines whether the pedestrianfacing a roadway. No support is provided when a pedes-[ II ] Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance① Initiatives to Reduce Pedestrian Accidents

Pedestrian Safety Support Using V2P Communication Technology

trian is on a sidewalk but is not facing a roadway. Support
is necessary if the pedestrian is facing a roadway since thepedestrian may step into the road.
2.6. Communication Protocol for P2V
This section focuses on the technique to reduce mutual
coupling with existing systems using adjacent frequency
bands, and on the communication protocols for power saving.
2.6.1. Coexistence with existing systems
Radio frequency interference with the 760MHz band cellular system (LTE Band 28) is a problem for the operationof a P2V system using the 760 MHz band. It occurs whenantennas for different wireless systems are placed in close
proximity (Fig. 11). When these antennas are built into one
terminal, it is one of the most severe conditions for operating a P2V system.
Figure 12 shows the proposed mutual coupling reduction
structure for ensuring isolation between these radio frequency (RF) front-end circuits. A cellular antenna andP2V communication antenna were built into a unit carriedby pedestrians. For cellular multi-input multi-output(MIMO) communication, there are two cellular antennas
as the main antenna and aux antenna. Three parasitic elements are installed around these antennas to reducemutual coupling. Band pass filters for cutting off the signalof the other system are mounted in the RF front-end circuit.
Figure 13 shows the improvement of isolation characteristics between these RF front-end circuits by the proposed
method. The red line in the figure is the target value forsuppressing interference between these systems(6). Isolation
between these antennas is greatly improved and meets thetarget value. From the result, it can be seen that the P2V
system can coexist with existing systems even under severe
conditions, such as being built into one terminal by applying this mutual coupling reduction technique.
2.6.2. Communication Protocol for power saving
In the future, power saving will be necessary to promote
the use of the pedestrian terminals. Figure 15 shows thecurrent consumption of the pedestrian terminal. Assumingthat the power consumption when sending data (radio
function: ON) is 100%, 25% consumption is achieved
when the radio function is OFF.

Fig. 11 Condition of radio frequency interference

Fig. 12 Mutual coupling reduction techniqueFig. 13 Isolation between RF front-end circuits (S12)Next, we examined the life patterns of the target users.
According to Traffic Statistics (7), a high proportion ofpedestrians involved in traffic accidents are elementary
school students. We recorded the behavior of elementary
school students, and studied their life patterns based on
the National Time Use Survey(5). It was found that elementary school students spent 8 to 11 hours outside and 1 to 4
hours traveling.
We evaluated the battery specifications by calculating thedaily power consumption based on the time that elementary school students spend outside. This time was categorized between scenarios requiring support (i.e., Radiofunction ON) and scenarios not requiring support (Radiofunction OFF). Figure 14 compares the current consumption with reference to a smartphone battery.
From the above results, the power consumption of thepedestrian terminal can be suppressed and the terminal
can be operated for one day by switching the radio function ON or OFF according to the scenario.

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Pedestrian Safety Support Using V2P Communication Technology

Fig. 14 Comparison of current consumption

Fig. 15 Comparison of battery capacity and power consumption

2.7. Prototype of Terminals
2.7.1. Overview
To achieve efficient use of the system, the pedestrian terminal and the in-vehicle terminal share the same hardware
configuration as illustrated in Fig. 16. They are distinguished by different working modes: pedestrian mode andvehicle mode.
Prototypes were developed considering usability and battery duration performance so that they can be handledeasily and tested smoothly.

A backpack capable of holding every piece of equipment
was selected as the pedestrian terminal with ease of usability for the tester. Antennas were installed on the backpack.
A dedicated antenna for the in-vehicle terminal was
selected, and power is supplied to the terminal via a cigarette lighter socket as shown in Fig. 17.
2.7.2. Antenna Design
For the pedestrian terminal, the performance of theantenna should be ensured considering the effect of the
body of the pedestrian carrying the backpack.
The antenna selected for the pedestrian terminal requiresa ground aluminum plate. Hence, the antenna was installed
on a plate, which was placed in the cover pocket of the
backpack.
As shown in Fig. 18, the main antenna and the subantenna for V2X communication were placed at the edgeof the ground plate to achieve performance compensation
through reception diversity, while maintaining gain in theforward direction when placed in the backpack. A GNSS
antenna was placed between the above two V2X antennas.
It was also placed on the ground plate and faces the zenith
direction.

Fig. 18 Antenna configuration of pedestrian terminal

Figure 19 shows the communication distance distribution. It is measured through the antenna gain when the
prototype terminal is actually placed on a human body.

Fig. 16. System configuration of terminal prototypes

socketFig.17. Exterior views of terminal prototypes
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The distance becomes shorter at angles at which the head
and the antenna overlap, but it still can reach the target
value of 150 m or longer (400 m or longer is achievablewith reception diversity).
Figure 20 shows the performance of the GNSS antenna.
The antenna gain becomes poor at angles at which thehead and the antenna overlap under vertical plane conditions (2), but it is still -6 dBi or greater at angles between-30 and +30 degrees (0 degrees is the zenith position).

Fig.21. Pre-test
Table 2 Pre-test results
Scenarios requiring support(Succes Rate≥80%)

Scenarios not requiring support(Failure Rate≤20%)

Fig.20. GNSS antenna characteristics

2.7.3. Pre-Test Results
A test was conducted in the Odaiba area before theplanned extensive experiment by providing the pedestrianand in-vehicle terminals described in Section 2.7.1. For the
scenarios requiring support, alerts below a TTC of two
seconds were not counted and were excluded from theresearch scope. Successful instances of presence notification at the intersection, information provision, and warning/alert provision were counted. The correct support
operation rate was calculated based on the ratio of successful instances with respect to the number of tests. In thecase of the five scenarios not requiring support, the provision of any support information was counted as a false
support operation. The false support operation rate wascalculated based on the ratio of false information provision
with respect to the number of tests.
Results of the pre-test are shown in Table 2. The main factor that lowered the correct support operation rate waspoor adaptation to vehicle speed fluctuations. The main
factors that lowered the false support operation rate
resulted from the testing method, as well as positioning
and moving direction errors. This pre-test verified theeffectiveness of the developed technology for improving
the V2X communication distance and GNSS accuracy, andno obvious problem occurred.

Mobile Phone Network Utilization Type

3 Technology

Risk prediction was the target for the technology using
mobile phone networks. Pedestrian positioning detection
in intersections and data collection/broadcasting technology using Web technology for risk prediction are reported
below.
3.1. Pedestrian Detection within Intersection
3.1.1. Overview
Safety support measures for pedestrians are desirable.
Experiments were carried out using an application to
detect pedestrian positioning information and the likewithin an intersection to help reduce traffic accidents.
3.1.2. Purpose of Experiment
The experiment assumed that highly directional BLE beacons were installed in the vicinity of the traffic lights at asimulated intersection. This experiment aimed to verify
and clarify the pedestrian detection areas in the intersection area shown in Fig. 22

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Fig. 25 Pedestrian detection result (threshold: -62 dBm)
Fig. 22 Illustration of pedestrian detection in intersection area

3.1.3. System Overview
To help reduce traffic accidents, pedestrians in the intersection area are detected by signals received by pedestrians(smart phones) transmitted by the BLE beacons.

Fig. 26 Detection of pedestrian poolFig. 23 System overview

3.2. Development of Information Collection and Distribution Subsystem using Web Technology over Mobile
Networks
3.2.1. Basic Concepts of R&D Subsystems
This approach is a basic way to both collect informationfrom moving vehicles and deliver information to moving
vehicles and pedestrians over a mobile cellular networkfrom the standpoint of service coverage. The types ofpedestrian-related information for predicting risk andwarnings can be summarized as shown in Fig. 27. For
example, if you know a crowded crosswalk is ahead, then
you can minimize collision risk by turning left a block earlier.

Fig. 24 Sequence Diagram

3.1.4. Consideration
Based on the experimental results, it is possible to identify only the neighboring BLE beacon as shown in Fig. 22
by setting a threshold value. As a result, as shown in Fig.
23, it is possible to detect a pedestrian in the intersection.

Fig. 27 Pedestrian-related information

The identification of such data, including other vehicle
data (e.g. vehicle speed and hard braking) from cars thathave already passed through an area on all roads using the96[ II ] Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance① Initiatives to Reduce Pedestrian Accidents

Pedestrian Safety Support Using V2P Communication Technology

Fig. 28 Overall configuration of mobile network-based web platform for next generation ITS

mobile cellular network has already been proved effectivein earlier research.
In contrast, mobile phones and installed OS commonly
have built-in WRT (browsers), enabling the universe ofcompatible devices to be maximized, and minimizing theimpact of heterogeneous OS and apps. Rapid and global
deployment can be expected by using the Vehicle Web
APIs and generic sensor API currently being standardized
by W3C. Based on such concepts, we developed a mobile
network-based web platform as a part of the next generation ITS as shown in Fig. 28.
3.2.2. Development of Web Runtime
We developed and implemented a W3C Vehicle API to
web runtime in two ways: as a native WRT and as a polyfill, enabling us to replicate real-world browser heterogeneity. The native version has been fine-tuned for memory
optimization.
3.2.3. Providing Situational Awareness of Pedestrians
to Drivers
We developed a subsystem to upload short videos fromsmartphones to the cloud when the car is stopped or if
hard braking occurs (as shown in Fig. 29), as well as apedestrian-related information API for quantifying pedestrians and detecting sudden hard braking.

Fig. 29 Pedestrian-related information subsystem

3.2.4. Privacy Protection Measures
Privacy protection strategies will be required in circumstances where the driver can be easily identifiable by somekey data or by statistical analysis of the driving context.

An individual may drive a car driven by others. An individual may drive multiple cars and use various apps. To
whom does this data belong? Are there exceptions, such as
emergencies? Privacy settings will be adjusted each time a
different driver drives the car.
To cope with such issues, we have developed what we calla Personal Agent, which is composed of a proxy server for
privacy protection and which controls data transfer bymanaging privacy settings per the preferences of the individual and the situation.

4 Conclusion
As fundamental technologies to achieve a V2P communication system, we developed an accurate positioning technology for pedestrians and an advanced risk prediction
technology to predict possible collisions even with somepositioning errors. We also developed a communication
protocol that launches direct communication only when
support is required, thus reducing power consumption.
The effectiveness of these technologies was confirmed in a
pre-test.
To enable installation of the system in a smart phone, acompact 760 MHz band communication antenna wasdeveloped and correlation with cellular antennas was lowered to enable coexistence. We also analyzed the usage scenarios of the pedestrian terminals and confirmed that 760
MHz band communication terminal could run for 24hours using a smart phone battery.
Pedestrians are detected using BLE beacons at intersections. By detecting pedestrians on the intersection sidewalks, positioning data of the pedestrians can be provided
to vehicles.
A mobile network-based web platform has been developed in line with the current W3C activities, enabling thecollection of pedestrian-related information including
other vehicle data (e.g. vehicle speed and hard braking)from cars that have already passed through an area on all
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roads, and the provision of alerts.
In addition, a new privacy protection mechanism has
been implemented in the above subsystem.
Our aim is to contribute to reducing pedestrian traffic
fatalities to zero by popularizing pedestrian terminals.
Future work includes examining the feasibility of installingthe system on smart phones, studying the commercialization of the system, and improving the terminals.
References(1) Traffic Bureau, National Police Agency: Characteristicsof road fatalities in 2017(2) ITARDA, Survey concerning analysis methodology for
estimating the effectiveness of reducing traffic fatalities
for surveying and reviewing to achieve the national
goal of road casualties reduction (2015)(3) National Traffic Safety and Environment Laboratory:
Survey report on requirements for vehicle-to-pedestrian communication (2016)(4) Road Transport Bureau, Ministry of Land, Infrastructure, and Transport: Advances Safety Vehicle (ASV)
promotion plan report, 4th term (2011)(5) NHK Broadcasting Culture Research Institute, NHK
Data Book 2015 National Time Use Survey, 2016.
(6) T. Kinpara, et al., “Interference between Pedestrian to
Vehicle Communication and Cellular Systems in 700
MHz band,” IEICE Gen. Conf., A-14-6. Mar., 2016.
(7) ITARDA, Traffic Statistics (2014), 2015.
Guidance
Iwao Sasase, Keio University, Professor
Shunsuke Kamijo, The University of Tokyo, Associate
Professor
Motoki Shino, The University of Tokyo, Associate Professor
Author data
<Panasonic Corporation>(CNS, Innovation Center)1) Yoshiyuki Okubo, Manager
2) Masahito Sugahara, Chief Engineer
3) Tsuyoshi Ueno, Staff Engineer4) Shintaro Muramatsu, Staff Engineer5) Yoshimasa Shirasaki, Staff Engineer
6) Iketsu Ryu, Staff Engineer
7) Koichi Aihara, Staff Engineer
8) Jun Ishida, Engineer(AIS, Automotive Development Division)9) Hidenori Matsumoto, Chief Engineer10) Fukutaro Sekine, Staff Engineer11) Shinichi Takimoto, Staff Engineer
9812) Kenji Yanagi, Staff Engineer<Panasonic System Networks R&D Lab. Co., Ltd.>(Technology Center)
1) Noriaki Minamida, Senior Manager
2) Shingo Sumi, Assistant Manager
3) Hiroyuki Kakizaki, Staff Engineer<NTT DOCOMO, INC.>(Smart-life Solutions Department)
1) Ken Kumagai, Senior Manager
2) Mizuho Katayama, Manager
3) Hiroto Sugishita, Manager
4) Kentarou Kaneda, Assistant Manager
5) Keisuke Izumi
6) Yasuhiro Onozuka<KDDI Research, Inc.>,(Future Design Division 2)
1) Tatsuhiko Hirabayashi, Principal Analyst2) Shinsaku Kiyomoto, Senior Manager
3) Masahiro Fujiwara, Senior Analyst
4) Haruo Takasaki, Analyst5) Naonori Kato, Analyst
6) Yasuyuki Moriguchi, Analyst
7) Yuto Kakiuchi, Analyst
8) Takayuki Goto, Analyst

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Research and Surveys for Enhancing Driving Safety
Support Systems (DSSS) Utilizing Radio Waves, andResearch and Surveys for Establishing Technology to
Provide Vehicle/Pedestrian Detection Information
Toward the Realization of Automated Driving
Masafumi Kobayashi (UTMS Society of Japan)
ABSTRACT: To promote the realization of safe driving support and automated driving, it is essential to observe the conditions surrounding
the vehicle, including beyond the vehicle’s line of sight, and to provide vehicles with traffic information in real time that contributes to theprevention of traffic accidents using roadside sensors. Therefore, research and surveys were conducted into a system that provides vehicles withconstantly changing traffic information using radio communication. In fiscal 2014 and 2015, research and surveys were conducted into theenhancement of driving safety support systems (DSSS) promoted by police, and the specifications of low cost versions of the systems, with theaim of implementing the systems across the country. In the following fiscal year of 2016, examinations were carried out with an eye toward therealization of automated driving. These involved the classification of traffic accident types and vehicle behavior at intersections, identification of
the types of moving objects (vehicles, pedestrians, bicycles, and the like) that need to be detected, together with the required range of detection
to ensure vehicle safety in each accident scenario, followed by an examination of the configuration of roadside sensors required for detection.

1 Purpose of the Research and Surveys
Safe driving support and automated driving systems are
expected to have the following effects: (1) reducing traffic
accidents, (2) mitigating traffic congestion, (3) reducing
environmental burden, (4) providing mobility support for
the elderly, and (5) increasing the comfort of driving. In
particular, at a time when Japan is becoming a super-aging
society, and as a country aiming to achieve the world’s safest road traffic environment, realizing the practical use ofsafe driving support and automated driving systems, andmaking them widely available at an early stage is ofextreme importance. Also, to enable safe driving support
and automated driving, it is important to observe the conditions surrounding the vehicle, including beyond thevehicle’s line of sight, and to provide vehicles with traffic
information in real time that contributes to the preventionof traffic accidents using roadside sensors. Therefore, theseresearch efforts and surveys were designed to enhance
driving support systems through the development and verification of a system that provides vehicles with constantly
changing traffic information utilizing radio communication with 700 MHz band. This report presents the resultsof the research and surveys conducted over a period of
three years from fiscal 2014 through 2016.

2 Procedure of the Research and Surveys
2.1 Research system
Through the examination, research, and development ofintelligent transport systems (ITS), the UTMS Society ofJapan promotes ITS for surface roads and pursues standardization activities for domestic and international ITS.
From the end of the 1990s, UTMS Japan has promoted
research and standardization activities for driving safety
support systems (DSSS) with the aim of reducing traffic
accidents by supporting driver recognition, judgment, andoperation using ITS technologies, to create conditions for
comfortable driving. Before launching these research
efforts and surveys, a new examination body (the DSSS
Enhancement Examination Sub-working Group) wasestablished within the Vehicle-Infrastructure Cooperation
Systems Working Group, which has promoted the researchand standardization of DSSS.

3 Research Results for Fiscal 2014
Services regarded as having the potential to produce thedesired effects on DSSS were developed as shown in Table
1 from the perspective of the types of traffic accidents that
occur at intersections or curves with poor visibility due todriver inattention, and from the perspective of the violation of traffic regulations such as traffic signals and roadsigns.
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Research and Surveys for Enhancing Driving Safety Support Systems (DSSS) Utilizing Radio Waves, and Research and Surveys for
Establishing Technology to Provide Vehicle/Pedestrian Detection Information Toward the Realization of Automated Driving
Table 1 List of safe driving support services

For each of the developed services, the functions andoperational flow required for each of the components of
the infrastructure system, that is, the traffic control center,signal controllers, vehicle-to-infrastructure radio communication devices, as well as roadside equipment including
roadside sensors and on-board systems, were compiled ina system definition document. The research also examinedcommunication protocols to establish secure communication between vehicles and roadside infrastructure even in
an environment where vehicle-to-vehicle and vehicle-toinfrastructure communications coexist, specifications forthe message sets provided to on-board units via vehicle-toinfrastructure communication, and the identification of
performance requirements for roadside sensors whenusing a 79GHz band high-resolution radar. Furthermore,
to reduce installation costs for DSSS and promote nationwide implementation and broader coverage, surveys andresearch were conducted into low-cost versions of DSSS
that would be both inexpensive and satisfy the required
functions. The following proposed revisions were developed.
(1) Simplification of roadside systems aimed at reducing
equipment and construction costs· Elimination of the need for infrared beacons that
notify the location of points of origin upstream ofintersections (Location reference by GPS)· Integration of roadside equipment (integration of
information relaying and determination devices withITS radio equipment)(2) Standardization of management functions of traffic
control centers· Reduction of the number of central computers supporting DSSS and streamlining through standardized
processing· Reduction of data preparation costs through revisions
to road shape information requirements(3) Revisions to detection areas to reduce the number ofroadside sensors.
Figure 1 shows the proposed architecture reflecting these
proposed revisions.

100Fig. 1 Proposed revisions to system architecture

5 Research Results for Fiscal 2015
To verify the revisions developed based on the surveys
and research into the low cost versions of DSSS conductedin fiscal 2014 (i.e., the revisions to road shape information
requirements, revisions to the detection areas of DSSS
pedestrian sensors, and revisions to the detection areas of
DSSS vehicle sensors), a verification experiment was carried out using roadside systems installed on a test course
in fiscal 2014 by the National Police Agency for a research
project called “Creation of an internationally open researchand development environment” (NPA 6). System acceptance was assessed through a questionnaire survey. Basedon the results of the experiment, constraints under which
the revisions to the detection areas for pedestrians andvehicle sensors can be applied, such as the range of sight ofoncoming traffic from a right-turning vehicle, were
defined. Also, with respect to infrastructure maintenancein cases where road shape information was changed due toroad improvements or other reasons, which had been an
issue for system operation, guidelines relating to the operation and maintenance of road shape information weredeveloped.

6 Research Results for Fiscal 2016
In fiscal 2016, research and surveys for establishing technology to provide vehicle/pedestrian detection information towards the realization of automated driving was
conducted. The configuration of the roadside sensors
required for detection were examined after classifying traffic accident types and vehicle behavior at intersections andidentifying the types of moving objects (vehicles, pedestri-[ II ] Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance② Development of Basic Technologies to Reduce Traffic Accident Fatalities

Research and Surveys for Enhancing Driving Safety Support Systems (DSSS) Utilizing Radio Waves, and Research and Surveys for
Establishing Technology to Provide Vehicle/Pedestrian Detection Information Toward the Realization of Automated Driving
ans, bicycles, and the like) that need to be detected,
together with the required range of detection to ensure
vehicle safety in each accident scenario. These efforts also
examined a new DSSS for left-turn support using radio
communication based on an existing right-turning support
system. Functional specifications for a pedestrian recognition (at left turns) enhancement system were examined
and a model system was developed for verification. Furthermore, to ensure the operation and management of
DSSS, research examined the functions of traffic control
centers, and the operation and management of roadsideequipment, including security management during thelifecycle of the roadside equipment. This includes theinstallation of new roadside equipment and the relocationand disposal of existing equipment.

7 Conclusion
This research project, which was carried out over a periodof three years from fiscal 2014 through 2016, definedrequirements for the enhancement, installation, and widespread deployment of DSSS that provide vehicles with constantly changing traffic information using radio communication. In the future, further research and surveys
must be pursued on the basis that this DSSS will evolve
into an infrastructure system designed for automated driving.

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Development of Practical Roadside Sensor Utilizing
Millimeter-Wave Radar Technology
Yoichi Nakagawa (Panasonic Corporation)
ABSTRACT: This article reports the results of a research initiative to apply high-resolution millimeter-wave radar to roadside sensors for the
purpose of reducing pedestrian accidents using cooperative driving support. Millimeter-wave radar technology installed at intersections andthe like can realize sensing functions, such as the detection of pedestrians and bicycles with high accuracy, regardless of the weather and time.
To facilitate the practical adoption of this technology, detection processing software compatible with high-resolution radar has been developed and verified under severe weather conditions such as hard rain and snowstorms. A prototype 79 GHz band millimeter-wave radar system was installed at actual intersections to evaluate the detection performance of crossing pedestrians and so on. This report mainly describes
the development of the radar technology and the progress of the public road experiment.

1 High-Resolution Radar Technology
1.1. Summary
We are carrying out a research initiative to apply the latest
radar technology to infrastructure systems in consideration of the evolution of millimeter-wave devices and sensor software. Specifically, we are promoting technologicaldevelopment and demonstration experiments with the aimof applying high-resolution millimeter-wave radar to roadside sensors at intersections, assuming the adoption of thissystem by Intelligent Transport System (ITS) applications
such as cooperative driving support(1)(2). In order to provide so-called look-ahead information as driving support,
an appropriate wireless communication technology thatshares sensor data in real time will be used in addition tosensor technology that accurately measures the speed ofapproaching vehicles and the position of crossing pedestrians (Fig. 1).

Fig. 1 Coverage of the roadside radar installed at intersection

There are growing expectations for spatial imaging using
millimeter-wave radar, and short-range wide-angle scan102

ning is becoming a standard function of high-resolution 79
GHz band radar. The maximum effective detection distance of this radar is about 30 to 40 m for pedestrians andabout 70 to 100 m for automobiles, depending on the radarcross section (RCS) of the target. A typical planar antennaconfiguration is capable of detecting the location of a target
up to a field of view (FOV) of about 90°. However, thisconfiguration can measure range up to a FOV of 150°. Further, since the usable frequency bandwidth exceeds 1 GHz,
the theoretical range resolution is 15 cm or less. Therefore,
scattering points existing in the same range bin are
reduced, and the angular resolution is effectively improved.
In addition, since a Doppler frequency shift occurs in theecho of a moving target, the velocity of the target can be
estimated from the Doppler frequency. When the carrier
wave is 79 GHz, the wavelength is about 4 mm. For example, a velocity resolution of 0.1 m/s can be obtained withan observation time of 20 ms. Because of its high range
resolution, radar has excellent separation performance formultiple targets that are close to each other and have slight
velocity differences. For pedestrian detection, it is possible
to analyze the Doppler frequency spread due to the vibration of limbs as a feature value superimposed on the Doppler shift. Indeed, understanding the echo characteristics
inherent in such targets has led to the extension of radar
detection functions(3).
1.2. Radar detection software
Algorithms for realizing millimeter-wave radar detection
are generally implemented as software to increase theoptions of the device. This detection software often
includes a filtering block for extracting candidate cells andmeasured values (range, angle, Doppler frequency, andpower) from scan data of mesh-like cells outputted by theradar device, a clustering block for grouping multiple candidate cells for each target, and a tracking block for making
time-series data frames correspond with each other. The

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detection result output depends on the application using
the radar system, but includes information such as position, speed, movement trajectory, number of targets, andtarget type.
In order to improve the effective detection accuracy of aradar system, it is essential to optimize clustering andtracking based on the echo characteristics of targets (Fig.
2). For example, the echoes of vehicles observed with highresolution radar are separated into a large number of scattering points. Therefore, it is necessary to treat spatiallyspread candidate cells as part of the same group using the
Doppler frequency or the like. Pedestrian detection has
low distance dependence when resolving scattered points,
but the echo intensity drops discontinuously over time.
Therefore, it is necessary to interpolate the measurement
position using the preceding and succeeding data framesconsidering that a pedestrian is an object moving at low
speed.

Fig. 2 Radar data processing to detect targets

The roadside sensor equipment consisted of a millimeterwave radar device as the sensor unit and an industrial personal computer (PC) as the control unit realizing theapplications. When a camera is used as a roadside sensor,
the control unit performs both a process to extract the target from the image data, and a process to estimate the distance and the velocity. However, with a radar system, since
the distance and the velocity of the target are included in
the data generated by the sensor unit, it is possible to suppress the processing and cost requirements of the control
unit compared to a camera-based system.

2 Roadside Sensor System
2.1. Basic design
A millimeter-wave radar with the performance and functions described above was installed at the roadside of intersections and used to detect passing pedestrians andvehicles. The radar was installed at intersections to detectpedestrians and measure the traffic flow. This configurationis considered to be applicable to both safety support andsignal control systems. Although cameras are generally
used to recognize objects, radar can obtain stable accuracy
regardless of the weather or time to detect pedestrians andcount the number of vehicles.
When this type of roadside sensor is installed, it is necessary to prevent blocking by vehicles in front of the detection target. The height of the traffic signal on which thesystem is installed is a guide of sensor range. For example,
when the installation height is 5 m above the ground andthe vertical plane beam width of the millimeter-wave radaris 10 degrees, the effective range coverage for passenger
cars is calculated to be about 15 to 75 m. This is because
the radio link is designed considering the regulations of 79
GHz band high-resolution radar(4), in which the sensitivity
characteristics within the data frame over a period of 100
ms or less is used as an indicator.
Furthermore, since pedestrian echoes have large fluctuations in power and random variations, it is necessary tostatistically analyze measurement data and identify its
characteristics. Specifically, based on an experiment using
standard dummies that can simulate walking, the difference in the RCS of an adult and child is about -4 dB (Fig.
3). When these fluctuation characteristics are represented
by 95 % values of cumulative probability, the difference
between a passenger car and pedestrian instantaneously
becomes as much as -20 dB. Therefore, it was calculated
that the detection distance range of pedestrians is about 20
to 40 m, although the actual range depends on the installation conditions of the radar.

Fig. 3 Measurement of pedestrian dummies for link design

2.2. Multiple-radar integration
This section briefly describes the features and functionsof a system for installing the radar sensors at the diagonalcorners of an intersection to achieve coverage of the entire
intersection. This multiple sensor system is an effective wayof preventing blockage by large vehicles and complementing the blind areas generated below the sensors. As these
multiple sensors must be integrated to detect the positionsof targets precisely, the raw data outputted by the radar
devices are transmitted and aggregated in real time while
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ensuring time synchronization.
Cases of crossing pedestrians or bicycles passing throughthe blind spots of drivers are a cause of accidents at intersections on public roads. A system that simultaneously
operates radars installed at the diagonal corners of anintersection can also improve detection accuracy byincreasing the scan data samples (Fig. 4). When a vehicle isinside the common area within the intersection, echoes
scattered from the front of the vehicle are observed by one
radar and scattering from the sides and back are dominantat the others. That is, integration processing is needed to
identify data indicating different positions for a target
vehicle.

In other words, the practical adoption of millimeter-wave
radar requires technical verification assuming hard raincaused by typhoons or the like.
3.2. Design for heavy rainfall
The attenuation characteristics with respect to the amountof rainfall based on the results of experimental analysis
were modeled and reflected in the radio link design of the79 GHz radar system. Specifically, experimental rainfall
data was used to obtain a relational expression concerning
rain intensity and attenuation per unit distance. For example, when the line margin for detecting a pedestrian 40 m
ahead is designed as +10 dB, it is possible to estimate witha margin of +5 dB at a rainfall intensity of 50 mm/h and 0
dB at 100 mm/h. Alternatively, it is also possible to estimate the effective coverage at a distance of 32 m when alink margin of +10 dB can be secured at a rainfall intensityof 50 mm/h, and 27 m at 100 mm/h in the same way (Fig.
5).

Fig. 4 Verification of simultaneous operation of multiple radars

We have also considered applying this intersection-based
radar system for merging support on highways(5). Often,
the merging point of a highway is a place in which it is difficult for a vehicle moving up the lamp and a vehicle traveling on the main lane to detect the presence of each otherusing onboard sensors. Therefore, it may be possible torealize smooth automated driving by identifying the situation of vehicles on the main lane and that of merging vehicles in real time and transmitting this information to
nearby vehicles.

Technical Verification under Severe

3 Weather Conditions

3.1. Objective
For radars where stable operation is expected even in bad
weather, heavy rainfall is the most severe condition. Even
short-range millimeter-wave radar is unable to ignore theinfluence of attenuating radiating radio waves due to scattering from raindrops during heavy rainfall. In contrast,
the attenuation is low during heavy snowfall despite theworsening in visibility. This is because radio waves areabsorbed by water and the actual precipitation duringsnowfall is low, and because snow manifests as ice grains.
104

Fig. 5 Pedestrian detection performance in heavy rainfall

To reduce the sensitivity of millimeter-wave radar toechoes scattered by raindrops, it is necessary to optimize
the detection threshold in order to determine the existenceof the desired targets. In this optimum design, the physicalcharacteristics of randomly generated raindrop echoes are
extracted. Here, the characteristic that the received powerof the raindrop echoes is inversely proportional to thesquare of the range is utilized. This characteristic is confirmed by analyzing measured data under heavy rainfall.
Furthermore, to reliably suppress false alarms due to raindrop scattering, the threshold of radar detection, which isa distance function, is adjusted in correspondence with theassumed rain intensity. That is, an adjustment method foruniformly shifting the threshold set in inverse proportion
to the square of the range is used. It was confirmed thatraindrop sensitivity can be suppressed to 0.1% or less using
the rain intensity data at 50 mm/h acquired in the attenuation experiment. However, since this raindrop sensitivity
and the desired target detectability have a trade-off relationship, the threshold must be set appropriately according
to the system requirements.

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3.3. Experiment during snowfall
In cold climates where the amount of snowfall is large,snowstorms sometimes cause poor visibility. Therefore,
radar is regarded as potentially particularly effective compared to visible cameras and laser imaging detection andranging (LiDAR). As described above, robustness duringsnowfall can be secured relatively easily if radar technology
compatible with severe rainfall conditions can be established. This section describes experiment results for a 79
GHz band millimeter-wave radar conducted in a region
with heavy snowfall.
In this experiment, a pillar was temporarily installed at
the side of a single-lane road and installed with a prototype
radar. To detect vehicles traveling behind large-sized vehicles, the installation angle was adjusted so that the vehicles
are irradiated from the rear (Fig. 6). Moreover, the measurement accuracy was verified by counting the number ofvehicles passing through to the radar detection software.

Fig. 6 Prototype 79 GHz radar experiment

Several consecutive 24 hours of data, including severe
weather conditions such as snowstorms, were selected toverify the radar measurement accuracy. Table 1 shows theaccuracy verification results (vehicle counting data) for
each day with heavy snowfall, including sleet and snowstorms. Although some over-counting and non-counting
occurred, a detection accuracy of 99% or more wasobtained in both conditions. Furthermore, a functionalverification was carried out by extracting all the time periods in which a vehicle was not present for more than two
minutes, but no false alarm occurred in either condition.
When analyzed in more detail, cases occurred in which
the number of large vehicles was over-counted or two
vehicles driving close to each other were counted as one.
However, the occurrence of these events is not attributable
to weather conditions such as rainfall or snowfall. Suppressing the over-detection of large vehicles has a trade-off
relationship with the separation performance of vehicles inproximity with each other. In other words, if parameters
are adjusted to suppress excessive detection of trucks or
the like, non-detection of multiple vehicles close to othertends to increase. In practice, algorithms and parameters
will be optimized to meet the performance requirements
required by roadside sensor applications.
Table 1 Verification of vehicle count accuracy in snow

Detection Accuracy Verification on Pub-

4 lic roads

An experimental 79 GHz radar system has been installedat an actual public road intersection, and the detection
performance of pedestrians on the crosswalks and themeasurement of traffic volumes on the roads leading to theintersection were verified. The verification field is the intersection between a single-lane road and a two-lane mainroad, which is common on Japanese public roads. The traffic volume of this intersection is high despite the narrowroad width. Also, because it is located near a subway station, pedestrian and bicycle traffic is high in the morningand evening, when the roads are also crowded.
The experimental system was installed in an existing signal pillar, and consists of a radar unit attached 5 m above
the ground and a control PC inside a cabinet. In this verification experiment, the radar device was adjusted to facediagonally across the intersection. Figure 7 shows the relationship between the detection range for pedestrians andeach lane, and the radar has a field of view (FOV) on thehorizontal plane of 70°. Since the vertical plane beamwidth of the radar antenna is about 10°, a blind area ispresent at ground level about 15 m from the radar installation point.

Fig. 7 System configuration for public road experiment

The present system is set to output the scan data at a
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Development of Practical Roadside Sensor Utilizing Millimeter-Wave Radar Technology

frame period of 50 ms, the maximum detection distancefor a pedestrian is about 40 m, and a passenger car can bedetected up to about 80 m. When the detection target is apedestrian, the power of the echoes fluctuate randomly, sointerpolation processing using multiple data frames isrequired. In general conditions with a walking speed of 1
ms, a moving distance of 20 cm occurs in the processing
period of 200 ms. That is, even if the processing is performed in a period corresponding to multiple frames, theinfluence on positioning accuracy is slight. In addition, inorder to avoid a collision, the moving direction of thepedestrian may be also required. In this case, the tracking
process for estimating the velocity and trajectory of thetarget is executed using a time series of coordinate position
data sets.
Samples of the evaluation results are shown in Table 2 fora case in which one of the crosswalks shown in Fig. 7 is set
as the detection area. When performing interpolation processing using four frames, a detection rate of 95% or more
was achieved, and a false alarm rate of about 1% was
obtained. The main reason for the occurrence of false
alarms is multipath scattering caused by vehicles passing infront of the desired target parallel to the crosswalk. Possible countermeasures include setting an upper velocity limit
for pedestrian detection.
Table 2 Detection accuracy for crossing pedestrians

It is necessary to detect pedestrians on crosswalks in drivers’ blind spots as a driving support application at intersections. From another viewpoint, for example, in traffic
control applications, it is required to measure directionspecific traffic flows at intersections. In Japan, traffic flow
on public roads is measured with ultrasonic sensors
installed above each lane. Therefore, the total cost of
deployment as an infrastructure system could be suppressed if roadside millimeter-wave radar systems installed
for driving support can also measure the number of vehicles passing through multiple lanes.

5 Conclusion
As described above, we have carried out research anddevelopment for applying the latest millimeter-wave radar
technology to the roadside sensors at intersections. Specifically, we implemented clustering technology for scatteredechoes obtained by high-resolution radar and verified a106

roadside sensor system diagonally covering an entire intersection. Moreover, we demonstrated the radar detection
function in rainfall exceeding 50 mm/h and during snowstorms. A 79 GHz radar prototype is currently operational
at an actual intersection, and we are working to develop
technology to improve detection accuracy through longterm data acquisition. In particular, it is expected that a
detection rate of 95% or more and a false alarm rate of 1%or less can be achieved with respect to the detection ofpedestrians and bicycles.
References(1) M. Yasugi, et al.: Development of 79GHz Band Radarfor Realizing Safety Mobility (in Japanese), PanasonicTechnical Journal Vol. 61, No. 1 (2015)(2) Y. Nakagawa: Infrastructure Radar System as NextGeneration ITS Utilizing ICT, ITU-AJ New Breeze Vol.
27, No. 3 (2015)(3) I. Ryu, et al.: Pedestrian Recognition Using 79GHz
Radars for Intersection Surveillance, European Radar
Conference (2016)(4) ARIB STD-T111 Version 1.1 (2017)(5) T. Hayashi, et al.: A study of infrastructure radar technologies using 79GHz band on V2I application for
merging support at highway junction, ITS World Congress (2017)

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Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance③ Estimating the Effectiveness of Measures to Reduce Traffic Accident Fatalities

Study of Japanese Traffic Accident Patterns for Estimating Effectiveness of ADAS Based on ITARDA
Macro Database
Toru Kiuchi, Satoko Ito (Institute for Traffic Accident Research and Data Analysis)
ABSTRACT: In Japan, the authors analyzed accident data using automated driving systems under the auspices of the Cross-ministerial Strategic Innovation Promotion Program (SIP) of the Cabinet Office of the Government of Japan. Based on ITARDA macro data from 2013, 255
SIP accident patterns with three or more fatalities were established and studied for four years. This article describes the background, accident
patterning methodology, a pattern sheet sample, analysis of sheets, and some study results using the 255 SIP accident patterns. It also introduces the future plans for these SIP accident patterns in a national accident database.

1

BackgroundThe number of Japanese traffic accident fatalities in 2017
was 3,694, a decrease of 210 from the previous year and thelowest number since 1948 when the National Police
Agency started keeping traffic accident statistics.

Fig. 1 Trend of traffic accident fatalities in JapanThe Institute of Traffic Accident Research and Data Analysis (ITARDA) was established in 1992 as a public benefit
corporation approved by the National Police Agency andthe Ministry of Transportation and Construction. It conducts comprehensive and scientific research into traffic
accidents from the three standpoints (people, roads, andvehicles), and provides the results of its research to the
public and private traffic safety organizations to help themrealize safer road traffic. The 10th Traffic Safety Master
Plan (in March, 2016) states that “by 2020, the number oftraffic accident fatalities should be reduced to 2,500 or lessto realize the world’s safest road traffic environment.” To
achieve this goal, it is necessary to reduce the number oftraffic accident fatalities by about 1,200 in the remaining
three years.
Started with Prime Minister Abe’s remarks aiming forworld-leading’s technologies at the 107th Council for Science, Technology and Innovation (March, 2013), theCross-ministerial Strategic Innovation Promotion Program(SIP) is being strongly supported by the Japanese government. SIP is promoted beyond the boundaries of the ministries and sectors, and is allocated its own budget, frombasic research to completion (practical application/commercialization).

Fig. 2 Eleven projects of SIP

The 11 SIP themes launched in 2014 include one called“Automated Driving System.” This theme targets theachievement of national goals such as reducing traffic accidents, the realization and dissemination of automatic driving systems, cooperative development with the Tokyo
Metropolitan Government with the 2020 Tokyo Olympics
and Paralympic Games as a milestone, the development
and demonstration of systems including large-scale demonstration experiments, as well as the development of basic
technologies to reduce the number of traffic fatalities andtraffic congestion.
ITARDA has been conducting research to help estimate
the effect of basic technologies on automated driving systems aiming to reduce the number of traffic accident fatalities. The title of this research is “Analysis of traffic accident
patterns using the latest ITARDA macro database,” and ispart of a survey and study for achieving the national goalof reducing traffic accident fatalities.

