2

Establishment and Utilization of Traffic Environment Data

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

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

1

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

Need for road traffic environment
data

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

Dynamic data

Link

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

Base

Laser scanner

Radar

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

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

Fig.2: Concept of dynamic map

Mechanism of automated driving
On-board sensor data

ITS traffic prediction support information

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

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

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

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

2

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

Activities to utilize road traffic
environment data

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

3

Overview of the FOTs in the Tokyo
waterfront area

3.1. Positioning of the FOTs

The first phase of SIP-adus

The Second phase of SIP-adus

What we aim to be

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

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

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

Realization of advanced mobility
and logistics services

Practical application of advanced

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

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

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

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

Establishment of a static
data platform

Reduce traffic congestion bycontrolling traffic flow

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

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

Large-scale FOTs of highprecision 3D maps
〜2018

Accident reduction using
V2X communication

Real-time distribution of
traffic regulation information〜2022

Fig.3: Road Traffic Environment Data Roadmap

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

Chuo Ward
Koto Ward

Tokyo Waterfront City area

Minato Ward

Metropolitan Expressway
Ota Ward

Kawasaki Ward

Haneda Airport area

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

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

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

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

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

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

Antenna after entrance(merging assistance information)

Airport West Entrance
Test vehicle

Test vehicle

Passage closed
Main line vehicle information
Gate
information

Gate information
provision server

Passage of test vehicle +radio wave command

Vehicle sensor
processing unit

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

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

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

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

V2N

760 MHz band

LTE/4G
Cloud

Within 200-300 m

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

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

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

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

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

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

Short andmedium-range
serversHigh
precision
map

LTE

Data conversion and vehicle control output
V2I terminal

GNSS
receiverMobilerouterOn-board
equipment for
FOTs

Automated driving
and driverassistance systems

Dynamic Map
viewer

Fig.10: V2N information provision network architecture

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

Fig.11: SPaT information provision through V2N

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

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

Fig.14: Provision of rainfall information via V2N

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

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

PUSH system

ServerSpecify distribution
area from vehicle

Receive information on thespecified area from the server(PULL)

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

Fig.15: Information provision method via V2N

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

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

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

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

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

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

Fig.16: Demonstration of prototype automated vehicles

4

Conclusion

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

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2

Establishment and Utilization of Traffic Environment Data

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

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

1

Overview of the FOTs

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

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

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

Fig.1: FOTs Area

SIP通し.indb 41

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

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

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

Fig. 2

1) The Tokyo Waterfront City Area Field Operational Tests

Data management anddelivery section*3

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

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

Traffic signal
prediction informationLTE

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

Test vehicle onboard
equipment(BOX-PC)

Fig. 4 Fig. 4

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

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

Event
Event

Automated driving anddrive assistance system(prepared by test
participant)

PUSH
PUSH

PUSH

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

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

Vehiclelocation
Vehiclelocationis isregularly
uploaded
regularly
uploaded1

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

Fig.2: Test system structure

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

2021
Nov.

Dec.

2022
Jan.

Feb.

Mar.

Apr.

May

June

July

Aug. Sept. Oct.

Nov.

Dec.

Rainfallinformation
Lane-specificroad traffic
information

FOTs

Mock
emergency
vehicleinformation forvehicles on
emergency
calls

Traffic signal
prediction
information
Field Operational
Test WG

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

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

VehicleVehicleindicates
area indicates
numberarea number

PULL
PULL

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

3

Fig.4: PUSH method and PULL method

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

Creation and evaluation of the V2N
information delivery system

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

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

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

42

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

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

Table 1: Overview of rainfall information
Information source
Provided
information

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

Impediment location
4.1 km (lanes 1 and 2)

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

1.5

140

2.0

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

60

4.0

40

4.5

20
0

10:02

10:01

10:00

Traffic congestion
dissipated at 4.4km point

09:59

09:57

2.00

09:56

Entered traffic
4 congestion area
at 3.3 km point

09:58

5.0

3.00

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

1.00
0.00

80

3.5

09:55

Precipitation (mm/h)

降水量（mm/h）

10min. forecast
10分後予測値

100

Driving Speed

3.0

Speed（km/h）

Distance（km）

Current
現在値value

4.00

120

2.5

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

Driving Trajectory

Ariake有明JCT
Junction

Tatsumi辰巳JCT
Junction

Kasai

葛西JCT
Junction

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

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

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

Fig. 8

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

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

Delivery method PUSH method

Mock emergency vehicle

Viewer screen

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

Metropolitan Expressway Haneda Route and Bayshore Route

Delivery method

PULL method

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

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2.Establishment and Utilization of Traffic Environment Data

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

Daiba1) The Tokyo Waterfront City Area Field Operational Tests

Central Odaiba No. 1 (North)

Drive
route

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

Central Odaiba No. 2 (South)
Viewer screen
Daiba

Drive
route

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

DriverouteDrive

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

Teleport
EkimaeCentralOdaibaNo. 2 (South)

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

Vehicle speed

Mock emergency
vehicle speed

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

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

No. 2(South) DR 2↓

Ekimae

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

Mock emergency vehicle
driving route
Test vehicle driving route

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

Central Odaiba No. 2 (South)