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Study of Japanese Traffic Accident Patterns for Estimating Effectiveness of ADAS Based on ITARDA Macro Database

This article describes the background, accident patterning
methodology, a pattern sheet sample, analysis of sheets,and some study results using the 255 SIP accident patterns.

2 Patterning of ITARDA Macro Data

quently, we chose three or more fatalities as the thresholdto cover over 80% of the total traffic accident fatalities in2015. This resulted in 255 SIP patterns.
2.2.

Pattern sheet example

2.1. Methodology for patterning
In the first year of this SIP research, the ITARDA macro
accident database for 2013 was studied and classified using
the accident classification items shown in Table 1.
Table1 Items and classifications for patterning

Fig. 3 Example of SIP accident pattern sheet (Japanese only)

Public road, Expressway

Table 2 at the bottom of this page shows the numbers offatalities and patterns classified by combinations of accident types and primary or secondary parties. Changing thethreshold of the number of fatalities to five or more, fouror more, or three or more increases the number of both
fatalities and patterns, as well as the coverage ratio. Conse-

Figure 3 shows an example sheet of the 255 SIP patterns.
This pattern sheet shows car-to-car crossing accidents at
signalized intersections on public road excluding expressway. The secondary party (blue car) is coming from theright side of the primary party (red car). The accident summary table at the top left shows the keywords from theclassification items for patterning shown in Table 1. An
accident diagram is shown on the right. The middle tableshows the number of accidents involving fatal, serious, andminor injuries included in this pattern, and the number offatalities, seriously injured, and slightly injured occupants.
In addition, the bottom table shows the total number ofaccidents and casualties for the year as a reference.

Table 2 Numbers of fatalities and patterns, and ratio covered (note: numbers are not revised)Public road

Expressway

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Study of Japanese Traffic Accident Patterns for Estimating Effectiveness of ADAS Based on ITARDA Macro Database

Fig. 4 Example of detailed SIP analysis sheet (Japanese only)

Detailed analysis was carried out for each pattern andanalysis results. Information such as whether the accident
occurred during the day or at night, in fine or rainy
weather, or on urban or rural roads, whether the primary
party violated any regulation, as well as the travel speed,
human factors, and age group in the accident pattern wereclarified. The results of this detailed analysis were prepared
for all accident patterns.

3 Application of 255 SIP Patterns
Over four years of studies, the 255 SIP patterns wereapplied to various topics, including the following.
1) Estimation of the effectiveness of ADAS provided withpedestrian detection technologies for reducing fatal
car-to-pedestrian accidents
2) Estimation of the effectiveness of ADAS provided withlane keeping technologies for reducing accidents inwhich single cars run off the road3) Identification of patterns with 20 or more fatalities anda fatality rate of 5% or more among fatal car-to-bicycle
accidents
4) Identification of trends in the number of accidents andfatalities of each pattern over nine years, and to investigate the reasons for these trends

year because of the decreasing total number of road fatalities in Japan. In the first year, 255 SIP patterns were established using 2013 accident data, and the fatalities
categorized in these patterns accounted for 80.0% of thetotal traffic accident fatalities. However, in 2015, after only
two years, the coverage rate fell to 76.8% and to 75.5% in2016.
Currently, in the final year of the research project, we plan
to review the existing patterns and identify potential new
patterns to improve the coverage rate. For example, in single-vehicle accidents involving a car or motorcycle, thesepatterns are extremely fragmented according to the classification of the traffic accident statistical codes. In the caseof single-vehicle accidents, cars or motorcycles that departfrom their lane for some reason might collide with fixed
objects, or collide with nothing and drive off the road. Insuch cases, the collision object is decided by the road environment. Therefore, to improve the coverage rate, we areinvestigating the possibility of reducing the number ofaccident patterns by combining collision objects. From thestandpoint of active safety, there is an idea that all colliding
objects may be integrated. In contrast, from the standpointof passive safety, it is necessary to separate collision objects
with high fatality rates such as utility poles and sign posts.
In addition, we are also studying patterns in which thenumber of traffic accident fatalities has increased in recent
years, and new patterns such as single-bicycle accidents,
which were not covered four years ago.
After these studies, it should be possible to finalize new
SIP patterns in a national traffic accident database forJapan.
References(1) ITARDA: Traffic accident statistics for 2017 (2018).
(2) NPA: Characteristics of traffic accident in 2017 (2018).
(3) CAO: The 10th Traffic Safety Master Plan (2016).
(4) ITARDA: SIP-adus annual report for 2014 (2015).
(5) ITARDA: SIP-adus annual report for 2015 (2016).
(6) ITARDA: SIP-adus annual report for 2016 (2017).
(7) ITARDA: SIP-adus annual report for 2017 (2018).

Details of the results of these topics are introduced inannual reports published by ITARDA (see the references).

4 Issues to be Solved and Future Plans
From the standpoint of fixed point observation, the 255
SIP patterns have been maintained for the past four years.
However, the coverage rate has been decreasing year by
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Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance④ Analysis of Nationwide and In-Depth Accident Data, and Development of Simulation Technology

Development and Substantiation of Simulation Technology for Estimation of Detailed Traffic Accident
Reduction Effects
Sou Kitajima (Japan Automobile Research Institute)
ABSTRACT: The aim of this project is to develop novel multi-agent traffic simulation software capable of predicting the impact of automateddriving systems on traffic safety. The software is designed to incorporate variables for human perception and recognition, decision-making,and errors, dependent on age and gender. In this study, the software was applied to a 6 x 3 km2 area in Tsukuba city. Over a simulated periodof time that included more than 500 agents (vehicles, drivers, and pedestrians), the software simulated and identified at least five types ofaccidents. Different automated driving technology penetration scenarios were applied to conduct preliminary estimations of the potential
impact of these technologies on safety.

1 Introduction
Automated vehicles are regarded as a promising means ofhelping to achieve safer roads(1). Comprehensive estimations of the potential impact of such vehicles on traffic
safety at a national level require the application of simulation methodologies. Within the Japan SIP-adus strategic
research program, the aim of this project is to develop andverify a multi-agent traffic simulation methodology capable of estimating the safety improvement potential of automated vehicle technologies.

ule includes three management modules and two traffic
participant models. If an accident occurs during a simulation, log data such as collision velocity, collision angle, andthe driver’s age are recorded to calculate the probability ofserious injury.

2 Methodology
2.1. Simulation Concept
Automated driving systems have the potential to help
achieve the Japanese government’s target of realizing theworld’s safest traffic environment. Although studying
methods of assessing the impact automated driving functions is an important part of this approach, there are several challenges, such as the development of pragmatic
behavior models of various traffic participants. From thestandpoint of research ethics, we cannot assess impacts on
fatality reduction by field operational tests (FOTs) in thereal world. Therefore, research is under way into the development of macro traffic simulation software that consistsof multi-agent traffic participants. This simulation aims togenerate both normal traffic flows and critical traffic situations.
2.2. Simulation components
2.2.1. Overall structure
Figure 1 shows the composition of the multi-agent traffic
simulation. The simulation is composed of scenario files,
road data, and a simulation module. The simulation mod110

Fig. 1 Composition of multi-agent traffic simulation

2.2.2. Behavior model of traffic participants(1) Driver behavior model
The driver behavior model is composed of three mainprocesses including perception/recognition, decision making, and actions. This simulation incorporates two types ofdriver visual fields to simulate various safety confirmation
actions (Fig. 2). Various typical driver errors, such as inattentive driving, aimless driving, misjudgment, and inadeEgo vehicle

Fig. 2 Outline of driver agent model[ II ] Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance④ Analysis of Nationwide and In-Depth Accident Data, and Development of Simulation Technology

Development and Substantiation of Simulation Technology for Estimation of Detailed Traffic Accident Reduction Effects

quate operation are reproduced by utilizing the driver
behavior model. Furthermore, since drivers have diverse
characteristics due to age, gender, character, and drivingstyle, the simulation also includes four different driver personality attributes.
(2) Pedestrian behavior model
Figure 3 indicates the pedestrian behavior model used toreproduce road crossing behavior. In Japan, about 25% of
fatal accidents are collisions with a crossing pedestrian at a
non-intersection area. It is important to develop a pragmatic model based on pedestrian safety confirmation
actions. There are six parameters for reproducing roadcrossing behavior, such as walking velocity. The distribution of parameters was reflected in the behavior model
based on fixed-point observation and examination(2).

Fig. 5 Road network for functional testing

2.3.2. Simulation conditions
Typical automated driving penetration rates were applied
to compute and compare the results of diverse technology
penetration scenarios. The simulation contains multiple
driving modes, including manual driving, autonomous
emergency braking (AEB), lane departure warning (LDW),
and automated driving (Table 1). It is important to set up
mixed scenarios consisting of various driving modes toestimate safety impacts more practically.
Table 1 Technology penetration scenarios

Fig. 3 Outline of pedestrian agent model

2.2.3. Advanced driver assistant system (ADAS)/automated driving
Advanced driver assistant system (ADAS) and automateddriving specifications can be defined by the number of sensors, detection range, and angle. Figure 4 shows an example of an automated driving system. In this article, theestimation of accident reduction effects is computed
through this system.

3 Simulation Results
3.1. Reproduction of realistic traffic flows
To obtain pragmatic estimations of accident reduction
effects, it is essential to reproduce realistic traffic flows via
simulation. The current simulation can reproduce approximately 500 agents with individual behavior (Fig. 6). Traffic
densities and travel velocities are set beforehand based onroad traffic census data.

Fig. 4 Specifications of sensors for automated driving system

2.3. Functional testing of multi-agent traffic simulation
2.3.1. Road network
Figure 5 shows a simulated area that was created for thedetailed modeling of a part of Tsukuba City. The road network is composed of various routes, such as an expressway,
major roads, and narrow streets.

Fig. 6 Multi-agent traffic simulation including 500 agents

3.2. Reproduction of accidents
Typical accidents such as head-on collisions occur occasionally due to driver agent error. Figure 7 shows an example of an accident. The driver agent started to depart fromthe driving lane because of driver error (i). Lane departure
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Development and Substantiation of Simulation Technology for Estimation of Detailed Traffic Accident Reduction Effects

continued even though the vehicle reached the opposite
lane (ii). Although the driver agent perceived the lane
departure and began an emergency operation (iii), it was
not effective at avoiding a collision (iv).

Fig. 7 An example of a head-on collision at a basic road section

3.3. Comparison between simulated results and accident statistics
Figure 8 compares simulated results for 100% manual
driving with traffic accident statistics. These accidents
occurred in proportions and locations similar to those ofreal-world accidents.

Fig. 9 Comparison of accident occurrence locations (scenarios 1, 3, 5)

The simulation can adopt different automated driving
technology penetration scenarios to estimate the potential
impact of different technologies on safety.
3.5. Comparison of number of accidents and accident
rate
Under 100% manual driving, the system simulated a totalof 859 accidents of five types. The number of predictedaccidents decreased as the level of automation increased,falling to 156 accidents at the highest simulated automation level (25% of vehicles with AEB + LDW and 75% withlevel 4 automated driving) (Table 2). Figure 10 shows that
all the considered technologies contributed to an absolutedecrease in accidents. This decrease was dominated by
reductions in rear-end and lane departure-related crashes.

Fig. 8 Comparison of accident occurrence locations between simulation
results and real world accidents

Table 2 Synoptic table of simulation results

3.4. Frequency and occurrence locations under different simulation conditions
Figure 9 shows the simulation results under different simulation conditions to estimate the accident reduction effectof different systems. Each result shows different characteristics in terms of frequency and occurrence locations. This
simulation is an effective way of quantitatively estimating
the safety impacts of different levels of penetration ofadvanced driving assistant systems or automated driving.

Fig. 10 Comparison of relative accident rates and compositions

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Development and Substantiation of Simulation Technology for Estimation of Detailed Traffic Accident Reduction Effects

4 Concluding Remarks
Novel multi-agent traffic simulation software was developed and applied to an 18km2 area in Tsukuba City, Japan.
Over a simulated period of time with more than 500
agents, the software simulated and identified five types ofaccidents. Different automated driving technology penetration scenarios were applied to estimate the potential
impact of different technologies on safety. Future work will
also address the validation of behavior models and the
expansion of the current methodology to wider regions.
References(1) Cabinet office, Government of Japan: White paper on
traffic safety in Japan 2017, pp. 68-72 (2018)(2) Measurement and Analysis of Road Crossing Behaviorsof Pedestrians in a Street without Crosswalk by Experiments (in Japanese) (2017).

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Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance④ Analysis of Nationwide and In-Depth Accident Data, and Development of Simulation Technology

Development and Evaluation of Technologies forConstructing Driving Video Database
Kazunori Nomoto, Hiroshi Tanigawa, Akito Adachi (Japan Automobile Research Institute)
ABSTRACT: The safety of automated driving systems, which are regarded as a promising means of helping to reduce traffic accidents, isgreatly dependent on functions for recognizing pedestrians and objects, and on making early and accurate judgments of potential collisionrisks. The collection of large volumes of driving data and the development of technologies and tools for using this data efficiently are recognized as common issues for strengthening competitiveness in the development of such recognition and judgment functions. This article
describes efforts undertaken to research and evaluate the utility of a driving video database and simulation evaluation technology needed fordeveloping camera image recognition technology.

1

Development Aim

Human error is the cause of over 90% of traffic accidents,
and nearly all fatal accidents occur on ordinary roads.
Implementing automated driving on ordinary roads isbeing promoted as a national project in Japan with the aimof reducing fatal traffic accidents.
There is intensifying competition to research and develop
techniques capable of accurately judging at an early timing
the presence and potential risk of a collision with pedestrians and two-wheeled vehicle riders, who account for alarge percentage of traffic accident fatalities. In addition toautomated driving systems for ordinary roads, applicationof these techniques is also being targeted at advanced
driver assistance systems (ADASs) that have already been
implemented in production vehicles, and efforts are being
made to further improve them and expand their application.
Original equipment manufacturers (OEMs) and suppliers
are proceeding with R&D activities using data collected indriving tests conducted using vehicles equipped with proprietary systems developed by each company. However, an
enormous amount of time and expense is required toobtain the large volumes of driving video data needed fortraining image recognition engines and for conducting
simulations to evaluate the recognition performance of
these systems. Meanwhile, the camera image data collectedby these proprietary systems is highly dependent on the
hardware used, such as the specifications and mounting
positions of the cameras. Moreover, companies cannot easily share their data with others since images may containconfidential information about products under development. Consequently, there is a need to construct a largescale driving video database that is premised on shared use
by every company involved.
The aim of this project is to improve R&D efficiency andcompetitiveness by providing large volumes of teaching114

data to OEMs and suppliers for use in developing automated driving technologies, as well as to universities,
research institutes, IT business ventures, and the like,which are looked to for research on cutting-edge technologies such as artificial intelligence (AI). Such data is neededfor developing recognition engines for pedestrians andtwo-wheeled vehicle riders along with artificial intelligencefor predicting the risk of a collision using driving environment recognition systems based on camera images.

Overview of Research and Development

2 WorkIn this driving video database project, large volumes ofdriving video data (approximately 1,500 hours, 4.2 petabytes (1015) and 100,000 scenes) were collected for use indeveloping and evaluating the performance of pedestrianrecognition systems. The associated key technologies forconstructing an easy-to-use database were also developed,
including functions for tagging data and conducting data
searches.
Over a three-year period from fiscal 2014, activities were
undertaken to develop and verify an automated driving
system for universal services, as well as technologies forconstructing a driving video database in the Cross-Ministerial Strategic Innovation Promotion Program (SIP). Fromfiscal 2017, this project commissioned by the Ministry of
Economy, Trade and Industry was launched to research,develop and verify an advanced automated driving system
for social implementation. This project undertook researchand development work necessary for achieving an
advanced automated driving system.
2.1. Research and development objectives
2.1.1. Construction of driving video database
The image data collected in this database is limited toscenes involving pedestrians, two-wheeled vehicles, including bicycles, and motor vehicles, based on the opinions of

[ II ] Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance④ Analysis of Nationwide and In-Depth Accident Data, and Development of Simulation Technology

Development and Evaluation of Technologies for Constructing Driving Video Database

people concerned with the development of automateddriving technology. In addition, the camera system used
was configured to take wide-angle forward images and360-degree images of the surroundings in order to record
the presence and movement of pedestrians, two-wheeledvehicles, and other objects around the vehicle. This isbecause one of the issues raised in the Euro NCAP (European New Car Assessment Programme) 2020 Roadmap isthe prevention of accidents at intersections.
The vehicle-mounted cameras used in automated driving
systems have different specifications depending on the
manufacturer, and their performance is being improved
every year as result of further technological progress. Thisdriving video database is intended to provide teaching datafor use in simulations to evaluate the performance of suchcamera systems. The image quality must be as good as or
better than that of the vehicle-mounted cameras to beevaluated. Taking into account the specifications of cameras currently used in ADASs and projected performance
improvements over the next five years, it was concluded
that the cameras should have approximately twice thenumber of imaging elements as current cameras and awider horizontal angle of view, among other specifications.
Accordingly, a Hi-Vision (high-resolution two-megapixel
imaging element) camera system was adopted to provide
the desired performance.
Existing image recognition databases are capable of recognizing faces and handwritten characters, and the volumeof data needed at the level of practical performance is said
to be in the order of several million items. The pedestrian
databases currently provided by research institutes in othercountries contain still images, but video image data isneeded to analyze pedestrian behavior at intersections andother places.
Based on these considerations, it was decided to record
four million frames of different pedestrian types, including
men, women, and children of all ages, in different posturesand carrying different objects, and 107,497 video images ofwalking scenes, including walking along the side of roads,
at intersections, crossing roads, and other situations.
2.1.2. Construction of simulation evaluation environment
Data collected by driving test vehicles on public roads
provides useful information for use in technological development because it includes various types of real-world
traffic environments, such as different road structures, natural environments and the presence of pedestrians, nearby
vehicles, and other objects. However, it would be impossible to record every driving situation that might occur inthe real world. One conceivable way of complementing
actual equipment evaluations and simulations using

recorded driving data would be to build a virtual environment and to conduct simulations under a variety of given
conditions. Based on actual recorded images, computer
graphics (CG) technology, like that widely used in thevideo game industry and elsewhere in recent years, wasapplied to create a virtual space on a computer. In this way,
an evaluation environment was constructed for conducting
simulations under virtual conditions and its utility wasverified. Various adverse circumstances were created foruse in the simulations by freely combining natural conditions, such as daytime, nighttime, and rain, as well as the
positions and movements of pedestrians and nearby vehicles.
2.1.3. Promotion of database use by the private sector
The existence of the driving video data recorded in thisproject was publicized widely among OEMs and suppliers
that are developing automated driving systems, as well as
among research institutes, universities, IT venture businesses, and other entities involved in cutting-edge coretechnologies such as AI. Sample data was made public inorder to collect a wide range of information about various
aspects, such as needs concerning insufficient data, as wellas technologies or tools for practical use of the database.

3 Construction of Driving Video Database
3.1. Data collection
Various types of sensors are available for detecting a vehicle’s direction of travel and surrounding circumstances,including cameras, millimeter radar, LiDAR devices andultrasonic sensors, among others. A monocular camera
system was selected for recording forward wide-angle andall-around image data because the data volume is smaller
than a stereo camera system and high-resolution wideangle images can be obtained. Because the distance accuracy of a monocular camera system to potential object isinferior, long-range scanning LiDAR sensors were used inparallel. A system was adopted for synchronized collectionof both image data and LiDAR data.
The camera system consisted of five 2.3-million pixel
monocular Hi-Vision cameras. One was a forward remotemonitoring camera with an angle of view of approximately
90° and the other four had an angle of view of approximately 180° and were positioned at the front and rear andon both sides of the vehicle. Corresponding to the cameras
were five LiDAR sensors and large-capacity data recording
devices with a maximum recording time of six hours. The
vehicle was equipped with two types of GPS devices formeasuring its position. The appearance of the vehicle thatwas developed for collecting driving video data is shown in
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Fig. 1.

Fig. 1 Vehicle developed for collecting driving video data

3.2. Data processing
In order to use large volumes of data efficiently and effectively according to a particular purpose, it is necessary to
be able to comprehensively search all the related data by
specifying certain priority conditions These might include
driving scenarios such as straight-ahead travel or turning
right or left at an intersection, conditions such as daytime,nighttime, or rain, and the presence, positional relationships, and other aspects of pedestrians or nearby vehicles.
For that purpose, it is essential to classify scenes so that
objects of interest can be found in specific time frames inindividual driving scene files that contain, for example,
scenes of pedestrians or bicycles crossing the road. Therefore, all the images in this driving video database havebeen classified into 27 types of scenes.
Figure 2 shows a breakdown of the overall 258,364 driving scenarios. The database contains 107,497 pedestrian
scenes obtained from city streets, shopping/entertainment
streets, sightseeing areas, and cold regions, and 6.45 million frames of pedestrian image data. As a result of analyzing, classif ying and organizing a ll t he dat a, t hedevelopment targets of 4 million frames of pedestrian
image data and 107,497 scenes of pedestrians walking were
ultimately obtained.

multifaceted evaluation of the data with respect to quality,ease of use, utility, and other aspects. Although the results
confirmed the utility of the database, room for further
improvement was also found.
In addition, four briefing sessions were held from January
to March 2018, which were attended by 112 participantsfrom 81 organizations. Figure 3 shows one of the ninesample driving scenarios shown to the attendees. The aim
was to encourage use of the database by organizations
engaged in R&D work on cutting-edge core technologies,
including the development of recognition engines forpedestrians, two-wheeled vehicles, and the like, as well as
the development of AI for predicting the risk of a collision.
People from research institutes and other organizations
accounted for approximately 35% of the total attendees,
which shows that the aim of broadening the range of R&D
activities was accomplished. JARI has also independently
taken the initiative to supply 288 sample datasets to interested parties, and has launched activities to encourage useof the driving video database by private sector organizations.

Fig. 3 Driving video data showing a pedestrian crossing the road against
sunlight

Construction of a Simulation Evaluation

4 Environment

Fig. 2 Breakdown of the numbers of targeted objects in driving scenarios

3.3. Database evaluation
A portion of the driving video database was provided to
the representatives of eight organizations, including OEMs,
suppliers, and research institutes in order to perform a116

It would be difficult to record every situation that mightoccur in the real world, including various weather conditions, near misses and so on, by driving test vehicles on
public roads to collect driving video data. Constructing avirtual environment and conducting simulations under awide variety of given conditions is regarded as a more
effective method. For that reason, a simple prototype simulation environment was constructed and its utility was verified.
Specifically, some of the recorded driving data was used
to extract playbook-like scenarios of vehicle behavior of
the vehicles, pedestrians and other traffic participants
appearing in those scenarios. In addition, from among themodels of structures, people, vehicles and the like devel-

[ II ] Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance④ Analysis of Nationwide and In-Depth Accident Data, and Development of Simulation Technology

Development and Evaluation of Technologies for Constructing Driving Video Database

oped by the private sector, ones closely resembling actual
objects were selected and various simulation conditions
were determined, including the weather. Computer graphics technology was then used to recreate virtual driving
scenarios on a computer. It should be noted that the models of structures, people, and vehicles used were preparedin advance on the basis of actual photographic materials sothat they would be equally recognizable by image recognition engines and AI in the same way as actual objects.
Consequently, they could be used in the evaluation ofimage recognition performance and eliminated any concerns concerning personal information.
Evaluations performed in a prototype virtual simulation
environment were compared with evaluations of driving
scenarios obtained using test vehicles. This was accomplished by positioning people and vehicles on a test coursein the same manner as in the virtual driving scenarios andthen recording images of the test driving scenarios. Differences between the two driving scenario datasets were evaluated using the same recognition engine, and the utility ofthe virtual simulation environment was verified by the factthat nearly identical results were obtained.
In order to verify the safety and utility of an automateddriving system, driving tests must be conducted using test
vehicles equipped with the system to be evaluated. A prototype environment was constructed that can use virtual
simulation evaluations to verify object recognition andvehicle control algorithms at the design and development
stage before conducting actual driving tests. The utility ofthis environment was also verified in this project.
The simulation evaluation environment was constructedwith a high level of reality by combining digital map data
(accurate road structures, lane configurations, and the like)and object assets (nearby structures, guardrails, trees, andthe like) using MATLAB/Simulink and Prescan, as an
example of a general-purpose evaluation environment.
This was carried out to realize a usable simulation environment for conducting comprehensive safety evaluations of
the recognition and judgment functions of automateddriving systems.

ation environment using computer graphics technology
was also verified.
References(1) Report on Development and Verification of Autonomous Driving System and Technologies for Constructing a Driving Video Database in the Cross-Ministerial
Strategic Innovation Promotion Program (SIP) in Fiscal 2014 (in Japanese).
(2) Report on Development and Verification of Autonomous Driving System and Technologies for Constructing a Driving Video Database in the Cross-Ministerial
Strategic Innovation Promotion Program (SIP) in Fiscal 2015 (in Japanese).
(3) Report on Development and Verification of Autonomous Driving System and Technologies for Constructing a Driving Video Database in the Cross-Ministerial
Strategic Innovation Promotion Program (SIP) in Fiscal 2016 (in Japanese).
(4) Report on Research and Demonstration Project forSocial Deployment of Highly Automated Driving:
Automated Valet Parking Demonstration Experimentand Automated driving system Research and Development, in Fiscal 2017 (in Japanese).

5 Conclusion
Efforts were undertaken in this project to research andevaluate the utility of a driving video database and simulation evaluation technology needed for developing camera
image recognition technology. As a result, approximately
1,500 hours of driving image data amounting to 4.2 petabytes and 107,497 scenes of pedestrian image data werecollected in the database. The utility of a simulation evaluSIP-adus: Project Reports, 2014-2018

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Development of Basic Technologies to Reduce Traﬃc Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance⑤ Visualization of Local Traffic CO2 Emissions

Development of Tool for Assessing Impact of Automated Driving Systems on Traffic Flow and CO2
Emissions
Daisuke Oshima and Takashi Kurisu (Pacific Consultants Co., Ltd.)ABSTRACT: We have developed a tool for visualizing the CO2 emissions of vehicular traffic, which can quantitatively evaluate how the introduction of automated driving systems might affect traffic flow and, consequently, changes in the amount of CO2 emissions from vehiculartraffic. To develop this evaluation tool, we combined traffic simulation with CO2 emissions models and performed necessary verifications inaccordance with an international joint report issued as part of NEDO’s “Development of Energy-saving ITS Technologies” project. In addition, we used the tool to evaluate certain automated driving systems in specific scenarios, where the tool proved applicable to the evaluationof those systems.

1 Purpose
Automated driving systems are expected to reduce traffic
fatalities and alleviate traffic congestion, possibly resulting
in lower CO2 emissions. This project aims to develop an
evaluation tool for visualizing the CO2 emissions of vehicular traffic, which can quantitatively evaluate how the
introduction of automated driving systems might affecttraffic flow and CO2 emissions from vehicular traffic, as atool for assessing the social impact of such systems. The
evaluation tool is intended to help set priorities for thetechnological development of various automated driving
systems, help determine how best to implement such systems in society, and help raise public awareness throughvisualization of the amount of CO2 emissions, thus promoting the practical and widespread use of such systems.

2 Development Plan
2.1. Basic configuration of evaluation tool for visualizing CO2 emissions of vehicular traffic
As a technique for estimating CO2 emissions based onchanges in vehicular traffic flow, a method that combines
traffic simulation with CO2 emissions models was established in the “Development of Energy-saving ITS Technologies” project (Energy ITS project) conducted from
FY2008 to 2012. This method involves simulating the
movements of all vehicles traveling in a study area, calculating CO 2 emissions based on the reproduced vehicle
movements using CO2 emissions models, and summing
the results for all vehicles to estimate the CO2 emissions
from all the vehicular traffic (Fig. 1).
An international joint report titled “Guidelines for assessing the effects of ITS on CO2 emissions” issued as part ofthe project specifies the requirements to be met in the cal118

culation process. The results of calculations that comply
with these requirements are deemed internationally reliable. To develop the evaluation tool, we combined the traffic simulation with CO2 emissions models in accordancewith the international joint report.

Fig. 1 Method for estimating CO2 emissions from vehicular traffic flow1)

2.2. Scope of evaluation
The following five automated driving systems wereselected for evaluation by the tool:• Green wave driving utilizing traffic signal control
information, etc.
• Advanced Rapid Transit (ART) (smooth accelerationand deceleration of buses)• Truck platooning• Automated driving on expressways and general roads• Local traffic control systems (automated last-miletransport and automated valet parking)
The introduction of automated driving systems might
have the following three main effects leading to lower CO2
emissions: (1) changes in travel demand caused by
improvements in the convenience of automobiles, (2)changes in vehicle behavior caused by system control, and(3) fewer traffic jams caused by traffic accidents (Fig. 2).
The area enclosed by the dotted lines in Fig. 2 was selected
as the scope of application of the evaluation tool, and theeffects shown outside the dotted box were regarded as[ II ] Development of Basic Technologies to Reduce Traﬃc Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance⑤ Visualization of Local Traffic CO2 Emissions

Development of Tool for Assessing Impact of Automated Driving Systems on Traffic Flow and CO2 Emissions

given conditions. That is, the technology does not estimate
how introducing automated driving systems will change
travel demand or reduce traffic accidents.

Fig. 2 Effects of automated driving systems on CO2 emissions fromvehicular traffic3

Development of Local Traffic CO2 Emissions Visualization Technology

3.1. Modeling of changes in traffic flow caused byintroduction of automated driving systems through traffic simulation
3.1.1. Identification of CO2 emissions reduction mechanisms and determination of requirements for the assessment tool
The international joint report states that the mechanismsof how the measures under evaluation might affect driverand vehicle behavior and help reduce CO 2 emissions
should be represented by diagrams called “reference models”. Additionally, the report recommends using the refere n c e m o d e l s to ve r i f y t h at t h e a s s e s s m e nt to ol
appropriately considers these mechanisms and that, in
actual evaluations of automated driving systems, theireffects are quantified based on the models.
Therefore, in preparation for simulation-based modeling,
we defined a mechanism for reducing CO2 emissions foreach automated driving system being evaluated. As an
example, Fig. 3 shows a reference model of truck platooning.
Next, based on the mechanisms of how the automateddriving systems might help to reduce CO2 emissions (represented by the created reference models), we identified
the requirements for the simulations to be performed toevaluate each system.

Fig. 3 Reference model of truck platooning

3.1.2. Modeling of automated driving systems andimplementation of models in traffic simulations
In accordance with the requirements identified in 3.1.1,
we modeled the automated driving systems and implemented these models in traffic simulations. For example,
we modeled platooning trucks that maintained a consistentfollowing distance (modeling of truck platooning) andautomatic overtaking and merging maneuvers (modelingof automated driving on expressways) and implemented
the models. Then we tested the operation and confirmedthat the automated driving systems were represented by
the traffic simulation as intended.
3.2. Development of technology for estimating emissions from changes in driving behavior caused by automated driving
3.2.1. Expansion of CO2 emissions models
We used existing CO2 emissions models as base models(some of which were created in the Energy ITS project).
However, because CO2 emissions models of diesel passenger cars and hybrid minivans (which are becoming more
popular in recent years) and buses (evaluated in this project) were not available, we bench-tested these types ofvehicles to obtain data on their driving behavior and CO2
emissions, and created CO 2 emissions models for thembased on the data.

Fig. 4 Bench-testing to create CO2 emissions models

3.2.2. Creation of CO2 emissions models assuming
automated driving systems have been introduced
We created CO2 emissions models assuming that automated driving systems have been introduced and haveaffected the driving behavior of vehicles. For truck platooning, for example, we created a CO2 emissions modelthat reflects the reduced air resistance of following trucks
due to the shorter following distance; for automated driving on expressways and general roads, we created a CO2
emissions model that reflects optimal starting (acceleration), cruising, and stopping (deceleration) maneuvers.

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Development of Tool for Assessing Impact of Automated Driving Systems on Traffic Flow and CO2 Emissions

3.3. Development of technique for estimating CO2
emissions reductions due to reduced traffic accidents
In order to estimate CO 2 emissions reductions due tofewer traffic jams caused by traffic accidents (Fig. 2), it isnecessary to perform calculations while randomly changing the locations of accidents prevented by the automateddriving systems. Therefore, we adopted a method of estimating CO2 emissions reductions by using a simplified
evaluation simulation and macro CO2 emissions models.
(The simplified evaluation simulation technique assesses
the impact of a reduction in the traﬃc capacity while taking into account the space-time propagation of congestion.
This technique is based on the simplified kinematic wave
theory using data obtained from a dynamic traffic flow
simulation that assumes normal traffic conditions notaffected by accidents or other incidents.)
To determine the impact of accidents on traffic flow (tobe used as an input value in simplified evaluation simulation), we analyzed traffic congestion caused by accidents
using probe data and data on accidents on general roads inthe 23 wards of Tokyo.

4 Evaluation in Model Cities
We evaluated automated driving systems using the developed evaluation tool. Before conducting evaluations, we
compared observed data (such as traffic volume and travel
speed) with the results from the traffic simulation to verify
that the current traffic conditions in the study area were
accurately reproduced by the simulation, as recommended
in the international joint report. Here, evaluation results
for truck platooning are described as an example.
4.1. Simulation conditions
We performed calculations for the Tomei–Shin-Tomei
network (Yokohama-Aoba IC – Toyota JCT – Tomei-Miyoshi IC) (shown in Fig. 5) assuming 24 hours of travel
between 4:00 a.m. on a weekday and 4:00 a.m. on the next
weekday in September 2016. We also assumed that:- 50% of heavy vehicle trips between Tokyo and Nagoya
were made by platoons of four vehicles maintaining a fol-Fig. 5 Network used for platooning simulation
120

lowing distance of 4 m (approximately 3,000 trips made byplatooning vehicles, which account for 1.7% of the total
number of heavy vehicle trips (approximately 182,000
trips) on the Tomei–Shin-Tomei network).
- 1,000 of the 3,000 vehicles departed at night (between
9:00 p.m. and 1:00 a.m. the next day).
- The platooning vehicles traveled on the Shin-Tomei route(shown in red in Fig. 5) at a target speed of 80 km/h (thetarget speed of conventional heavy vehicles is 75–95 km/h).
4.2. CO2 emissions estimation results
The reduction in CO2 emissions from the base case level
was estimated at 0.3% for the 1,000 platooning vehicles(midnight), and 0.8% for the 3,000 platooning vehicles (24hours). The main possible causes of these reductions are
the reduction in the air resistance of following vehicles due
to platooning and a reduction in the average speed of thesurrounding traffic due to the vehicle platoons traveling at
an average speed of about 80 km/h. In this project, theevaluation focused on weekdays, when congestion is low.
We believe that platoons of vehicles traveling on weekends
or holidays might yield a more significant reduction
because platooning helps to reallocate road space.

Fig. 6 Evaluation results of truck platooning

5 Conclusion
We have developed an evaluation tool that can quantitatively evaluate how the introduction of automated driving
systems might affect traffic flow and CO2 emissions fromvehicular traffic. Although automated driving systems are
expected to help alleviate traffic congestion and reduce CO2
emissions, they may fail to deliver the expected benefits,
especially during the transition period in which such vehicles and conventional vehicles share the road. The evaluation
tool can be used to find the desired parameters, such as theperformance or penetration rate of automated driving systems, and to predict and evaluate what effects the systems
might have under various conditions. We hope that this tool
will be used in various studies to enhance the potential benefits to society of introducing automated driving systems.

[ II ] Development of Basic Technologies to Reduce Traﬃc Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance⑤ Visualization of Local Traffic CO2 Emissions

Development of Tool for Assessing Impact of Automated Driving Systems on Traffic Flow and CO2 Emissions

Reference(1) “Report: Establishment of Reliable International Evaluation Methods,” Development of Energy-saving ITS
Technologies project, New Energy and Industrial
Technology Development Organization, 2013

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Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance⑥ Analysis of Social and Industrial Aspects of Automated Driving

Analysis of Social and Industrial Aspects of Developing and Popularizing more Advanced AutomatedDriving Systems
Yoshihiro Suda, Takashi Oguchi, and Koichi Sakai (The University of Tokyo)
ABSTRACT: The following are regarded as preconditions for encouraging the development and widespread use of more advanced automateddriving systems (ADS): Clarification of the impacts on society and industry inside and outside Japan, as well as the risks that will result fromsuch changes, the implementation of measures to mitigate these impacts and risks, the formulation of scenarios from a long-term perspective,
and greater understanding of ADS on the part of the general public. This study clarified the evolution of ADS, proposes a future vision forthese systems and scenarios for issue resolution, and defined specific actions to prepare for the implementation of ADS through a study teamcomposed of academic specialists from various fields.

1 Aims of This Study
The following are regarded as preconditions for encouraging the development and widespread use of moreadvanced automated driving systems (ADS): Clarificationof the impacts on society and industry inside and outside
Japan, as well as the risks that will result from suchchanges, the implementation of measures to mitigate these
impacts and risks, the formulation of scenarios from along-term perspective, and greater understanding of ADS
on the part of the general public. A study team was established to clarify the evolution of ADS, propose a future
vision for these systems and scenarios for issue resolution,and define specific actions to prepare for the implementation of ADS.

2 Establishment of Study TeamThe team that was established to conduct this study is primarily made up of key figures from universities in wideranging areas such as traffic engineering, mechanical/
robotics engineering, informatics, urban planning, cybersecurity, and other engineering fields, as well as public economics, civil procedures, technology management, andother socioeconomic fields, in addition to the fields of education, cultural anthropology, and so on.

122

ping, and whether users will respond positively to such
systems. It is possible that level 3 systems will be developed
as a part of the technological progress toward level 4 systems.

Fig. 1 Evolution of automated driving systems

Although level 3 systems may be popular with professional drivers and other people with advanced driving
skills, such systems are not expected to be favored by theaverage driver. Therefore, it is possible that they may notactually be adopted in society.

Future Vision for Automated Driving Sys-

4 tems and Scenarios for Issue Resolution

3 Evolution of Automated Driving Systems

4.1. Preconditions for study
Based on the two-level evolution discussed in Section 3
above, analysis was conducted for two different timeframes: the near future (by around 2025) and the mediumto long-term (by the late 2030s). Figures 2 and 3 show theanticipated technological development and dissemination
status for each of these time frames.

Level 2 and 4 systems are regarded as most likely torespond to societal needs (Fig. 1).
Level 3 ADS are affected by issues such as whether users
will be able to resume driving tasks in response to system
requests while engaged in secondary activities such as nap-

4.2. Future vision from the standpoint of societal needs
Societal needs can be categorized from the standpoint ofroad traffic issues, public transport systems, and logistics
systems. This section will consider in greater detail ways toensure mobility for vulnerable transport users (both those[ II ] Development of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance⑥ Analysis of Social and Industrial Aspects of Automated Driving

Analysis of Social and Industrial Aspects of Developing and Popularizing more Advanced Automated Driving Systems

who drive and those who do not have a driver’s license)
within the category of public transport systems, as well as
the future of logistics systems (ways to resolve the driver
shortage and reduce logistics costs).