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

Provided
information

Provision scope

Traffic signals in Waterfront City area

Delivery method

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

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

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

SPaT information for intersections in the designated rectangular area in the

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

Specifiedintersection
PUSH method

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

Teleport Ekimae
8

Drive
route

Daiba

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

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

Teleport Ekimae

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

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

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

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

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

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

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

Data aggregation
serverTest vehicle on-board
equipment

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

Test vehicle on-board

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

Traffic signal
cycle length

Data aggregation
serverShort/medium-range
area server

Test vehicle on-board
equipment

Delay time

Traffic signal
cycle lengthOnesecondinterval

Data aggregation
serverDelay time

Short/medium-range
area server

Test vehicle on-board
equipment

1 minute
requestcycle

Maximum delay time of slightly
over 1 second

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

Data aggregation
serverShort/medium-range
area server

Test vehicle on-board
equipment

Specify intersection forwhich information is desired

Delay time

Delay time

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

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

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

3

Effectiveness of traffic environment
information

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

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

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

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

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

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

4

Conclusion

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

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2

Establishment and Utilization of Traffic Environment Data

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

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

1

Purpose of the Study

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

2

Study of the socio-functional requirementsof the SPaT Information Center

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

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

Note: “Distribution Center” is a tentative name.

Predicted SPaT informationTraﬃc Control Center
Dedicated line

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

Predicted SPaT
information

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

LTE line

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

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

Method where predicted SPaT information iscreated at the Traﬃc Control Center

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

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

Fig.1: SPaT information provision methods

Prefectural policeiiNational Police
Agencyi

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

i

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

Non-competitive area

Competitive area

￥￥

i

ii

i

￥￥

SPaT Information
Center￥i i

Cost burden￥
Beneficiary

￥
￥
i i

Distribution ￥￥Center

Fig.2: Schematic of a social implementation model

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

Necessary social requirements

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

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

Relaying SPaT
information

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

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

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

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

Fulfillment ofsocialrequirements

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

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

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

Fig.3: Conditions for Implementing Entities

Technical
connections tocustomers

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

3

Proceduralconnections tocustomers

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

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

3.1. Overview of the Control Center-based Method

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

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

Methods of providing SPaT Information

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

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

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

300ms

0 error

300ms

㸩＋

㸫

The control
center-based method
Theoreticalerror value

380ms

380ms
150ms

The control
center-based method Legend
prototype
Error verification result

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

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

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

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

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

SPaT information monitoring

Monitors whether the distribution of SPaT information has been stopped.

SPaT information verification

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

MQTT broker

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

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

4

Verification by Prefectural Police Model System

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

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

Traffic Control Center
Traﬃc signal control block

Systems managementblock

Terminal control blockTraﬃc signal information
distributor
To SPaT information
aggregator

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

LTE line
SPaTl information
roadside unit

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

4.4. Verification MethodTraﬃc signal controller

Centralized control

Non-centralized control (point control)

LegendThe centralized
control-based method

Fig.6: Capturing of video

Indicates new or an update.
Indicates a modification.

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

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

Number ofrecall controls

Number ofright-turn
sensitive
controls

SystemNumber ofcontrols

Nighttime
Number of
flashes

The control
center-based
method9 Note60

90Centralized
method

6 Note33

40Controller-based
method

5

1

0

4

4

Methods

Item

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

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

Verification items

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

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

5

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

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

National Police Agency
aggregation system

Predicted SPaT information
Intersection management
information

SPaT Information Center
operatorPrimary role: Information
generation

Primary role: Information
aggregation

Primary role: Information
distribution

Generation of predicted SPaT
information

Receiving and transmitting of
SPaT information

Managing the distribution ofpredicted SPaT information

Registration of intersection
management information

Communication
history inquiry

Guarantee of accuracy, etc.

SPaT information
aggregator

SPaT Information
Distribution Center

Prefecture A

Traﬃc signal
information
distributor

Prefecture Z

Traﬃc signal
information
distributor

Private SPaT Information
Distribution Center

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

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

The controllerbased method

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

Obtaining of information on the direction of vehicle travel

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

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

National Police
AgencyPrivate companies
etc.

SPaT information
aggregator

SPaT Information
CenterSPaT Information
Distribution Center

DatabaseD
C
MVNO environment

B

Z

On-board device(Android device)E

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

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

Measured performance of each section

1100
1000
900
800
700
600
500
400

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

300
200
100
0

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

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

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

ਦ̏̎೧ౕ‫ڧ؂ৄݗ‬Competitive areas

Traffic Control Center(Prefectural police
headquarters)

National Police Agency SPaT
Information Aggregation System

In the Traffic Control Center
Traffic
signalinformation
distributor

Distribution Center(businesses, etc.)

Predicted SPaT information

SPaT Information Center(NPA)

Building a new NW
environment

SPaT information/aggregator

RouterRouterFW

FW

SPaT information
aggregator

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

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

For verification

L2SW

Verification
deviceLTE

Simulated on-board device:
Laptop PC or Android device

GNSS

Fig.10: Testing environment for FY2022

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

6

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

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

2

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

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

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

7

Conclusion

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

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

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

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

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

Establishment and Utilization of Traffic Environment Data

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

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

1

Purpose and Outline

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

2

Our Future Visions

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

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

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

Automated vehicle

With Information
provision

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

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

Solutions forthe needs

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

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

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

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

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

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

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

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

Required immediacy

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

Event detection, etc.

Several hundred m upstream

Several km upstreamHigh

A few tens ofseconds

Emergency braking, etc.

A few
seconds

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

Fig.2: Scope of project3

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

Study of each component technology

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

Data item

Average speed

Link speed(5 min)Event occurrence point

A few
minutes

Low

Table 1: Used probe data
Aggregate link
unit

Number of vehicles by
speed classification Note 1)

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

Speed Classification

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

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

Generation time 18:30of delivered
18:35
information

18:3518:40

18:4018:45

18:4518:50

18:5018:55

18:5519:00

19:00

Layer 5Layer 4Layer 310

Layer 25

Layer 14

Layer 6

19: 05Layer 6

19: 10

19: 20Integrated from Layer 1 to 3 to

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

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

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

Layer 28

Layer 16

Layer 6Layer 5Layer 4Layer 312

Layer 2
10

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

19: 1519:0019:05

Layer 2

Layer 1

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

Probe operator

Public institution

OEM, etc.

vehiclesProbe operator
serverIntegration andgeneration servers

OEM and otherservers

VehiclesProbe operator
server

FOT system
serverFOTs in the Tokyo
waterfront area
Consortium server

Test vehicle

Functionality thatshould be present

Basic format

Cooperative area

This test

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

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

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

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

<Pattern 1>

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

Left direction
speed
V1

V2

Linear speed

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

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

Number of vehicles by speed
classification (speed distribution)

DRM link(100m equidivision)

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

V4

Frequency<Pattern 2>

V3

With
congestion...

Possible lane-specific congestion if speed
variation occurs

Number of vehicle events
Indicator
Left indicator
Right indicator

NormalLeft steeringBrake

Low speed range

High speed zone

Speed

:

Number ofcases

15 cases
0 cases
15 cases
9 cases

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

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

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

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

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

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

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

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

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

4

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

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

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

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

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

FY2021

FY2022

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

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

Field operational test planning

Test systemdevelopment

Test system
improvements/expansion

Field
operational tests

Dec. 2021 Jan. 2022

Probe data online
connection
destination added

Field operational tests

Feb.
2023

Systemimprovements

Field operational
tests

Field
operational tests

・Additional data

・Improvedfunctionality

Spring (April, May)

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

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

2) Study and evaluation of probe processing technology

Field operational
tests

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

Fig.6: Overall schedule for FOTs

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

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

Lane-level roadtraffic
information

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

Simulated emergency
vehicle location
information

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

V2N SPaT (Signal
Phase and Timing)
information

Linked distributionfunctionHigh
precision
Map

High precision map
Tied/conversion

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

Data server

Distribution server

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

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

Rainfallinformation

Simulated emergency
vehicle location information

V2N traﬃc signal control
information

Mobile terminal(Mobile router)
BOX-type
PC

Vehiclecontrol

Viewer display
terminal
Correspondence and output conversion withhigh precision maps

Fig.7: Overall structure of the FOT environment

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

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

Information distribution to OEM center

Information source

Data representation format

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

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

HTTP

1 minute cycle

JASPAR: JSON format
Content：attention

HTTP

1 minute cycle(2) Rainfall information

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

5-minute cycle

JASPAR: JSON format
Content: Environment

HTTP

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

Proprietary binary format

UDP

As required

Proprietary binary format

Web Socket

As required(4) Traffic signal schedule information

Proprietary binary format

UDP

As required

Proprietary binary format

Web Socket

As required

Distributor B

Collection cycle

Data representation format

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

Haneda Line inbound toward branch at Hamasakibashi JCT junction
Ginza area

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

Occurrence of lane-level
congestion on the Haneda
Line side

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

Suzugamori IC

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

Katsushima IC

Shibaura IC
ShibauraJCT

Oi PA
Kuko-Nishi IC

Tokai
JCT

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

Shiodome ICTokyo Bay TN

Oi JCT
Oi-MinamiIC

From
Hanedaarea

Hamasakibashi
JCT

From Rainbow Bridge area

Shio
dom

Oi IC

Tokyo PortTN area

Rinkai Fukutoshin IC

LegendLane-level congestion on the
Bayshore Route mainline

Kuko-Chuo IC

Metropolitan Expressway
Main line merging point

Ariake JCT

Main line road-split point

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

IC merging
IC merging

C NTT InfraNet

Shinonome JCT

PA

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

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

Congestion tail*Junction

Generated
information

Primarily Tokai JCT merging point！
！

Within all testing areas

Lane regulation

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

Direction of travel resolution(100m units)
0*

Merging

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

B Lane regulation

A-2 Congestion tail (passing support)

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

100

Congestion tail(by lane)

200

300

400

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

500

！
！

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

Uses the same
logic

！
！
！

！
！

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

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

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

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

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

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

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

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

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

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

Bayshore Route 10-15 minutes
Eastbound
beforeHaneda Line
Inbound14

12 Last 10 minutes
10 Last 5 minutes
8

5.2

6

8.4

6.9

6.5

8.0

15-20 minutes
before9.5

8.6

20-25 minutes
before25-30 minutes
before

10.4

10.2

10.5

8.8

8.9

Layer 5Layer 64
20
Layer 1

Layer 2

Layer 3Layer 4

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

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

40%

30%

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

Haneda Line Inbound100%

Average: 5.2 units/5 min.