Fig. 2 Technological development and dissemination status by around2025

Fig. 3 Technological development and dissemination status by the late2030s

4.2.1. Ensuring mobility for vulnerable transport users(when users drive themselves)
By around 2025, level 2 private vehicles with advanced
driving safety support systems are expected to appear.
Restriction-relaxed licenses will enable these vehicles to be
operated by everyone, from young people to senior citizens. Legal systems will evolve along with technologicalprogress, such as through the establishment of new restriction-relaxed licensing classifications that enable people todrive only vehicles equipped with advanced driving safety
support systems.
By the late 2030s, although such systems will handle
almost all driving tasks, fully-featured shared control systems (level 2) will exist that make it seem as though thedriver is operating the vehicle by himself or herself.
4.2.2. Ensuring mobility for vulnerable transport users(users without driver’s licenses)
By around 2025, low-speed, small-ridership transport
services using level 4 ADS will appear, although these will
be limited to certain areas such as sparsely populated
regions, planned housing developments, and dedicatedroads.
By the late 2030s, level 4-5 ADS dedicated vehicles (ADSDV) will be employed for regular route buses and ridesharing services. In addition, new modes of transport willappear. Level 4-5 ADS-DV for car sharing services thatprovide a private transport environment will be available
for individual use, and on-demand and shuttle type level
4-5 ADS-DV small-ridership type ridesharing services will
also be available.
4.2.3. Resolution of the driver shortage and reductionof logistics costs
By around 2025, there will be widespread use of level 2
vehicles as lead vehicles and electronically coupled trucks(level 4 vehicles) as following vehicles on certain expressway sectors. On ordinary roads, level 2 restriction-relaxedlicenses for truck drivers will make it unnecessary to haveadvanced driving skills to operate trucks. There will also bemixed passenger and cargo transport on regular routebuses and in ridesharing services (also with level 2 restriction-relaxed licenses), in accordance with demand.
By the late 2030s, it will be possible to operate level 4-5
ADS-DV in all sectors of both expressways and ordinary
roads. There will also be mixed passenger and cargo transport and ridesharing services using level 4-5 ADS-DV regular route buses, in accordance with demand.
4.3. Social impacts and issues of automated driving systems from the standpoint of societal needs
Tables 1 through 3 show specific issues as viewed fromthe perspective of societal needs, scenarios for progress intechnological development, social impacts, negative
impacts and issues, and scenarios for issue resolution.

Actions in Preparation for Implementa-

5 tion of Automated Driving systems

The key specific issues that must be resolved throughcooperation among industry, government, and academia
to implement driving automation systems in society are as
follows:• Human factors• Cooperation on infrastructure, urban planning, andmobility support (collaboration with society)• Technologies for self-driving cargo transport (contribution to economic activities)• Legal systems and insurance systems

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Analysis of Social and Industrial Aspects of Developing and Popularizing more Advanced Automated Driving Systems

Table 1 Social impacts and scenarios for issue resolution related to ensuring mobility for vulnerable transport users (when users drive themselves)

Table 2 Social impacts and scenarios for issue resolution related to ensuring mobility for vulnerable transport users (users without driver’s licenses)

Table 3 Social impact and scenarios for issue resolution the driver shortage and reduce logistics costs

6

Conclusion

The following items were conducted in this study.
• Establishment of study team
• Clarification of ADS evolution• Proposal of future vision for ADS and scenarios for
issue resolution
124• Proposal of specific actions in preparation for the
implementation of ADSIIDevelopment of Basic Technologies to Reduce Traffic Accident Fatalities and Congestion, and Initiatives to Raise Social Acceptance⑦ Initiatives to Raise Social Acceptance of Automated Driving Systems

Future Needs of Automated Driving Systems, andAnalysis and Research of Issues Arising out of such
Needs
Akiko Narita (SC-ABeam Automotive Consulting)
ABSTRACT: To create social acceptance of automated driving systems, public events called “citizens’ dialogues” have been held since FY2016.
These events provide information about automated driving to the public to facilitate understanding of these systems. In addition, we havebeen sorting and analyzing new awarenesses and visions of these systems based on opinions and responses, including the concerns and expectations of the public, to provide input for guiding future research and development activities, as well as to foster social acceptance. At the
dialogue sessions held over the past two years, opinions were exchanged among a variety of stakeholders, accurate information on automateddriving was provided, and expectations and concerns about automated driving were extracted. The positive outcomes of the initial events andacceptance of the activities are illustrated by dissemination of the activities to provincial areas during FY 2018.

1

Background and Objectives of the Research

It is essential to create social acceptance of the Strategic
Innovation Promotion Program (SIP) - Automated Driving for Universal Service (adus) in order to direct researchand development outcomes toward practical application,commercialization, and deployment within society. In
doing so, it is important to further social acceptance ofautomated driving systems through public events to facilitate understanding of driver support systems whose trials
are already in progress, as well as the technologies andbenefits of automated driving now being studied anddeveloped. It is also important to assess and validate theopinions and responses presented at such events.
Toward this objective, events called “citizens’ dialogues”
started in FY2016 as a forum for interactive communication in which participants discussed and exchanged opinions about the social needs of automated driving systems
and the various associated constraints. Thus, to realize thegoals of automated driving at the societal level, efforts arebeing made to heighten social acceptance by providing
information to the public and by addressing community
concerns and public needs.

2 Activities and Outcomes of Research
2.1. Activities in FY2016
In FY2016, three public “citizens’ dialogue” events were
held. The first dialogue recruited university students, whowill forge the next-generation of car users, and opinions
were exchanged on the theme “Social change: the realization of automated driving.” In this of age of fewer andfewer young people holding a driver’s license or owning acar, views were shared on what university students expectfrom automated driving and what kind of society they
believe they have to build from various perspectives,
including the perspective of drivers and inhabitants. When
asked about the characteristics of a future that accepts
automated driving vehicles, participants responded that it
will allow effective utilization of time, increase convenience
in regions without public transportation, and create a newuse of roads. The first citizens’ dialogue ended with a summarizing conclusion that “a network is being formed to
discuss social innovation and ethics through public-private
cooperation. This kind of movement will become important from now onward.”
The second citizens’ dialogue invited professional drivers
and transport industry workers. Under the theme “Connections between automated driving and society,” views
were exchanged regarding how society and future automated driving are mutually connected from the viewpointsof people working at transportation sites and elsewhere.
The topics of discussions included how a society that hasrealized automated driving would differ from the present
society, how the relationship between vehicles and society
would change, and whether lifestyles and other aspects oflife would change in terms of fears, expectations, and eveneducation. Labor shortages and the aging of the population
were highlighted as problems of those holding vehiclerelated jobs. At the same time, high expectations for automated driving were presented. Concerns about automateddriving were the usage of automated driving itself as wellas the problematic aspects of trying to foresee the extent towhich such systems would be built. Concern was also
expressed about who should take responsibility in the caseof an accident and whether the division of the roles of thesystem and humans at level 3 would work properly.
Regarding education targeting correct understanding of
automated driving, it was suggested that it would be
important to effectively use automated driving through the
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involvement of and education by people from different
walks of life such as education by manufacturers, education by those experienced in automated driving, and dissemination of information by motor journalists.
The third citizens’ dialogue invited legal and insurance
professionals and university law students to participate in a
discussion session themed “The rights and responsibilities
of drivers,” which had been touched upon in the first andsecond dialogues. Should the law protect drivers or manufacturers? In the context of issues at each stage of the evolution of automated driving, legal professionals, law
students, and business professionals representing the automobile industry, who also participated in the second dialogue, exchanged opinions on the future relationshipbetween humans and artificial intelligence (AI) from alegal perspective. As for the problems of fully automateddriving, various views were presented from the positions ofbusiness and legal professionals. Concern was expressedthat it would take a long time for society to understandautomated driving, because the safety of automated driving
vehicles remains unconfirmed. Some participants emphasized that it was difficult to discuss who should bear
responsibility in general, because the situation would differ
from case to case, such as the matter of maintaining
accountability for functional limitations. Participants also
said that the scope of product liability should be extended
to programs and data rather than limited to tangible goods,that a scheme similar to the compulsory automobile liability insurance has to be established, and that because thefirst priority is to identify the cause of accidents, the persons in question should cooperate in the cause-identification process rather than being held liable for negligence.
Therefore, criminal punishment should be avoided.”
2.2. Outcomes achieved in FY2016
The citizens’ dialogues in FY2016 achieved the following
outcomes:
Creation of social acceptance of automated driving was
discussed in the three SIP-adus citizens’ dialogues. Various
parties including university students and business and legal
experts actively shared their views from a variety of angles.
Their conversations deepened understanding of the technology, benefits, and issues related to the automated andcooperative driver support systems that are already realized. They also suggested directions automated driving
should take in the future. In particular, it is a significant
achievement of the citizens’ dialogues that ideas generated
by university students and other young people who, as
mentioned earlier, will forge the future of automated driving, contributed innovative suggestions to existing norms.
Furthermore, a survey of participants conducted after the126

citizens’ dialogues showed that over 80% of participants
were satisfied, that their understanding of automated driving had deepened, and that their expectations for the realization of automated driving had increased. These results
demonstrate that this activity heightens social acceptance.
2.3. Activities in FY2017
In FY2017, SIP organized two citizens’ dialogues as aforum for interactive communication to extract and analyze future needs, to discuss new awarenesses and visions,and to gain new insights through exchanges with the public. SIP conveyed accurate information through panelists,
the general audience, and others who participated in the
citizens’ dialogues, and the outcomes, published on the
SIP-adus website, received broad media coverage. Whilebuilt on the achievements of the previous fiscal year, theFY2017 citizens’ dialogues featured the following five elements:(1) Collaboration with the Tokyo Motor Show: this popular event allows information to be spread widely among the
public(2) The first dialogue was designed to focus on the distribution of information and the second on dialogue among
the public(3) Active utilization of the ideas and perspectives ofyouth, who are the next- generation leaders(4) Theme setting based on the perception of automateddriving as an ecosystem, such as a city or MaaS rather thanas an independent system(5) The introduction of new approaches including online
posting tools and graphic-enhanced recordings to collectthe opinions of a large segment of the population and, as aresult, activate dialogues
The first citizens’ dialogue held in collaboration with
Tokyo Motor Show was made possible through the joint
cooperation of the event’s organizer, the Japan Automobile
Manufacturers Association. Through the positive effects ofteaming with a large-scale external event, the SIP-adus citizens’ dialogue attracted 399 applications and 311 visitors.
The theme “Mobility and urban design” was decided with
the intention of allowing the general public to find a personal relevance in automated driving in terms of travel anddaily life by considering both transportation and cities as a
combined living space, and to extract needs from futureoriented and public viewpoints by considering future
urban design.
The session was divided into three parts: (1) imagining an
ideal life and travel based on the issues and needs of current mobility and cities, (2) presentation of opinions on
automated driving technologies necessary for an ideal life
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Future Needs of Automated Driving Systems, and Analysis and Research of Issues Arising out of such Needs

mated driving, and (3) sharing of opinions on the elements
required to realize these ideals and future mobility and cities.
The audience was given an opportunity to post opinions
online. In a post-event survey, 372 questions were posted
online and 216 questionnaires were returned, providing
many useful insights. The results of analysis of online
responses are summarized below.
(1) The need to accurately convey information on automated driving
Many respondents perceived that the activities of SIPadus were not correctly understood, expressed concerns
about automated driving in terms of mixed traffic, andplaced importance on safety and security. This made clear
the need to correctly convey what is possible and what is
not in order avoid overestimations or distrust of automateddriving, and it made clear the existence of issues of how toconvey what is possible and what is not. In addition, there
was the opinion that sharing the vision for the future is aneffective way of helping people to understand the yet-to-berealized technologies associated with automated driving.
(2) Responding to diverse needs
Although citizens’ dialogues have been ongoing in Tokyo
since the first year, a large part of the audience suggested
the need for diversity, such as the participation of people
from non-urban areas and the elderly. Also, some felt thatthe development of flexibility and agility would be necessary to respond to diverse needs.
(3) Attention also needs to be paid to factors other than theautomobile industry
It is necessary to think about automated driving not as a
singular thing but within a whole design that encompasses
various elements, such as urban planning, changes in ways
of working, regions, and attributes. Additionally, some
respondents felt that logistics and parking-lot management
may be improved by automated driving.
The second dialogue in FY2017 set the theme as “Futuresociety and MaaS.” Based on mega-trends including agingof the population, urbanization, and globalization, ideas
were discussed on what kinds of needs would be created inlife and travel in the year 2030, what kinds of services
would meet those needs, what would be required to realize
those services, and what roles automated driving would
play in that context. The second citizens’ dialogue led toawareness of the following:(1) Future needs for MaaSIn addition to just moving to a destination, travel itself orother aspects can be the purpose of travel. The purposes oftravel will become increasingly more diversified.
Travel can also become an opportunity for socializationand communication with others.
(2) Ideas for addressing needs
Data linkage will be important in service deployment.
One issue is how to share data already owned by individual
firms. In addition, increased added value by packaging, forexample, will possibly spread and expand services.
(3) Toward realization of services
Another issue is how to best show people automated driving services that have not yet been realized.
Moreover, the concept of data collection and utilizationin the minds of the public will change. The introduction ofInternet of Things (IoT) technology may create opportunities for decentralized automated transportation that suits
each region. Another issue is the necessity of a platform toconsolidate travel-related data. Published data will be combined and used in transportation. It will be further processed by the private sector to provide customized services.
Collaboration will also become important. Existing transit guidance is effective in everyday life, but it cannot comprehensively address unexpected situations. Guidance forthe best modes of travel requires data linkage.
2.4. Outcomes achieved in FY2017
The outcomes of the citizens’ dialogues in FY2017 aresummarized below. These indicate the directions of future
research and development.
(1) Consideration of a decentralized automated model inline with regional characteristics
In the dialogues, the needs and issues concerning mobility were approached by members of the general public on
the panel. A wheelchair user said, “I can drive by myself
now, but as I get older I’m worried whether I’ll be able tomove about as freely as I can now.” An exchange student
from France commented that travel in Japan in the middleof the night was inconvenient. Also, while there wereunique perspectives such as “ultimately I’d love for myhouse to move,” it was also pointed out that in a disasterhit area movement that is important to one person might
be considered unreasonable to another. From the individual environments and backgrounds of the public panelists
it became clear once again mobility is perceived as having
diverse needs and issues.
Also, in metropolitan areas, particularly in the center ofcities, public transportation networks are well developed
and, although there are issues such as with commuting, it
is non-urban areas that tend to have the most deep-rooted
concerns in terms of regular transport. This was reflectedin the opinions of the public panelists, and also in many ofthe people who responded to the questionnaire survey andonline posting tool.
Many people also voiced expectations that automated
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Future Needs of Automated Driving Systems, and Analysis and Research of Issues Arising out of such Needs

driving systems could respond to more diverse needs. As
shown in the survey analysis of the first dialogue and theawareness brought about from the second dialogue, opinions were that decentralized automated services would bean effective way of addressing needs that vary regionally. A
nationally unified infrastructure environment should probably be somehow secured, also from a public notion. However, instead of simply expanding a standardized service, adecentralized automated model that matches different
traits and regions, while securing a fixed platform for thewhole of society, will probably be accepted by each regional
area as something sustainable.
(2) The necessity of perceiving automated driving not as a
singular thing but within the common social foundation
In the first dialogue about cities, and the second one
about MaaS, it was understood that better services shouldbe created by considering these services within the common social foundation, in which a variety of players are
involved as an ecosystem. One of the speakers in the second dialogue explained that “there is a need for a platformthat can smoothly and seamlessly match the needs of users
who want to move with the suppliers of mobility, including
trains and buses.” People affected by the heavy snowfall in
January raised the problems of fragmentary information
on public transport operation and that information fails to
respond to true mobility needs. For this reason, there
should be a platform where data is linked and utilized
across different businesses. However, multiple issues need
to be resolved before such a platform can be built andoperated. Also, in order to realize automated driving services, governments and private enterprises must work
together on a common social foundation, fulfilling theirrespective roles as part of an ecosystem, thereby preserving
sustainable socioeconomics.
(3) Offering a platform to convey information
Since the citizens’ dialogues have a mission to bring aboutsocial acceptance, conveying information is an importantfunction of these events.
While more information related to automated driving
systems has become available in recent years, accurate
information will not be conveyed to the public unless it isactively obtained out of interest. This is reflected in the firstsession’s survey results that found around half of the participants were unaware of SIP-adus activities. There is aneed to convey correct information on automated driving
systems to those whose response to these systems is passive, and to avoid overestimations and information that is
untruthful.
The citizens’ dialogues were planned as a forum to bring
together SIP-adus members, intellectuals, and the generalpublic for mutually meaningful communication. Properly
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offering accurate information to the public at these occasions is highly significant in bringing about social acceptance.
There was also the opinion that actualizing services will
also deepen understanding of these systems, further identify system needs, and cause these systems to be seen as
something attractive.
(4) Future possibilities for citizens’ dialogues
The first dialogue attracted a wide general audience byteaming the event with the Tokyo Motor Show. The factthat the dialogue was able to convey information to a large
number of the general public can be considered a significant outcome in this research, whose objective is to fostersocial acceptance.
Conversely, we held the second dialogue with interested
parties only. In terms of the dialogue with the public andthe needs that were pointed out, the benefit of a smallerscale event is that it allows deeper conversations.
A total of 18 panelists from the general public participated this year. The event was held in the dialogue form tofocus on interactive discussions, which resulted in a limited number of panelists per dialogue. Additionally,
because the event was held in Tokyo, it was not possible tosource issues and needs related to non-urban areas.
Another opinion expressed in the two dialogue sessions
was that transport should match the separate issues ofindividual regions. This issue is best understood by thepeople living in these regions. It was concluded that, byholding citizens’ dialogues in regions where interests arediverse, inviting participants from different walks of life,
including the elderly and people engaged in child rearing,perhaps the needs and issues of mobility not understood inTokyo will start to come forth.
2.5. Activities in FY2018
Over the past two years, the citizens’ dialogues haverevealed that in metropolitan areas public transportation
networks are well developed, and that, although there areissues related to commuting, it is non-urban areas that
tend to have the most deep-rooted issues. In these nonurban areas, while the aging of the population and depopulation accelerate the scrapping of public transportation
networks, new means of travel are required to meet theneeds of growing tourism. In addition, a sense of insecurity about mixed traffic still exists. Therefore, automateddriving has to be accurately understood so that it is notoverestimated or distrusted as it spreads socially.
Taking the above matters into account, FY 2018 activities
will pay attention to regions interested in introducing automated vehicles as a public transportation system. In
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administrators of these regions express willingness tointroduce roadside stations and automated driving systems
across hilly and mountainous terrain, a dialogue-form
meeting will be held on a per region basis to discuss the
adoption of automated driving. Furthermore, at the Tokyo
Motor Festival to be held in early October, a symposiumon the safety and security of automated driving will be heldwith the aim of eliminating concerns and correcting/facilitating understanding about automated driving. In this way,efforts to create and increase social acceptance will bemade to help realize automated driving.

3 Conclusion
Having started in FY2016, this project is now in its thirdyear. The project has provided information on automateddriving to various stakeholders and audiences, and has
provided a platform for exchanging, summarizing, andanalyzing the opinions expressed. The project has also provided a platform for extracting the needs, expectations,and concerns about automated driving. Guided by the
project results, efforts have been made to further social
acceptance of these systems. This activity has facilitatedunderstanding of these systems through dialogues with thegeneral public. It has demonstrated that it is a useful meansof creating social acceptance for the incorporation of automated driving into the societal structures of Japan.

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III Next-Generation Urban Transportation

Next Generation Transport

Masayuki Kawamoto (University of Tsukuba)
ABSTRACT: As Japan’s society ages, the percentage of traffic accident fatalities involving elderly people is increasing. For thisreason, it is necessary to develop more flexible, efficient, and useful public transport systems. With this background, the SIPadus Next Generation Transport Working Group decided to develop a partially automated driving system, in addition to therelated services and advanced infrastructure applications for public transport. These services and applications include a precise docking system, an advanced public transportation priority system (PTPS) application, an Advanced Rapid Transit(ART) information center, and a walking assistance service system for vulnerable road users. We set the 2020 Tokyo Olympic
and Paralympic Games as the milestone for the initial implementation of these systems.

Fundamental needs of Public Transport
More than half of traffic accident fatalities are people aged
65 years and older, and about 70% of those are pedestrians
and bicycle users. Furthermore, more than 50% of elderly
traffic accident victims lose their life within 500 metersfrom their home. To reduce the overall number of traffic
accident fatalities, countermeasures are required from thestandpoint of both vehicles and pedestrians.
Advanced driver support systems that assist elderly drivers whose driving skills have decreased with age have been
proposed. However, since these systems include several
controversial points, the best way to help elderly or handicapped people lead active lives through mobility is to promote more flexible transport systems with advanced
accessibility characteristics.
Public transport systems are becoming more and more
important. However, the limitations of public transportation, such as the relatively lower fares that can be earned
from these systems and the necessity of making themavailable to all, make them hard to operate in depopulatedareas. Although a fully automated bus service has thepotential to reduce driver costs, high initial costs makethese systems difficult to deploy. One potential solution is acombination of new technologies with a completely evolutional business model that has yet to be practically realized.

Advanced Rapid Transit (ART)The initial objectives of this project were to improve thequality of urban public transport and to resolve the shortage of highly skilled bus drivers. One of the first working
items was to develop an automated precise docking systemfor buses. This is a control technology that enables a bus tocome alongside a bus stop precisely, without any large gap
between the bus entrance and the edge of the bus stop. This
will help wheelchair users and visual impaired persons to
get on and off the bus without missing their step. Evenskilled drivers need this system because it is a hard task to
perform every time, especially when it is dark. Of course,
this system will also help less experienced bus drivers as
well. Buses using this system were given the name
Advanced Rapid Transit (ART).

Development of Precise Docking System
SIP-adus was requested to implement some of items from
our activity by the 2020 Tokyo Olympic and Paralympic
Games. However, we realized that it would be too difficult
to complete and transfer the specifications to a potential
manufacturer within a year (considering that vehicle man-

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[ III ] Next-Generation Urban Transportation

Next Generation Transport

ufacturers usually require a further three years for theirwork). Although we decided to utilize our basic specifications as much as possible, more reliable sensor hardware
was needed to achieve production level quality, whichmeant that we had to use an existing product already on
the market.
This tentative solution uses a dedicated optical guideway
on the road surface. One concern about this solution was
the possibility that general road users might misunderstand the meaning of this visible guidance line (unlike
some examples in Europe, we planned to put this line on
general roads).
To evaluate this concern, two types of experiments wereconducted. One involved selecting the color of the guidance line to reduce the impact on general road users while
also allowing robust detection by the precise docking system. Another involved proving ground testing with a variety of test subjects to see whether they misunderstand theline. According to these test results, road authorities wereapproved to use this system within limited locations. The
authorities also asked us to develop a new system that does
not use dedicated visible markers instead of this short termsolution.

New System Development for Precise
Docking
Two different approaches were conducted.
One used an invisible guidance line created by infra-red(IR) reflective paint that only IR cameras can detect andcannot be confused by general road users. One issue of thissystem is heat radiation from the road surface during
sunny days in summer. Although some potential countermeasures were identified, we needed to give up thisapproach at the end of the fiscal year for several reasons.
The other approach involved a sensor fusion system thatuses existing roadside objects, including road signs andstructures around the bus stop, to detect the precise location. This system does not require any dedicated infrastructure. As some experiments have found very positive
results, ensuring robustness would be a final key to reachthe goal.

Advanced PTPS
Lack of speed and chronic operational delays are major
reasons why bus transit loses value and service quality.
Advanced public transportation priority systems (PTPS)
can help to resolve these issues. These systems have a wide
range and can cover large numbers of buses simultaneously

around a targeted intersection using radio wave beacons.
This feature provides the new capability of coordinating
which bus of several approaching the same intersection
should be prioritized. Flexible prioritization factors can be
adopted, such as the number of passengers in the bus, orwhether the bus is critically delayed or classified as an
express. These factors and the priority threshold values canbe controlled as required at the ART information center.

ART Information Center
From the standpoint of operational information, buses
have the disadvantage of unstable time delays compared toother transportation, such as airplanes and trains.
In addition to the active involvement of PTPS prioritization, the latest AI and big data technologies can be used toestimate the delay time to individual destinations for eachpassenger more accurately with good probability. These aremajor requirements for the ART information center, as
well as the two data handling requirements described
below.

Congestion Prediction
Rather than vehicle congestion on roads, the ART information center handles congestion caused by the motion
and behavior of pedestrians. The timing and method of
information provision is important for people wishing to
go to an event. Both congestion prediction before the eventand real time feedback of the on-going situation are very
important for information users. This information provision should also carefully consider various psychologicalfactors.

Walking Assistant System
People using public transportation have to walk fromtheir origin, as well as to their final destination. Walking
assistant systems will be very helpful, especially for vulnerable road users such as wheelchair users, the visually
impaired, and elderly people to find the optimum routewith smaller barriers. One challenge of this approach
includes the creation of a standardized open database.
Although many kinds of trials are under way in several
places, it will not be easy to gather the data from these trials into one single information platform. This project aims
to provide an open template for this information.

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III Next-Generation Urban Transportation① Development of Next-Generation Urban Transportation Systems

Survey about Feasibility of Requirements for NextGeneration Urban Transportation System
Tatsuya Hashimoto (Advanced Smart Mobility Co., Ltd.)ABSTRACT: To make it easier for bus users, including wheelchair users, to get on and off the bus, a survey was conducted to help develop an
automated control technology that stops the bus so that the gap between the bus entrance and the bus stop is minimized. Section 1 reports theresults of an investigation about the permissible gap and step between the bus entrance and the bus stop. Sections 2 and 3 describe the performance of recognition technology used for controlling the accurate arrival of the bus at a bus stop. Section 4 describes the performance of thearrival control using the recognition technology described in Section 3.

1

Clarification of Permissible Gap for Accurate Arrival Control of Bus at Bus Stop

1.1. Purpose of investigation
This survey investigated the steps and gaps between a bus
stop and bus entrance, and defined the permissible steps
and gaps that would allow wheelchair users to get on andoff the bus.
1.2. Outline of experiment
As shown in Fig. 1, we made a wooden pedestal frame tosimulate a bus stop, and used the terrace at the entrance ofa building to simulate the entrance of a bus. We used amanual wheelchair and an electric wheelchair for theexperiment. The test subjects were six healthy volunteers(ages: 20s to 60, one female test subject), who participatedin the experiment after learning how to handle both themanual and the electric wheelchair adequately. There wasno assistant, and the experiment was carried out in theingress/egress environments listed in Table 1. Accessibility
was evaluated using the four levels listed in Table 2.
1.3. Results of the experiment
Tables 3 and 4 show the results for the different stepheights using the manual and electric wheelchair, respectively. Tables 5 and 6 show the results for the different gaps.
From the evaluation results shown in Tables 3 to 6, thepermissible step height was set to 30 mm and the permissible gap was set to 60 mm.
Table 3 Step difference evaluation using manual wheelchair

Table 4 Step difference evaluation using electric wheelchair

Fig. 1 Experimental situation

Table 5 Gap evaluation using manual wheelchair

Table 1 Implementation content

Table 2 Evaluation index

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Survey about Feasibility of Requirements for Next-Generation Urban Transportation System

White Line Detection for Accurate Ar-

2 rival Control of Bus to Bus Stop

A white line is laid along the path of the bus to the bus
stop, and accurate arrival control is performed based on
the amount of deviation between the white line as detectedfrom the camera image and the bus.
2.1. White line detection algorithm
White line candidate points are extracted using the whiteline width. The widths of the white lines in the input
images are made constant by converting the images to abird’s-eye view. Therefore, pairs of edge points with a fixedwidth can be extracted as white line candidate points. The
white lines are detected by applying Hough transformation
to the white line candidate points. Figure 2 shows example
images from each processing step. The deviation amount iscalculated by converting the image coordinates to the bus
body coordinates.

Fig. 4 Evaluation results of deviation detection accuracy

between the white line and a point 3 meters ahead of thebus. In Figure 4, the vertical axis indicates the detection
result, and the horizontal axis indicates the actually measured value. White lines were detectable with an error of±15 mm up to a deviation amount of 0 to 1350 mm.

Curbstone Detection for Accurate Ar-

3 rival Control of Bus to Bus Stop

The amount of deviation between the curbstone and bus
is detected from a point cloud obtained using LiDAR, andaccurate arrival control is performed based on the detected
deviation amount.
3.1. Curbstone detection algorithm
The distance deviation d and angular deviation θ between
the curbstone and bus as shown in Figure 5 are detectedfrom the point cloud obtained by LiDAR. The normal of

Fig. 2 Image processing example

2.2. Evaluation of white line detection
Figure 3 shows an example result of white line detection,
and Figure 4 shows the evaluation results of the deviation
amount, which indicates the detection accuracy. The
amount of deviation was defined as the lateral distanceFig. 3 Example of white line detection result

Fig. 5 Relationship diagram of required deviation amountFig. 6 Flow of curb detection process
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Survey about Feasibility of Requirements for Next-Generation Urban Transportation Systemeach point is calculated, and points on the solid surface as
indicated by the red points in Figure 6 are extracted as the
curb candidate point cloud. The required amount of deviation is calculated by approximating the curbstone candidate point cloud with a straight line.

between the bus entrance and the curb at the bus stop was
set to 4 cm ± 2 cm. As shown by the experimental resultsin Figure 9, an evaluation was performed 10 times andconfirmed that the bus arrival control can be performed
with the target accuracy.

3.2. Evaluation of curbstone detection
As shown in Figure 7, LiDAR was attached to the front of
the bus, and the accuracy of the detected deviation amount
was evaluated using the arranged blocks as a simulated
curbstone. Table 7 shows the detection results of the distance deviation amount. The deviation was detected withan error of 0.01 m or less. Table 8 shows the detection
results of the angle deviation amount. The angle deviation
was detected with an error of 0.7 degrees or less.

Fig. 8 system configuration

Fig. 9 Results of bus arrival control experimentFig. 7 Evaluation scenario
Table 7 Detection results of distance deviation

Reference(1) T. Fukao et al.: Preceding Vehicle Following Based on
Path Following Control for Platooning, Preprints of
IFAC AAC (2013)
About the author
1) Tatsuya Hashimoto, Environmental Recognition Technology Development, Engineering Div., Advanced
Smart Mobility Co., Ltd.

Table 8 Detection results of angle deviation

Accurate Arrival Control of Bus to Bus

4 Stop Based on Curbstone Detection

The arrival control accuracy of a bus to a bus stop basedon curb detection using LiDAR was evaluated. Figure 8
shows the system configuration based on path-following
control (1). From the results of the wheelchair ingress/
egress experiment in Section 1, the target of the gap
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III Next-Generation Urban Transportation① Development of Next-Generation Urban Transportation Systems

Development of Sensing and Control Technology for
Precise Docking of Advanced Rapid Transit system
Sadahiro Kawahara (JTEKT Corporation)
ABSTRACT: This research is part of the Strategic Innovation Promotion Program(SIP)/Autonomous driving system project and examines theissues of the so-called Advanced Rapid Transit (ART) automated driving system, which is considered to be a promising next-generationtransport system for urban areas. This article describes the docking control project, which aims to clarify basic control issues related to sensing, steering linkage, integrated control with the brakes, and so on. These items are being researched and development to realize docking
maneuvering technology with excellent functionality.

1 Overview
A precise docking control system, i.e., an automated precision docking control strategy that aims to minimize thegap between the entrance of a bus and a bus stop, is an
important part of realizing a high-quality advanced Rapid
Transit (ART) system. Automated precise docking control
should contribute to the smooth and safe embarkment anddisembarkment of passengers, including the disabled andparents with strollers. This system must be robust in various environments and achieve the best route for docking
even in severe conditions. It should also achieve smoothbraking and steering control to prevent accidents on thebus and improve passenger comfort. Cooperative docking
control with the bus driver should also contribute toenhanced safety.

2 Issues for Precise Docking Control
Steering systems for large vehicles like commercial buses
or trucks usually have a complicated structure. The transfer
characteristics of the steering system may be asymmetricor non-linear because of long linkages or free play atmechanical connection points. This is one of the difficulties that must be overcome to realize a precise docking
control system. Since this issue is difficult to solve throughmechanical improvements, the addition of a phase advance
control algorithm that compensates for the asymmetric or
non-linear steering system characteristics may be an effective countermeasure.
This section described the measurement of steering system characteristics such as phase delay. Factors related toprecise docking ability were investigated based on measured steering and vehicle behavior, and a concept for precise docking control was identified.

2.1. Test mule and system structure
2.1.1. Test mule
A large bus was used as a test mule. Figure 1 shows theappearance of the test mule and Table 1 lists its specifications. The axle load was measured with the driver present.

Fig. 1 Appearance of test mule (large bus)
Table 1 Specifications of test mule (large bus)

2.1.2. Precise docking control system
Figure 2 shows the structure of the precise docking control system. Calculation for the docking control is performed using an integrated control ECU (dSPACE GmbH:
MicroAutoBox II(*a)). Distance to the lane separation line issupplied by a camera (Mobileye, Inc.: ME560(*b)) and RTKGPS system (GeneSys Elektronik GmbH: ADMA-GECO+(*c)). Vehicle yaw angle, yaw rate, and acceleration
values are supplied by a gyro sensor (MEMSIC, Inc.:
NAV440(*d)) mounted on the floor of the passenger compartment close to center of vehicle gravity. The vehicle
velocity and running distance are calculated from wheel
speed pulses.
The steering angle and deceleration commands calculatedby the integrated control ECU are sent to the steering actuator ECU and braking control ECU.
(*a) Trademark of dSPACE GmbH(*b) Trademark of Mobileye, Inc.
(*c) Trademark of GeneSys Elektronik GmbH(*d) Trademark of MEMSIC, Inc.

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Fig. 2 Control system

Calculation for steering angle commands is based on thepath following control method.(1) The basic concept of thismethod involves following an assumed trajectory (target
trajectory).
The target angle δc is calculated by equation (1).

V : velocity, M : vehicle weight, β : slip angle, γ : yaw rate,lf : distance from gravity center to front axle,
lr : distance from gravity center to rear axle,
J : yaw moment of inertia, Kf : cornering power of front tire
Kr : cornering power of rear tiree2 : deviation of x direction,
e3 : deviation of yaw angle,
ωr : yaw rate of reference vehicle,
Figure 3 shows a block diagram of the control system. The
target trajectory is installed as a lookup table to define thelateral distance between the vehicle and the lane separationline against the travel distance. The lateral gap e2 between
the measured and target value is estimated. The heading
angle difference e3 is estimated from the heading angle output of the camera or the RTK-GPS system.

Fig. 4 Overview of steering system

2.1.4. Brake system
An electronically controlled brake system (WABCO
Holdings Inc.: EBS (*e)) controls the braking force alongwith signals from other controllers. Figure 5 shows an
overview of the braking control system.
The braking control ECU receives deceleration commands from the integrated controller, calculates the airpressure for each wheel, and sends commands to the braking control unit.
(*e) Trademark of WABCO Holding Inc.

Fig. 5 Overview of braking control system

Table 2 compares the target and actual deceleration value.
The characteristics are not linear and the variation is not
small.
Table 2 Comparison of average target and actual deceleration (N: 5)

Fig. 3 Control system block diagram

2.1.3. Steering system
The steering system of the test mule consists of hydraulicpower steering integrated with a ball screw type gear box.
Figure 4 shows an overview of the steering system. The
actuator for steering angle control is mounted on the steering column. This actuator receives commands from theintegrated controller and controls the steering angle using
the PID control algorithm. The lateral displacement of thetie rod is measured as an alternative value to the tire angle.

136

To avoid the effect of these characteristics, the following
control method was introduced to achieve a precise longitudinal stopping position. The target deceleration is calculated from the travel distance S, the assumed stopping
position St, and velocity v, using equation (2).

a : deceleration, S : travel distance,
St : assumed stopping position, v : velocity
When the difference between the actual and target deceleration, or the deceleration deviation is large, the target
deceleration is compensated continuously to adapt to thesituation at that time using this calculation. This method
also helps to reduce jerk. The target deceleration is also
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Development of Sensing and Control Technology for Precise Docking of Advanced Rapid Transit system

between the actual and target deceleration following equation (3).

tionship between the steering and tire angles in a sine wave
command. The tire angle response is delayed from thesteering angle. The amount of free play is estimated as asteering angle of 11 degrees.

a' : Corrected deceleration, x : Coefficient for correction
The correction coefficient x is estimated considering thedeceleration at that timing, based on the results shown in
Table 2.
2.2. Improvement of precise docking system algorithm
2.2.1. Optimization of control gain
In the current control method, gain k2 for compensating
the lateral distance is constant in the docking control andother modes. Figure 6 shows an example of lateral distanceresults using constant control gain k2 fitted for a straightline trajectory. This result indicates that the error increases
during docking control after a distance of 180 meters.

Fig. 6 Vehicle movement under control gain fitted for straight trajectory

A control method was introduced that selects one of twovalues for gain k2 based on whether the bus is traveling in astraight line or performing a docking procedure. Figure 7
shows the example of the results using this gain selection
method. The difference between the target and actual values is reduced, demonstrating that this is an effective control method for achieving the planned trajectory.

Fig. 8 Relationship between steering and tire angles

The steering angle command is estimated considering
these characteristics created by mechanical free play. Whenthe angle command direction is reversed, the free playvalue is added to the angle command. Figure 9 shows thedifference in trajectory with and without free play compensation. Lateral movement is reduced especially on
straight trajectories.

(a) Without compensation of steering free play(b) With compensation of steering free play
Fig. 9 Improvement by compensation of steering free play

2.2.5 Braking control
The brake system is controlled based on equation (2). The
deceleration command is 0.5m/s2 in the initial braking area

Fig. 7 Vehicle movement under separate control gains
fitted for straight or docking trajectories

2.2.2. Compensation for free play of steering linkage
Mechanical free play exists in normal steering systems,
and the free play in the steering system of large vehicles,
such as this test mule, is larger than in normal passenger
cars. This large free play has adverse effects such as delayon the control results. A compensation control method
was studied to improve this issue. Figure 8 shows the rela-Fig. 10 Vehicle movement under braking control
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Development of Sensing and Control Technology for Precise Docking of Advanced Rapid Transit system

and 0.2m/s2 at the stopping position. This method contributes to reducing the braking distance and accidental deceleration deviation at the bus stop. Figure 10 shows anexample of vehicle behavior with braking control.

Study of Issues Toward Minimization of

3 Infrastructure

A fundamental study into multiple sensing methods andcooperation between sensors is in progress with the aim ofrealizing more precise docking.
3.1. Characteristics of RTK-GPS and front camera
To build a better sensing device selection method, thecharacteristics of the RTK-GPS and front camera systemwere measured. Figure 11 indicates the lateral position difference between the RTK-GPS and front camera. Thisvalue relates to the actual distance. The value from thecamera is delayed compared to the value from the RTKGPS (0.3sec).

Fig. 11 Distance difference between camera and RTK-GPS

specific guidance lines or magnetic markers on the road,side cameras for boundary and position detection ofpainted lines or curbstones to improve accuracy, and Lidarshould be considered.
Multiple sensing methods are preferable to achieve sufficient accuracy under every condition, and a better sensing
method should be selected or a cooperative sensing procedure should be adopted.