80%

Information
freshness 4 or less

60%

Information freshness 5
20％

40％
10％

10％

13％

14％ 13％

11％

9％

6％

7％

2％

5％

0％
0

1

2

3

4

5

6

7

8

9

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

11

12

13

14

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

15

0％

Cumulative composition

Number of samples ratio

50%

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

N=100 Note 1)

Deviation distance from observed congestion tailNote 2)

t
por
e re
Fals

【Direction-of-travel accuracy【 Lane direction accuracy

Note 3)Note 4)

】 51%

】 63%

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

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

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

5

Future Direction and Issues

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

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

6

Conclusion

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

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

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2

Establishment and Utilization of Traffic Environment Data

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

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

1

Background and Purpose of the study

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

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

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

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

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

Day 1

Day 2

Day 3

Day 4

Initial implementation

Implementation

Adoption rate 30%+

Adoption rate 50%+

Merging assistance to adjust speed
on merging lane

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

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

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

Cooperative assistance for mainlane vehicles with roadside
infrastructure

Merging assistance in heavy trafficwith negotiation by inter-vehiclecommunication

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

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

Merging assistance navigation via
roadside infrastructure

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

Cross-section data sensing + forecast
information delivery

Area covering measurement + successive
information delivery

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

Acceleration lane
starting point

Successive
sensing of mainlane traffic

Successively deliver mainlane traffic information
Acceleration lane
starting point

Navigate each vehicle to merge
alternately (during heavy traffic)

Request to adjust
inter-vehicle gap
or to change lane

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

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

Up to tight traffic flow

Traffic flows including
heavy traffic environment

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

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

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

2

Selection of Area for the Simulation

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

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

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

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

5
4
3

Haneda Line (inbound)
Airport West entrance

2

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

1
0
0

50

100

150

200

250

300

Distance from hard nose to taper edge (m)

350

400

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

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

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

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

4％

New ICOld IC

〜390

〜370

〜350

〜330

〜310

〜290

〜270

〜250

〜230

〜210

〜190

〜170

〜150

〜130

〜90

〜110

〜70

〜50

〜30

〜10

0％

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

Other IC2％

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

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

3

Study of Day 2 System Feasibility

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

100 points

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

Communication area
Sensing area

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

Merging in sufficientgap is possible

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

Fig.5: Road alignment for the simulation

99 points~

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

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

1.15 sMerging without
enough gap
7.0 km/h

Merging area

14.0 km/h

Relative speed

Fig.4: Merging evaluation score map (tentative)

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

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

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

Verification scope of parameters

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

Length ofcommunication area

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

Information delivery
delay

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

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

The shorter the length, thegreater the negative impact

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

4

Detailed Verification of the Day 2 System

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

0-99 points

53

With assistance 20%
automated vehicles

21

29

43

25

0

100 points

20

40

35

60

80

100

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

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

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

64

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

0 points

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

1.0

1.5

(3) Deceleration speed [G]

0.3

0.2

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

Inter-vehicle distance [m]100

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

60

40

0 points or lower: Merging without
enough gap

20

0

-50

-25

0

25

50

Relative speed [km/h]

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

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

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

Saturation level: Low

90
80

Average driving speed [km/h]

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

0:00

23:55

Saturation level: High

70
60
50
40
30
20
10

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

0

50

100

150

200

250

Number of passing vehicles [vehicles/5 minutes]

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

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

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

Concept Planning and Feasibility
Verification of the Day 3 System

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

6

Expectations of Technological
Outcomes in Fiscal 2022

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

7Conclusion

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

[References]
(1)

(2)

(3)

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

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

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2

Establishment and Utilization of Traffic Environment Data

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

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

1

Purpose and Overview of Research

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

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

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

2

Construction of Model System

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

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

Standard data (held by prefectural police)
Data creation

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

not tentatively linked

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

not tentatively linked(2) Tentative linkage XX%

Road sign prediction system modification

tentatively
kinked(3) Tentative linkage XX%

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

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

Compare with current linage data

Fig.1: FOTs flow chart

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

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(1) Technological Development Concerning the Generation of Traffic Environment Data
5) Improvement of Data Accuracy of Traffic Regulation Information

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

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

Precision evaluation
Precision
evaluation
Correct

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

Incorrect

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

Fig.2: Evaluation flow in FOTs

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

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

Road sign type, etc.

Number of
extractions

Extraction
rate

Still images of principal signs

297

281

95％

Still images of supplementary
signs

131

129

98％

Video of principal signs

219

214

98％

Video of supplementary signs

112

111

99％

Road markings

261

248

95％

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

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

Image recognition system

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

Operating PC

Road signs/markings
position prediction system

Signs/Markings data extraction
Signs/Markings data recognition

Still image

Add signs/markings position information

Signs/
Markings
CSV

Video

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

Extraction

Recognition

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

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

Illustration of Process

Identify areas and extract images
By TOSCO CORPORATION

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

Estimate distance and angle
Position
estimates

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

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

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

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

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

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

Fig.4: Designateddirection only sign

Fig.5: One-way street sign

*Revied version of current standard format

Number ofsigns

Number of
extractions

Extraction
rate

Parking prohibited

106

106

100％

Maximum speed 40km

15

15

100％

Crosswalk/bicycle crossing

14

14

100％

Designated direction only

13

13

100％

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

11

3
(11)

27％
(100％)

Maximum speed 50km

11

11

100％

297

274

92.3％

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

Fig.6: Structure of expanded standard format

Table 3: Recognition results
Class

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

Number of images(1,600 images)

Percentage
1,539

96.2％

More than 2 meters - within 4 meters

59

3.7％

More than 4 meters

2

0.1％

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

3

Prototype System

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

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

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

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

Expanded standard format data

Is there a diﬀerence? Reason? =Reconduct research?

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

Image recognition system

Road signs/markings position prediction system

not tentatively linked

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

Drive
video

Collected
image

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

tentatively kinked

Is the sign wrong? The regulation wrong?
Other?

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

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

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

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

Success

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

Precision
evaluation(4) Correct linkage

Correct

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

Incorrect

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

(5) Incorrect linkage

Fig.11: Evaluation flow of the prototype system

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

National Police Agency

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

Video
UploadVerify and revise
extraction output

Precision evaluation

Extraction
Recognition

Signs/Markings
extraction output

Outputdata

Roadsigns.
csv

Position prediction© OpenStreetMap.org

Fig.9: Image recognition system configuration

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

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

4

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

Traffic regulation
information

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

5

Conclusion

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

FY2021

Scope ot this research

FY2022Standard F

Prepare configuration plan

Add data items for annual statistical report
Expanded F
FOTs

FY2023 andlater

Expanded FStandard F

FOTs

Data format

Reflect FOTs (Field
Operational Tests)
results

Expanded F[Fixed]Expanded F
Transition

Simultaneous operation

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

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

Fig.13: Concept of future operation

Cooperation with high precision 3Dmap

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

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

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Establishment and Utilization of Traffic Environment Data

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

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

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

Overview of Research and Development

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

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

Optical beacon
Detects only when passing directly
under the optical beacon

Cable
network

Vehicle passage Control center
information

Cable network
SPaT switching
information

Priority passage

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

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

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

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

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

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

FY2021
Effectiveness verification phase

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

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

Fig.2: Technological development overall process2

Traffic signal priority control in thistechnology development

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

Intersection 0

Optical beacon
position -150

Red light

Actualspeed(1) No control

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

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

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

Optical beacon
position -150

-300

Calculation
speed (opticalbeacon)Actual speed

Red light
Green light(2) With control

Optical beacon use
fixed point only
Time (seconds)

Fig.4: Conventional traffic signal priority control issues

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

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

Calculated
speed (GNSS)

Red light
Green light

Actualspeed

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

GNSS (final determination position)

Optical beacon (final determination position)-150

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

4

Traffic signal priority control FOTs

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

Time (seconds)-300

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

3

the same system.

Detailing functional and technicalrequirements

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

Item

Contents

Verification area

Shizuoka City, Shizuoka Prefecture

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

Target intersection

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

Verification period

PTPS

FAST

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

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

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

24 hours including weekends andnational holidays

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

Ambulances: 3

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

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

Shizuoka City
Shizuoka
Hospital

Shizuoka City
Emergency
CenterJapan Red Cross Hospital
JR

Saiseikai
GeneralHospital

tion
a Sta
uok
Shiz

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

CentralsystemLow-level
unit/terminal
support unit

Display
deviceTrunknetwork

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

MobilerouterLocation
information, etc.

Mobilephone
network(LTE)

Terminal system

Signalcontroller

Signalcontroller

Signalcontroller

On-board
unit

Public vehicle (bus) Emergency
vehicle (ambulance)

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

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

Verification items

1 Priority control operation

2 Control effects

Objective/evaluation

Results

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

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

Basic operation: Equivalent to or better than
optical beacon control

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

3 GNSS accuracy

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

Validation against the measurement cycle
4 Extension time

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

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

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

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

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

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

AbechoKagoueZaimokucho

NakachoMean value

1.58

2.74

2.39

2.49

Maximum value

4.95

5.12

4.84

4.89

Minimum value

0.16

0.99

0.63

0.63

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

Unit: mUnit: mEvaluation site

In front of the Japan Red Cross
Hospital

From Japan Red Cross Hospital to Egawacho

Mean value

2.59

3.19

3.23

Maximum value

9.71

11.57

20.00

Minimum value

0.42

1.44

0.50

Egawacho

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

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

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

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

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

Bus travelroute
09:06:03
500

Intersection lightcolor status(execution history)
Distance from intersection

400

300

200

100

0

-100

-200

-300

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

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

22:34:39

22:34:30

Reduce green
light time by
10 seconds

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

22:34:13

22:34:05

22:33:56

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

Intersection lightcolor status(execution history)

Kuroganecho Nishi
intersection stop
line location

22:33:39

Kuroganecho Nishi Intersection passing location

Optical beacon (set)22:33:30
500

400

300

Optical beacon (set)

200

100

0

45 m

-100

-200

Distance from intersection-300

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

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

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

5

Simulated emergency vehicle
information transmission FOTs

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

Scope of maintenance in this R&D

Information collection anddistribution server

On-board unit

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

Router

LTE router

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

Simulated emergency vehicle

On-board unit

LTE router
GNSS
satellite

GNSS module

Connection diagram

Fig.11: Simulated emergency vehicle

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

System configuration chart

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

Standard

Communication control procedure

UDP

Transmission cycle

Every 2 seconds

Number of GNSS information units stored

Max. 20 (100 ms)