Compatibility of Driver Operation and

4 Automated Control

In the case of a level 1, 2, or 3 automated system, thedriver is required to avoid pedestrians and other objects
during the docking procedure. Since one of the concerns
in this procedure is a delay in the driver’s avoidance operation, sharing of responsibility by the driver and automateddriving system is preferred. In this case, the driver should
always be aware of the driving operation.
This research proposed and evaluated the effect of a prototype system operated based on driver intention (measured using an input force from the driver). This system
proposes that the driver should always be touching thesteering wheel. Driver steering characteristics using the
proposed system were evaluated and analyzed using a driving simulator.
The adoption of a shared control method in the automated docking control system should be studied based on
this result.

5 Conclusion
Fig. 12 Time difference between camera and RTK-GPS

The lateral movement is at a similar level as shown in Fig.
13.

Fig. 13 Vehicle movement using RTK-GPS data

3.2. Future study of sensing for precise docking control
The sensing resolution with RTK-GPS or front camera
images is insufficient to realize further accurate control,
and additional infrastructure is necessary. For example,
138

The effect of steering system delay with respect to vehicle
behavior was investigated as part of the study into actuators and controls for an ART system.
The prototype system described above was evaluated, andit was found that the proposed system concept of sharedcontrol is effective.
However, since the targets for this system are difficult toachieve with current buses, items to be developed toachieve integrated steering and braking control are being
investigated.
Current position recognition capabilities are not sufficientto achieve the targets under all environments. Difficult to
detect disturbances must also be considered, for example
the impossibility of detecting objects in the dead areas ofvehicle-mounted sensors.
Further research and studies to solve these issues are
required in the future.

[ III ] Next-Generation Urban Transportation① Development of Next-Generation Urban Transportation Systems

Development of Sensing and Control Technology for Precise Docking of Advanced Rapid Transit system

References(1) Jun Yoshida, Toshiyuki SUGIMACHI, Takanori
FUKAO.: Autonomous Driving of a Truck Based on
Path Following Control. Transactions of the Japan
Society of Mechanical Engineers Series C., Vol.77,
No.783, p. 4125-4135 (2011).
(2) Sadahiro Kawahara, JTEKT Corporation, BR Next
Generation Transpotation System Promoting Section,
General manager, Vehicle dynamics and Humanmachine interface

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139

III Next-Generation Urban Transportation① Development of Next-Generation Urban Transportation Systems

Survey about Precise Docking of Next-Generation
Transportation Systems for Strategic Innovation Program (SIP) – Automated Driving System
Ryohta Wakai (The Institute of Behavioral Sciences)
ABSTRACT: Precise docking at bus stops is a key technical requirement for achieving the practical implementation of the next-generation
Advanced Rapid Transit (ART) transportation system. Among the control methods for precise docking that are being studied in this researchand development project, the road surface guidance lines described in this article are regarded as an effective means of reducing control error.
However, some concerns remain, such as their use on roads not used by buses, on which they may cause needless driver confusion. This study
investigated guidance lines for precise docking that are readable by cameras mounted on ART vehicles, and conducted several verification
experiments to confirm their safety. The effect of this precise docking system on the driving behavior of ordinary vehicles and the recognition
rate of these guidance lines were investigated in experiments held at a test course.

1

Introduction

1.1. Purpose of the Survey
This project studied guidance lines for precise docking
that are readable by cameras mounted on Advanced Rapid
Transit (ART) vehicles, and several verification experiments to confirm their safety were conducted. Throughverification in the laboratory and on a test course, weinvestigated how well the vehicle-mounted cameras recognized the guidance lines, the effect of the guidance lines on
the driving behavior of ordinary vehicles, as well as therecognition rate and control error of a precise docking system.
1.2. Study Flow
This study was conducted in the following order:

and that can be recognized by vehicle-mounted cameras.
Specifically, we studied guidance line design proposals taking the following into account.
1) Colors used for road markings such as white, yellow,
red, and blue should not be used.
2) To allow the camera to recognize the guidance lines, use
a guidance line color with a high contrast ratio with thecolor of the underlying pavement.
3) A proposal that includes putting markings on the roadto indicate bus-only use should be considered.
4) We also considered a design that changed the shape ofthe guidance lines.
* The guidance lines are double-dotted lines that can berecognized by the camera to be used.
Based on the above, the following six proposals were usedas the design proposals.

2. Investigation of guidance line design proposals■ Investigation of the guidance line design proposals■ Study on the concept of verification

5. Summary of results■ Selection of a guidance line design proposals■ Identification of issues to be mvestigatedFig. 1 Study Flow

Investigation of Guidance Line Design

2 Proposals

2.1. Guidance Line Design Candidates
When a vehicle passes over guidance lines installed on
the road, drivers are inclined to turn the steering wheel
toward the roadside or step on the brakes. Therefore, wemust consider design proposals for guidance lines that
ordinary drivers will not confuse with official traffic lines
140

Fig. 2 Guidance Line Design Proposals

2.3. Narrowing Down the Guidance Line Design Proposals
Finally, we selected one proposal from these six proposals
based on the results of verification performed in the laboratory and on the test course. The criteria for narrowing
down the proposals were as follows, based on the results ofpreliminary consultations with relevant organizations.

[ III ] Next-Generation Urban Transportation① Development of Next-Generation Urban Transportation Systems

Survey about Precise Docking of Next-Generation Transportation Systems for Strategic Innovation Program (SIP) – Automated Driving System
Table 1 Narrowing Down the Guidance Line Design Proposals

3 Lab-based Verification
3.1. Overview of Verification
We verified the guidance lines in the laboratory by measuring the image contrast ratio of camera recognition forthe different guidance line colors under various lighting
conditions, etc. Since the experiment results showed thatthe recognition performance of the system was satisfactory,
the guidance line color and other specifications wereselected.
In the laboratory experiment, test pieces consisting of different guidance line colors and underlying pavements were
exposed to light simulating daytime sunlight and nighttime street lights, to measure the image contrast ratio ofcamera recognition. The conditions used in the verification
experiment in the laboratory to confirm the system recognition performance were as follows.
Table 2 Conditions of Verification Experiment in the Laboratory

3.2. Overview of Verification Results
The following results were obtained from the lab verification:
1) Guidance line colors
While both green B and A had an image contrast ratio
close to white during the day, green B had a higher measured image contrast than A. Therefore, green B was usedas the candidate for green.

2) Reflecting material
The contrast ratio of the glass beads and AWT (a highperformance product) was almost equal, and the “BrightGrip” product had a slightly lower contrast ratio. Since
AWT costs 1.5 times as much as (general-purpose) glass
beads, a relatively inexpensive general-purpose material
can be used.
3) Base material
The contrast ratio of red iron oxide is approximately 0.1
lower than that of asphalt. Even white guidance lines cannot be recognized by the system on top of heat-insulating
pavement.
4) At night
Even green B has a contrast ratio which is approximately
0.1–0.2 lower than that of white guidance lines. The
required luminosity is 60 lux or more.

Fig. 4 Basic Performance Evaluation: Morning / Sun /Evening + Dry / Wet

4 Test Course-Based Verification
4.1. Overview of Verification Experiments
4.1.1. Overview of Verification Experiments
The effect of the guidance lines on ordinary drivers wasstudied on the test course. Test drivers drove on a test
course marked with guidance lines while their driving
behavior was monitored, and driver awareness during
driving was then evaluated by a form-based survey.
The verification experiments were carried out for threedays (Sunday, Feb. 11 to Tuesday, Feb. 13, 2018). We used
the V2X Urban Proving Ground Course in Tsukuba, which
is owned by the Japan Automobile Research Institute(JARI) as the test course. A total of 32 test drivers participated in the experiments, considering the balance between
genders and age groups.
Based on the results of prior investigation, we selected
proposal 1 (reference) (guidance line 2: white), proposal 2(guidance line 1: green), proposal 4 (guidance line 3: “ForBuses” marking) as the guidance line patterns used in theverification experiments.
A schematic diagram of the test course is shown below:

SIP-adus: Project Reports, 2014-2018141[ III ] Next-Generation Urban Transportation① Development of Next-Generation Urban Transportation Systems

Survey about Precise Docking of Next-Generation Transportation Systems for Strategic Innovation Program (SIP) – Automated Driving System(Green)Guidance line (1)No guidance line

(White)Guidance line (2)Guidance line (3)(Green, annotated)

Fig. 5 Test Course for Driving Experiments

4.1.2. Details of Verification Experiments
In the verification experiments, each test driver drove
through four sections: three with guidance lines and one
without. We measured the traveling speed and position(lateral direction) to check the operating situation of thebrakes and steering wheel while driving along the guidancelines, and observed the physical effects. Here, we used GPS
data at one-second intervals to measure the traveling speed
and recorded the traveling position by taking video images
showing the distance to the roadside line using a camera
mounted on the vehicle. We also conducted a form-based
survey immediately after the test drivers drive over theguidance lines for the first time and after passing over all
the guidance lines to ask them how they reacted when they
noticed the guidance lines. Because drivers are especiallystrongly affected when seeing the guidance lines for the
first time, the test drivers were grouped into three groups
and the driving routes were set so that each group experienced a different type of guidance line first.
4.2. Verification of Effect on Drivers Passing along
Guidance Lines
4.2.1. Effect on Braking
The effect on braking when passing through the guidanceline section was verified. During the first experience (firstsight) of the guidance lines, the average traveling speed of
all the test drivers and the standard deviation variance did
not differ depending on the type of the guidance lines.
Although the behavior of individual test drivers showed no
rapid deceleration (defined as 0.3 G as a value indicating asudden surprise), some test drivers released the accelerator
to reduce speed a little.
4.2.2. Effect on Steering
The effect on steering when driving through the guidanceline section was examined. Although no major differenceswere observed depending on the type of the guidance line,
there was an overall tendency to drift to the right. No
major differences were observed between the areas withand without guidance lines. The behavior of individual test
drivers showed a lateral change of approximately 20 cm foreach guidance line.
For the guidance lines 2 (white) and 3 (text marking), we
checked the driving behavior of three test drivers with142

large steering wheel operation. One driver drove to theright side within the range of the lane in the case of guidance line 2 (white), while the other two misunderstoodthat guidance line 3 (text marking) was a bus-only lane,and drove along the guidance line section from right toleft.
4.3. Verification on Effect of Guidance Lines on Driver
Awareness
The main results obtained from the form-based survey
about the guidance lines after the first experience and after
passing over all the guidance lines were as follows:
Table 4 Main Answers to Form-based Survey

4.3. Verification Test Results on Test Course
Based on the results of both the lab-based and the test
course-based verification, we decided to employ guidanceline 1 (green).
Table 5 Results of Guidance Line Selection*We evaluated construction practicality based on the results of interviews
with suppliers who mark road surfaces.

5 Conclusion
This study verified the color and other elements used for[ III ] Next-Generation Urban Transportation① Development of Next-Generation Urban Transportation Systems

Survey about Precise Docking of Next-Generation Transportation Systems for Strategic Innovation Program (SIP) – Automated Driving System
guidance lines for precise docking from the viewpoints oflegal compliance, system recognition, construction practicality, and likelihood of confusion with official traffic lines.
As a result, we decided to employ guidance line 1 (green B).
However, in the real-world implementation phase, it will
be important to let various users on public roads understand that these guidance lines are for buses, together withthe benefits realized by precise docking in order to increasesafety. It will be necessary to convey the importance of theguidance lines and precise docking among the generalpublic, verify the recognition performance of the guidancelines under conditions other than the test environment (onpublic roads, at night, etc.), and investigate how to install,
maintain and manage the guidance lines in the real-world
implementation phase.

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143III Next-Generation Urban Transportation② Sophisticated Local Transportation Management

Examination of Enhancement of Bus Priority Control
in Next-Generation Urban Transport Systems
Toru Mabuchi (UTMS Society of Japan)
ABSTRACT: Next-generation urban transport systems are being developed in order to ensure safe and smooth traffic around the venues ofthe 2020 Tokyo Olympic and Paralympic Games, and to secure means of transportation for the elderly in Japan’s super-aging society. The
UTMS Society of Japan developed a system that performs bus priority control using vehicle-to-infrastructure communication via the 700
MHz radio frequency and conducted verification through field operational tests. The equipment specifications and communications standards established in this development will be adopted as standard specifications to promote future the introduction and dissemination of thesystem.

1

Purpose and History of the Development

1.1. Purpose of PTPS enhancement
The Universal Traffic Management System promoted by
the National Police Agency of Japan aims to realize a safe,
comfortable, and environmentally friendly traffic environment through the provision of real-time traffic information
to drivers using two-way communication with individualvehicles via infrared beacons, and through the active management of traffic flow. As a sub-system of UTMS, Public
Transportation Priority Systems (PTPS) have already been
put into practical use.
In recent years, driving safety support systems (DSSS)
using vehicle-to-infrastructure communication via the 700
MHz radio frequency band have been practically implemented. Because the roadside radio equipment of thesesystems is installed near intersections and provides a communication range of several hundred meters, this radio
equipment also has the potential to be utilized for PTPS. In
order to resolve various issues with the existing PTPS, the
UTMS Society of Japan is working to enhance these systems using vehicle-to-infrastructure communication via
the 700 MHz band.
1.2. Background of the development
In fiscal 2014, studies and research were carried out,
including surveys about the current situation of publicroad transport, as well as current situation surveys and literature surveys and analyses about bus priority control.
In fiscal 2015, requirements for the enhancement of PTPS
were identified and a basic design was developed.
In fiscal 2016, a central computer was developed byequipment manufacturers in accordance with the basic
design and a model system was set up in Tokyo by the
Tokyo Metropolitan Police Department. An on-board unit
supporting the system was also developed by an automotive manufacturer. In parallel, design verification was con144

ducted through simulations.
In fiscal 2017, using the above-mentioned model system,
effectiveness verification was performed in a real-world
environment involving vehicles.

2 Details of PTPS Enhancement
2.1. System design policies
This system was designed to enhance PTPS, which are
currently realized by infrared beacons, through the utilization of the 700 MHz radio frequency band. The enhanced
system was designed to satisfy the main PTPS upgrade
requirements shown in Table 1. These enhancement
requirements were compiled and determined based on the
results of questionnaire surveys performed at each prefectural traffic control center, as well as the results of interviews bus companies, with consideration also given to theviability of the system.
Table 1 Main requirements for enhanced PTPS

2.2. Bus priority control function(1) Purpose
A PTPS that uses infrared beacons estimates a bus arrival
time at an intersection using bus passage information collected by infrared beacons installed upstream of the intersection. However, because the arrival time is predictedusing a design speed containing a certain margin, one issue
is the wasted green traffic signal time resulting from the[ III ] Next-Generation Urban Transportation② Sophisticated Local Transportation Management

Examination of Enhancement of Bus Priority Control in Next-Generation Urban Transport Systems

difference with the actual speed. To resolve this issue, theperformance of the priority control was improved.
(2) Functional overview
Figure 1 shows a conceptual diagram of the system andFig. 2 shows an example of green traffic signal extension(“green extension”) control operation. The ITS roadsideradio equipment receives real-time vehicle information(described below as “vehicle-to-vehicle communication
information”) transmitted over the 700 MHz radio bandfrom a bus equipped with an on-board unit supporting thesystem and traveling toward the control target intersection.
Based on the vehicle location data included in the vehicleto-vehicle communication information received from thebus, the system determines the passage of the bus throughthe first bus passage check point (passage determination
point), estimates the arrival time of the bus at the intersection using a design speed close to the actual speed, decides
whether to provide priority control at the intersection, as
well as the operations to be provided (green extension/red
truncation), and then performs signal control. The systemfurther determines the passage of the bus through the second passage check point located around 50 m upstream ofthe stop line, estimates the bus arrival timing at the stop
line using a design speed with an adequate margin, andprovides a further green extension as needed.

Fig. 1 System image

Fig. 2 Example of green extension control(3) System configuration
Figure 3 shows the system configuration. The bus ID, priority request information indicating the presence of a priority request, and vehicle-to-vehicle communication
information are sent from the on-board unit to the ITS

wireless roadside unit. Service information, such as the bus
passage checkpoint loci, maximum green extension time,
maximum red truncation time, and the like are sent fromthe ITS wireless roadside unit to the on-board unit.
The ITS wireless roadside unit determines the passage ofthe bus through the bus passage check point and sends thepriority request information to the central computer. The
central computer sends signal light information to a signal
controller based on the timing of the priority request information, by which signal control is performed.

Vehicle (on-board unit)
Fig. 3 System configuration

3 Effectiveness Verification
3.1. Effectiveness verification by simulations(1) Outline of effectiveness verification
The purpose of the effectiveness verification by simulation was to confirm the system’s effectiveness in advanceand in theory prior to verification with a model system,
and also to confirm effectiveness under various conditions
that cannot be tested with a model system. To this end,
verification was carried out using a microscopic traffic flow
simulator on a virtual route of about 3.5 km with ten intersections and on a model route with the target intersection
in front of Tokyo Wangan Police Station.
(2) Main verification items and results of verification on
virtual route
The verification of basic functions in dedicated bus lanes
confirmed that the enhanced PTPS can reduce bus travel
times by 4% or more compared to the existing PTPS. Even
without dedicated bus lanes, the enhanced PTPS was able
to reduce bus travel times by about 3.9% to 5.8% comparedto the existing PTPS.
The analysis of maximum green extension times confirmed that bus travel times can be further reduced byabout 8% by changing maximum green extension times
from 10 to 20 seconds. However, since this increases travel
times on minor roads, a decision needs to be made foreach intersection on which the system is to be introduced.
Analysis of the negative effects of GNSS positioning
errors confirmed that, when positioning errors are within
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Examination of Enhancement of Bus Priority Control in Next-Generation Urban Transport Systems

the range of +/- 30 m, the effect on the reduction in travel
time is 0.5% or lower.
(3) Main verification items and results of verification onmodel route
The verification of basic functions confirmed that theenhanced PTPS can reduce bus travel times by about 2%
compared to the existing PTPS. This value is almost identical to the expected value calculated in theory. Therefore,
the priority control was judged to be operating as designed.
To validate the number of bus passage check points, asimulated comparison was made between cases with two
and three points, the results of which confirmed that bus
travel times can be very slightly reduced by 0.1% whenthree points were provided. However, considering that theexisting signal controllers cannot be used for realizing sucha system and because of the low cost effectiveness of providing three points, a system with two check points was
judged optimal.
3.2. Effectiveness verification using model system(1) Outline of effectiveness verification
The purpose of the effectiveness verification using amodel system was to verify system operation under various real environmental conditions, to verify design methods, and to identify the influence of parameters on theeffectiveness of the system, thereby obtaining findings
capable of maximizing the effects of such parameters witha view to implementing the system in other areas in the
future.
The verification was conducted using the model systeminstalled in fiscal 2016 by the Tokyo Metropolitan Police
Department at the intersection in front of Tokyo Wangan
Police Station and test vehicles (two buses, one light truck
and three passenger cars) prepared by automotive manufacturers.

https://maps.gsi.go.jp/
Fig. 4 Test site (Tokyo Odaiba area)(2) Main verification items and results of verification
Before implementing the bus priority control, the rangeof ITS wireless communication using the 700 MHz bandand the GNSS accuracy were checked. Some routes are146

covered by radio communication with a range of about 300
m or more, and others with a range of about 200 m. Therefore, confirmation is necessary for each area where the system is introduced. GNSS accuracy errors are a few metersor less in most areas, with relatively large errors observed
on route 3, which was assumed to be due to the effects ofbuildings beside the road.
In the verification of bus priority control operation, threeoperation cases (with green extension control, without red
truncation control, and without control) were confirmed
to be operating normally based on the logs in the on-board
units and central computer. In particular, it was confirmedthat green extension times were extended appropriately atbus passage check point 2 (see “Figure 5 Green extension
control results”).
The effectiveness evaluation at the intersection downstream of the bus stop confirmed that effective priority
control can be achieved by setting the first bus passage
check point further upstream, although it is difficult topredict the bus acceleration status.

Fig. 5 Result of the green extension control

4 Conclusion and Future Issues
Through the real-world verification using ITS wirelessroadside units, on-board units, and a central computer
supporting the enhanced PTPS, it was confirmed that bus
priority control using the 700 MHz ITS wireless communication is achievable.
The operation verification required time and effort
because logs had to be collected from on-board units andthe central computer, respectively for analysis. It would bedesirable to realize a function that can readily determine
the success, failure, and effectiveness of bus priority control. Also, when applying the system to intersections wherelarge GNSS errors are detected, it is recommended that theaccuracy of GNSS be confirmed in advance and the occurrence of wasteful green traffic signal time be verified after
applying the system.

III Next-Generation Urban Transportation② Sophisticated Local Transportation Management

A Study on the Speediness and Safety of Advanced
Rapid Transit
Daisuke Oshima, Akira Mitsuyasu, Takane Imagawa (Pacific Consultants Co., Ltd.)ABSTRACT: This study on the speediness and safety of the Advanced Rapid Transit (ART) system involved the building of on-board units fora Public Transportation Priority System (PTPS), and the evaluation of the effectiveness of the resulting Advanced PTPS. Specifically, the basic
requirements were designed for the advanced PTPS on-board units, which communicate with roadside units that control traffic signal timings(i.e., to extend the green light interval or truncate the red, based on bus locations sent by the on-board units). These units also have a priority
arbitration function, which grants priority over other buses when a bus meets the criteria for priority, as well as an HMI function, which notifies the priority status information and supports passage through the intersection. The extent to which Advanced PTPS improves timelinessand speediness, and its effects on general traffic were evaluated. It was clarified that Advanced PTPS and priority arbitration can improve bothspeediness and timeliness.

1 Overview
A Public Transportation Priority System (PTPS) is ameans for improving the timeliness and speediness ofbuses running among general traffic. PTPS ensures priority
for public transportation by means of priority signal control, which controls the timing of traffic signals to allow
public transportation vehicles to pass through signalizedintersections without stopping, and includes the introduction of priority roads for public transportation. PTPS has
been introduced throughout Japan. A vehicle-to-infrastructure communication system using optical beacons,
which is conventionally used in PTPS, communicates once
before the vehicle enters an intersection. In contrast, theNational Police Agency, which oversees traffic managers
across Japan, considered that more flexible priority signal
control could be realized by utilizing the 760 MHz bandthat had been allocated to ITS following the digitalizationof terrestrial television broadcasting, and the Agency has
been examining the development of roadside units (roadside ITS radio sets, hereinafter referred to as “roadsideunits”) for that purpose. Therefore, the following researchand development has been conducted in the current project from FY2015 to FY2018:(1) Examination of the functional requirements for onboard units that communicate with Advanced PTPS roadside units and the building of prototypes(2) Simulated evaluation of the extent to which Advanced
PTPS improves timeliness and speediness and how it
affects general traffic(3) Evaluation of the effectiveness and influence ofAdvanced PTPS through a field operational test(4) Research into the introduction requirements, issues,and effectiveness of the nationwide deployment ofAdvanced PTPS

2

Examination of Functional Requirements
for On-Board Units that Communicate
with Advanced PTPS Roadside Units andBuilding of Prototypes

2.1. Functions of Advanced PTPS
A conventional PTPS detects the passage of target vehicles using an optical beacon installed at only one point
upstream of an intersection, predicts the timing of arrivalof the vehicle at the intersection, and extends the green
signal or truncates the red. Meanwhile, by using 760 MHz
band radio communication, Advanced PTPS can (1)improve communication performance over obstacles anddistance, (2) allow continuous communication, (3) allow
interactive communication, and (4) cover buses on all
approach routes with one beacon. These measures will help
to overcome the restrictions on installation locations for
PTPS roadside units, reduce the introduction cost, react tovehicle speed changes in real time, inform the bus driver ofthe status of priority control on the relevant approach
route, and detect the approach of multiple buses and determine which bus is to be prioritized. This research aimed todevelop on-board units with the following three functions.
The first is a function for communicating with the roadside units. A roadside unit has two determination points(the first and second virtual beacon positions) upstream of
an intersection to detect the passage of a bus, predicts thetime of arrival of the bus, which is making a priority
request, at the intersection at each point, and controls thesignal timing (extends the green light interval and truncates the red) in two stages accordingly. Meanwhile, theon-board unit transmits position information, the validityof the priority request, and other information to the roadside unit so that the unit can detect the passage of the bus.
The second function is the HMI, which allows the onboard unit to identify the priority control status by receiving downlink information (e.g., DSSS signal information)
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A Study on the Speediness and Safety of Advanced Rapid Transit

research and the like. Determining the threshold based on
the operation status of multiple buses was accomplished bymultiplying the highest priority index by a certain coefficient. Among the priority indices, “crowdedness” wasdefined based on an approximate classification of the number of passengers estimated by the ART Information Center function using in-vehicle cameras.
Fig. 1 Image of multistage control in Advanced PTPS

from the roadside unit so that the unit can assist passage
through the intersection (acceleration/deceleration support), while providing the driver with priority request
information. Figure 2 shows the intersection passage support on the screen of the HMI under development. If thesignal turns yellow when a bus enters a signalized intersection and the bus brakes hard, it is dangerous since it may
cause passengers in the bus to fall over. Therefore, minimizing the frequency of buses stopping in signalized intersections, while providing drivers with intersection passage
support information, contributes to safer bus operation.
The third function is priority arbitration. For example, if
several buses approach an intersection at the same time,
this function transmits “Priority request: Valid” to theroadside unit only for a bus that urgently requires priority
due to a significant delay or the like, and “Priority request:
Invalid” for other buses of lower priority, so that the bus inurgent need is prioritized. The validity of the priority
request is determined by the on-board unit by comparing
the vehicle status index with the preset threshold (the criterion for priority). For the priority index, the delay time,congestion degree, combinations thereof, and specific timezone and route designation were examined. In addition,
more appropriate priority arbitration may be possible byidentifying the bus that urgently needs priority in real-timefrom the real-time operation status of multiple buses.
Therefore, two approaches were tried: the first used a fixed
threshold preset in the on-board unit (static threshold),
and the second used a threshold that was successively
updated based on the operation status of multiple buses(dynamic threshold) in collaboration with the ART Information Center function, which is separately conducting

148

Fig. 2 Intersection passage support screen

2.2. Examination of functional requirements for onboard units and building of prototypes
An Advanced PTPS on-board unit was designed to transmit and receive information using a 760 MHz band radio
set, identify the vehicle status from the information provided by GPS and CAN, obtain information in collaboration with existing bus location systems and the ART
Information Center function, which is separately conducting development, determine the priority request necessity,and provide appropriate information to the driver using
HMI such as displays. The main requirements are listed inTable 1. In addition, an on-board unit that meets therequirements was developed. As shown in Fig. 3, the unit
consists of an on-board PC connected with a 3G USB dongle for 3G communication, a sensor unit for receiving GPS
information and vehicle speed pulses, a monitor with an
HMI function, an antenna for 760 MHz communication,
and a 760 MHz radio set.
2.3. Functional verification
Functional verification of the developed on-board unit
was carried out using passenger cars and the roadside unit
installed at the intersection in front of Tokyo Wangan
Police Station in FY2017. The results confirmed that the
on-board unit worked as designed: that the setting information can be transmitted correctly between the vehicle
and infrastructure, and that the HMI of the on-board unit
is accurate enough to inform the driver of the priority status. Figure 4 shows the difference in time required forvehicles to pass through the signalized intersection resulting from the extension of the green signal, which was confirmed through the verification. The priority arbitration
function was also verified. Specifically, the on-board unit
was confirmed to be able to determine the validity of thepriority request of the vehicle based on the threshold preset in the unit and to transmit the validity information to
the roadside unit. Furthermore, the on-board unit was
confirmed to be able to determine the threshold after considering the ART statuses (the delay time and congestion
degree) of multiple vehicles in collaboration with the ART
Information Center function, and then determine thevalidity of priority request accordingly.

[ III ] Next-Generation Urban Transportation② Sophisticated Local Transportation Management

A Study on the Speediness and Safety of Advanced Rapid Transit

Table 1 Main requirements for an Advance PTPS on-board unit

Fig. 4 Difference in transit time due to extension of green signal time3

Simulation Evaluation of the Extent towhich Advanced PTPS Improves Timeliness and Speediness and How it Affects
General Traffic

The extent to which Advanced PTPS improves timelinessand speediness, and how it affects general traffic were evaluated through simulation using the micro traffic simulation software “VISSIM.”3.1. Verification on Tokyo Ring Road No. 2
Simulated verifications were carried out in some sections(Kachidoki intersection, Harumi 5-chome intersection,
and Toyosu New Market intersection) of Tokyo Ring RoadNo. 2, as shown in Fig. 5. The results showed that introduction of PTPS reduced the travel time by approx. 30 seconds(about 7%) (Fig. 6). Priority arbitration decreased the
maximum delay from the schedule by 69 seconds (about8%). Priority arbitration also decreased the average delay
time by approx. 30 seconds (about 13%). Furthermore, due
to variation in delay time, the dynamic threshold is lessdispersed than the static threshold after priority arbitration(Fig. 7).

Fig. 3 Configuration of the Advanced PTPS on-board unit

3.2. Simulated verification assuming a virtual local city
Simulated verification assuming a local city other thanTokyo was carried out by constructing a virtual network as
shown in Fig. 8. A network was constructed to include amain road running east–west and two narrow east–weststreets sandwiching it, intersecting with five main roads
running north–south and several narrow streets parallel tothem. Then, the road through which the eastbound andwestbound buses pass (hereafter referred to as the “centermain road,” see Fig. 8) was changed to a two-, four-, or sixlane road in order to simulate several cases of road structures and regulations. Advanced PTPS was set up at eachof three signalized intersections in the center of the centermain road.

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Fig. 7 Results (Variation in delay time)

Fig. 5 Simulated verification sections of Tokyo Ring Road No. 2

ing in less variation in travel time. Moreover, it was shownthat additionally introducing Advanced PTPS reduced theaverage travel time by more (reduced by 16% in the case ofa six-lane road), significantly improving the speediness andtimeliness of buses.

4Fig. 6 Results (travel time)

As a typical result, Fig. 9 shows the number of buses
delayed by five minutes or more in terms of average travel
time per passenger on a six-lane road. On the four- or sixlane road with a dedicated bus lane, the average travel timeof buses was found to be significantly reduced, also result-

Evaluation of the Effectiveness and Influence of Advanced PTPS through Field
Operational Test

To verify the improvement effect on the speediness ofbuses, a field operational test using the roadside units
installed at three points on Tokyo Ring Road No. 2 isscheduled to be conducted from November, 2018. Tworoute bus-size buses will be used for the test. The test isscheduled to be performed along a section from the
Toyosu Market Entrance to the Ariake Station area on
Tokyo Ring Road No. 2 where Advanced PTPS roadside

Fig. 8 Simulated virtual network assuming a local city150

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6 Conclusion

Fig. 9 Results (Average travel time and number of delayed vehicles)

units have already been installed. For details, refer to“Large-scale Field Operational Tests on Advanced Rapid
Transit.”5

Research into Introduction Requirements,
Issues, and Effectiveness of Nationwide
Deployment of Advanced PTPS

5.1. Introduction requirements and issues
To identify the feasibility of introducing Advanced PTPS
in cities across Japan, a survey of relevant organizations
such as local governments and bus companies was conducted mainly in areas where bus rapid transit (BRT) was
recently introduced and areas that are considering introducing BRT. The results showed that the challenge ahead is
how to ensure the speediness and timeliness of buses andthat many local governments, especially those that are considering introducing BRT, need to introduce PTPS in orderto solve the challenge. However, issues of project cost andgaining a consensus on introducing PTPS were also found.
In one area where PTPS using optical beacons has alreadybeen introduced, there was an opinion that although beacons need to be updated, it is difficult to quickly introduce
new types of facilities due to the cost. Meanwhile,
Advanced PTPS controls multiple routes and intersections
with one roadside unit. Therefore, if the superiority of
Advanced PTPS over optical beacons in the initial installation cost can be demonstrated, it may possibly persuadeorganizations to introduce Advanced PTPS. There was also
an opinion that it is difficult to gain a consensus on introducing PTPS. To do so, it is important to present the quantitative effects of PTPS by, for example, estimating theeffects on general traffic in addition to the improvement inspeediness and timeliness of buses.
5.2. Simulation verification assuming a local city
In the future, we plan to select two cities from the area
surveyed, simulate bus operations on actual bus routes in
the cities, and verify the effects of introducing Advanced
PTPS.

This research showed the following:(1) Regarding the examination of functional requirementsfor on-board units that communicate with Advanced PTPS
roadside units and building of prototypes, the basic
requirements for Advanced PTPS were examined, and prototypes were built based on the results. One of the featuresof the prototype unit is that it supports a priority arbitration function, that is, when several ART vehicles approachan intersection at the same time, the PTPS on-board unit
in each ART vehicle determines the priority of the vehicle
and determines the validity of the priority request, so thatthe vehicle to be prioritized is determined appropriately. In
addition, by linking with the ART Information Center
function, it was possible to dynamically control the priority considering the operation statuses of multiple ART
vehicles.
(2) Regarding research into the introduction requirements, issues, and effects of the nationwide deployment of
ART and Advanced PTPS, a survey of relevant organizations such as local governments and bus companies was
conducted. The results showed that many local governments need to introduce Advanced PTPS but there areissues of project cost and gaining a consensus.
(3) Simulated evaluations of the extent to which
Advanced PTPS improves timeliness and speediness, andhow it affects general traffic were carried out using a road
network in a virtual local city and Tokyo Ring Road No. 2.
The results showed that Advanced PTPS and priority arbitration can improve both speediness and timeliness. In theremaining research period, we plan to conduct demonstration experiments on public roads as well as simulations inorder to confirm the effectiveness of Advanced PTPS on
actual public roads. We also plan to verify its effectivenessthrough simulation in local cities with a view to nationwide deployment. In the future, it will be necessary tocoordinate actual installation with bus companies and local
governments in areas where there is a strong need for
PTPS, examine the HMI of the PTPS on-board unit andthe method of priority arbitration after considering costs
and operation, and make proposals according to the local
situation, to encourage the actual introduction ofAdvanced PTPS.

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Development of ART Information Center Function
Chiemi Tsuchiya, Kojin Yano, Miyu Tsukamoto, and Junko Ushiyama (Hitachi, Ltd.)ABSTRACT: The functions of an Advanced Rapid Transit (ART) information center were investigated to help realize services using ARTrelated information and to associate various public traffic information. Experimental receiving/publishing data APIs were produced to realize
five ART information center functions. A bus information-provision service for bus users was developed to exemplify the function of utilizing
the information collected and stored in ART information center platform. We plan to investigate the usefulness of the information and theservice provided by ART information center function in a large scale field operational test from October to November 2018.

1 Purpose
An Advanced Rapid Transit (ART) information center isan open platform for the collection and utilization of information related to public transportation. Its purpose is tohelp resolve transportation-related issues by providing
valuable information and services to both the operators
and users of public transportation, including ART (Fig. 1).
In this report, we describe platforms within the ART information center and a bus information-provision service as
an example of the utilization of the information collected
and stored in the ART information center platform.

Center Function

Fig. 1 Overview of ART information center function

Functions and Services of ART Informa-

2 tion CenterWe defined the following two rules for the ART information center. One is to collect and store information related
to public transportation, such as bus operation information and various information obtained from on-board
devices in the ART system or buses. The other is to modify
the information collected and stored into an easy-to-use
format and share it with traffic business operations andapplication developers. The functions of the ART information center and information provision services are shownin Fig. 2. The information collected and stored in the ART
information center, such as information from the on-board
devices in buses, is shown in the upper part. The information and services provided to business operators of public
152

transportation and passengers are shown in the bottom.
The following five functions of the ART information centerare shown in the middle.
· PTPS priority mediation function· Getting on/off announcement support function· Degree of crowding announcement support function· Dynamic connection announcement support function· Forecasting of congestion function
The getting on/off announcement support and forecastingof congestion functions were developed in collaboration
with SIP-PJ “Improving the speediness of ART withadvanced PTPS” and SIP-PJ “Investigation of methods forforecasting traffic congestion and guiding congestion
avoidance”, respectively. These functions collect and store
traffic information, which is processed in accordance witheach user’s requirements and shared with them. As a result,the information given by the ART information center will
help passengers to utilize public transportation more conveniently.
2.1. ART information center platform
The ART information center is composed of five functions as shown in Fig. 3. In the inbound data platform (1)and data portal platform (2), the common interface andAPIs are prepared so that plural business operators canutilize different data structures in various system applications. Six APIs for receiving data were developed (e.g., a
bus location data-receiving API and a bus crowding information-receiving API). Six APIs for publishing data werealso developed (e.g., a PTPS priority decision-providing
API and a target bus estimated time of arrival (ETA)-providing API). The data format, such as longitude and latitude, depends on the business area and data processing
systems. Therefore, the ART information center also has adata format transformation function. The data storage andanalysis platform (3) collects data using the APIs, modifies/transforms the data in accordance with the users’ purposes, and shares the data with them. The support platform
for application developers (4) supplies the application
developers with the data stored in the ART information
center using the development support tool/function. The

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Development of ART Information Center Function

Fig. 2 Functions of the ART information center and information provision services

Fig. 3 Block diagram of the ART information center platform

integrated operation platform (5) manages security andresources by controlling the ART information center.
2.2. Bus information provision service
In the SIP-PJ “A research of the pedestrian support system common platform”, field verifications were performed
with the cooperation of people with limited movement
capabilities, such as visually impaired people and wheelchair users. As a result, the necessary information fortransfers was studied and evaluated. It was found that thesepeople want to know how close an approaching bus is tothem and how close their bus is to the destination bus stop.
Information on approaching buses on each route is alreadyprovided in some areas. However, it is difficult to specify
which bus should be selected when the bus stop serves plural routes. It is important to supply bus passengers withinformation about the approaching bus that they want toride and information about how close the bus is to the destination bus stop from their point of view. We developed
an application that notifies bus passengers about how closeand how crowded approaching buses are to exemplify thefunction of utilizing the information collected and stored
in the ART information center platform. The application is
run on two smartphone platforms (Android OS and iOS).
The outline of the information service about how closeand how crowded approaching buses (hereinafter called
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the “bus information-provision service”) are shown in Fig.
4. The bus information-provision service can collaborate
with the navigation service. The information is createdusing the information about the travel route obtained by
the navigation system and the information stored in the
ART information center platform (bus stop, crowding
level, timetable, estimated arrival time, etc.), and notified
to bus passengers. The information displayed on the screen
can be provided audibly using a smartphone, and switched
ON or OFF.
The bus information-provision service is started by pushing the button shown on the page of the route found via
the navigation service (1). On the smartphone screen, theestimated arrival time and the crowding level are shownfor the selected bus (2). In the development, we categorized
four levels of bus crowding. In addition, we defined occupied/vacant information for wheelchair spaces. Bus passengers are notified of whether the bus they are planningto ride has reached the nearest bus stop or not and how
close the bus is to their bus stop (3)(4). After getting on thebus, they are notified of whether the bus has reached thenearest bus stop and how close the bus is to the scheduled
destination bus stop (5)(6). The information is notified by
pop-up displays, sounds, or vibrations.
The bus crowding level is categorized using images fromon-board cameras. An on-board tool for image analysis
was developed in this project. The crowding level information processed by the on-board tool is sent to the ART
information center platform at intervals fixed in advance.
Some of the information handled by the on-board tool isshown in Table 1. The camera images are not stored in the
ART information center platform from the standpoint of
personal information regulations.
Last year, we took pictures of the inside of a bus and analyzed them (Fig. 5). Four cameras were used in the bus,with one focusing on the wheelchair space. As a result, it
was confirmed that the analysis of the limited area
observed by the four cameras was an effective way of identifying the crowding level of the whole bus. Camera images
are shown in Fig. 6. It was clarified that the crowding level

Display degree ofcrowding in a bus

Fig. 4 Outline and use scenarios of bus information-provision service
Table 1 Information handled by the on-board tool (extract)

Crowding level
Crowding Information154(1 : unoccupied seats, 2 : All seats occupied, 3 : Space in Aisle, 4 : Fully crowded)

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Development of ART Information Center Function

Fully occupied seats

Fig. 5 Field verification of providing bus crowding information

Fig. 6 Images from on-board cameras

Fig. 7 Operation of the ART information center function in large-scale field operational test

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155[ III ] Next-Generation Urban Transportation② Sophisticated Local Transportation Management

Development of ART Information Center Functionof the whole bus is difficult to identify using images fromthe rear of the bus due to interference by standing passengers in non-step buses because the floor is higher at therear. Therefore, to precisely understand the crowding level,
we decided to use a combination of three camera images
taken at the front, center, and rear.
These results will be verified in the test to be held in
October.