GSI Map

Route A
Route B
Route C
Route D
Route E

K J

I H G

L

F
Q

R

D

S

P
A

M

B

C
GSI Map

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

NO
Fig.13: Evaluation sites

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

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

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

A

B

C

Unit: mD

E

Mean value

2.41

2.07

2.49

2.90

1.98

Maximum value

4.73

3.51

3.87

5.30

4.67

Minimum value

0.59

0.57

0.38

0.97

0.16

Evaluation results (Part 2)
Evaluation siteF

G

Unit: mH

I

J

Mean value

2.14

3.22

5.29

8.37

5.09

Maximum value

4.60

5.27

9.92

17.67

14.10

Minimum value

0.16

0.19

1.41

1.12

0.92

Evaluation results (Part 3)
Evaluation site

K

L

Unit: mM

NO

Mean value

2.35

1.27

2.18

3.20

2.41

Maximum value

5.23

2.19

3.62

20.81

5.36

Minimum value

0.82

0.34

0.50

0.45

0.70

Evaluation results (Part 4)
Evaluation siteP

Q

Unit: mR

S

Mean value

2.55

2.23

2.56

1.92

Maximum value

8.28

3.47

3.79

2.19

Minimum value

0.25

0.39

0.13

1.56

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

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

Evaluation site

L

M

N

Unit: mO

Mean value

1.13

2.32

1.90

2.75

Maximum value

1.96

3.56

2.97

5.36

Minimum value

0.63

1.56

0.85

0.70

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

Evaluation site

P

Q

R

Unit: mS

Mean value

2.39

3.47

3.79

1.91

Maximum value

6.97

3.47

3.79

1.91

Minimum value

0.28

3.47

3.79

1.91

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

L

M

N

Unit: mO

Mean value

1.41

2.04

4.47

1.99

Maximum value

2.19

3.62

20.81

4.89

Minimum value

0.34

0.50

0.45

0.77

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

Evaluation site

P

Q

R

Unit: mS

Mean value

2.79

0.99

1.33

1.94

Maximum value

8.28

2.08

2.19

2.19

Minimum value

0.25

0.39

0.13

1.56

6

Conclusion

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

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

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2

Establishment and Utilization of
Traffic Environment Data

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

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

1Introduction

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

2Outline of Activities

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

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

FY2019Q3Q4FY2020Q
1 Q2

FY2020Q
3 Q4

FY2021Q
1 Q2

FY2021Q
3 Q4

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

Phase I
Selection of Use Cases

Phase IIClarification of communication requirements

Phase III
Study of communication methods

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

Development of roadmap

Fig.1: TF Study Members and Overall Schedule3

Study of communication methods

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

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Functional classification

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

Target areas

Expressways + prefectural and municipal roads

Overview

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

Target vehicles

Privately owned vehicles

Image of the use caseContinuous information provision of mainline traﬃc

Speed adjustment

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

Infrastructure

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

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

Sensor data

Speed, position

Rich content-

V2I

Topology

One-to-many

Control Usage

Speed adjustment, vehicle distance adjustment

Data category/ content of
information

Remarks(communication
requirements,
etc.)

MessageCommunication

Responsiveness

Required

Data amount

Small

Fig.2: Use case example: merging assistance
Table 1: Example of communication requirements
Functionalclassification
Use cases
No.
Message namea. Merging/lane change assistance
Cooperative merging assistance for mainline vehicles with roadside infrastructure
networka-1-3
Negotiation request Negotiation response
LocationControl request
Update request
Update response
information

Communication
V2I (I to V)
V2I (V to I)
methodRoadside
MessageNon-specific vehicles
infrastructure
destination
From 6 seconds
Target area
before the start ofWithin the control(minimummerging until therequest range
range)
merging point
Number oftransmission
1 vehicle
1 vehicle
vehicles per area
Connecting road:
Required
66.7 - 116.7 m
66.7 - 116.7 m
communication
Main line: 111.1 distance266.7 mConnecting road: 20 Connecting road: 20
Maximum - 70 km/h
- 70 km/h
relative velocity Main line: 20 - 120 Main line: 20 - 120
km/h
km/h
5,236 bytes (4,986 +Maximum data
250)
287 bytes
size
Estimated number of(2)vehicles: 184
Periodic/
Periodic
Aperiodic
Aperiodic
Transmissioninterval

PAR per packet
Maximum
acceptable delay
in radio area

100ms

V2I (I to V)

V2I (V to I)

Specific vehicle

Specific vehicle

Within the control
request range

Within the control
request range

1 vehicle (x numberof controlled
vehicles)Connecting road:
66.7 - 116.7 m
Main line: 111.1 266.7 m
Connecting road: 20- 70 km/h
Main line: 20 - 120km/h

48 vehicles (numberof control vehicles,
during congestion)
Connecting road:
66.7 - 116.7 m
Main line: 111.1 266.7 m
Connecting road: 20- 70 km/h
Main line: 20 - 120km/h

273 bytes(2)287 bytes(2)

Aperiodic

Aperiodic

not specified

PAR ≥ 99%
(tentative)

PAR ≥ 99%
(tentative)

PAR ≥ 99%
(tentative)