Verification in Large-scale Field Opera-

3 tional Test

The ART information center function will correlate andshare the data in the large scale field operational test toverify the results of the pedestrian support system common platform and improvements in the speediness of ART
using advanced PTPS (Fig. 7). The field operational test
will be conducted from October to November in 2018.
In the test to verify the improvements in the speediness of
ART using advanced PTPS, the ART information center
platform obtains bus position and delay data from the onboard Advanced PTPS. The ART information center platform gives it a priority request threshold set by the PTPS
priority mediation function.
In the test to verify the results of the pedestrian support
system common platform, the ART information center
platform provides bus passengers with a bus information
sharing service using the crowding degree announcement
support function and getting on/off announcement support function via a smartphone application. The test participants will be asked whether these functions supply
information and services as well as expected. The usefulness of the information and services will be also evaluatedin the field operational test.

4 Conclusion
The ART information center is a traffic information infrastructure that collects various kinds of information on
public transportation, modifies it into an easy-to-use format, and shares it to traffic business operation and application developers. Here, we developed five functions and sixnecessary APIs. It was confirmed that external systems canhandle the data obtained by the APIs developed in thisexperiment. We developed an application to share theinformation to bus users to demonstrate how well the dataprocessing in the ART information center platform is
working. In a field operational test to be held in the near
future, we will investigate the usefulness of the informationand services provided by the ART information center plat156

form.

III Next-Generation Urban Transportation② Sophisticated Local Transportation Management

Investigation of Methods for Forecasting Traffic Congestion and Guiding Congestion Avoidance
Tsutomu Yabe, Go Hagihara, Noriyuki Izumi, Yuichi Mohri (Institute of Behavioral Sciences)
Yoshihiko Watanabe, Jun Yamazaki, Shunsuke Minami, Rieko Otsuka (Hitachi, Ltd.)ABSTRACT: The purpose of this study is to identify behavioral and psychological characteristics by analyzing a large quantity of data aboutmajor events held in Tokyo in close cooperation with relevant entities, including the Olympic Organizing Committee, the Tokyo Metropolitan
Government, and railway/bus operators. We are conducting social experiments regarding behavior modification and guidance targeted atrelatively major events to form and verify hypotheses of models to be used for congestion prediction. Based on these verification results, we
intend to suggest a new scheme that can provide congestion predictions and traffic information appropriate for the 2020 Olympic and Paralympic Games to minimize impacts on individuals attending or affected by the games, mainly by targeting people who will visit the Olympic
and Paralympic Games and citizens that live or work in Tokyo and use public transportation including Advanced Rapid Transit (ART).

1

Objectives and Goals

This study is conducting experimental surveys to estimate
the effects of measures to avoid congestion, considering
examples of methods to forecast traffic congestion andcongestion-avoidance guidance. Then, based on theseresults, traffic conditions during a major event are being
simulated, and draft plans for a demonstrative experimentare being drawn up. Effective traffic congestion-avoidance
measures will be suggested based on the results of thisdemonstrative experiment.

Outline of Overall Information Provision

2 Scheme

2.1. Basic understanding
To consider the prediction and mitigation of congestion
during a major event like the Tokyo Olympic and Paralympic Games, a comprehensive framework is required to predict overall transportation demand based on the dailytransportation demand. Especially for areas where greatertransportation demand than normal is expected, thisdemand may not be satisfied by individual (existing) transportation facilities. Therefore, it is important for thesefacilities to work in cooperation with each other. In thiscase, citizens that usually use these transportation facilities
must play an active role in reducing demand, including
behavior modification.
To establish an overall scheme, it is also necessary to provide information on transportation services that matches
the variety of visitors, including their nationalities, disabilities, ages, and the like, as well as their purpose of travel(including travel to Olympic venues, sightseeing, eating,and so on). At the same time, attention should be paid toprovide information responding to unforeseen contingencies or emergencies.
2.2. Proposal of overall information provision scheme
For this scheme, it is necessary to 1) assume what transportation entities/purposes correspond to the following
two types of transportation demand: normal daily trans-

Fig. 1 Overall scheme of providing information for realization
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portation in Tokyo and transportation for visitors (including transportation demand for the Olympic games), 2)
provide hourly predictions for these two types of demandcorresponding to the transportation entities, and 3) implement measures that allow to provide appropriate transportation services corresponding to the traffic volume.
At the same time, it is also important to provide transportation services and information for visitors of different
nationalities, level of disability, age, and so on, including
responses to unforeseen contingencies or emergencies. For
this reason, to establish an overall information provision
scheme, the issue with the highest priority is cooperation
between the Organizing Committee of Olympic Games,
the national and local governments responsible for transportation administration, the police, transportation operators, and other authorities. Based on this cooperation, it
will be necessary to study the contents of information, how
to provide the information, as well as the timing, feasibility, and impacts to encourage various transportation entities to change behavior.

Various Transportation Entities and Pur-

3 poses to be Considered

It is necessary to focus on various transportation “entities”
and “purposes” to predict congestion and to provide information for a major event. Transportation entities can beroughly divided into “individuals living in Tokyo” and“individuals visiting Tokyo.” Some individuals living inTokyo are residents of Tokyo, some are employees commuting to Tokyo, and some are “operators” who run businesses in Tokyo. Furthermore, it is necessary to consider
individuals with mobility limitations, including elderly anddisabled persons. Furthermore, although some visitors toTokyo will be attending events, others will be there on
business or the like. Also, some individuals living in Tokyo
may also intend to attend events. By focusing on the transportation entities and purposes mentioned above, the different transportation characteristics of travelers can be
identified (Table 1). For example, transportation for commuting in Tokyo is concentrated in the morning and theevening, mainly by rail. Transportation for commuting is
also steady with small fluctuations.

Table 1 Transportation characteristics corresponding to various transportation entities and purposes, and matters to be considered158

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Investigation of Methods for Forecasting Traffic Congestion and Guiding Congestion Avoidance

4

Outline of Traffic Congestion Forecasting and Congestion-avoidance Guidance(Current Situation)

We are trying to avoid traffic congestion based on a
behavioral modification process, by providing the appropriate information depending on individual attributes andsituations. We are also trying to forecast traffic situations
dynamically in cooperation with the ART Information
Center (Figs. 2, 3).
Most recently, we have simulated traffic situations using a
traffic simulator (by applying agent-based modelling), integrated with behavioral modification based on the informa-tion provided (Fig. 4).

Fig. 2 Simulation output (example)

Fig. 3 Outline of traffic congestion forecasting and congestion-avoidance guidanceFig. 4 Outline of urban traffic forecasting and information provision (current situation)
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III Next-Generation Urban Transportation③ Improvement and Popularization of Accessible Transportation (Initiatives for People with Restricted Access to Transportation)

Development of Movement Support System for People with Mobility Constraints
Yukiko Hatazaki (UTMS Society of Japan)
ABSTRACT: As Japan faces a super-aging society, movement support for people with mobility constraints has become an important social
issue that needs to be addressed. To cope with this situation, technology development, as well as research and surveys are in progress to help
realize safe, secure, and smooth movement support for people with mobility constraints that also satisfies convenience and economic rationality requirements. The UTMS Society of Japan is working on the enhancement of the existing Pedestrian Information Communication Systems(PICS) and proceeding with studies to help realize services that provide intersection crossing support using smartphones.

1 Purpose
During the 2020 Tokyo Olympic and Paralympic Games,
heavy traffic is expected due to visitors and spectators of
the Games. Therefore, the provision of movement support
for people with mobility constraints, including wheelchairusers, has become an important element for the success of
the Games. Movement support for people with mobility
constraints is also a critical social issue in Japan, whichneeds to be addressed as the country faces a super-aging
society. To cope with this situation, technological development, as well as research and surveys are in progress tohelp realize safe, secure, and smooth movement support
for people with mobility constraints that also satisfies convenience and economic rationality requirements.

2 Background of the Development
The overall plan for the development of moving support
systems for people with mobility constraints is shown in
Fig. 1.
Research, surveys, and basic design activities were conducted from fiscal 2014 through 2015, during which problems in the existing systems were identified and draft
measures were proposed. The results found that existing
Pedestrian Information Communication Systems (PICS)
are not as widely used as desired due to cost and maintenance problems resulting from the systems’ high installation costs and availability, which is limited to the users of a
dedicated hands-free terminal or long cane. Therefore,
draft measures were proposed to provide services using
smartphones that have become increasingly common, notonly among people without disabilities, but also amongpeople with mobility constraints.
In fiscal 2016 and after, an experimental system was set
up and field verification is underway to identify the specifications of the system.
160

Figure 1 Overall plan

3 Outline of the New System
3.1. Services to be realized
The following two services were decided to be realized.
(1) A “signal information provision service” that provides
information on the name of the intersection and thepedestrian signal status.
(2) A “pedestrian green signal time extension service” that
extends the green time of the pedestrian signal.
3.2. Communication protocol
Bluetooth connection, which is standard with relatively
new smartphone models, was chosen as the protocol forcommunication between the roadside equipment andsmartphones.
3.3. User interface
Using a smartphone while walking on a busy street may
result in accidents. This behavior is already recognized as asocial issue. Therefore, smartphone applications were
examined and a simple screen display was designed so thateven people with weak eyesight can recognize the current
traffic signal status displayed on the screen based on avoice or vibrotactile user interface.

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Development of Movement Support System for People with Mobility Constraints

3.4. Determination of service area
From the aspect of cost, it was decided that the poles on
which traffic signal controllers are installed should be used
for the Bluetooth transmission/reception units (hereafterreferred to as “BLE beacons”). As a result, the communication area is within the radius of the pole on which the signal controller is mounted, which depends of the locationof the pole. If the service is realized using one BLE beacon,
it is necessary to transmit radio waves to the farthest sidewalk. However, this results in differences in the area oneach sidewalk in which communication can be established.
As a solution, it was decided that a location should only be
defined as part of the service area when it is within a certain range from the center of the intersection, based on
information from the smartphone GPS receiver. The relationship between the communication area and service area
is shown in Fig. 2.

Figure 3 Range of radio wave coverage from two BLE transmission/
reception units

3.6. Display of remaining pedestrian green signal
time/wait time
Indication of the remaining pedestrian green signal time
and the wait time until the pedestrian signal turns green
has been implemented using countdown timers as a function of pedestrian signals. In order for visually impaired
individuals to decide whether or not to start crossing astreet, it was decided to introduce the same function to thedeveloped system through smartphones.

Figure 2 Service area

3.5. Application to large-scale intersections
Because the guaranteed range of radio wave coverage by
one BLE transmission/reception unit is defined as a radiusof 70 m, radio waves cannot cover the required range inthe case of large-scale intersections. As a result, areas
where tactile paving is installed for visually impaired individuals to wait to cross streets may not be covered by theservice in some cases. People with visual impairment often
feel unsafe when crossing intersections with long crossing
distances. Therefore, it is important to provide support atlarge-scale intersections. To cope with this, more than one
BLE transmission/reception unit must be installed at largescale intersections to enable provision of the service.

Figure 4 Pedestrian signal lamp (indication of time passed)

4 Model Verification
4.1. Experiment location
A verification experiment was carried out at the Shinmisato Ekimae intersection in Saitama Prefecture in fiscal
2016 and at the Shintoshin (Nishi) intersection in Saitama
Prefecture in fiscal 2017. The service was experienced byvisually impaired people, and a questionnaire survey was
conducted.
4.2. Results of the experiment
Evaluation was carried out using ten devices running“Andorid6.9” or higher, or “iOS” or higher and supporting
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Development of Movement Support System for People with Mobility Constraints

Bluetooth 4.0 or higher, with a dedicated application
installed. The results found that all devices were able toproperly provide the service. The questionnaire survey forvisually impaired people who experienced the service
showed that about 90 percent of the participants thoughthat the service was necessary, and many comments werereceived to open-ended questions. The results demonstrated that visually impaired people are highly interested
in this movement support service at intersections.

5 Conclusion(1) Through model verification, it was confirmed that thedeveloped system can be applied to real intersections.
(2) A questionnaire survey of people that experienced theservice and discussions with experts confirmed theeffectiveness of the movement support service at intersections using smartphones.
(3) Based on the results of the model verification andexaminations, draft experimental specifications anddraft standards were prepared. The draft specifications
and standards prepared are described below:· System definition document (draft) for a movement
support system for people with mobility constraints
using smartphones· Written proposal (draft) for the examination of specifications for BLE roadside equipment· Standards (draft) for an application that communicates between BLE roadside equipment and smartphones· Written proposal (draft) for a BLE roadside equipment inspection manual· Written proposal (draft) for the examination of acomprehensive inspection manual for a moving support system for people with mobility constraints
using smartphones

6 Future Issues
In the future, it will be necessary to develop a concrete
operational procedure for the whole system, with a view
toward differentiation between the new system and otherexisting pedestrian support systems, as well as cooperation
with other applications such as navigation systems.

162III Next-Generation Urban Transportation③ Improvement and Popularization of Accessible Transportation (Initiatives for People with Restricted Access to Transportation)

Research into Common Pedestrian Support System
Platform
Junko Ushiyama, Masao Yamamoto, and Haru Ando (Hitachi, Ltd.)
Hideki Yamazaki, Hikari Nakazawa, and Yayoi Horie (Navitime Japan Co., Ltd.)
Satoshi Watanabe and Satoshi Hashimoto (Zenrin Co., Ltd.)ABSTRACT: To improve pedestrian accessibility through route guidance adjusted to the individual characteristics of each pedestrian, thefollowing studies, investigations, and developments were performed. First, the information necessary for transfers and the method of collecting this information were studied and evaluated through field verifications. As a result, data collection applications on smartphones wereadopted as a low-cost and continuous method for information collection. Second, demonstration experiments to develop walking networks
using the data collection application were implemented. In addition, the serviceability of common platform information was evaluated using
a prototype personal navigation application on smartphones, and a field verification of route guidance corresponding to the personal attributes of pedestrians. Furthermore, cooperation between personal navigation systems and advanced PICS was studied. A large-scale demonstration experiment regarding route guidance will be performed in October 2018.

1 Outline and Objectives
Pedestrian support systems aim to realize a safe andsecure traffic environment for a wide range of people,
including elderly and disabled persons, by reducing traffic
accident fatalities and improving the accessibility of public
transportation. These systems should be able to provide
public transportation information necessary for movingfrom a departure location to a destination (common platform information), as well as route guidance suited for different types of pedestrians. The objectives of this project
are to study easy and efficient methods to collect this common platform information, and to evaluate its serviceability and acceptance through demonstration experiments inwhich various kinds of people with restricted transportation capabilities participate.

Study of Common Platform Information

2 and Collection Methods

2.1. Investigation of common platform information
Figure 1 outlines the demonstration experiments performed between 2015 and 2017. With the cooperation ofpeople with restricted transportation capabilities, demonstration experiments were performed in the Tokyo Bay
area each year. We assumed that different information
would be required in accordance with the type of personwith restricted transportation capabilities (e.g., wheelchairusers, visually impaired persons, the elderly, etc.). The
importance of the information was investigated by extracting common platform information for each pedestrian
attribute through field verifications and surveys, andthrough route guidance demonstration experiments.

Fig. 1 Outline of demonstration experiments between 2015 and 2017
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Research into Common Pedestrian Support System Platform

Table 1 shows the results of the investigation. The common platform information was classified into nine categories. All the people with restricted transportation
capabilities responded that they required “vertical transportation” and “detailed information of public transportation”. In addition, the results show that information on“crosswalk intersections”, and “sidewalk road conditions” isalso important. Figure 2 shows photos of the route guidance field verification.
2.2 Collection of common platform information
It is obvious that exact and updated information is thebest for users. However, it is not acceptable for the sustainability of the services to expend large amounts of time andmoney to collect and update this information. This projectused a data collection application, through which common
platform information can be easily obtained throughmobile devices such as smartphones and published on theInternet. The application collects common platform information by users posting information about bumps, roadwidth, gradients, slopes, studded paving blocks, and crosswalks. In addition, we aimed to develop an accessibility
map suitable for people with restricted transportation
capabilities by collecting global navigation satellite system(GNSS) probe information.
A demonstration experiment to collect common platform
information was performed in November 2017 (Fig. 1).
Sixty-three people with restricted transportation capabili-ties such as wheelchair users, visually impaired persons,baby buggy users, and the elderly participated. The participants used smartphones installed with the data collection
application, moved around the Toyosu-Ariake area, andposted important information using the application.
Table 2 shows a summary of posted data categorized by
the attributes of the people with restricted transportation
capabilities. The total number of posts was approximately
1,700 and contained features corresponding to the attributes of the users. Visually impaired persons mainly posted
photos and comments, and information regarding key
landmarks for transportation. Wheelchair users posted
much information regarding pedestrian walkways. The
elderly posted both information regarding themselves aswell as other information that they thought might be helpful for other people with restricted transportation capabilities.
We investigated transportation routes in the demonstration experiment using questionnaires. GNSS tracking
information and responses to the questionnaires were analyzed, and recommended routes were formulated from theanalysis results. A demonstration experiment regarding
route guidance adjusted to the individual characteristics ofeach pedestrian will be held in 2018 using a personal navigation application containing walking networks developed
based on the common platform information obtained
through the previous demonstration experiments and field
verifications.

Table 1 Importance of common platform information (results of verification by demonstration experiments)

Fig. 2 Scenes from the field verification performed in the Shimbashi-Ariake-Toyosu area in 2016164

[ III ] Next-Generation Urban Transportation③ Improvement and Popularization of Accessible Transportation (Initiatives for People with Restricted Access to Transportation)

Research into Common Pedestrian Support System Platform

Table 2 Results of posts classified by attributes

gation application for route guidance and an advanced
Pedestrian Information and Communication System(PICS) was researched, and a preliminary experiment was
performed to examine the feasibility of support on crossing walkways and facilitate safer transportation of visually
impaired persons. In our research in 2017, we designed a
PICS terminal application and a personal navigation application as separate programs, and developed a cooperation
function between these applications. The demonstration
experiment proved the acceptance of a personal navigation
system that cooperates with an advanced PICS, especiallyfor visually impaired persons.

Fig. 3 Sample screen of personal navigation system

3

Route Guidance Corresponding to Attributes of People with Restricted Transportation Capabilities

3.1. Prototyping of personal navigation application
A prototype personal navigation application was createdto evaluate the common platform information used forroute guidance corresponding to the attributes of each typeof person with restricted transportation capabilities. This
personal navigation application shows the results of route
searches based on the attributes of the user, and navigates
the preferred route. Helpful information during transportation such as barriers and barrier-free facilities are shownas pins on the guidance maps (Fig. 3).
3.2. Demonstration experiments of route guidancecorresponding to attributes
In the 2018 demonstration experiment, the serviceabilityof the provided public transportation information will be
evaluated, in addition to the serviceability of route guidance (refer to the Development of ART Information Center report). Figure 4 shows the planned scenarios of thedemonstration experiment. The preliminary experiments
over the last three years have proven that route guidance
adjusted to individual characteristics is acceptable, andthere are high expectations of providing common platform
information.

Fig. 4 Planned scenario of demonstration experiments

5 Conclusion
Three years of demonstration experiments showed thatroute guidance corresponding to the attributes of people
with restricted transportation capabilities is highly acceptable. Continuous collection of common platform information and the practical application of route guidance
adjusted to individual characteristics are likely to be implemented in the near future.

Preliminary Experiment of Cooperation

4 with Advanced PICS

The acceptance of cooperation between a personal naviSIP-adus: Project Reports, 2014-2018

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IV Field Operational Tests

Outline of Large-scale Field Operational Test

Masato Minakata (Toyota Motor Corporation)

1

Back Ground

In SIP-adus, various research and development measures
have been organized around five major themes since 2016,
and a large-scale field operational test (FOT) is set as a
deployment milestone.
Overseas FOTs include the Smart City Challenge in the
United States, a demonstration experiment on automateddriving technology in the entire city in Columbus, Ohio. In
Europe, the AdaptIVe Project, the largest comprehensive
automated driving demonstration experiment, is being
carried out in the context of the Seventh Framework Programme (FP7) by EU member states. Similarly, in the
United States, Waymo and Uber are conducting social
demonstration experiments on civic mobility services
using automated driving vehicles. Demonstration experiments on public roads are also being conducted in various
countries around the world.
The FOT environment for automated driving on publicroads in Japan has already become comparable to that ofother countries thanks to the cooperation of the National
Police Agency, the Ministry of Land, Infrastructure, Transport, and Tourism and other organizations. As long as
there is a driver in the driver’s seat, FOTs can be carriedout without applying for permission with the government
agencies.

2

Overview

Our way of thinking about large-scale does not, in any
way, refer to the number of vehicles participating in a FOT.
The meaning we attribute to the large-scale expression is “
way of approaching comprehensive themes”, “implementation over a wide area”, and “internationally open”.
2.1. Objectives
The objectives of large-scale FOTs are1) Stimulating research and technological development by
providing the necessary places and infrastructure.
2) Industry, government and academia evaluation, frommany viewpoints, of the common themes raised at these
open opportunities to identify problems.
1663) Judging whether to put SIP-adus research results into
practical use.
4) Inviting overseas manufacturer participation and leading international cooperation and standardization.
5) Using public relations to promote automatic driving systems and foster social acceptance.
2.2. Themes
SIP-adus carried out large-scale FOTs covering the five
major themes of ① dynamic maps, ② human machineinterface (HMI), ③ cybersecurity, ④ pedestrian accident
reduction, and ⑤ next-generation urban transport. These
five major themes are the fundamental technologies in thecooperative areas of the industry, and are indispensable forthe social implementation of automated driving systems. It
is therefore important to confirm the effectiveness andpracticality of these cooperative areas via the FOTs.
2.3. Implementing Organization
Under the Promotion Committee, a plan for large-scale
FOTs was studied by the large-scale FOT planning task
force. Based on that plan, the National Energy and Industrial Technology Development Organization (NEDO)
acted as the administrative secretariat to manage the overall operation of the large-scale FOTs and administer public
experiment funding on a per theme basis. The organization
granted funding implemented its FOT plan, including therecruitment and the management of FOT participants.
2.4. Participants
SIP-adus recruited domestic and overseas automakers,
automobile parts and system suppliers, universities,
research institutes, and venture companies to openly participate in the dynamic map and HMI FOTs with expectation of receiving feedback from wide range of theperspectives. The preparation of some experimental equipment and the infrastructure necessary for the FOTs werecarried out by the SIP-adus and provided to the participants. In total, 22 domestic and overseas groups participated in the FOTs (Fig. 1).

[ IV ] Field Operational Tests

Outline of Large-scale Field Operational Test

Fig. 1 Participants in SIP-adus Large-scale FOTs

2.5. Area
FOT on the public roads are indispensable for curryingout the demonstration experiments for practical application.
In the large-scale FOT, SIP-adus planned the use of highways and ordinary roads, primarily in the Kanto region,and reached prior agreement with the stakeholders in therelevant regions to carry out the test on public roads forthe first time in Japan. In addition, SIP-adus conducted a
FOT in Okinawa to verify the social acceptance for an
automated driving bus as the next-generation of urban
transport. To solve the increasingly serious problem oflimited mobility due to the shortage of transport services
and logistics in depopulated areas, the social acceptance ofmobility services based on road-vehicle cooperation using
michi-no-eki roadside rest areas as a starting points wasevaluated in semi-mountainous areas where super aging isadvancing rapidly. For some FOTs, the test courses werealso used from the point of view of ensuring safety.
2.6. Period
The SIP-adus large-scale FOTs started in October 2017,with the implementation period determined separately foreach theme. All experiments were completed in December
2018.

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IV Field Operational Tests① Large Scale Field Operational Tests

Dynamic Maps
Yoshiaki Tsuda, Yukishiro Saito, Yukio Takahashi (Mitsubishi Electric Corporation)
Shinya Muroyama, Kazuki Hirao (Aisan Technology Co., Ltd.)
Sosuke Hirano, Junichi Kamichika (Increment P Corporation)
Kiyohiro Yamauchi, Yumi Takehana (Zenrin Co., Ltd.)
Shintaro Osaki (Toyota Map Master Incorporated)
Ryota Kataoka, Miho Shikata (Pasco Corporation)
ABSTRACT: Through the large-scale field operational test (dynamic maps), we designed static information update data for dynamic maps
necessary for automated driving systems and location referencing methods to link static information with dynamic information, and collaborated with test participants to evaluate these static information update data and location referencing methods. Based on the static information
evaluation results, we created map updating guidelines. We also determined that information usage intent varied by automated driving level.
Our findings indicate that linking static information and other information can be effective for automated driving system vehicle control, andwe verified dynamic map theory through actual field operational testing.

1 Introduction
Dynamic maps are high-accuracy 3D maps overlaid withconstantly changing information regarding nearby vehicles, the status of traffic signals, and other driving conditions. Dynamic maps are composed of four layers, as
shown in Fig. 1: static information, semi-static information, semi-dynamic information, and dynamic information. Static information is the high-accuracy 3D map itself.
It is composed of actual features, such as carriageway lines,
street signs, and traffic signals, and virtual features, such aslane links, carriageway links, and intersection areas. Semistatic information consists of future road transport information such as traffic congestion forecast information,scheduled traffic restrictions information, and weather
forecast information. Semi-dynamic information is composed of road traffic information such as accident information and traffic congestion information. Dynamic
information consists of traffic signal information andinformation regarding physical objects on or near roads,
such as the positions of nearby vehicles and pedestrians.
Location referencing methods have been defined to linkthese four layers, from static information to dynamic information.
Section 2 of this article provides an overview of the largescale field operational test (dynamic maps) (the field operational test). Section 3 presents the methods used to evaluate
map data updates, semi-dynamic information (traffic flow
information and lane-specific restriction information) anddynamic information (e.g., traffic signal information, crosswalk pedestrian information), and the results of these evaluations. Section 4 contains a summary of our results.
168

Fig. 1 Dynamic map four layer structure

Overview of the Dynamic Map Field Op-

2 erational Test

2.1. Dynamic Map Field Operational Test ScheduleWith the participation of test participants, over the
15-month period from October 2017 to December 2018,
this test verified the specifications and accuracy of prototype dynamic maps, the linking of information such as
dynamic information and semi-dynamic information withstatic information, and the system used to distribute thisinformation. Table 1 shows the schedule of the Dynamic
Map Field Operational Test.

[ IV ] Field Operational Tests① Large Scale Field Operational Tests

Dynamic Maps

Table 1 Dynamic Map Field Operational Test schedule

2.2. Field Operational Test Area
In order to implement this field operational test, static
information was prepared for routes starting from theJapan Automobile Research Institute urban area simulation
test course and extending to the Shimizu Ihara Interchange
via the Joban Expressway, Metropolitan Expressway, Tomei
Expressway, Shin-Tomei Expressway, and other roads, aswell as for the Odaiba area, as shown in Figs. 2 and 3.

verification test contents, evaluation methods and evaluation contents, and verification test equipment configurations.
Table 2 Dynamic Map Field Operational Test Participants

2.4. Equipment and Software Used in the Field Operational Test
The following equipment and software was used to distribute (1) static information, (2) semi-dynamic information, and (3) dynamic information in this verification test.

Fig. 2 Dynamic Map Field Operational Test Area (Expressways)

Fig. 3 Dynamic Map Field Operational Test Area (Odaiba Area)

2.3. Field Operational Test Participants and Implementation System
The twenty participants shown in Table 2, including Japanese and foreign motor vehicle manufacturers, suppliers,
and universities, took part in the field operational test. A
total of ten dynamic map field operational test working
group sessions were held. The working group discussed(1) Static information (map data and map update data)
Static information map data and map update data were
distributed to test participants on DVD. Incremental map
update data alone was also distributed via LTE to verify
envisioned actual operation.
(2) Semi-dynamic information (traffic flow informationand lane-specific restriction information)
Traffic flow information generated from probe information acquired from vehicles was distributed via LTE. Lanespecific traffic restriction information was also distributed
using ETC 2.0 roadside wireless devices in actual operation.
(3) Dynamic information (traffic signal information, crosswalk pedestrian information, vehicle detection information)
Dynamic information was distributed via two distribution methods that are in actual operation. Advanced infrared beacons were used to distribute traffic signal
information. ITS wireless roadside devices were used to
distribute traffic signal information, crosswalk pedestrian
information, and vehicle detection information.
LTE smartphones, ETC 2.0 vehicle-mounted devices, 760
MHz receivers, and receiving terminals for storing data
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[ IV ] Field Operational Tests① Large Scale Field Operational Tests

Dynamic Maps

output from receiving terminals were lent to test participants in order to receive information types (1) through (3).
Figures 4 to 7 show the testing systems and devices used in
the field operational test.
API software and dynamic map viewers installed inreceiving terminals were used to verify received data. Fig-ure 8 shows an example of traffic signal information for the
Odaiba area displayed on the dynamic viewer.
Test participants installed these devices and software in
test vehicles, drove on actual roads, and conducted evaluation verification.

Fig. 4 Equipment and Software Used in the Field Operational Test

Fig. 7 Receiving Terminal

Source: Mitsubishi Electric Corporation
Fig. 5 ETC 2.0 Vehicle-Mounted Device

Source: DENSO CORPORATION
Fig. 6 760 MHz receiver
170

Fig. 8 Example of Traffic Signal Information for the Odaiba Area
Displayed on the Dynamic Viewer

[ IV ] Field Operational Tests① Large Scale Field Operational Tests

Dynamic Maps

3 Evaluation Methods

Table 4 Overview of Traffic Flow Information and Information
Distribution Area

3.1. Static Information
The five types of map update data shown in Table 3 werecreated based on factors such as static information
improvement requests, and map update evaluation was
performed.
As the map update data example in Fig. 9 shows, map
update data was prepared for areas in which there wereroad extensions or changes to road shapes.
Table 3 Locations for Which Map Update Data Was Created

Only congested lanes are shown.

Fig. 10 Traffic Flow Information (Lane-Specific) Display Example(2) Overview of Lane-Specific Restriction Information
A test system was created to test the use of lane-specificrestriction information distributed by ETC 2.0 roadsidedevices in dynamic maps. This test system was then provided to test participants. When participants passed ETC
2.0 roadside devices on actual roads, they received lanespecific restriction information and evaluated the linking
of static information and lane-specific restriction information (see Fig. 11).

Provided data

Only lanes with traffic restriction are shown.

Fig. 9 Example of Map Update Data (Metropolitan Expressway - Horikiri/
Kosuge Junction Inner Lanes)

3.2. Semi-Dynamic Information(1) Overview of traffic flow information
With regard to traffic flow information, the project coordinated with a separate SIP-adus project to distribute two
types of traffic flow information—the road-specific andlane-specific information shown in Table 4—via LTE. Figure 10 shows an example of the displayed traffic flow information.

Fig. 11 Lane-Specific Restriction Information Display Example

3.3 Dynamic Information
A test system was created to test the use of traffic signal
information and other information provided by advanced
infrared beacons and ITS wireless roadside devices indynamic maps. This test system was then provided to test
participants. When participants passed intersections on
actual roads, they received TSPS/DSSS information andevaluated the linking of static information and traffic signal and other information (see Fig. 12).
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171

[ IV ] Field Operational Tests① Large Scale Field Operational Tests

Dynamic Maps

varied by automated driving level. For level 3 and higher
automated driving systems, some participants indicatedthat lane-level traffic flow information was necessary. Table
6 shows the traffic flow information evaluation results.

Fig. 12 Dynamic Information Display Example

4 Evaluation Results(2) Lane-specific restriction information evaluation results
We determined that lane-specific restriction information
usage intent varied by automated driving level. For level 2
and higher automated driving systems, some participants
indicated that lane-specific restriction information wasnecessary. Table 7 shows the lane-specific restriction information evaluation results.
Table 7 Need for Lane-Specific Restriction Information

Evaluations were performed on static information, semidynamic information, and dynamic information.
4.1 Static Information Evaluation Results
We determined that high-accuracy 3D map usage intent
varied by automated driving level. We found that level 2
and higher automated driving systems used high-accuracy
3D maps, and that feature and attribute information usage
intent for level 4 and higher automated driving systems
varied by company. Table 5 shows the results of the static
information evaluation.
Table 5 Static Information Usage Intent

4.3. Dynamic Information
We determined that dynamic information usage intent
varied by automated driving level. We also found that traffic signal information was required for all automated driving levels. We found that for level 1 and 2 automateddriving, participants planned to use crosswalk pedestrian
information and oncoming vehicle information as information for drivers, while for level 3 and above automateddriving, the information was necessary for vehicle control.
Table 8 shows the dynamic information evaluation results.
Table 8 Need for Dynamic Information

4.2 Semi-Dynamic Information Evaluation Results(1) Traffic flow information evaluation results
We determined that traffic flow information usage intent
Table 6 Need for Traffic Flow Information

5 SummaryThrough the large-scale field operational test (dynamicmaps), we designed test systems to link the dynamic map
static information update data and static information necessary for automated driving systems with dynamic and172

[ IV ] Field Operational Tests① Large Scale Field Operational Tests

Dynamic Maps

other information, and collaborated with test participants
to perform evaluations.
Based on the evaluation results, we created map updating
guidelines. We also determined that information usage
intent varied by automated driving level.
Our findings indicate that linking static information andother information can be effective in automated driving
system vehicle control, and we verified dynamic map theory through actual field operational testing.
This research and development work was carried out withthe collaboration of Program Director Seigo Kuzumaki,
members of SIP-adus, the large-scale field operational test
(dynamic maps) participants, as well as Dynamic Map
Platform Co., Ltd., Mitsubishi Research Institute, Inc.,
Sumitomo Electric Industries, Ltd., and NTT DOCOMO,
INC., which were involved in the preparation of test systems.
References(1) Automated Driving System Map Data Specification
Proposal Ver. 1.1 (2016)(2) Automated Driving System Map Data Encoding Specification Proposal (Test Data Encoding Specifications)Ver. 1.0 (2016)(3) Dynamic Map Semi-dynamic/Semi-static Information
Data Specifications (Proposal) Ver. 1.0 (2016)(4) Recommended Specifications for High-accuracy Maps
Used for Automated Driving (November 2016, Japan
Automobile Manufacturers Association, Inc.)(5) Materials Related to New Advanced Digital Road Surface Information for Advanced Driving Support (Japan
Digital Road Map Association)
About the author
1) Yoshiaki Tsuda is the chief engineer of the Information
Technology Systems Department at Mitsubishi Electric
Corporation’s Kamakura Works. He is engaged inresearch and development of social infrastructure systems such as ITS, DSRC, and high-accuracy 3D maps.

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173

IV Field Operational Tests① Large Scale Field Operational Tests

HMI
Keita Hosaka (Tokyoto Business Service Co., Ltd.), Makoto Itoh (University of Tsukuba),
Toshihisa Sato (National Institute of Advanced Industrial Science and Technology),
Satoshi Kitazaki (National Institute of Advanced Industrial Science and Technology)
ABSTRACT: In the HMI large-scale experimental demonstration, the results of research and development (knowledge and systems) areverified by corporate participation (in actual use). In FY 2017, a baseline for readiness was derived through public road experiments in which(1) the feasibility of driver monitoring systems (DMS) in the real world was verified, and (2) data with variability for use as a baseline in thereadiness index was acquired. In FY 2018, the relationship between how and what information on system functions is conveyed to the driverand handover performance are being verified experimentally on a test course. Also, various controlled driver conditions, DMS output, andhandover performance will also be verified experimentally on the test course.

1

HMI Large-Scale Experimental Demonstration

We conducted the experimental demonstration with enterprises as participants to verify the SIP-adus Human Factor
deliverables (knowledge and system) in an environment
closer to real-world situations. The data obtained was shared
between the organizers and the participants, and will prove
useful for further research and development by the individual companies. Furthermore, it is used as reference for “theJAMA HMI guidelines”, “ISO TC22/SC39”, and “UN-R79”.

2

Large-Scale Experimental Demonstration
in FY 2017 (Task B: Evaluation of drivercondition, development of DMS)

2.1. Purpose
The FY 2017 experiment was conducted on public roads
on to define driver readiness.
The purpose of the experiment was to derive the readinesscomponent index during manual driving to serve as a baseline. The methodology for the experiment involved having
each subject drive for about two hours in a vehicle equippedwith the DMS prototype for commercial vehicles (manual
driving) from the SIP large-scale experimental demonstration highway course. For the evaluation items, we analyzed
the relationship between the DMS output and driving information to derive the baseline readiness index (Fig. 1).

Driver state A

Driver state B

Fig. 1 Conceptual diagram of readiness
174

The baseline in the readiness index is considered to bevariation in readiness during manual driving. Specifically
it is necessary to consider internal driver factors driver
attributes (age, sex, driving frequency, etc.), and driverstate (presence or absence of focus on driving, drowsiness,fatigue) and external factors such as congestion or otherroad traffic environment changes and traffic environment
complexity, and quantitatively verify the relationshipbetween these values and the DMS output.
The research questions in “Evaluation of Readiness Component Indicators in Practical Use” are as follows.
RQ1: Does the relationship between driver condition (DMS
output) and the surrounding environment monitoring behavior change during manual driving?
RQ2: Can the DMS detect changes in state based on theawareness of the driver?
2.2. Implementation Plan
The participating companies performed the five items
below.
(1). Pre-experiment preparation state(2). Preparation of vehicle used in the experimental demonstration(3). Experimental demonstration(4). Post-processing of vehicles used for the experimental
demonstration (removal of equipment)(5). Submission of experimental data
The experimental conditions are as follows.
- Experimental vehicle
Manually operated commercial vehicles- Subjects
Each participating company selected nine or more
people who, as much as possible, are average drivers(three aged from 20 to 39, three aged from 40 to 59,
and three aged 60 or older).
- Route and driving time[ IV ] Field Operational Tests① Large Scale Field Operational Tests

HMI

A combination of two to three hours of long-distancehighway driving defined in SIP experimental demonstrations and half-an-hour to one hour of highway driving around Tokyo was used. The order of the routes wasbalanced, and a break time was set for safety.
The items measured in the experimental demonstration are
as follows.
- Driver, vehicle, traffic environmental data
Vehicle (CAN) information, GPS information, video
images around the vehicle- DMS output
Gaze, eyelid opening, blinking, saccades- Other items
Interviews concerning the driver’s state.
The staff member riding along observed and recorded
the surrounding environment and equipment operation.
2.3. Outline of Implementation
The FY2017 large-scale experimental demonstration was
conducted at six participating companies, with experiments carried out between November 2017 and March2018 (Table. 1). There were a total of 61 subjects (50 malesubjects (82%) and 11 female subjects (18%)). By age, 25
subjects (41%) were 20 to 39 years old, 24 (39%) were 40 to
59 years old, and 12 (20%) were 60 or older.

Fig. 3 Investigation of feasibility of DMS: blinking detection

In the context of studying the feasibility of DMS, we examined the relationship between the subjective evaluation of
drowsiness and DMS output. Fig. 3 above compares thecorrect answer (graph on the left) calculated from images
and the DMS output (graph on the right). Regarding the
subjective evaluation of drowsiness, results with high correlation were obtained in ‘blink duration’ rather than ‘PERCLOS’. A similar tendency was found in both the correct
values from images and the data from the DMS output.
Medium saccades (9 to 16
degrees) countTable 1 Outline of implementation
Fig. 4 Investigation of feasibility of DMS: detection of saccades

2.4.

Implementation Result

Fig. 2 Diagram of calculation of evaluation index

To verify the relationship between changes in driver condition or ambient environment and the DMS output, data
was extracted and verified as shown in Fig. 2.
For the driver status, drowsiness, fatigue, and thinking
were clearly ascertained from interviews at 15 minuteintervals, and the relationship between the value and the
DMS output was verified.

Next, we examined the relationship between thinking and
DMS output. Fig. 4 above verifies the relationship of the
DMS output (blink count, saccades (small), saccades(medium)) in terms of whether the driver is thinking or not.
In the case of thinking (≒ conscious but exhibiting inattentiveness), it was confirmed form the DMS output that the
number of blinks and the occurrence of smaller saccades
increased.
From these results, the following findings were obtained.
- It was confirmed that the DMS operates in a real-world
environment and detects the driver condition.
- Sufficient fluctuation (variation) in readiness was
acquired as a baseline for the readiness index.

3

FY 2018 Large-Scale Experimental Demonstration (Task A: Study of prior driver
knowledge and establish guidelines on
system functions)3.1. Purpose
Verify the relationship between how and what informaSIP-adus: Project Reports, 2014-2018

175

[ IV ] Field Operational Tests① Large Scale Field Operational Tests

HMItion on system functions is conveyed to the driver andhandover performance (driver and vehicle behavior) in
vehicles with automation corresponding to level 2 or 3.
Specifically, we examine the usefulness about requests tointervene (RtI), the meaning of RtI indications, and examples of scenarios requiring intervention to achieve a proper
handover.
3.2. Experiment Scenario
The subjects were separated into two groups with different prior knowledge (Table 2) and performed subtasks(SuRT) in the low speed range (approximately 30-50 km/h). An RtI was generated at the object avoidance event(TTC: 6 seconds), and the driver’s behavior was measured.
Table 2 Two groups with different degrees of prior knowledge

4

FY2018 Large-Scale Experimental Demonstration (Task B: Evaluation of drivercondition, development of DMS)

4.1. Purpose
Verify the relationship between controlled driver states,
DMS output, and driving handover performance in vehicles with automation corresponding to level 2 or 3. The
controlled driver states are conscious but exhibiting inattentiveness, distraction, and drowsiness.
4.2. Experiment Scenario
For the conscious but exhibiting inattentiveness and distraction state experiments, the three sessions of manual
driving, automatic operation only, and automatic operation
with subtask execution were carried out. Subtasks implemented Nback (1 back, 2 back) for the conscious but
exhibiting inattentiveness state and SuRT (difficult) for the
distraction state. In the experiment, an RtI was generated
before switching from automatic operation to manual driving (TTC: 6 seconds), and automatic operation (low speed)
and subtasking were performed with a preceding vehicle
present. The preceding vehicle changed lanes one second
after the occurrence of the RtI, and the object avoidance
behavior of the driver and the DMS output were measured(Fig. 5).
For the drowsiness state experiment, the two sessions ofmanual driving and automatic operation were carried out.
The automatic operation (low speed) was performed with a176

preceding vehicle present. The RtI is generated 25 min after
the start of driving, or when the examiner observing the
subject determines that the driver alertness is decreasing.
The preceding vehicle changed lanes one second after theoccurrence of the RtI, and the object avoidance behavior ofthe driver and the DMS output were measured (Fig. 5).

Fig. 5 Description of RtI event

FY 2018 Implementation Plan for Large-

5 Scale Experimental Demonstration

In the large-scale experimental demonstration of FY
2018, experiments were carried out efficiently. The methodology involved participating companies selecting twoeither carry out the two tasks as a joint experiment or as
separate experiments.
The experiment conditions were as follows.
- Automatic operation level of test vehicle
Corresponding to level 2 (hands on the steering wheel)or level 3 (hands off the steering wheel)- Experiment site
Test course (company test course or shared test course)- Subjects
10 people or more
The items measured in the experiment are as follows.
- Vehicle behavior data and in-vehicle environmental data
- DMS output (Task B only)- Questionnaire
We conducted the questionnaire before and after theexperiment.

6 Conclusion
In the FY 2017 experimental demonstration, (1) the feasibility of DMS in the real world was verified, and (2) datawith variability for use as a baseline in the readiness index
was acquired.
In the FY 2018 large-scale demonstration experiment, wesought to elucidate the following two points. The first (Task
A) is the relationship between how and what information
on system functions is conveyed to the driver during automatic driving and handover performance. The second(Task B) is the relationship between the controlled driverstates and the DMS output, and the operation handover
performance.

IV Field Operational Tests① Large Scale Field Operational Tests

Information Security
Ken Okuyama (PwC Consulting LLC)
ABSTRACT: A field operational was conducted based on the information security evaluation method for vehicle systems established in FY
2017. In FY 2018, a larger scale information security field operational test will be conducted with an increased number of participants. The
evaluation and implementation approach were reviewed and will be finalized as Information Security Evaluation Guidelines for vehicle systems.

1 Project Overview
The Cross-ministerial Strategic Innovation Promotion
Program (SIP) Automated Driving System / Large Scale
Field Operational Test: Information Security Field Operational Test conducted research and analysis on cybersecurity threats related to automated driving, developed acybersecurity evaluation method/protocol at the vehicularlevel aimed towards international standardization and formulated a plan to conduct technical research through black
box testing of the vehicle systems provided by the participants in the field operational test.
This document summarizes the details of the information
security trial research conducted based on the information
security evaluation method that was developed and the
management of the field operational test.

2 Information Security Trial Researchwere conducted.
Evaluation Period: 15th December, 2017 to 9th February
2018 (36 days)

2.3. Details of the Evaluation
2.3.1. Evaluation Items
The specific evaluation items covered in this project arelisted in the table below. The evaluation items that werecovered are marked with “○”, items that were not covered
because they did not fulfill the prerequisites are marked
with “×”, and items that were not covered due to lack offeatures or required applications are marked with “-”.

2.1. Objectives and Scope
In the information security trial research, an evaluation
was conducted for vehicle systems based on the evaluation
method described in the draft of the Information Security
Evaluation Guideline formulated in FY 2017.
Through the evaluation trial based on the guideline, thepracticality and validity of the Information Security Evaluation Guideline was assessed. At the same time, vulnerabilities in the tested vehicle systems that could potentiallylead to cyberthreats were tested from an attacker’s viewpoint, and advice was given for any issues discovered that
required remediation.
2.2. Evaluation Period and Schedule
The evaluation was conducted over eight weeks; from the
setup of vehicle systems to be evaluated, to reporting theevaluation results. Within this period, seven weeks werespent on obtaining the firmware during the reconnaissancephase. Due to time constraints, only part of the work
related to escalation of privileges and action on objectives

2.3.2. Format of the Evaluation Report
The results of trial research were reported and covered
the following items;
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Information Security(1) Evaluation results(2) Criticality(3) Evaluation details(4) Evaluation procedure(5) Assumed risk
(6) Condition for successful attack(7) Improvement plan
The criticality of the items was judged based on the following criteria:· HighCases where failing to remediate a discovered vulnerability could lead to a highly critical cybersecurity compromisethat would severely impact business operations (e.g. product recall, disruption of business operations). In addition,
the attack leading to the compromise does not require hightechnical capability or cost. Necessary measures such as
vulnerability remediation must be implemented immediately.
· Medium
Cases where failing to remediate a discovered vulnerability could lead to a highly critical cybersecurity compromisethat has considerable impact on business operations (e.g.
significant deterioration of business performance). An
attack leading to the compromise requires some degree oftechnical capability and cost. Implementing measures suchas vulnerability remediation as necessary is recommended.
· Low
The discovered issue in the system does not have any
immediate impact on cybersecurity, but implementing
appropriate measures is expected to provide an improvement in the cybersecurity level. It is recommended to
implement certain measures in the future.
· InfoThe discovered issue in the system may have some impact
on cybersecurity. It is recommended to consider implementing certain measures considered.
2.4. Summary of the Trial Research
Through the trial research, it was discovered that thereconnaissance phase of the developed evaluation procedure required a longer period than originally expected.
This was recognized as an improvement point for the
future management of field operational tests. Variation in
evaluation results for multiple systems was also recognized
as an issue to be addressed in future field operational tests.
The actual evaluation results will not be disclosed in thisdocument as they contain highly sensitive information
related to the tested systems.

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Management of Information Security

3 Field Operational Test

The field operational test will be planned and managed
taking into consideration the previously explained trial
research results and with an increased number of vehicle
systems to be evaluated.
3.1. Field Operational Test Management Plan
3.1.1. Field Operational Test Schedule
The schedule of the field operational test in FY 2018 is asfollows:*1 Evaluation work will be conducted for two months within the set period(August - December) for each participant.

3.1.2. Preparation for Field Operational Test
For the field operational test, participating companies
were requested to provide the following items and/or services.
· Contract Phase- Property loan agreement, non-disclosure agreement· Items related to evaluation target system- Vehicle system, vehicle system components, telematics
service (ECU, service account, server, application)· Preparation for evaluation work
- Set up of evaluation target system (initial connection,functionality check)
3.1.3. Confidential Information Handled in Field
Operational Test and Scope of Disclosure
Scope of disclosure for the confidential information handled in the field operational test was restricted as shown in
the following table. PwC and the individual participants
shall sign a non-disclosure agreement to apply and ensure
stricter confidentiality management.

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Information Security

sion through one external interface produce the same
results for the other interfaces.

3.2. Reviewing the Evaluation Method
As previously mentioned, several issues were foundthrough the trial research and the evaluation method forthe field operational test was reviewed to reflect the necessary improvements.
3.2.1. Review of Evaluation Method in the Reconnaissance Phase
The reconnaissance phase, which turned out to be themost time consuming phase of the trial research, wasreviewed as follows:- Hardware reverse engineering work will be contracted
to a third party vendor with specialized skills and timing
will be concentrated in the initial period of the phase. This
will allow the reconnaissance phase to be completed infour weeks (one month).
3.2.2. Review Evaluation Method in the Intrusion
Phase and Onwards
The intrusion phase was reviewed so that a deep-dive into
the procedures from intrusion to action on objectives
phase will be performed based on the following prioritization.
- Priority
1. Active attacks through cellular network
2. Passive attacks through cellular network
3. Active attacks through Wi-Fi
4. Passive attacks through Wi-Fi
5. Active attacks through Bluetooth
6. Passive attacks through Bluetooth
Intrusion will be attempted using all attack methods
against all external communication interfaces within thestandard evaluation period of one month. The escalation ofprivilege and action on objectives phases are evaluatedafter successful intrusion into the vehicle embedded system. Therefore, it can be assumed that a successful intru-

3.3. Planning of Evaluation Guideline Update
Through the management of the field operational test, theevaluation guideline is to be finalized as follows:
3.3.1. Implementation of Threat Analysis
The field operational test aims to evaluate the robustnessof the vehicle systems against attacks from three communication interfaces: cellular, Wi-Fi and Bluetooth.
Alternatively, as the number of target areas for the penetration tests to be conducted within a limited timeframe isexpected to increase in the future, threat analysis will be
integrated into the evaluation guidelines in order to enableprioritization of the targets.
3.3.2. Refinement of Evaluation Criteria
The evaluation criteria and the elements used as input for
the evaluation are defined as follows to ensure a reproducible evaluation method.
(1) Evaluator Skill
Identify necessary skills of an evaluator. Self-check shallbe conducted by the evaluator, then cross-checked by the
responsible manager.
(2) Procedures based on Guideline Evaluation Items
Clarify procedures described in the guideline. Based on
work evidence and results, evaluate that each evaluation
items were conducted with minimal variation.
(3) Evaluation Workload
Set the standard evaluation period at two month (forty
working days) with two staff members and perform theevaluation accordingly.
Manage the field operational test based on this set evaluation workload and evaluate the validity of the elements
through the test results, to reflect any necessary improvements into the evaluation guideline.

4 Conclusion
In the Information Security Field Operational Test, a penetration test will be conducted based on the vehicle cybersecurity evaluation method that was developed.
Based on the test results, the Information Security Evaluation Guideline will be summarized to contribute to thestandardization of the penetration testing method for vehicle security evaluations.
About the author
1) Ken Okuyama, Technology Consulting Manager,
Cybersecurity, PwC Consulting LLC
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IV Field Operational Tests① Large Scale Field Operational Tests

Pedestrian Collision Reduction
Kosuke WATABE (Nippon Koei Co., Ltd.)ABSTRACT: The goal of this project is the reduction of pedestrian collisions. For this purpose, various demonstration experiments withattention evaluation tools were conducted using communication technology (V2P). In the demonstration experiments, functionality andeffectiveness were verified under the actual traffic conditions, and the impact of promoting the social value of the provided service was examined. The service transmits alerts, conveys information, and provides notifications of the presence of an intersection between cars and pedestrians, and was assigned a certain evaluation. This report presents the overall process and the results of the project for the two years of FY 2017
and FY 2018.

1

Introduction

This research aimed to demonstrate the effectiveness ofpedestrian collision reduction support technologies under
actual traffic conditions, and to achieve the promotion ofsocial value for said technologies by carrying out verification tests and validations of mutual warning functions in
portable devices that incorporate vehicle-to-pedestrian(V2P) communication technologies, high precision pedestrian positioning technologies, and action prediction technologies. Systems using these technologies provide services
such as transmitting alerts, conveying information, andproviding notifications of the presence of an intersection.
This is a two-year project running from FY 2017 through FY
2018. In order to determine the effectiveness of the V2P technologies and the issues to solve, we are conducting several
experiments and considering measures in scenarios where thetechnology is effective at reducing pedestrian accidents.
Experiments to examine the reliability of V2P devices werecarried out during FY 2017. In FY 2018, a survey was conducted among the public, and the effectiveness of the devices
was evaluated and verified in scenarios requiring support.
V2P assistive technology was applied separately to thedeveloped products (mobile terminals) of the counterparts
involved, based on the assumption that two-way communication was established between the service for pedestrians and in-vehicle devices.

support. Category A) consists of five scenarios: (A1) pedestrian crossing an uninterrupted road section, (A2) crossing
an intersection with poor visibility, (A3) right turn at anintersection (both with and without traffic signals), (A4)left turn at an intersection (both with and without traffic
signals), and (A5) roads without sidewalks (Fig. 1). Category B) also consists of five scenarios: (B1) inside a vehicle,(B2) inside a building, (B3) on a pedestrian footbridge,(B4) on a sidewalk, and (B5) on or below an elevated structure (Fig. 2).
The experiment area must have enough space to allow us
to conduct the experiments for all scenarios safely. For thatreason, Odaiba and Ariake in Tokyo were the final choice.

2 Project Process
2.1. Preparation for Experiments
The first step of this project was to examine the reliabilityof V2P devices, and we started by designing experimentplans. To conduct experiments effectively, various scenarios were introduced and divided into two categories, A)
scenarios requiring support and B) scenarios not requiring

180

Fig. 1 (A) Five scenarios requiring support.

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Pedestrian Collision Reduction

ried out on the log data. The experiments were carried out
in the same location for the same scenario multiple times,
taking dependence on the surrounding environment into
account, to achieve the target number of samples.
2.3. Evaluation of Devices
The results of the experiments show the goals were mostly
achieved (Figs. 4 and 5).
The correct operation ratios in scenarios A1) and A2)
tended to be slightly low. There was some excessive detection and other issues involving notification transmission
and reception methods at intersections, and there is stillroom for improvement in the information notification
methods.
In the scenarios in which support was not required, theFig. 2 (B) Five scenarios where support is not required.

Experiment plans were established as laid out below to establish assessment methods for objectively and quantitatively
verifying whether the provision of information to both pedestrians and vehicles allowed each of them to avoid danger.
· Evaluation by check sheet· Construction of a visualization system and database withwhich to evaluate collected data and logs
2.2. Experiments for Reliability of Devices
The experiments were carried out from February 13 to 15,
2018 with 2,604 samples collected for ten scenarios in 14
locations. A total of 150 people, including the testing staff,
participated.
In the experiments, V2P communication systems wereoperated. These systems are composed of pedestriandevices (Fig. 3) and in-vehicle devices. The goals of theseexperiments were to achieve a correct device operation
ratio of more than 80% for the scenarios requiring support,
and an incorrect operation ratio of less than 20% for the
scenarios where support was not required.
Pedestrians and drivers with the appropriate devices passed
through each scenario, and we examined how correctly eachdevice operated. A shotgun method was introduced in orderto gain reliable sampling effectively. In addition, condition
verification, trend analysis, and factorial analysis were car-Fig. 3 V2P communication system used for verification.

Fig. 4 Results of scenarios requiring support.

Fig. 5 Results of scenarios where support was not required.
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2.4. Experiments for Examining Behavior Change
Demonstration experiments based on general monitor
tests were conducted from 22 to 23 July 2018 and in midNovember 2018.
In these experiments, we analyzed what changes in action
and consciousness are observed when information such as
alerts is provided to pedestrians and drivers in scenariosthat require support. In scenarios A1 and A2, where many
accidents between vehicles and other road users occur,
experiments were also conducted in cases where no information was provided.
Since these types of accidents are common in children(especially 7 years old boys) and elderly people, we classified them as children (lower grade), adults, elderly people(65 years old and over), and carried out an evaluation
demonstration experiment with thorough safety measures.
(1) Verification using test course
Using the JARI test course, we verified behavior change ina situation where the pedestrian and the vehicle approacheda scene where there were many pedestrian accidents.
Forty-eight pedestrians and 32 drivers cooperated in theexperiments.
The car was driven at 50 km/h, and 30 km/h for Case A1(crossing on a single road), and 30 km/h for Case A2(intersection outside the line of sight), with the pedestrian
walking at a normal pace in accordance with the approachof the car were the conditions applied.
In pedestrian experiments, the frequency of visual confirmation increased in cases where information is provided,
and the average time to behavior change (stopping) tends
to be longer when it is not provided. This was not limited
to a particular age group, but was constant, with an especially large difference observed in the elderly (Fig. 7).
In contrast, in the experiments with drivers, the behavior
leading to deceleration changed in all age groups.
Proportion ofvisual checks (%)

ratio of incorrect operations was high in the scenarios
related to altitude such as (B3) pedestrian footbridges and(B5) on or below elevations, with information notifications
being unintentionally sent and other issues. This was
attributed to the fact that the high bridge is a slope structure, and it was inferred that there was a case that itmatched after altitude detection
The problems and tasks were identified for the next verification.
We also studied whether or not it would be possible tosecure data by having pedestrians walk freely and undirected throughout an area for the main verification (survey), but this was found to be extremely difficult.

OverallChildren

AdultsElderly people

Fig. 7 Verification of pedestrian behavioral change (A1).

(2) Validation on public road
As in (1), the Odaiba district was used as a test site, with
XXX pedestrian monitors and XXX driver monitors participating in a scenario requiring support. In a scenario where
information provision is required, the monitor experiences asituation where the pedestrian and vehicle encounter one
another. After that, we assessed the situation using log andcamera data, and also conducted a questionnaire on theevaluations, tasks and requests concerning information provision, to examine the effectiveness of the system.

3 Conclusion
This study has made it possible to evaluate that providing
information such as alerts via V2P two-way communication is effective for both pedestrians and drivers. In addition, there were needs and issues related to terminal
requirements (functional requirements, requirements forweight reduction required for portability) and behavior
changes. It would be greatly fulfilling if the results obtainedin this research proved useful for the social implementation of this system.

Fig. 6 Validation of behavior change (A1, A2).
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IV Field Operational Tests① Large Scale Field Operational Tests

Advanced Rapid Transit
Daisuke Oshima, Akira Mitsuyasu, Takane Imagawa (Pacific Consultants Co., Ltd.)
Junko Ushiyama, Chiemi Tsuchiya, Masao Yamamoto (Hitachi, Ltd.)ABSTRACT: A large-scale field operational test on the introduction of advanced rapid transit (ART) will be conducted with the aim of developing a public transportation system that is convenient and easy to use for various users, including the elderly and people with disabilities.
Specifically, there are plans to: verify the effectiveness of advanced public transportation priority systems (PTPS) to reduce the time required
for ART, verify the precise docking control system at bus stops to provide safe and quick boarding and alighting for all passengers, verify theeffectiveness of the route map and guide information created by aggregating and integrating barrier-free information collected from people
with restricted access to transportation and other people, as well as the provision of congestion forecasts by station and time based on the
results of analyzing congestion around stations near a venue when events are held. A field operational test on the ART Information Center
function as the foundation for realizing services utilizing ART-related information as well as organic and flexible cooperation of ART systems
by collecting, managing, and providing various traffic information is also included in the plan.

1

Overview

A large-scale field operational test on ART is being carried out on the five elemental technologies (ART swiftness
based on utilization of advanced PTPS, precise docking
control at bus stops, ART movement support system forpeople with restricted access to transportation, guidancefor congestion prediction and avoidance, and ART Information Center function) in order to verify the effects of thetechnologies developed.

2.2. Overview of test
The implementation period, place, and other details of
the field operational test are shown in Table 1 and Fig. 2.
Table 1 Overview of advanced PTPS field operational test

(See Fig. 2)

Fig. 1 Visualization of advanced rapid transit development

2

Improvement of ART Swiftness throughAdvanced PTPS

2.1. Purpose of test
In the context of the development of Advanced PTPS, thistest verifies the extent to which advanced PTPS, which
detects the approach of vehicles at an intersection and controls the priority signal accordingly, and priority arbitration for multiple vehicles simultaneously approaching anintersection, improve the swiftness of ART. In addition,
this test also examines the interface that informs the bus
driver of the priority status of the driver’s vehicle.

Fig. 2 Traveling route of advanced PTPS field operational test vehicles

2.3. Verification items(1) Verification of the effect of advanced PTPS on bus
operation.
(2) Verification of priority arbitration when two buses withroutes that cross simultaneously approach an intersection.
(3) Verification of HMI.

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3

Sensing and Control technologies Related to Precise Docking Control in Advanced Rapid Transit System

3.1. Purpose of test
This test verifies issues in the practical application of the
precise docking control system based on the integration ofsensing technologies such as vehicle position detection,and vehicle control technologies such as braking and steering.
3.2. Overview of test
The implementation period and other details of the field
operational test are shown in Table 2 and Figure 3.
Table 2 Overview of verification test of precise docking control

See Fig. 3

height difference and gap between the ground and the stepof the bus are reduced by placing a temporary platform on
the sidewalk.

4 Pedestrian Support System
4.1. Purpose of test
4.1.1. Field operational test on provision of bus boarding information
This test examines the effectiveness of the public transportation transit guide, bus boarding guide, and crowdedness information provided as a service by the ART
Information Center function.
4.1.2. Field operational test on route guidance according to individual characteristics
This test evaluates the usefulness and acceptability of theroute guidance according to individual characteristics for awide variety of users including people with restricted
access to transportation through a personal navigation app
utilizing information (common infrastructure information) necessary for movement, collected in FY 2017.
4.2. Overview of test
4.2.1. Field operational test on provision of bus boarding information
The implementation period and other details of the field
operational test are shown in Table 3 and Fig. 5.
Table 3 Overview of field operational test on provision of bus boarding
information

Fig. 3 Traveling route of precise docking control field operational test
vehicle3.3. Verification items(1) Confirmation and verification of issues under realworld conditions
This test confirms and verifies various issues under various road, traffic, and communication environments byperforming precise docking control on public roads and atbus terminals.
(2) Verification of ease of boarding and alighting between
platform and bus
This test also verifies how precise docking control affects
the ease of boarding and alighting for passengers when the

See Fig. 5
day

Fig. 5 Route of field operational test on provision of bus boarding
information

Fig. 4 Scenes of precise docking control184

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Advanced Rapid Transit

4.2.2. Field operational test on route guidance according to individual characteristics
The implementation period and other details of the field
operational test are shown in Table 4.
Table 4 Overview of field operational test on route guidance according toindividual characteristics

day

4.3. Verification items
4.3.1. Field operational test on provision of bus boarding information
This test uses questionnaires to verify the usefulness ofproviding public transportation-related information, suchas transit guide information and boarding/congestion notification information while moving between Toyosu andAriake using public transport including assumed ART
buses.
4.3.2. Field operational test on route guidance according to individual characteristics
The prototype personal navigation app displays route
search results according to the characteristics of the various users, including people with restricted access to transportation, and provides route guidance. This test verifies
the acceptability of the route guidance according to individual user characteristics by asking people with restricted
access to transportation to move around Toyosu Station
while carrying a smartphone with the personal navigation
app installed.

Guidance methods for predicting and

5 avoiding congestion

5.1. Purpose of test
Looking ahead to the Tokyo Olympic and Paralympic
Games, this test examines the method of providing congestion avoidance guidance by offering appropriate information according to individual attributes and scenarios, on
the basis of the application of dynamic congestion prediction and human behavior modification processes in collaboration with the ART Information Center function. This
field operational test provides information on large-scale
events and verifies the effectiveness of the method throughquestionnaires.
5.2. Overview of test
The field operational test is conducted using the Sumida

River Fireworks Festival and Jingu Gaien Fireworks Festival as large-scale events that attract hundreds of thousandsof visitors. Specifically, congestion prediction informationand congestion avoidance recommendation information
are provided to visitors using the existing route search sites
or the app (see Fig. 6). The congestion prediction information provides real-time information on the degree of congestion (such as congestion forecasts by station and time)
based on the route search history. The congestion avoidance recommendation information suggests ways to avoid
the congestion, such as “come to the venue earlier” and “getoff the train at another station and walk a little.”

Fig. 6 Example of providing information in field operational test (appinformation screen)

5.3. Verification items
This test conducts a web-based questionnaire for app
users in order to verify the effectiveness of efforts to promote behavior modification of visitors by providing information. Specifically, this test shows the effectiveness ofproviding information, such as the percentage of visitors
who have changed the stations or railway lines they use or
the time they leave home to visit the event.
5.4. Results of verification
The tabulated questionnaire results show that about 90%
of respondents who viewed the provided information
answered that the information was useful, and about 20%of them answered that they had modified their originalplanned route. In addition, another 40% or so of respondents answered that they made a decision after reading thearticles because they originally had not decided how to go
to the venue, which indicates the usefulness of the provided information.

6 Functions of ART Information Center
6.1. Purpose of test
The ART Information Center plays the function of information linkage in the above-mentioned large-scale field
operational test on the improvement of ART swiftness
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Advanced Rapid Transit

based on utilization of advanced PTPS and of the pedestrian support system (see Fig. 7).

Fig. 7 Visualization of operations of ART Information Center function

6.2. Overview of test
The implementation period and other details of the field
operational test are shown in Table 5.
Table 5 Overview of field operational test on functions of ART Information Center

6.3. Verification items
Two demonstration confirm that the functions of the
ART Information Center function, including the PTPS priority arbitration function and boarding information support function, work without any problems and whileproviding useful information.

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IV Field Operational Tests② Regional Tests

Feasibility Study of Advanced Automated Driving
System for Public Buses in Okinawa
Takayuki Ando (Advanced Smart Mobility Co., Ltd.)ABSTRACT: In Okinawa, traffic jams are a serious social problem that should be resolved, and there are high expectations for the development of an advanced rapid transit (ART) system using automated driving technology. This feasibility study aimed to resolve various technological- and service-related issues to facilitate the introduction of an automated ART driving system in Okinawa. Although this study foundthat some functions of the proposed system were feasible, such as lane keeping, velocity, approach, and lane change controls, it also found thathigher recognition capabilities and higher control accuracy will be necessary before a level 4 or 5 driverless automated driving system can be
adopted. In addition, issues related to cost, laws, infrastructure, and support for disabled people will also have to be resolved before this system
is ready for introduction.

1 Traffic environment in Okinawa
Traffic jams in Okinawa have become a serious social
problem that should be resolved. Okinawa has no railways
except a monorail and the utilization rate of public transportation is extremely low(1). At the same time, car ownership has been increasing and more tourists are borrowing
rental cars. In this situation, there are high expectations
that an advanced rapid transit (ART) system using automated driving technology can promote the use of public
transportation.

inputs to the engine ECU, and deceleration realized bycontrolling the pneumatic pressure. RTK-GPS and magnetic sensor were used for localization. LiDARs, cameras,
and radar were used for detection.
2.3. Course
Figure 2 depicts the course of the experiment, which was
conducted in Ginowan city and Kitanakagusuku village inDecember 2017. The length of this course is 20 km, and it
includes Route 58 which is the main road on the island of
Okinawa. Another experiment was conducted in Nanjo
city in March 2017.

2 Experimental Conditions
2.1. Aim
The main aim of this study was to clarify potential issues
created by the future introduction of automated buses in
Okinawa, by examining the feasibility and acceptance oftechnologies, such as lane keeping control, velocity control,
object detection and avoidance, approaching control,
remote monitoring, and the like.
2.2. Vehicle
The small bus shown in Fig. 1 was remodeled into an
automated driving bus. A steering motor was mounted on
the column shaft, acceleration was automated by voltage

Fig. 2 Map of the Experiment Course

3 Specification and Experimental Results
3.1. Object Detection
The automated bus detected objects using LiDARs, cameras, and radar, as shown in Fig. 3. The types of objects,
such as cars, bicycles, and pedestrians were identified using
AI. As shown in Fig. 4, road maps were used to distinguishobjects in the same lane of the bus from those in otherlanes. These methods reduced the non-detection and misdetection of objects.

Fig. 1 Automated Driving Bus
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Figure 7 defines the error in the lateral position when lane
keeping control is active. It shows an error of within ±0.2 m,
which indicates that the bus does not deviate from its lane.

Fig. 3 Object Detection Using Sensors

Fig. 7 Lateral Position Error

3.3. Approach Control
To make it easier for passengers to get on and off, the bus
must stop closely and accurately to the bus stop. Bus stop
approach control using sensors such as a LiDAR, magnetic
sensors, and RTK-GPS was trialed as shown in Fig. 8.
Fig. 4 Distinguishing Objects Using Road Maps

3.2. Lane Keeping Control
Lane keeping control adopted path following control(2).
Figure 5 defines errors in the lateral position and yaw angleof the vehicle from the reference path. The lateral positionand yaw angle were measured using RTK-GPS and theerror was calculated as follows.

Fig. 8 Approach Control to Bus Stop

The experimental results in Fig. 9 show that the distance
between the curbstone and the bus was within 4.0 cm ±2.0
cm when the automated system uses LiDAR for curbstone
detection.

Fig. 9 Distance between Curbstone and Bus
Fig. 5 Definition of Error

In Fig. 6, δ is the steering wheel angle calculated anddirected by the lateral position and yaw angle feedback
control. Ky and Kθ are the feedback gain of this controller,
and Gy,θ) is the transfer function of the vehicle.

Fig. 6 Lateral Position and Yaw Angle Feedback Control

188

3.4. Object Avoidance and Lane Change Control
Figure 10 shows the object avoidance and lane change control procedures when preparing to change lanes to the right.
Object Avoidance Control
Detects
objects
using
front sensors
Tells the driver
to be readyfor object
avoidance

Tells the driver
to be readyfor lanechangeIf the driver
does not cancel
it, the object
avoidance
control starts

If the driver
does not cancel
it, the lanechange control
startsFig. 10 Object Avoidance and Lane Change Control

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Feasibility Study of Advanced Automated Driving System for Public Buses in Okinawa

The experiment found that it is difficult to change lanes
when the road is crowded or the speed of the bus is much
lower than other vehicles.

labor costs of a fully automated system would be necessary
to pay for the introduction of the automated driving system.

3.5. Velocity Control
Acceleration and deceleration commands are calculated
by a velocity controller that generates a reference velocity
in real time. When the bus detects an object in front of it,
such as a vehicle stopped at a red light, adaptive cruise
control (ACC) decelerates the bus as shown in Fig. 11.

3.8 Override
Unexpected steering overrides occurred 2.3 times andbrake overrides occurred 4.0 times per 20 km on average.
Figure 14 depicts a steering override case, in which a large
bus had stopped on the bus route and there was small
space to avoid it. Higher recognition capabilities andhigher control accuracy are needed for a level 3, 4, or 5
automated driving system.

Fig. 11 ACC Results

3.6. Remote Monitoring System
The system allows a remote observer to check the vehicle
position, speed, state, and whether an error has occurred.
The observer can stop or start the vehicle by watching live
videos taken by cameras inside and outside the vehicle.

Fig. 12 Remote Monitoring System

3.7. Questionnaire results
Passengers became more positive about riding an automated bus than before the test ride. However, they identified many issues to be resolved before such a bus could be
introduced, such as cost, laws, infrastructure, support fordisabled people, safety, and the like.

Fig. 13 Questionnaire Results

Many business operators answered that a level 2 or 3
automated driving system had few merits since the lower

Fig. 14 Case of Steering Override

4 Conclusion
Although many issues related to technologies and services remain before an automated ART system can be
introduced in Okinawa, some of were resolved in this feasibility study. Higher recognition capabilities and higher
control accuracy are necessary requirements for a level 4 or
5 driverless automated driving system. In addition, issues
related to cost, laws, infrastructure, and support for disabled people will also have to be resolved before this system is ready for introduction.
References(1) The Japan Times: Okinawa set to launch first railway
project, https://www.japantimes.co.jp/news/2014/02/20/
national/okinawa-set-to-launch-first-railway-projectto-ease-chronic-traffic-jams, (February 20, 2014).
(2) T. Fukao et al.: Preceding Vehicle Following Based on
Path Following Control for Platooning, Proceedings of
IFAC AAC (2013).
About the author
1) Takayuki Ando, Assistant Manager, Control Development, Engineering Div., Advanced Smart Mobility Co.,Ltd.

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IV Field Operational Tests② Regional Tests

Field Operational Tests for Automated Driving Systems in Rural Areas
Ministry of Land, Infrastructure, Transport and Tourism Road Bureau ITS Policy andProgram Office
ABSTRACT: Due to the progressive aging of the population in Japan in recent years, ensuring means of transportation in rural areas has
become a pressing problem. In the cross-ministerial Strategic Innovation Promotion Program (SIP), field operational tests (FOTs) have been
conducted for automated driving services (ADS) in rural areas where the aging of the population is progressing with particular rapidity, usingroadside rest areas (called “Michi no Eki” in Japanese). These tests have been conducted nationwide since September 2017, and tests were
performed in 13 locations during FY 2017. The tests are verifying the effects of ADS on road transportation in anticipation of their fully
fledged introduction, as well as social acceptance, benefits to the community and so on. The test results verified that ADS could successfully
and smoothly navigate even steep winding roads in mountainous areas, even when there was snow on the road. However, the test results also
identified issues such as parked vehicles on the road and vehicles coming from the opposite direction. The tests also showed that participants
have high expectations of ADS, felt that ADS is considerably reliable, and that the introduction of these services would have a substantial
beneficial impact on communities. SIP will continue to work toward the introduction of ADS in rural areas.

1

Background

The aging of society is progressing much more rapidly inrural areas than in the nation as a whole. The aging of the
population in these areas is thought to be roughly ten years
ahead of the national average, with an aging index of 31%
in 2010 compared to the national average of 23%. In addition, the total length of bus routes that were abolishednationwide from FY 2007 to FY 2015 comes to 13,000 km,
forty percent of truck drivers are 50 years of age or older,
and in 2017 alone, the number of people aged 65 or older
who voluntarily surrendered their driver’s licenses came to400,000. This is around 17 times the number (approximately 20,000) who did so in 2007.

Amidst these trends, around half of all rural residents
aged 75 or older leave the house less frequently after voluntarily surrendering their driver’s license. Presumably thesesenior citizens have fewer opportunities to leave theirhomes and go to places that they would ordinarily frequent, such as supermarkets and other commercial facilities, hospitals, local authority offices and financial
institutions.
Roadside rest areas (called “Michi no Eki” in Japan) havebeen constructed in 1,145 locations nationwide as of April
2018. Eighty percent of these are located in rural areas.
Thirty-nine percent are located within one kilometer of a
hospital or clinic, and 35% are located within one kilometerof a facility that offers public services. Forty-four percent
have bus stops for express buses, route buses, and on-

Fig. 1 Locations of FOTs in FY 2017190

[ IV ] Field Operational Tests② Regional Tests

Field Operational Tests for Automated Driving Systems in Rural Areas

demand buses. Large numbers of such rest areas function as
centers for transportation and community life in rural areas.

Table 2 Passenger cars selected

2 Purpose
Field operational tests (FOTs) are being conducted at 13
roadside rest areas nationwide in anticipation of the introduction of automated driving services (ADS). These FOTs
are aiming to verify the effects on road transportation of
the fully fledged introduction of ADS in rural areas by2020 from the viewpoints of technology and businessmodels.

3 FOTs in FY2017
At first, five locations for FOTs were selected for the primary purpose of technical verification. Eight locations
were also selected for the primary purpose of studying
business models. Apart from these 13 locations, anotherfive regions were also selected for the purpose of conducting a feasibility study of business models. Figure 1 showsthese locations. FOT committees were established in eachlocation to verify research items, routes, transportation
systems, to coordinate with related entities, and to conduct
testing and verification.
Second, four types of vehicles were selected through an
open bidding process. Various types of models equippedwith different driving technologies were selected, as shownin Table 1. These vehicles are basically operated at low
Table 1 Buses selected

speeds.
Third, the route of each test was set on the basis of thelocation of the roadside rest area. The routes travel aroundsurrounding villages, private residences and facilities
including hospitals, local authority offices, agricultural
centers, and other important facilities. The total distance
was a few kilometers. The routes were divided into two
types of section: dedicated sections for level 4 automateddriving and those for level 2 automated driving. Following
the constraints defined in current regulations, the level 4
sections were set in a special manner.
Finally, the items tested were (i) the effect on road infrastructure, road management, and road transportation systems, (ii) the reliability of ADS, (iii) business feasibility, (iv)the human machine interface (HMI), especially relating tosocial acceptance, and (v) impacts on the local economy.
3.3. Case study – Hitachi Ohta roadside rest area.
This section introduces the FOT conducted at the Hitachi
Ohta roadside rest area as one of the case studies. It was
implemented for one week starting on Sunday November
19, 2017. It used the vehicle supplied by Yamaha Motor as
shown in Photo 1. This is a cart type vehicle with 7 seats
and a forward camera for object recognition. The total
length of the route was 3.2 km, which includes 500-meter
sections for level 4 automated driving. The route connects
the Hitachi Ohta roadside rest area with several bus stops
for passengers and farm products.
The FOT concentrated on technical aspects, namely, (i)the effects on road infrastructure and the like, (ii) the reliSIP-adus: Project Reports, 2014-2018

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[ IV ] Field Operational Tests② Regional Tests

Field Operational Tests for Automated Driving Systems in Rural Areas

ability of ADS, and (iii) the HMI. It was expected that ADS
would find it difficult to navigate sectors with overgrownvegetation and narrow lanes. Furthermore, in terms of reliability, the FOT verified the effect of rain on the detection
capability of the forward camera. One of the unique pointsof this FOT is the collection of agricultural crops anddelivering them in cooperation with express buses. Photo 1
shows several scenes from the FOT.