PAR ≥ 99%
(tentative)

Not specified

Assumed 100ms

Assumed 100ms

Assumed 100ms

3.3. Technical Testing of Communication Systems
The feasibility of the use cases were evaluated from theperspective of the feasibility of communication by applying thecommunication requirements defined in Section 3.2 to the SIPUC selected in Section 3.1. In the evaluation, the applicability of
the following two communication methods to twenty of the
SIP-UC use cases was studied, excluding the five use cases forwhich information distribution over a wide area by V2N(Vehicle to Network) communication is clearly suitable.
(1) Expansion of 700 MHz Band ITS(2) Application of C-V2X(*) as an example of a new \communication method*Adopted as an example of utilization of the 5.9 GHz

band frequency
C-V2X (Cellular V2X: X stands for ‘everything’)
The actual evaluation was conducted by Kyocera
Corporation (hereinafter referred to as "Kyocera") and NEC
Corporation (hereinafter referred to as "NEC"), who were
entrusted with this task. Of these, Kyocera was responsible
for the evaluation of the 700 MHz Band ITS, while NEC was
responsible for the evaluation, roadmap development, andoverall summary of the new method based on C-V2X.
Regarding these evaluations conducted by Kyocera andNEC, please refer to the details in Section 2 (2) 1) "Researchon Communication Methods to Realize Cooperative
Automated Driving Use Cases" that follows this section. Thissection briefly describes the results of the testing.
In the evaluation of 700 MHz Band ITS, Kyocera
evaluated the feasibility of adding all twenty SIP-UC to theexisting ITS services that support safe driving. As a result,among the V2I (Vehicle to Infrastructure) communications
between vehicles and the roadside infrastructure, information
distribution from the roadside infrastructure to vehicles was
feasible in all cases. On the other hand, some of therequirements for information distribution from vehicles tothe roadside infrastructure could not be met. As for V2V(Vehicle to Vehicle) communication, eight of the eleven use
cases in SIP-UC that assumed V2V communication could
not meet the requirements.
The use cases that failed to meet the requirements werecharacterized by the need for a function (called "negotiation")in which the vehicle that is the source of information
identifies the vehicle to be communicated with and exchanges
individual information with it. The negotiation function is amechanism that does not exist in the 700 MHz Band ITS,
which is based on broadcasting, and this evaluation
confirmed that a new communication method, as well as a
new communication bandwidth in some instances, arerequired to realize such use cases.
On the other hand, NEC’s evaluation using C-V2X

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

focused on the evaluation of packet arrival rate (PAR),
reception level, and the impact of shadowing, based on
merging assistance through negotiation and services aroundintersections, which could not be realized in the evaluationof the 700 MHz Band ITS. As a result, it was confirmed that
there were cases in which the requirements could not be met,such as sudden deterioration of communication conditions
due to shadowing and a drop in packet arrival rate due toroad reflection and other effects.
The results of the technical testing are summarized below.
·F or infrastructure-to-vehicle communications under V2I, alluse cases can be met by expanding the 700 MHz Band ITS.
· Some of the vehicle-to-infrastructure communication use
cases under V2I and all of the V2V communication use
cases did not meet the requirements· A review of the detailed conditions for each individual use
case scenario and the application of new communication
methods are necessary· For use cases that require advanced communication, suchas negotiation between vehicles, the requirements were notmet even in the C-V2X evaluation.
From these results, it was found that it will be difficult torealize all of the SIP-UC simply by adding new methods, andit was concluded that "further study is needed".

4

Development of roadmap

The SIP-UC defined by the TF are classified into two
categories: those that are essential for driving automatedvehicles and those that support safer and more comfortable
driving. The timing of their realization varies, including thosethat can be realized through the extension of safe driving
support, those that require advanced mechanisms, and thosethat have already been positioned as national policies by ITSrelated ministries and the national government. Therefore, inPhase III, the deployment timeframe for each of the SIP-UC
use cases was arranged and defined using a timespan ofapproximately five years, based on information from thevarious related parties. In addition, because the realization of
the use cases requires a wide range of issues to be solved, notonly technological but also including the legal system, businessmodels, and clarification of the roles and responsibilities
among service providers, at the same time the TF identified
the necessary issues to realize the SIP-UC and included themin the deployment plan.
In the deployment plan, the use cases that can be extended
from safe driving support to automated driving and the use
cases that are essential for the driving of automated vehicles
are expected to be realized around 2025, and the 700 MHz
Band ITS can be utilized. The use cases for safer and more

comfortable automated driving, which are expected to berealized from 2030 onwards, will require advanced
communications such as the negotiation that is describedabove, and will require the introduction of new communication
methods and the securing of frequency bandwidth. Based onthese considerations, the TF developed the roadmap shown in
Fig.3 and reached the following conclusions.
· It is assumed that new communication methods fornegotiation, etc. will be required around 2040
· If we anticipate, for example, 30% adoption of cooperative
automated vehicles around 2040, introduction of the newcommunication methods will be required from around 2030
· The 700 MHz Band ITS can be utilized for use cases thatwill commence earlier, before 2030
Further, in order to realize each use case, it is very
important to have timelines to address the many other issues,
not only the results of the TF’s study of communication
methods.
Regarding the deployment plan and roadmap, pleaserefer to the details in Section 2 (2) 1) "Research on
Communication Methods to Realize Cooperative AutomatedDriving Use Cases" that follows this section.