Snow

Vehicle on-street
parking

Pedestrian

Photo 1 Scenes from FOT

4 Results of FOT
4.1. Effects on road structure, road management, androad transportation systems(1) Road structure
The ADS was able to navigate smoothly even on steep
winding roads and was not affected by road alignments.
The ADS drove smoothly even in sectors with steep
inclines. However, in some cases, ADS recognized thesteep incline as an obstacle. Furthermore, in sectors withno sidewalks and narrow road shoulders, ADS detected

Photo 2 Examples of obstacles
192

pedestrians and bikers as obstacles and stopped orrequested the user to drive manually.
This FOT also indicated the importance of cooperation
with the local community. For example, signposts thatindicate dedicated ADS sections are needed to caution
pedestrians and bikers not to enter the sections concerned.
At intersections with no traffic signals, when ADS
detected or needed to make way for approaching vehicles,
in some cases, the vehicle either stopped or required manual driving to avoid the vehicles. When approaching intersections with poor visibility and the like, ADS also required
manual intervention in advance in some cases.
(2) Road management
During the FOT, cases occurred in which the ADS
detected plants or weeds on the roadside and stopped or
required manual driving. This shows that it is essential forroad administrators to perform appropriate plant management. Also, in areas with heavy snowfall, ADS was able todrive smoothly even when the road was covered with snow
or compacted snow (approximately 10 cm). In some cases,
however, snow that had been pushed to the sides of theroad was detected as an obstacle on the road. Road administrators will be required to remove snow appropriately for
ADS.
(3) Road transportation systems
While ADS was able to merge smoothly onto and off twolane sections of roads, on single-lane and other narrowroad sections, there were cases in which the ADS regarded
approaching vehicles as obstacles and stopped or required
manual driving. In particular, it might be necessary to takemeasures for narrow road sections. Some potential solutions are to provide turnouts, to establish rules that givepriority to ADS transit through cooperation with the local
community, or to make such roads one-way.
Under mixed traffic environments, the ADS was able todrive smoothly when its vehicle speed was about the sameas the surrounding vehicles. However, traffic congestion
occurred in some cases when its speed was extremely
slower. It might be necessary to introduce dedicated sections, provide turnouts, or other measures. Also, when
there were parked vehicles on the road, ADS detected
them, and stopped or required manual driving. As a result,it may be necessary to introduce measures in cooperation
with the local community, such as posting signs indicating
ADS routes and regulations prohibiting drivers from parking their vehicles.
(4) Roadside rest areas as transit centers
There were cases in which ADS detected pedestrians or[ IV ] Field Operational Tests② Regional Tests

Field Operational Tests for Automated Driving Systems in Rural Areas

two-wheeled vehicles and stopped or required manual
driving. Measures will be required, such as the posting ofclear signs indicating ADS routes and securing adequate
spaces for two-wheeled vehicle parking.
When there was snow at the roadside rest area, in somecases, the vehicle parking spaces were not visible. The ADS
detected parked vehicles as protruding into the road, andstopped or required manual driving. It might be necessary
to remove snow as appropriate.
4.2 HMI including social acceptance
The research conducted questionnaire surveys with monitors who participated in FOTs as ADS passengers. The
questions were related to the HMI. There were 1,240
respondents. The ratio of male responders was 60.5% andthat of senior citizens aged 65 or older was 39.2%. The survey showed that 40% of respondents recognized the benefits of ADS. They hoped it would become a better
developed means of daily transportation. In contrast, themost frequently mentioned concerns were unforeseeable
issues and lack of maintenance. Figure 2 shows the change
in attitudes about the reliability of ADS before and afterriding the vehicle. The result shows that people tend to feelthat ADS is more reliable after riding in the vehicle. The
results from responders who experienced level 4 automated driving also show that more people felt that ADS isreliable after riding in the vehicle.
Another result showed the change in people’s intention to
use ADS in the future after their experience of ADS. Overall, there was a high inclination to use ADS in the future,
which became even higher after riding in the ADS vehicle.
People tended to worry about securing daily transportation in the future. Especially, those who intend to voluntarily surrender their driver’s licenses stated that they weremore likely to use ADS.
4.3 Impacts on local economy
The survey results showed that ADS would provide local
people with potential benefits. For example, they could
have the opportunity to go anywhere, take trips outside thehome, and make use of delivery services using ADS.

5 Conclusion
These FOTs have found that, despite several technicalissues that should be addressed, ADS is basically applicableto routes including roadside rest areas, hospitals, local
authority offices, supermarkets, and other facilities. To
achieve the government goal by 2020, it will be necessary
to implement additional FOTs that basically focus on business feasibility to help local communities introduce ADS
and to secure local transportation services at a reasonable
cost.

Fig. 2 Change in people’s attitudes about the reliability of ADS
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V

International Collaboration

Overview of International Cooperation Activities by
SIP-adusHajime Amano (ITS Japan)

1

BackgroundIn the past ten years, technologies for automated driving
have been dramatically improved not only by establishedautomobile manufacturers, but also by startups in theinformation service industry. The time between the start ofresearch and development of technologies to their deployment as commercial services is being shortened. A varietyof activities have been initiated in vehicle technologies,
infrastructure, standardization, regulatory issues and field
operation tests in every part of the world, even thoughautomobile manufacturers are operating in a global single
market. Therefore, international cooperation from the very
early stage of development is recognized as an essential
aspect of connected and automated driving projects for theinternational harmonization of standards and regulatory
frameworks.

2

Definition of the Goal of the ProjectAt the beginning of the project, its goal was defined forsharing with international stakeholders. The idea is
described as follows.
“An inclusive society, where different people in diverse
communities actively participate in generating values, will
enhance both the wellness of individuals and economic
development. Automated driving technologies integrated
with social innovations should provide everyone withmobility to fully exercise their capabilities, enabling thesustainable development of the society.”
Then the project was named SIP-adus: Cross-Ministerial
Strategic Innovation Promotion program, Innovation ofAutomated Driving for Universal Services – Mobility
Bringing Everyone a Smile –.

3

Focus Areas

The key international cooperation platform for connected
and automated driving systems has been the EU-US-Japan
Automated Road Vehicle Working Group, co-chaired by
the European Commission, U.S. Department of Transpor194

tation and the Japanese Ministry of Land Infrastructure,
Transport and Tourism. Through discussion with international counterparts, six important areas of international
cooperation were identified and six leaders were assigned
to coordinate that cooperation. As the project had madeprogress toward deployment, field operational tests wereadded as another area of cooperation. All international
activities are aligned to those thematic areas.
3.1. Dynamic Map
Digital map databases with a layered structure built ongraph network representation of roads will be expanded toinclude highly detailed description of the road structure
and its surrounding environment. The database will be
dynamically linked to real-time data from an integrated
sensing system onboard the vehicle and semi-real-timedata from V-to-X communications. Such databases canonly be developed and maintained through collaboration
across the industry sectors and public agencies.
3.2. Connected Vehicles
Higher levels of automation require a larger range of
observation of the driving environment. The deploymentof connected vehicle technology will be advantageous toautomated driving systems. Proximity will be sensed byintegrated sensors onboard the vehicle. Physically shieldedvehicles will notify each other through V-to-V communication. Beyond the horizon of sensing systems, V-to-I
communication will provide automated vehicles with additional information.
3.3. Human Factors
The shift between levels of automation will depend on thedriving environment and driver’s condition along the trip.
It is important to design automated vehicle systems toeffectively communicate with the driver so that the situational awareness of the driver is maintained and the transition between the levels of automation is properly
performed.
3.4. Cybersecurity
A connected car is a car that has become capable of providing various services, which enhance its convenience andsafety, utilizing DSRC and cellular communication. Also, it[ V ] International Collaboration

Overview of International Cooperation Activities by SIP-adus

uses onboard communication networks such as CAN, LIN,
and Ethernet. At the same time, however, there is a risk of
exposure to cyberattacks. Cybersecurity measures will haveto continue to be reinforced to keep systems secure in the
future. Therefore, it is essential to share information andpromote collaboration among industry and government
across the conventional boundaries between sectors.
3.5. Impact Assessment
Enhanced safety is the objective with the highest priority
in vehicle automation. However, automated vehicle technology is only one part of measures to avoid traffic accidents. Field research on vehicle crashes, the modelingvehicle behavior, and the evaluation of a variety of measures are the foundations to take an integrated approachthat makes the most effective use of new technologies.
Socioeconomic impacts are also important aspects of theassessment, which will provide a foundation for fosteringsocial acceptance of connected and automated vehicle
deployment
3.6. Next-Generation Transportation Systems
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 transit systems featuring
automated driving technologies and on-demand operation
will reduce travel time while making trips comfortable forpassengers, and enhance efficiency for operators. In contrast, small vehicles with enhanced driver assistance for
personal use are also anticipated to provide aged or handicapped users with a level of mobility that will encouragethem to actively engage in social activities.

4

Major International Cooperation
Activities

4.1. Networking
The members of SIP-adus have actively participated ininternational conferences to share ideas and experiences.
Those opportunities include the Automated Vehicle Symposium hosted by the Transportation Research Board andAssociation of Unmanned Vehicle System International,
and the European Conference on Connected and Automated Driving hosted by the European Commission.

open to the public, and closed workshops for in-depth discussion with invited experts. Each plenary session is pairedwith a workshop for a focus area of international cooperation. Presentations and poster sessions on SIP-adus subprojects were organized. Trial rides of prototypes andcommercial models with connected and/or automateddriving technologies were provided for international participants.
4.3. Internationally Open Platform of Field Operation
Tests with Shared Resources Provided
Large scale field operation tests on public roads started in2017, and are open to any party meeting the minimum
qualifications to participate. Test facilities and operation
management are provided. Dynamic map data for the testsites and equipment for connected services are provided at
no charge. The only requirement for the participants is toactually use those shared resources, submit evaluation
reports, and engage in review discussion to improve andaccelerate standardization. Participants who test theirvehicles have to arrange all other resources by themselves.
4.4. Standardization
Results from SIP-adus, namely human factors, dynamic
map and vehicle control systems, have been reflected ininternational standardization activities at ISO TC22 Road
Vehicles and TC204 Intelligent Transport Systems. In addition, collaboration with industry standardization bodies
has expanded.

5

Conclusion

SIP-adus has been actively engaged in international cooperation, gained recognition, and developed a network
among global experts. This is quite a unique achievement
for the Japanese community, which generally has a reputation for being closed.
About the author:
Hajime Amano
President and CEO
ITS Japan

4.2. SIP-adus Workshop on Connected and AutomatedDriving Systems
Annual workshops have been organized for in-depth discussion among international experts. The Workshop is
composed of plenary sessions with a series of presentations
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Dynamic Maps
Satoru Nakajo (the University of Tokyo)
ABSTRACT: The international harmonization of dynamic maps was pursued based on the results of SIP-adus research activities within Japan.
The main task of the activities was to broaden the result of R&D to professionals outside Japan and to get feedback for the direction of thefurther research. The international harmonization activities include fostering international understanding of dynamic maps, arrangement ofstatic map features arrangement, the promotion on SIP-adus FOTs, and moving standardization activities forward. The relationship withrelated activities in other countries is just at the beginning stage. Continuous action will be needed to achieve international harmonization of
dynamic maps.

1

Basic Approach

The international harmonization of dynamic maps was
pursued based on the results of SIP-adus research activities
within Japan. The main task of the activities was to broaden
the result of R&D to professionals outside Japan and to get
feedback for the direction of the further research. The
activity started at the beginning of the SIP program in
2013.
The main actions included:- Making presentations at international conferences,including the ITS world congress, the Transportation
Research Board (TRB) annual meeting, the Transport
Research Arena (TRA), the Autonomous Vehicles
Symposium (AVS) and other related events within andoutside Japan.
- Organizing SIP-adus workshops (dynamic map session) in Tokyo.
- Discussing the topic at the EU-US-Japan Trilateral
Automation in Road Transport (ART) working group.
- Preparing for, and holding discussions at, meetings
related to the above, as well as discussing with otherrelated stakeholders within and outside Japan, including the International Standard Organization (ISO) andthe Open Auto Drive Forum (OADF).
The main outcomes of the actions included:- International understanding of dynamic maps.
- Arrangement of static map features.
- Promotion of SIP-adus field operation tests (FOTs).
- Moving standardization activities forward.

International Understanding of Dynamic

2 Maps2.1. Preparing visual contents
The concept underlying dynamic maps was defined asfour layers of data progressing from static, semi-static,
semi-dynamic to dynamic. There were several use cases
involving employing dynamic maps in automated driving
196

systems. The international cooperation team created presentations to facilitate understanding. Figures 1 and 2 present the results of that work. Figure 1 shows the wholeconcept underlying dynamic maps and their creation. It
illustrates the four layers of data and was created from 3D
common platform data including point clouds, graphics
and probe data. It also shows that it was possible to use thedata directly from vehicles via an API, as well as via OEM
servers.

Fig. 1 Concept underlying dynamic maps

Fig. 2 Example of dynamic map use

Figure 2 shows an example of dynamic map use. It is possible to estimate the position of a vehicle by comparing thehigh definition 3D map data and the sensed data.
2.2. Presentations at conferences
Based on the results of the R&D and other preparedmaterials, the international harmonization team made presentations at conferences, including the ITS world congress, events held by TRB, TRA, AVS, and the European

[ V ] International Collaboration① Leading International Debate on the Six Key International Collaborative Themes

Dynamic Maps

Road Transport Telematics Implementation Coordination
Organization (ERTICO), the OADF, the EU-US-Japan Trilateral ART WG and SIP-adus workshops. The team madepresentations outside Japan ten times during FY 2017.

Table 2 List of FOT participants

3 Arrangement of Static Map Features
SIP-adus has endeavored to harmonize static map features. The research team surveyed the automated driving
systems use cases provided by the Japan Automotive Manufacturers Association (JAMA) and existing specifications
in FY 2015 and selected 34 features. The team also discussed with major map providers in other countries andselected five features to harmonize.
In FY 2016, the team remodeled the map data based on
the data list provided by JAMA, and re-selected 14 basic
static map features. Table 1 shows the basic map features
and the map features to harmonize internationally, which
include:- Center line
- Lane lines- Lane edges- Stop lines- Pedestrian crossings- Carriageway links
Table 1 List of features to harmonize

4 Promotion of SIP-adus FOTs
The research team and the international harmonizationteam actively worked to promote participation in FOTs
from outside Japan. The contributions included translating
related documents and specifications into English, preparing test static map data covering over 700 km, and presenting the FOT plans extensively.
As a result, 21 organizations participated in the FOTs. The
organizations include major Japanese and German original
equipment manufacturer (OEMs) and suppliers. Table 2
shows the participants in the FOTs.

＊

FOT participants are for dynamic maps or the human machine interface(HMI)

5 Support for Standardization
5.1. Support for ISO activities
SIP-adus supported several dynamic map-related ISO
standardization items with ISO/TC204/WG3 members,
include;- ISO 17572-4: Precise relative location referencing.
- ISO 22726: Dynamic data and map database specification for connected and automated driving systemapplications.
- ISO 14825: Geographic data files (GDF).
In the context of international harmonization activities,
the team supported the organization of a joint meetingwith ISO/TC204/WG3 and the OADF. The meeting washeld in July 2017 in Aix-en-Provence, France. At thatmeeting, both organizations informed each other of theirstatus at the time and agreed to discuss further corroboration.
5.2. Collaboration with the OADF and Related Organizations
SIP-adus has discussed collaboration with the OADF several times since 2016. The team participated on an ongoingbasis starting with the 5th meeting in October 2016 in Beijing, China. The team started to make presentations on
SIP-adus dynamic maps at the 6th meeting.
SIP-adus and the OADF worked together to hold 8th
OADF meeting in Tokyo in conjunction with SIP-adus WS
2017. The meeting was held in the same week as the WS,
and many digital map professionals attended both events.
The team also supported further collaboration, such as the
SENSOR Interface Specification (SENSORIS) specificationand the ERTICO workshop in Japan in 2018.
At the ITS world congress 2018 in Copenhagen, SIP-adus
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Dynamic Mapsand the OADF organized a Special Interest Session (SIS).
In that session, both representatives discussed related topics for further collaboration. Figure 3 shows one of theslides presented at the SIS. It explains the relationshipbetween SIP-adus and other related standardization organization within the OADF.

Fig. 3 Presentation at ITS world congress 2018

6 Conclusion
International harmonization activities for dynamic maps
were actively pursued, and included fostering international
understanding of dynamic maps, the arrangement of static
map features, the promotion of SIP-adus FOTs, and moving standardization activities forward.
The R&D on dynamic maps and related dynamic data
distribution by SIP-adus will continue. The relationshipwith related overseas activities is just at the beginningstage. Continuous actions will be needed to achieve theinternational harmonization of dynamic maps.

References(1) Cabinet office of Japan: SIP-adus Dynamic Map Trial
Production and Evaluation (FY 2015) (Japanese)
http://www.sip-adus.go.jp/wp/wp-content/uploads/
co_2015_co1-1_doc4.pdf(2) Cabinet office of Japan: SIP-adus Dynamic Map Trial
Production and Evaluation (FY 2016) (Japanese)
http://www.sip-adus.go.jp/wp/wp-content/uploads/
cao_2016_cao1-01_01.pdf

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Connected Vehicles
Norifumi Ogawa (Mazda Motor Corporation)

1

Introduction

In terms of international cooperation activities concerning connected vehicles, we have focused on sharing information from Japan about how cooperative ITS with radio
communication systems is being applied to automateddriving, surveying the trends in Europe and the UnitedStates and transmitting the information we obtained to thepeople involved in Japan. The detailed activities include
leading the SIP-adus Breakout Workshop, sharing and collecting information on the trilateral conference among
Japan, the U.S, and Europe, participating in the international conferences on automated driving in Europe and theUnited States, as well as making presentations at conferences, visiting various projects conducted in Europe andthe United States, exchanging information among members, and more. The activities are shown below.

2 Activities Summary
2.1 Conferences Attended
The conferences we attended are shown below.
(1) Oct., 2015 SIP-adus Workshop and Trilateral Meeting(2) Apr., 2016 Transport Research Arena and Trilateral
Meeting(3) May, 2016 Grand Cooperative Driving Challenge,
Final Event(4) July, 2016 Automated Vehicle Symposium and Trilateral Meeting(5) Nov., 2016 SIP-adus Workshop and Trilateral Meeting(6) Jan., 2017 Transport Research Board and Trilateral
Meeting(7) Apr., 2017 Connected and Automated Driving Conference and Trilateral Meeting(8) June, 2017 Drive Me (visiting) and Adaptive, Final
Event(9) July, 2017 Automated Vehicle Symposium and Trilateral Meeting(10) Oct., 2017 ITS World Congress(11) Nov., 2017 SIP-adus Workshop and Trilateral Meeting(12) Jan., 2018 Transport Research Board and Trilateral

Meeting(13) Apr., 2018 Transport Research Arena and Trilateral
Meeting(14) June, 2018 ITS America Annual Conference, Connected Vehicle Pilot Program (NYC) (visiting) andSmart Columbus (visiting)(15) July, 2018 Automated Vehicle Symposium and Trilateral Meeting
2.2 Conference Summary
Summaries of the conferences listed above are presented
below.
(1) Oct., 2015: SIP-adus Workshop and Trilateral Meeting
SessionThe U.S. Department of Transportation (USDOT) introduced V2X and truck platooning, which were conductedin New York, Tampa (Florida), and Wyoming, as a Connected Vehicle Pilot Deployment Program (CV Pilot).
The EU introduced truck platooning as well as a cooperative ITS corridor to connect the Netherlands, Germany,
and Austria. They also introduced communication interoperability between European countries, the privacy of probe
information, cybersecurity as future issues.
Breakout Workshop
The Michigan DOT from the U.S., a Dutch organizationfrom the EU, as well as the Ministry of Internal Affairs andCommunications, the Metropolitan Police Department,
the National Institute for Land and Infrastructure Management, auto manufacturers, and electrical manufacturers
from Japan took part in this workshop.
They exchanged information on current and future activities in their own countries, and shared issues about reducing accidents through the spread of connectivity.
(2) Apr., 2016: Transport Research Arena (TRA) and Trilateral Meeting
With respect to connected vehicles and security, it wasagreed in the Trilateral Meeting that the exchange of information would continue. Also, the EU introduced the Declaration of Amsterdam, which was issued as a jointstatement on automated driving by the Ministers of Transport in EU countries.
In TRA, a great deal of time was spent on automateddriving and energy issues. We are facing the question of
how to solve issues that cross national borders, such as theintegration of measures from various countries and the
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Connected Vehicles

commonization of communication infrastructures.
(3) May, 2016: Grand Cooperative Driving Challenge,
final Event
The project started in 2011 and concluded in 2016. A
large-scale verification test on the highway from Helmond,the Netherlands, was carried out over two days, demonstrating the performance of the cooperative vehicle–infrastructure systems with lane changes between platooning
vehicles. After the event, we were able to have an information exchange with TNO (a research institution), which
has been leading the project, allowing us to foster ties with
Europe.
(4) July, 2016: Automated Vehicle Symposium and Trilateral Meeting
The EU reported that the AdaptiVe project, which made acomprehensive study of automated driving technology, thedevelopment of laws, and other factors, would complete
the final demonstration at the end of June, 2017.
The U.S. announced that Columbus, Ohio had been chosen for the Smart City Challenge project aimed at solving
overall urban transportation problems.
Japan introduced the national projects, such as a largescale verification test starting in 2017 conducted as part of
SIP activities.
(5) November 2016: SIP-adus Workshop and Trilateral
Meeting
SessionThe results of a SIP study indicated that a V2V emergency
vehicle approach warning system could reduce the delay
before the arrival of emergency vehicles. In addition, communication between vehicles on a main road and merging
vehicles was reported to facilitate smooth merging.
Finland introduced its E8-Aurora project designed tooffer a driving testing environment.
Breakout Workshop
The list of participants includes the European Commission, the Finnish MaaS Project, Spain, and Belgium fromthe EU, as well as automakers and electronic manufacturers from Japan.
With Japanese members introducing their activities
related to various services utilizing signal information, theparticipants shared information of the status of practicaluse of ITS and held a Q&A session.
(6) January 2017: Transport Research Board and Trilateral Meeting
At the Trilateral Meeting, members discussed future connectivity-related activities and confirmed that information
sharing would be their main focus.
The U.S. announced the guidelines for automated driving
and the issuance of “Notice of Proposed Rule-making”
concerning the mandatory installment of in-vehicle V2V200

devices. The EU announced its expected launch of a comprehensive project on automated driving. Some updates
were shared with members, including the holding of thefirst international conference on automated driving.
(7) April 2017: Connected and Automated Driving Conference and Trilateral Meeting
This was the first expert conference on automated driving
held in Europe.
There were many political reports presented, including
the agreements on cross-border test zones (Cross Border
Self-driving Test Zone between France and Germany), andcross-border legal frameworks for tests. Meanwhile, theindustrial sector is actively working on matters such as situation-based use of 5G mobile networks and DSRC, withan eye toward automated driving.
(8) June 2017: Visit to Drive Me and Adaptive final event
Drive Me is a large-scale test conducted using 100 automated vehicles (Volvo-made). For one year starting in September 2017, it investigated the level of driver confidencein Level 4 automated vehicles and the level of traffic participants capability to interact with them, with the aim of elucidating the challenges involved in automated driving.
The Adaptive project is aimed at solving HMI related
issues and legal challenges, including the social impact of
the realization of automated driving (e.g., decreased CO2
emissions, reduced number of accidents), through technological approaches, namely the development of automatedvehicles, verification tests/simulations with those vehicles,
and more.
· In the test-ride event, members test drove a Volvo truck
performing automated driving on the Autobahn.
(9) July 2017: Automated Vehicle Symposium and Trilateral Meeting
Members visited Gomentum Station (a test course for
automated driving). Currently, automated driving tests,
safety support system tests, and last-mile evaluations using
the EasyMile self-driving bus for unmanned driving, areunderway.
At AVS, there were many reports on studies that centeraround automated vehicles being conducted to improvetransportation services as part of more comprehensive
efforts encompassing public transportation systems in thecontext of national- and state-level projects.
At the Trilateral Meeting, it was reported that many projects were brought to an end around 2017 in Europe, which
has entered a new phase for launching next-generation
projects.
(10) October 2017 ITS World Congress
In both America and Europe, measures to reduce traffic
accidents, congestion, and CO2 emissions have been taken
by applying connected vehicles featuring automated driv-

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Connected Vehicles

ing and communication technologies. Such activities areprogressing from the research to the large-scale test phase.
The activities are taking comprehensive approaches that
include not only the development of automated driving
technologies but also the creation of legal systems andsafety standards.
(11) November 2017 SIP-adus Workshop and Trilateral
Meeting
SessionThe United States team introduced two tests, the Connected Vehicle Pilot Deployment program and the Smart
City Challenge. The European Commission started its
Cooperative ITS (C-ITS) activities in 2014 and, in cooperation with automotive makers and public agencies released
a report on C-ITS Platform Phase 1 in 2016. The following
year, it announced Phase 2, which specified the tasks toaddress in 2017, and a revised version is planned forrelease in 2018.
Breakout Workshop
The participants included ERTICO and suppliers fromthe EU, USDOT and other groups from the U.S., and representatives of the Ministry of Land, Infrastructure, Transport and Tourism, vehicle manufacturers, electronics
manufacturers, communication carriers, and other industries from Japan.
In terms of information sharing, issues concerning crossborder communication infrastructure and 5G usage were
presented by the EU, while the U.S. talked about the
CARMA project and investments in DSRC and other tests.
Also, the expansion of the infrastructure remains an issue
in each area.
(12) January 2018 Transport Research Board and Trilateral Meeting
In the U.S., the details of the CV Pilot have been madeclear.
CARMA, a new automated driving project run by
USDOT (FHWA), started considering a software platform
for column traveling, speed adjustments, and crossing control.
Europe will launch the 5G Car project, which focus on
studying communication systems for automated drivingusing mobile networks.
(13) April, 2018 Transport Research Arena and Trilateral
Meeting
The EU issued the specification for the final stage of the
ITS-G5 (DSRC), and announced that tests were to be performed in various countries, as it aims to achieve practicalapplication starting 2019. In the meantime, the pace of forward-looking activities, including lively discussions on the
5G communication system (next-generation portable communication), has quickened.

(14) June 2018 ITS America Annual Meeting, Visit toConnected Vehicle Pilot Program (NYC), and to Smart
Columbus
We visited the NYC Transportation Bureau (NYC DOT)
and investigated the progress of the CV Pilot project andwere given an on-site tour of a traffic control center in the
NYC DOT.
At the ITS America annual meeting, Mark Reuss, VP at
GM, gave a keynote speech on automated driving functions and V2X (DSRC) installation plans, which attracted
the interest of the audience. Also, a number of reports on
tests of safety driving support systems using V2X performed throughout the U.S. were provided at the session,indicating the steady progress of activities aimed at thepractical applications of these systems.
We visited Columbus, Ohio to observe the Smart Columbus initiative, which studies the application of the latest
technologies to achieve efficient urban transportation. The
project will conduct tests on transportation as a whole,including the provision of traffic information to information terminals, automated driving technology to cover thelast mile after the end point of public transportation routes,
the use of electric vehicles, and truck platooning.
(15) July, 2018 Automated Vehicle Symposium and Trilateral Meeting
A report comparing the performance of DSRC and Cellular V2X was made by Qualcomm.
Cellular V2X has a longer wave propagation distance than
DSRC. However, there were objections from the DSRC
side.
The ICT4CART (an EU project) aims to utilize level 4
automation operations in the real world using various ICT
technologies such as C-ITS, C-V2X, LTE, and G5 toincrease the reliability of V2V, V2I, and V2N during transitions between each mode of communication.

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Human Factors
Satoshi Kitazaki (National Institute of Industrial Science and Technology)
Makoto Ito (University of Tsukuba)
Tatsuru Daimon (Keio University)
Kiyozumi Unoura (Honda R&D Co. Ltd)
Hiroki Mori (Toyota Motor Corporation)
Takashi Sunda (Nissan Motor Co. Ltd)ABSTRACT: The EU-US-Japan trilateral cooperation on human factors has been active since Japan joined the cooperation framework in
2015. The human factors WG has been discussing specific topics. The first topic was the Out-of-the-Loop Concept. The outcome of the discussions was summarized and published as a technical paper in 2018. The WG then started discussion on the next topic, mental models, and isplanning to publish a paper in 2019. The human factors sessions in the SIP-adus Workshop have been great opportunities to promote theresearch and development activities on human factors in Japan, as well as to obtain valuable feedback for the project. Through international
cooperation, the presence of Japan in the field of human factors has grown extensively.

1 Trilateral cooperation on Human Factors

Possible deliverables- Reports/papers that summarize discussion

1.1. Position of the Working GroupThe Human Factors Working Group (WG) is officiallyplaced under the Steering Group and expected to work on
general human factors issues. However, the WG has been
focusing on human factors in automated driving since
Japan joined the WG in 2015. Therefore, the Human Factors WG and Human Factors Sub-Group under the Automation WG became one and the same (Fig. 1).
1.1. Members
The Trilateral Human Factors WG has been led by three
co-chairs, Chris Monk, NHTSA, US, Johan Engström,
Volvo, EU, and Satoshi Kitazaki, AIST, Japan. Emma
Johansson (Volvo) took the co-chair position of EU when
Johan Engström left in 2017. In each region, 5 or 6 humanfactors experts from academia, industry and government
form a team under the leadership of the co-chair (Table 1).
1.2. Statement of Work made in 2018
Objective- To share understandings of human factors in automateddriving.
Scope- Exchanging information about plans and findings ofresearch on human factors in automated driving in eachregion.
- Identifying and discussing critical human factor issues
that have not been highlighted.

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Fig. 1 Trilateral cooperation organization as of 2017.

Table 1 Members of the Human Factors WG (as of 2018)

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1.3. Achievements
1.3.1. Out-of-the-Loop Concept Paper
The loop concept is important in understanding driver
engagement in the driving task with level 2 and level 3
automated driving systems. However, the terms, in-theloop and out-of-the-loop, have been used with different
concepts. Natasha Merat of the University of Leeds took
the leadership of the project on this issue and invited morehuman factor experts to the WG in 2016. The WG worked
on a technical paper to precisely define the concept andhow it can be measured based on reviews of concepts inthe literature. The definitions are given below. The paper
was published online (Merat et al., 2018).
In the loop: In physical control of the vehicle and monitoring the driving situation.
On the loop: Not in physical control of the vehicle, butmonitoring the driving situation.
Out of the loop: Not in physical control of the vehicle, andnot monitoring the driving situation, OR in physical control of the vehicle but not monitoring the driving situation.
1.3.2. Mental models
The next focus, which had been discussed in the WG inparallel to the final phase towards the publication of theloop concept paper, was determined to be mental models.
Trent Victor of Volvo volunteered to lead this new projectin 2018. The user mental model may be considered theinternal “picture” the user has of the system functions
based on given knowledge. It may also grow through experiencing the system. An appropriate mental model is
expected to shift a knowledge-based understanding of thesystem functions to a rule-based one, and make necessary
driver responses quicker and faster. However, incorrect
mental models may cause negative effects on users such as
misuse or overtrust. The challenge is to design the system
to foster an appropriate mental model in the driver. The
target of the WG is to publish a paper in 2019.

research project for the purpose of feeding discussion outcomes into the project. Posters were also presented every
year on the progress of the SIP-adus human factors
research project.
Table 2.1 Presenters and titles of presentations in the human factors
plenary session at SIP-adus Workshop 2015

Table 2.2 Presenters and titles of presentations in the human factors
plenary session at SIP-adus Workshop 2016

Table 2.3 Presenters and titles of presentations in the human factors
plenary session at SIP-adus Workshop 2017

2 SIP-adus Workshop
The field of human factors has been one of the areas offocus in plenary sessions and breakout sessions of the SIPadus Workshop. The presenters in plenary sessions werefrom academia, industry and government in the EU, theUS and Japan (Table 2). Activities, findings and plans concerning human factors in various sectors in various regions
have been shared among the experts and the audience. The
breakout sessions were organized as round-table or group
discussions on specific topics (Table 3). The topics werechosen to be relevant to the SIP-adus human factors
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Table 2.4 Presenters and titles of presentations in the human factors
plenary session at SIP-adus Workshop 2018

Table 3 Topics of human factors breakout session at SIP-adus Workshop

3 Conclusions
The trilateral cooperation framework and the annual SIPadus Workshop have produced great opportunities to promote the research and development activities on humanfactors carried out in Japan, as well as to obtain valuable
feedback to the project. Satoshi Kitazaki has been invitedas the Principal Investigator of the SIP-adus human factors
research project to many international workshops, conferences and meetings to present the progress and findings ofthe project. Through international cooperation, the presence of Japan in the field of human factors has grownextensively.

References(1) Merat, N., Seppelt, B., Louw, T., Engström, J., Lee, J. D.,
Johansson, E., Green, C. A., Kitazaki, S., Monk, C., Itoh,
M, McGehee, D., Sunda, T., Unoura, K., Victor, T.,
Schieben, A., and Keinath, A. : The “Out-of-the-Loop”
concept in automated driving: proposed definition,measures and implications. : Cognition, Technology &Work, https://doi.org/10.1007/s10111-018-0525-8 (2018).

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Information Security
Takashi Imai (President, Toyota InfoTechnology Center Co., Ltd.)

1 Security
1.1. International Trends in Measures against Cyberattacks on Vehicles
Governments and industry groups are currently working
on development of laws, regulations and guidelines inorder to respond to cyberattacks on vehicles. The UnitedStates was the first to take action. In July 2015, the SPY Car
Act was introduced in the Senate. The bill includes requirements to develop cybersecurity standards to protect vehicle
control systems from hacking, standards to ensure privacyof the data collected in the vehicle and a cyber dashboard
to rate how well vehicle security and privacy are protected.
In 2016, Auto-ISAC and the National Highway Traffic
Safety Association (NHTSA) issued, in January and October respectively, best practices related to automotive cybersecurity. Although some issues remain since the guidance
introduced in these best practices were merely conceptual,they serve an important role in that certain degree ofguidelines were presented by the government and industry
groups that are in a position to impose directives on automakers. Along with these guidelines, the Society of Automotive Engineers (SAE) issued J3061 in January 2016
which specifies process-based cybersecurity measures.
J3061 covers cybersecurity measures to consider in eachphase of the product lifecycle from planning, development,
and operation to the disposal of the product after its
launch. However, much room remains for improvement, as
no specific methods regarding threat analysis and risk
assessment were identified in the guideline. In September
2017, the SELF DRIVE Act was passed by the House ofRepresentatives. Section 5, CYBERSECURITY OF AUTOMATED DRIVING SYSTEMS, notably, requires auto makers to develop cybersecurity plans. The act applies to all
non-commercial road vehicles to be sold after it comes into
effect, and automakers must be aware of its importance as
violating that law means they will not be allowed to supply
any automated driving systems (including partially automated vehicles) to the US market. In Europe, hardware
design for vehicle network and chips were studied in the
EVITA project from 2008 to 2011. In the Working Party on
Automated/Autonomous and Connected Vehicles under
the United Nations WP.29 forum, Europe has been working to lead the development of a guideline to ensure cybersecurity and data protection for connected cars andautomated driving. The contents were published in 2016

and will be updated as necessary in the future. In Japan,
the Japan Automobile Manufacturers Association (JAMA),
JasPar, and the Ministry of Land, Infrastructure, Transport
and Tourism are working together to establish minimum
mandatory standards for vehicle cybersecurity. Separately,
the Society of Automotive Engineers of Japan (JSAE) has
issued a cybersecurity analysis guide that summarizes theprocedures for security activities ranging from threat analysis to the definition of cybersecurity requirements. The
above presents the major trends in the United States,
Europe and Japan, all of which are currently in a period oftransition, which means that laws and guidelines are notfully established. The ISO and SAE aim to develop aninternational standard (ISO 21434) based on international
consensus by 2020. Until that standard is issued, automakers will be required to takes into consideration the laws,
regulations and guidelines previously mentioned and maketheir own decisions in establishing frameworks to ensure
the security quality of their products.
1.2. Efforts to Share the Outcome of SIP-adus GloballyWith these international trends in mind, we are working
on the development of a security evaluation guideline toprotect against cyberattacks as part of SIP-adus information security activities. The guideline simulates actual
cyberattacks and is considered highly effective. The novelty
in the evaluation method used in this guideline is that it
specifies common procedures and evaluation criteria forpenetration testing, the effectiveness of which is generally
considered to be highly reliant on the individual skills of
the tester. To assess the direction and necessity of the new
evaluation method, we are closely monitoring trends inJapanese automotive industry groups such as JAMA and
JasPar, as well as international standards (ISO 21434) and
WP.29 initiatives while working to foster common understanding and urge the spread of penetration testing in theindustry. In addition, we are consulting with the JSAE,
which is in charge of ISO 21434 and the National Traffic
Safety and Environment Laboratory, which is promoting
discussion on information security at WP.29, in order toensure that our project contributes to leading international
discussions.

About the author
1) Takashi Imai, President, Toyota InfoTechnology Center
Co., Ltd.

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Impact Assessment
Nobuyuki Uchida (Japan Automobile Research Institute) and Hiroaki Miyoshi (Doshisha
University)
ABSTRACT: Automated vehicles have the potential to transform the world’s road transportation system. Expected benefits include traffic safety,
transport network efficiency, energy/emissions, and personal mobility. Furthermore, as the existing transport systems are complex, prior to
implementation, automated vehicle technology must be understood and its effects carefully predicted prior to implementation to plan for thattransformation. This article introduces the international activities of SIP-adus with regards to assessing the impact of automated vehicles from thefollowing three perspectives: the Trilateral Impact Assessment Sub-Group for ART, as well as both safety and socioeconomic impacts.

1 Introduction
Automated vehicles (AVs) have the potential to transformthe world’s road transportation system. The potential benefits include traffic safety, transport network efficiency,
energy/emissions, and personal mobility. As it is being
introduced into existing complex transportation systems,
AV technology must be understood and its effects carefully
predicted prior to implementation to adequately plan forthat transformation. In this article, we introduce international activities with regards to assessing the impact automated vehicles from the following three perspectives: theTrilateral Impact Assessment Sub-Group for ART, as well
as both safety and socioeconomic factors.

Trilateral Impact Assessment Sub-Group

2 for ART

Members of the Trilateral Working Group on Automationin Road Transportation (ART WG) are working to address
the complexity of the impact of AVs in various areas. European researchers are looking at the possibility of applying
the Field Operational Test Support Action framework(FESTA, FOT Net 2016) to automation and sketching themechanisms through which automation potentially affects
our lives. The United States Department of Transportation(US DOT) has sponsored the development of a modelingframework that includes the areas of safety, vehicle operations, personal mobility, energy/emissions, network efficiency, travel behavior, public health, land use, and socioeconomic impact. Japan has been developing models of the
impact on safety since late 2015 under SIP-adus.
To coordinate the impact assessments performed in thefield of automated driving, the ART WG established an
Impact Assessment sub-group in 2015. The motivation was
the realization that, as field tests are expensive and mostly
done on a small scale, international harmonization would
be in everyone’s interest. With a harmonized approach,206

tests and studies can be designed to maximize the insights
obtained and arrange complementary evaluations aroundthe world.
The framework aims for high-level harmonization of
impact assessment studies globally. It is the first attempt at
harmonization by the three regions involved (EC, US andJapan). As automated driving covers many concepts, theframework does not give detailed methodological recommendations (i.e., methods to apply for calculating thestrength of various impacts) but it aims to facilitate metaanalysis across different studies. Therefore, the focus is on
providing recommendations on how to describe theimpact assessment study in a way that users of the results
understand what was evaluated and under which conditions.
The areas of AV impact may be divided into two large
groups: direct and indirect. Figure 1 depicts the impact
areas (Smith, 2017). Direct impacts are those which have aclear cause-effect relationship with the primary activity or
action. They are easier to capture, measure and assess, andare often (though not always) immediate to short-term innature. In Fig. 1, they are in the upper left, and include
safety, vehicle operations, energy/emissions and personal
mobility. The others are indirect impacts. Indirect impacts
can be characterized as secondary, tertiary, or still further
removed from the original direct impact.

Fig. 1 Framework of impact areas (Smith, 2017)

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Impact Assessment

Direct Impacts are those that can be measured in a field
operational test (FOT). They then can be scaled up to anational level and will also lead to indirect impacts. An
FOT can also provide insight into the infrastructure
requirements of an automation application.

Methodology of Safety Impact Assess-3 mentNearly all AV applications, ranging from Level 1 collision
avoidance systems to Level 5 self-driving vehicles, havepotential safety impacts. Ultimately, safety should be measured by accident statistics, such as fatalities, injuries andproperty damage for vehicle occupants and other road
users. Other road users may include pedestrians, cyclists,and slow-moving vehicles. One challenge with safety
impact assessment is that actual crashes are rare events,
even with a considerably large-scale FOT. Therefore, proxymeasures are often used.
In Japan, a novel multi-agent traffic simulation software
was developed as a part of the SIP-adus projects. The software can simulate and identify at least five types of accidents. Different automated driving technology penetration
scenarios can be set to estimate the potential impact of different technologies on safety. A summary of the achievements of the research project is described below.
Figure 2 shows a simulated area created to model a partof Tsukuba City in detail. The road network was composedof various routes such as an expressway, major roads, andold narrow streets in order to verify the simulation program.

technology penetration scenarios. There are multiple driving modes including manual driving, autonomous emergency braking (AEB), lane departure warning (LDW) andautomated driving (Table 1). It is important to set up
mixed scenarios consisting of various driving modes toachieve a more practical estimation of safety impacts.
In order to estimate possible accident reduction effects, it
is essential to reproduce a realistic traffic flow via simulation. There are approximately 500 agents that can behave
individually in our current simulation (Fig. 3). Traffic density and travel velocity are pre-set based on road traffic
census data.

Fig. 3 Multi-agent traffic simulation including 500 agents

As shown in Table 2, under 100% manual driving, thesystem simulated a total of 859 accidents categorized infive types. The number of accidents predicted decreased as
the level of automation increased, coming down to 156
cases for the highest automation level simulated (25% ofvehicles with AEB+LDW and 75% of Level 4 automateddriving). Figure 4 shows that all the technologies considered contributed to the absolute decrease in accidents,
which was predominantly represented by the reduction ofrear-end and lane departure-related crashes.
Table 2 Synoptic table of simulation results

Fig. 2 Road network for verification of simulator functions

Typical automated driving penetration rates are enteredin order to compute and compare the results of diverse
Table 1 Technology penetration scenarios

Fig. 4 Comparison of relative accident and component rates
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The above results in the SIP-adus project were presented
at the Autonomous Vehicles Symposium 2018 in San Francisco (Kitajima et.al., 2018) and drew the interest of theinternational community of experts. Several researchgroups expressed considerable interest in the multi-agent
traffic simulation model, and future collaborative research
regarding the simulation model is expected. Such international collaboration and discussions can address the methodology of safety impact assessment.

4 Economic Analysis of Safety Impacts
We also conducted analysis on safety impacts from theviewpoint of economic factors. Compared to passive safety
technologies, one of the most distinctive features of automated driving systems lies in the fact that the economic
benefits of those systems will be enjoyed not only by theirusers, but also by non-users. With AEB, for example, preceding vehicles will benefit more than following vehicles
equipped with on-board devices, regardless of whether
devices are install in the preceding vehicles or not. It canalso be said that automated driving systems are safetysharing systems. From an economics viewpoint, this
implies that it is difficult for automated driving systems to
be diffused properly via market mechanisms. Thus, economic incentives will be necessary to facilitate the diffusion of automated driving systems in society. We initiatedinternational discussion on these points at several international conferences (See Miyoshi, 2017).

5 Socio-Economic Impacts
Automated driving will bring about various significant
socioeconomic impacts.
The decrease in the opportunity cost of traveling when
SAE levels 4 and/or 5 automated driving becomes widespread in society could dramatically change human lifestyle, for example.
The national economy will also be affected by automateddriving, which will significantly change the cost and cost
structure of vehicles thanks to the change of inputs. In
addition, some advocates claim that automated driving will
result in a decrease in vehicle sales and ownership due tothe widespread use of car sharing and ride sharing. It is
possible that automated driving will significantly impact
the Japanese industrial structure. We estimated three categories of indexes of the power of dispersion of the automotive sector. The first category denotes the relative size of theinfluence on the entire industry (including the self-sector)
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in a case where the final demand for the industry increases
by one unit. The average value is 1. The greater the value of
an industry, the more heavily that industry affects thewhole sector. The second and third category indexes are
modifications of the first category. The third category index
denotes the impacts on other sectors. The effect on the selfsector is totally excluded. In the second category, only
direct effects of 1.0 on the self-sector are excluded. Figure 5
shows the three categories of indexes for Japan, Germany
and the US. Japan has the largest value in each of the three
categories, indicating that, among the economies of those
three nations, the Japanese economy will be the mostaffected by the diffiusion of automated driving systems. We
discussed this issue at the ITS World Congress 2017 (SeeMiyoshi and Kii, 2017)

Note) Calculated by author for the sector of “Motor vehicles, trailers andsemi-trailers”.
Data) 2011 Input-Output Tables compiled by OECD. Stat
Source) Miyoshi and Kii (2017)
Fig. 5 Indexes of the Power of Dispersion of Automotive Sector

References(1) Scott Smith: Benefits Estimation for AV Systems,
Socio-Economic Impact of CAD Session, 1st European
Conference on Connected and Automated Driving,
April 4, 2017, European Commission Charlemagne
Building, Brussels
https://connectedautomateddriving.eu/wp-content/
uploads/2017/03/1_Day2_B08_Smith_EC_CAD-.pdf(2) Sou Kitajima, Keisuke Shimono, Akito Adachi, Jacobo
Antona-Makoshi, Nobuyuki Uchida, Jun Tajima: A
multi-agent traffic simulation model to predict the
impact of automated driving systems on safety, July 11,
2018, Autonomous Vehicles Symposium 2018, Hilton
San Francisco Union Square, San Francisco.
http://auvsilink.org/AVS2018/Posters/Nobuyuki%20
Uchida_A%20multi-agent%20traffic%20Simulat i o n % 2 0 Mo d e l % 2 0 t o % 2 0 p r e d i c t % 2 0 t h e % 2 0
impact%20of%20Automated%20Driving%20sys
tems%20on%20safety.pdf(3) Hiroaki Miyoshi: Benefits of Automated Driving Systems: Traffic Accident Reduction, Socio-Economic
Impact of CAD Session, 1st European Conference on[ V ] International Collaboration① Leading International Debate on the Six Key International Collaborative Themes

Impact Assessment

Connected and Automated Driving, April 4, 2017,
European Commission Charlemagne Building, Brussels
https://connectedautomateddriving.eu/wp-content/
uploads/2017/03/2_Day2_B08_Miyoshi_EC_CAD_
v03.pdf (Accessed Sep. 18, 2018)(4) Hiroaki Miyoshi and Masonobu Kii: Macro Impact ofAutonomous Vehicles, SIS 51, ITS World Congress
2017, Montreal
http://en.sip-adus.go.jp/conference/its2017/file/conf_
its2017_sis51.pdf(Accessed Sep. 20, 2018)
About the authors:
1) Nobuyuki Uchida, Japan Automobile Research Institute, Safety division, Manager
2) Hiroaki Miyoshi, Doshisha University, Faculty of Policy
Studies, Professor, Public Economics

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International Cooperation Activities for Next-Generation Transport
Masayuki Kawamoto (University of Tsukuba)
ABSTRACT: Next-generation transportation and logistics are getting more attention as practical early applications of automated vehicle
technology as well as related innovative new services. Low-speed automated shuttles represent one of the major topics in this area and are thesubject of demonstrations with several business models targeting improvement in quality of life. Walking always accompanies the use of
public transport in daily life. Therefore walking support systems, especially for vulnerable road users, are key to promoting public transportthat encompasses upcoming shared mobility. Realizing driverless vehicle is still years away, but several applications making use of automatedvehicle technologies are being introduced to contribute reduction in traffic accident fatality.

Automated Public Transport Topic Trends
At almost the same time as our SIP-adus activities werelaunched in 2014, several existing international conferences started to include sessions relating to automatedvehicle systems. At those conferences, topics relating to
public transport and its services were few because thesmall size of the market for automated vehicles for public
transport did not draw much interest from major IT and
auto manufacturers. Recently, however, the situation has
been changing, and automation in public transport andfreight vehicles, as well as related services, has been gathering attention.

Fig. 2 EasyMile EZ10

Low-Speed Automated Shuttles
The major drivers of this trend were not IT giants or
major auto manufacturers, but rather small startup companies. One of the triggers for this development was the CityMobil2 project, partially funded under the auspices of the
EU’s Seventh Framework Programme for Research (FP7)which ran from 2007 to 2013.
EasyMile and Navya from France developed their auto-

Fig. 1 CityMobil2 Project
210

Fig. 3 Navya ARMA

mated shuttle for CityMobil2, which was a pilot demonstration project for automated road transport systems
across Europe. BestMile from Switzerland developed operation service systems for those automated shuttles. Now
EasyMile, Navya and BestMile are providing their products
worldwide including in the U.S. and Japan.

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International Cooperation Activities for Next-Generation Transport

Working Vehicle Automation

Fig. 5 Local Motors Olli

One reason for focusing on low-speed automated shuttles
as a practical application of vehicle automation is their limited operational domain. This makes it possible to choose
or set up a less critical domain for their operation wheremost risks can be predicted and avoided.
Similarly, long distance freight trucks and working vehicles such as refuse trucks and road cleaning vehicles represent another area of focus for the practical application of
automated driving. Automated truck platooning projects
are being conducted in the EU and the U.S., as well as inJapan.

Fig. 6 Ohmio LIFT

Another automated shuttle maker that is a little older
than the above companies, 2getthere from the Netherlands,
has also been providing commercial operation transport
products worldwide.
Olli, developed by Local Motors in the U.S., and Ohmiofrom New Zealand, are automated shuttles offering aunique concept and features that differ from the offeringsof the French makers.
Despite a background and environment favorable tomanufacturing such vehicles, Japan is still struggling tobring out original products.
The e-Palette concept from Toyota, which debuted at CES
2018, provides a ray of hope for the future of the automotive industry in Japan.

Fig. 8 Automated Refuse Truck

Fig. 9 Truck Platooning

The challenge of building self-driving trucks is being met
by Einride of Sweden which is developing the T-pod,
which is remotely controlled by the driver and the T-log,
which features automated driving capability.

Fig. 7 e-Palette Concept

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ies utilizing AI, IoT, open/big data, and renewable forms ofenergy are targeting improvements to the quality of life forall citizens. Valuable mobility-based services have emerged
as a new area of focus called mobility as a service, or MaaS.

Mobility as a Service
Fig. 10 T-pod

Fig. 11 T-log

Expanding Demonstrations from Small
Sites to Entire Cities
In their initial stage, most automated driving shuttle demonstration projects have been carried out in relatively limited areas like private campuses or small parks. However,some of those demonstration projects are now expanding
their concepts to entire cities, as exemplified by the Smart
City Challenge in the U.S.
In 2016, USDOT selected the city of Columbus Ohio as
the winner of its call for Smart City Challenge proposals.
This kind of project is not being conducted only in Columbus, but also in several cities in the U.S., with funding fromseveral sources.

MaaS is one of the biggest areas of focus in current international conferences for automated vehicle systems andintelligent transportation systems (ITS). There was aperiod of a few years when automated vehicle-related conferences only covered the topic of mobility services in thetitles of a few presentations, but it is now found in several
session titles, and is sometimes even the subject of thewhole conference.
The definition of MaaS is still unclear. However, it should
consist of new service value associated with mobility,
rather than merely simple transportation from A to B and
its attendant services. New service value will follow new
business models such as integrated sharing services orcompletely different cost allocations. It will take a little
more time for industry to find practical MaaS solutions.

User Acceptance of Automated Vehicles
Some demonstration projects are venturing to conduct
their operation in spaces that include pedestrians, taking
maximum care to avoid accidents, in order to evaluate user
acceptance of automated shuttle by both passengers riding
the vehicles and the pedestrians in the vicinity of the vehicle. The GATEway project in Greenwich, UK is one example, as is the Mcity project in Michigan, USA. Of course,
obtaining conclusive results concerning user acceptanceapplicable to various people will take time, but is also definitely an important process to implement future regular
operation of automated shuttles in shared spaces and similar areas.

Source: https://www.transportation.gov/smartcity/highlights
Fig. 12 USDOT Smart City Challenge

After those projects expanded to entire cities, automatedmobility became only one factor in innovation. Smart cit212

Fig. 13 GATEway project

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International Cooperation Activities for Next-Generation Transport

Fig. 14 Mcity project

Source: https://www.its.dot.gov/research_areas/attri/
Fig. 16 Diversified mobility services ATTRI

User acceptance studies have usually been conducted
through questionnaires involving a limited number of participants and a short evaluation period, but their results
have always included an unrealistically hopeful bias
because most demonstrations were carried out under ideal
conditions. A realistic user acceptance study, such as theone by PostBus in Sion, Switzerland, must consider daily
regular operation in a general (rather than ideal) environment.

What We Have Learned and What We
Should Do
The current results of several automated driving field tests
around the world indicate that realizing fully automateddriverless vehicles on public roads is still years away. In thenear future, even without a driver, introducing automatedvehicle services in actual markets will still require additional labor for tasks such as maintaining the vehicles andtheir dedicated infrastructure, securing operational safety,
and providing emergency support.
Continuous step-by-step studies are still required to reachour goal, which, it must be noted, is not to develop selfdriving vehicles. Our final goal is zero traffic accident fatalities. There are still issues to resolve before self-driving can
improve the safety and efficiency of public transportationand logistics through the use of automated technology inpreparation for future self-driving.

Fig. 15 PostBus SmartShuttle in Sion

Vulnerable Road User Support Systems
The activities of the Accessible Transportation Technologies Research Initiative (ATTRI) of USDOT have served asa reference for SIP-adus. Rather than the aging society
challenge faced by Japan, the ATTRI activities address themany veterans who need mobility services providing additional care. The two situations are similar in terms of theneed for mobility service offering several modes that caterto diverse groups of people.

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Activities toward International Standardization of
Dynamic Maps
Hiroki Sakai (Mitsubishi Research Institute, Inc.), Sumio Nishiyama (Kiwi-W Consortium)
ABSTRACT: In order to contribute to the international community, SIP-adus has supported the development of international standards forthe logical data model referenced by applications such as automated driving systems and for lane-level location referencing. For the logical
data model, an input proposal was developed by examining data models that will be necessary for those applications. For lane-level locationreferencing, a proposal for an international standard was created from use cases, requirements and general concepts for lane-level locationreferencing. SIP-adus will continue to support the international standardization of dynamic maps.

1 Purpose of Activities
As part of the cross-ministerial Strategic Innovation Promotion Program (SIP) activities covering automated driving systems (ADS), dynamic maps are being developed as
maps used in such systems. In order to contribute to theinternational community, SIP-adus aims for the international standardization of this outcome. Based on the history of ISO/TC204/WG3 (ITS database technology), whichhas promoted the international standardization of car navigation maps, candidates for the international standardization of dynamic maps are (1) data exchange, (2) API, (3)the data model (description of data type/attribute structure, methods of maintaining relevance between data
items, etc.), and (4) lane-level location referencing methodfor data exchange. Data exchange is already under development as GDF 5.1 (20524), an extension of GDF 5.0, which
is an existing standard in ISO/TC204/WG3. For the second
candidate, it is difficult to specify API without a data
model. In light of this situation, SIP-adus has supported
the development of an international standard for the logical data model referenced by applications such as ADS andfor lane-level location referencing.

Fig. 1 Relationship between ISO/TC204/WG3 Work Items.(1)214

2 Standardization of Logical Data Model
2.1. Overview
This work item standardizes the logical data model referenced by applications such as ADS.
The data envisioned for use in ADS is categorized into
static data (i.e. maps for high levels of driving automationand traditional map data) and dynamic data (e.g. traffic
and travel information). These types of data are interconnected to realize ADS. The data model for ADS shall havea structure specialized for driving automation anddesigned to be shared with other systems. GDF 5.1 provides detailed road data as static map data for ADS. ISO
14296 specifies the static map data and the attendant logical data model for car navigation systems and cooperative
ITS. Furthermore, dynamic data has already been specified
and used in many existing systems. Although it is necessary to specify the relationship between map and dynamic
data envisioned for use in ADS, no such relationship has
been provided so far.
Thus, the data system architecture related to the static anddynamic data flow was examined and, based on the results,
the logical data model was assessed, and an input proposal
was developed by examining other data models. The specification on map features and attributes for ADS provided
by the Japan Automobile Manufacturers Association wasreferenced when considering the necessary features. The
implementation of this specification could lead to cost
reductions in the maintenance and expansion of map
access libraries, as well as in the compilation and maintenance of map and map-related data for data providers, aswell as for connected and driving automation/vehicle control applications.

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Method 2: Delta from a reference point
The method used is selected according to road segment
type and/or usage. As a general rule, Method 1 is used torepresent events in lanes (excluding intersections) andMethod 2 is used to represent events at junctions. However, Method 2 can also be used to represent events inlanes.

Fig. 2 Proposed Architecture for Automated Driving System

2.2. Status of International Standardization
This work item was conditionally approved as PWI(22726) in April 2017. After that, in October 2017, throughexpanded joint meeting with existing WGs and external
SDOs, the Japanese delegation explained the scope of thiswork item and the contents of work to plan in the future,
while ensuring that there is no conflict with the existing
standards and establishing a cooperative relationship withthese WGs and SDOs. At the international conference inApril 2018, it was agreed to establish Part 1 as Architectureand data model for harmonization of static map data andPart 2 as Data model of static transitory and dynamic transitory data, and that the resulting documents would be
published as TS. Part 1 was approved as an NP in August
2018 and it is scheduled for publication as a TS in spring2020. Part 2 is planned to be moved to an NP proposal
after a consensus on the data type classification and data
items subject to standardization is reached (as of September 2018).

3Standardization of Lane-Level Location
Referencing

3.1. Overview
This work item standardizes location referencing capableof locating an object on a road within a specific lane.
ISO 17572 Part 1, Part 2 (pre-coded), Part 3 (dynamic)
have been published as the international standards forlocation referencing methods. However, none of themhandles location referencing at the lane level. Furthermore,
there is no other widely used standard covering locationreferencing at that level. Thus, a proposal for an international standard was created from use cases, requirements
and general concepts for lane-level location referencing.
There are two ways to reference location:
Method 1: Lane number counting

Fig. 3 Lane-Level Location Referencing Methods

3.1.1. Method 1: Lane Number Counting
The longitudinal location shall be referenced using a distance along a road section and a lane number counting
rule. The distance along a road section shall be expressed
as a percentage of the road section or a real distance. The
vertical location shall be expressed as a distance from theroad section. This method is used for the situations
described below.
- Road segments that have lane information.
- Specifying the lane.

Fig. 4 Explanation of Method 1

3.1.2. Method 2: Delta from a Reference Point
The location shall be referenced using the distance from areference point (Δx, Δy, Δh). This method is used for thesituations described below.
- Road segments in junctions.
- Specifying the relative position within a road (e.g. σ<25
cm accuracy).
In order to achieve a location measurement accuracy witha margin of error of σ<25 cm with this method, the following condition shall apply:- The method is used within a radius of no more than 200
meters from the reference point.

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Fig. 5 Explanation of Method 2

3.2. Status of International Standardization
This work item received NP approval in April 2017. The
CD ballot ended in July 2018, and CD comment resolution
concluded at the international conference in September
2018. It is scheduled to move to a DIS ballot proposal in
December 2018 and be published as an IS in the summerof 2019 (as of September 2018).

4 Conclusion
In order to contribute to the international community,
SIP-adus has supported the development of international
standards for the logical data model referenced by applications such as ADS and for lane-level location referencing.
For logical data models, an input proposal was developedby examining other data models. As of September 2018,
Architecture and data model for harmonization of static
map data has been approved as an NP (22726-1). SIP-adus
will continue to support international standardization,
aiming for publication as a TS in the spring of 2020. In the
dynamic maps for ADS proposed by Japan, how to associate externally provided information with map information
is an important consideration. Thus, in its examination of
static transitory data, dynamic transitory data and dynamic
data, SIP-adus will support cooperating with existing WGs
and external SDOs to avoid conflicts with related existing
standards. For lane-level location referencing, a proposal
for an international standard was created from use cases,
requirements and general concepts. SIP-adus will continue
to support international standardization, aiming for publication as an IS in the summer of 2019.
References(1) ITS Standardization 2017: Society of Automotive Engineers of Japan,
http://hq.jsae.or.jp/its/2017_bro_e.pdf (September 12,
2018)

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General Survey of International Trends Regarding
International Cooperation with Dynamic Maps
Sumio Nishiyama (Shobunsha Publications, Inc.),
Masahiro Koibuchi (Mitsubishi Research Institute, Inc.)
ABSTRACT: In order to contribute to the international community in the promising field of dynamic maps, a field expected to be integral todriving automation systems, SIP-adus investigated dynamic map data models and the map data structure of dynamic maps both in and outside Japan. Furthermore, this study worked to clarify differences in specifications of dynamic maps established by various nations. To achieve
compatibility between industry standards in Japan and in other countries, it also aimed to reinforce cooperative ties, through exchange ofviews and debate, between organizations worldwide that conduct research and development in the driving automation field.

1

Research Objectives

In order to contribute to the international community inthe promising field of dynamic maps, a field expected to be
integral to driving automation systems, SIP-adus investigated dynamic map data models and the map data structure of dynamic maps both in and outside Japan.
Furthermore, this study worked to clarify differences in
specifications of dynamic maps established by various
nations. To achieve compatibility between industry standards in Japan and in other countries, it also aimed to reinforce cooperative ties, through exchange of views anddebate, between organizations worldwide that conduct
research and development in the driving automation field.

2 Investigation of Dynamic Maps
The investigation gathered information such as the detailsof standardization documents and activities surrounding
the formulation of industry specifications for dynamic
maps. This was done through surveying public materials
and exchanging information with industry players at conferences and in individual meetings.
Table 1 Main Initiatives in the Global Formulation and Development of
Dynamic Map Specifications.

2.1. Initiatives outside Japan
The investigation compiled details concerning the activities of European organizations that are actively developing
industry standards.
2.2.1. NDS
The Navigation Data Standard Association aims todevelop a standard database format that is compatible withall car navigation systems.
The Navigation Data Standard (NDS) is a standard database format that maintains compatibility with all systems.
It separates the software and map data, and features immediate data updates.
2.2.2. ADASISThe Advanced Driver Assistance Systems Interface Specification Forum (ADASIS) aims to develop an interface for
ADAS applications and onboard maps.
The Advanced Driver Assistance Systems Interface Specification is an application interface for vehicle control thatprovides map information to ADAS.
2.2.3. SENSORIS
Initiated by HERE, SENSORIS aims to develop open
standards such as a format for processing and analyzing
information collected in the cloud from vehicle sensors.
It is studying specifications for uplinking vehicle sensor
data to a cloud center and those necessary for services that
result from the realization of vehicle sensor data.
2.2.4. TISAThe Traffic Information Service Association (TISA) aims
to develop open standards for traffic information and traveler information services.
It is developing two formats: TMC, for the transfer of traffic, weather and other information over FM channels, andTPEG, which uses digital broadcast to transfer information
related to traffic, public transport, the weather, and more.
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2.2.5. OADFThe Open AutoDrive Forum, consisting primarily ofEuropean organizations (NDS, ADASIS, TISA, SENSORIS) and related companies, is a platform that promotes
cross-domain debate and coordination to advance driving
automation.
It promotes the Auto Drive Ecosystem, which is a cycle ofmap production, delivery to vehicle, onboard cooperation
with ADAS modules, and vehicle (sensors) data feedback.
It has 61 participants, including auto makers and map
providers (as of Feb 2018).
It organizes meetings once every two or three months inEurope, the U.S., or Asia.
2.2. Initiatives in Japan
So far, SIP-adus has created documents defining requirements (draft) and specifications for basic map data andproduction (draft) for dynamic maps.
It has also created prototypes of high-accuracy 3-dimensional maps and conducted field operation tests.
As of December 2017, it has provided data for a subset of
local roads and expressways.
2.3. International Initiatives
Under TC 204, the technical committee for ITS standardization within the ISO, WG3 is working on the standardization of geospatial information and related matters withITS database technology at its core.
It defines the relationship between semi-static/semidynamic data and static data for dynamic maps. As of September 2018, the architecture and data model for
harmonization of static map data has received NP approval.

Besides the logical data model, there is current deliberation that aims for the publication of geographic data file
GDF5.1 (CD 20524-1, AWI 20524-2) and lane-level location referencing (NP 17572-4) by the ISO.

Fig. 1 Scope of PWI 22726.
Source: ITS Standardization 2017: Society of Automotive Engineers of Japan,
http://hq.jsae.or.jp/its/2017_bro_e.pdf (September 12, 2018)(1)

Fig. 2 Relationship between ISO/TC204/WG3 Work Items.
Source: ITS Standardization 2017: Society of Automotive Engineers of Japan,
http://hq.jsae.or.jp/its/2017_bro_e.pdf (September 12, 2018)(1)

Fig. 3 Japanese and International Activities Related to Dynamic Maps
Source: Created based on ITS Standardization 2017: Society of Automotive Engineers of Japan,
http://hq.jsae.or.jp/its/2017_bro_e.pdf (September 12, 2018)(1)218

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2.4. Japanese and International Activities Related to
Dynamic Maps
Domestic and international activities related to dynamic
maps are shown in Fig. 3.

3 Cooperation with Related Organizations
The investigation disseminated information regarding the
progress of current initiatives in Japan, and sought cooperative partnerships with appropriate organizations throughparticipation in conferences like OADF, as well as
exchanges of information with relevant parties.
Table 2 Participation in Conferences Hosted by Relevant Organizations.

industry specifications for dynamic maps. Furthermore,
the investigation disseminated information regarding thecurrent state of automated driving in Japan and sought out
cooperative partnerships with appropriate organizations
through participation in conferences like OADF andexchanges of information with relevant parties. For the
purpose of sharing information among Japanese stakeholders and discussing the direction of standardization
activities Japan should pursue, a conference called the
Dynamic Map Standardizing Strategy Initiative was established. Members of the conference consist of people fromacademia, the automobile industry and related fields. SIPadus will continue to seek out cooperative partnerships
with appropriate organizations through participation in
conferences like OADF, as well as the exchange of information with relevant parties.
References(1) ITS Standardization 2017: Society of Automotive Engineers of Japan,
http://hq.jsae.or.jp/its/2017_bro_e.pdf (September 12,
2018)

Information Sharing with Industrial

4 Sectors in JapanFor the purpose of sharing information among Japanesestakeholders and discussing the direction of standardization activities Japan should pursue, a conference called the
Dynamic Map Standardizing Strategy Initiative was established. Members of the conference are composed of people
from academia, the automobile industry and related fields.
Table 3 Dynamic Map Standardizing Strategy Initiative Schedule.

5 Conclusion
In order to contribute to the international community inthe development of dynamic maps, a field expected to be
integral to the automated driving system, this study investigated information such as details of the standardization
documents and activities surrounding the formulation of
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International Standardization for Human Factor
Issues in Automated Driving Systems
Motoyuki Akamatsu (National Institute of Advanced Science and Technology)
Satoshi Kitazaki (National Institute of Advanced Industrial Science and Technology)
ABSTRACT: This article reviews ISO activities on human factors in automated driving systems. Human factors issues have been discussed in
ISO TC22/SC39/WG8 (TICS on-board HMI) since 2014. Two technical reports have been published. One is for terms and definitions regarding human states and performance in the transition process. The other is for general ergonomic considerations for external HMI. Two otherdocuments, consisting of experiment guidelines or considerations on human factor issues, are under discussion. An outline of those documents, and the contributions of the SIP-adus Human Factors and HMI research project, are also described here.

1

Discussions of Human Factors Issues in
ISO Activities

International standardization activities regarding automated driving systems (ADS) have been conducted in ISO
TC (technical committee) 22 and TC204. TC22 is the committee for road vehicles and TC204 is for intelligent transport systems. The main items to standardize in TC204 arethe behavior of ADS, minimum functional requirements,
minimum performance requirements and system testingprocedures. In contrast, items covered in TC22 are morefor elemental technologies, such as functional safety,
design specifications, HMI/human factors, driver monitoring, event recording and component/subsystem verification. TC22/SC39/WG8 is a working group that addresses
human factor issues in ADS.
The first meeting within TC22/SC39/WG8 regarding
human factors in ADS was the Automated Vehicle Workshop during WG8 London meeting held in June in 2014.
At that time, the SAE had already proposed the definitions
for ADS levels, and VDA, Bosch, and BASt had also proposed separate, but similar, definitions. All the definitions
were developed from the point of view of system functions(ability), rather than from a human-centered point of view.
During the workshop, we discussed what items should bestandardized in terms of the human factor aspects of ADS.
As ADS is a state of art technology, it proved difficult toreach a common agreement about target standards because
the various ADS envisioned by researchers were all different. It was concluded that, in order to have a common
understanding of ADS human factors issues, we needed tostandardize terms/definitions and research protocols. In
addition, we needed to clarify human-system interaction
and system monitoring by the driver (user).
After the workshop, WG8 decided to establish a task force
220

for ADS human factors (TF-ADS). The US delegates nominated Dr. Myra Blanco of VTTI as a candidate to lead TFADS. There was discussion in the national committee ofJapan at the JSAE on how to deal with this matter. The
committee concluded that we should step into the leadership role for ADS standardization in order to reflect the
research outcomes of the SIP-adus project in international
standards. Then, the Japanese committee nominated Dr.
Ono of JARI (currently AIST) as a candidate to lead TFADS. In the end, WG8 agreed to make Drs. Myra and Ono
co-leaders of TF-ADS at the Bron meeting held in November 2014. The TF-ADS activities then started in the WG8Bron meeting. TF-ADS decided that the first document toestablish as an ISO document would be a technical reportwith a list of ADS human factors-related terms and theirdefinitions. The document was considered useful toachieve a common understanding of human factor issues
in ADS.

ISO/TC22/SC39/WG8 Activities on ADS

2 Human Factors

Since the Bron meeting in 2014, we have had two TFADS meetings every year. The TF-ADS members preparedmaterials for the terms related ADS human factor issues
and their definitions in those meetings. The co-leader fromthe US changed from Dr. Blanco to Dr. Schaudt in 2017.
After three years of discussions, the document was established as technical report TR21959 “Human Performanceand State in the Context of Automated Driving: Part 1 –Terms and Definitions”. This document is now at the preparation for publication stage at the ISO secretariat.
During TF-ADS discussions before the Gothenburg
meeting in May in 2017, we decided to prepare Part 2 of
the “Human Performance and State in the Context ofAutomated Driving” document. The contents would be
experiment guidelines to assess human factors in ADS. The

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aim of Part 2 was to include research finding and experiences obtained in the SIP-adus Human Factors and HMI
research project. The proposal was approved as a new work
item in the Orland meeting, and Dr. Kitazaki of AIST and
Dr. Shaudt of VTTI were elected as co-leaders. The workshop for the experiment guidelines document was held in
the Prague meeting in April 2018.

tion” is defined as an action initiated by the user of an
automated driving system to request manual control.
“Completion of driving manoeuver” is a takeover action
expected of the driver to successfully handle the system
limit. The time period until vehicle control performance isfully re-established after that completion is referred to as
the “control stabilization phase”.

In the Paris meeting in November in 2016, Dr. Shutko ofFord, in the US proposed the establishment of a document
for external HMI. He argued that automated vehicles
would arrive soon and other road users would therefore
need external HMI. These external HMI should be standardized in order to let those users understood the meaning of the HMI correctly. Then, the workshop was held at
the Gothenburg meeting in June 2017. In the workshop,
there were presentations, including one from the SIP-adus
Human Factors and HMI research project, and discussions
on the latest findings regarding external HMI. Since then,
TF-external HMI has started its activities. There has been
intense discussion in the TF that it is too early to establish
the design guide because we still do not have enough scientific evidence to specify the external HMI design. Therefore, the first document should be a general ergonomic
guide for considering external HMI. Also, the Japanesedelegates offered the opinion that the first step should be toprepare a document for experiment guidelines to examine
external HMI.

3.3. Performance measures in regaining control fromautomation.
Several variables can be used as measures of performance
regarding interventions by the driver. They can be classified into two categories. One is “time-related performancemeasures” and the other is “quality-related measures”.
“Takeover time”, “system deactivation time”, “intervention
time” and “control stabilization time” are defined as timerelated performance measures. “Safety-oriented objective
takeover quality measures”, such as number of crashes, and“Sensitivity-oriented takeover quality measures”, such as
standard deviation of lateral position of the subject vehicle,
are examples of quality-related measures. In addition,
expert assessments and subjective measures are included.

The followings sections introduce outlines of the documents under development in WG8.

3

TR21959: Human Performance and Statein the Context of Automated Driving:Part 1 – Terms and Definitions(1)3.1. Scope
The scope of the document is to cover basic terms anddefinitions related to driver performance and state in thecontext of automated driving. They are relevant to all levelsof automated driving functions that require a human/driver to be engaged or fallback-ready (SAE levels 1, 2 and
3). The terms are supposed to be used in human factors
assessment/evaluations.
3.2. Transition process models
A process model of the transition from manual to automated driving, and a process model of the transition fromautomated to manual driving were proposed (Fig. 1).
Terms related to takeover performance were defined inaccordance with the models. “Significant driver interven-

3.4. Measures of driver states
Attention, and the resources and tasks it demands, aredefined as general terms. “Monitoring the driving environment”, “monitoring ADS performance”, “object and event
detection and response (OEDR)”, “receptivity”, “situation
awareness”, “vigilance”, “operating mode awareness”, and“operating state awareness” are defined as terms for driverstates required for automation. OEDR and receptivity
come from SAE J3016 (2). Measures for these states aredescribed. Some other terms are referenced from SAE
J3114(3).
Terms such as “visually distracted/loaded”, “visual-manually distracted/loaded”, “cognitively distracted/loaded” and“mind wandering” are listed for states corresponding to
non-driving related activities. Terms, such as “hands on
wheel”, “hand off wheel” and “foot position” are listed as
driving position/ posture.
3.5. Driver readiness/availability
Readiness is a concept developed in the SIP-adus Human
Factors and HMI research project. Availability is a similar
concept developed in the KoHaf project in Germany.
Therefore, a compromise to use both terms was reached,
and they are defined as: the state of the driver during automated driving that influences successful driver intervention performance to regain control of the vehicle from thesystem to continue driving manually, avoid a hazard or
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bring the vehicle to a minimum risk condition. It is notedthat readiness/availability is a concept and measures for it
are to be developed.
3.6. Driver’s experiences and attitudes regarding driving automation systems
As driver state and intervention performance depends ontheir understanding and behavioral attitude with respect tothe use of the driving automation system, related terms aredefined.
Prior knowledge of driving automation systems and priorexpectations of driving automation systems are listed as
terms for “prior system image”. Knowledge of the system,functionality (purpose) of the system, functional limit androle of the user are listed as terms related to “education andtraining”. Mental model, trust, overtrust, and trust calibration are listed as terms for “user understanding of driving
automation systems”. Acceptance, reliance, overreliance,
underreliance, complacency, disuse, misuse and abuse arelisted as terms for “user’s use of driving automation system”. The document provides definitions of these terms.

4Road Vehicles: Human Performance andState in the Context of Automated Driving: Part 2 – Considerations in Designing
Experiments to Investigate Transition
Processes

Part 2 of the document is going to be prepared to help
researchers when designing experiments to assess driver
takeover performance. The title, structure and contents ofPart 2 document are under discussion. Chapters that may
be included are as follows. It should be noted that they are
all tentative and subjects to change after discussion in the
TF.
The chapter for “transition process model” describes themodel that is relevant to this document. The “Human factors that influence takeover performance and methods tocontrol/measure them in experiments” chapter will lists
factors regarding human functions to be controlled or
measured when conducting experiments. The “System factors that influence takeover performance” chapter will
describe design elements of ADS to be specified when
designing the experiment. The “Test scenario” chapter will
describe how to design test scenarios. The “Takeover performance” chapter will describe performance measurescollected to evaluate the system tested in the experiment.
The “Testing environment” chapter will describe features,
advantages and disadvantages of different types of testing222

conditions, such driving simulator, testing tracks and public roads.

5

TR23049 Road Vehicles -- Ergonomic Aspects of External Visual Communicationfrom Automated Vehicles to Other Road
Users(4)This technical report describes basic ergonomic aspects
to consider when designing and evaluating external HMI.
This document focuses only on visual communication
devices. There are eight chapters in the main body of thereport.
The “Current road user communications” chapter
describes communications that current road user performs. The “Potential ADS-DV communication” chapter
lists possible communication contents of visual external
HMI. They are vehicle state, such as vehicle speed andacceleration, and driving mode indicating that the vehicle
is moving in automated mode. The “Vehicle perception”
chapter explains that visual HMI should be perceived, recognized and understood. Also there is a possibility of presenting the state of the system in brief. The type of
information conveyed will be “guidance information” suchas prompting pedestrian(s) to start crossing the road and“intent information” such as the ADS-DV intends to stopat the crosswalk. The “Format of ADS-DV communication” chapter states that the format of the visual external
HMI should be not confused with existing lighting. The
chapter “Other considerations for ADS-DV communication”, discusses the need to make compromises with existing laws. The “Acceptance of ADS-DVs by the public”
chapter describes how trust is important for society toaccept ADS-DV and how understanding the communication involved is necessary. Standardization can play a keyrole in that respect.

6 Conclusion
Human factors issues in ADS are a hot topic these days.
When the ISO activities started in 2014, people were notaware of the importance of human factors in ADS. In thissense, WG8 made a good start. The SIP-adus Human Factors and HMI research project started in 2016 and has
been able to make to important contributions to ISO activities. The current tasks in WG8 are all targeted at technicalreports as ADS human factors are still under investigation.
We should prepare to publish standards since they have agreater impact on society.

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References(1) ISO DTR21959: Human Performance and State in theContext of Automated Driving: Part 1 – Terms andDefinitions.
(2) SAE J3016: Taxonomy and Definitions for Terms
Related to Driving Automation Systems for On-Road
Motor Vehicles. 2016(3) SAE J3114: Human Factors Definitions for AutomatedDriving and Related Research Topics. 2016(4) ISO DTR23049 Road Vehicles -- Ergonomic Aspectsof External Visual Communication from Automated
Vehicles to Other Road Users.

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SIP-adus: Project Reports, 2014-2018- Automated Driving for Universal Services ©2019 Cabinet Office, Printed in Japan(All rights reserved)

Publisher’s Office
		
		
		

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March 1, 2019
ISBN 978-4-904056-84-4