5

Conclusion

The TF defined 25 use cases to realize cooperative
automated driving that utilizes communication. It thenconducted a study of the communication methods thatshould be applied to these use cases and compiled them
together with the timeframe for realization of each use caseand issues that need to be solved in a roadmap.
In order for the early adoption of automated driving, it isnecessary to continuously study the application of use casesthat utilize the 700 MHz Band ITS and the newcommunication methods needed to realize the use cases that
require advanced communication in the future.
We hope that the study will be progressed steadily in the
future according to the roadmap that has been defined on
this occasion.
[References]
(1)

ARIB-STD T109 700 MHz Band Intelligent Transport Systems, https://www.arib.or.jp/english/std_tr/telecommunications/desc/std-t109.html,(accessed 25th October 2022)(2)

Study of utilization of new communication technologies including
V2X technology to automated driving system, 2019, https://en.
sip-adus.go.jp/rd/rddata/rd02/204s.pdf, (accessed on 25th October
2022)

(3)

SIP Use Cases for Cooperative Driving Automation-Activity Report of
Task Force on V2X Communication for Cooperative Driving
Automation in FY2019-, First edition, 2020, https://www.sip-adus.go.

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

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Research for V2X Communication for Cooperative Driving Automation (Overview)

2025-

20202022 (R4)USA

2023 (R5)

2030-

2035-

2040-

2024 (R6)

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

Transition from 5.9 GHz band DSRC
to C-V2X completed(FCC)
Start of deployment of V2N services in 5G (DG MOVE)(Reference) International trends

Development of V2X
safety evaluation criteria

Study of protocols

V2X becomes a(EuroNCAP)
subject ofassessment(EuroNCAP)
Study of advancement of ITS-G5(EuroNCAP)

EuropeR2R communication network construction using RSUs
(Czech implementation plan in C-Roads)

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

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

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

China

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

Formulation of IEEE 802.11bd

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

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

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

Japan

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

AssumedCommunication
technology

700 MHz band ITS

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

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

Study of specifications forsecurity and privacy measures

Security scheme study

Issuance of specifications for security and privacy measures
Security scheme formation

Study of policies for sharing in-vehicle devices

A-2) Formulation ofpolicies for sharing invehicle devices

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

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

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

Organization of concept of division of responsibility

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

Communication
standard formulation

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

OBU draft study

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

Study of OBU common specifications

OBU connectivity confirmation scheme
formulation

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

Equipping of 5.9 GHz OBU

Adoptionrate 30%

Adoptionrate 50%

Deployment of 5.9 GHz RSU

B-3) Equipment
development andpractical application

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

V2I(mediation)

V2V(Negotiation)

Fig.3: Roadmap for V2X Communication for Cooperative Driving Automationjp/rd/rddata/usecase.pdf, (accessed on 25th October 2022)(4)

ITS Forum RC-017: Study report on communication scenarios andrequirements for "SIP Use Cases for Cooperative Driving Automation",
2022, https://itsforum.gr.jp/Public/E3Schedule/p40/ITS_FORUM_RC017eng_v10s.pdf, (accessed on 25th October 2022)[Contacts]
ITS Promotion Office, Connected Supervisory Dept., Connected Company,
Toyota Motor Corporation, 1-4-18 Koraku, Bunkyo-ku, Tokyo 112-8701,
050-3165-1695, Contact: Hideaki Suganuma

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Establishment and Utilization of Traffic Environment Data

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

1) Research on Communication Methods toRealize Cooperative Automated Driving
Use-cases
Satoshi Kimura, Takeshi Nunomoto (NEC Corporation), Masato Ogawa, Tomoaki Konishi (KYOCERA Corporation)(Abstract) This project was conducted to formulate the roadmap for the timing of social implementation of communication
technologies necessary for the realization of an automated driving society, based on the study, verification, and field testsof the technical feasibility of wireless communication, including specific requirements for wireless communication
technologies, for the use-cases for cooperative driving automation utilizing the V2X as formulated by the Task Force on
V2X communication for cooperative driving automation, system implementation working group. Specifically, we examinedthe communication requirements for use-cases; verified whether the wireless communication cellular V2X system could
be used, formulated measures to solve any problems that arose, and then verified their effectiveness; verified whether
the existing wireless 700 MHz band Intelligent Transport Systems could be used, formulated measures to solve any
problems that arose, and verified their effectiveness; and developed the roadmap of communication requirements forcooperative automated driving as necessary for the realization of an automated driving society.
Keywords: cooperative automated driving, V2X, cellular V2X (C-V2X), 700 MHz band ITS

1A Study on V2X communication forachieving use-cases of cooperative driving automation (cellular V2X system)

1.1. Overview
Based on the SIP use-cases for cooperative driving
automation (hereafter "SIP-UC")(1) formulated by the Task Forceon V2X communication for cooperative driving automation(hereafter "TF"), the technical and communication requirements
for SIP-UC were studied jointly with Radio System Technology
Task Group of advanced ITS info-communication systemcommittee in ITS Info-communication Forum (hereafter "TG").
We verified the suitability of the cellular V2X system, one of theavailable wireless communication systems, to meet with thesecommunication requirements, formulated measures to solve
any problems that arose, and verified their effectiveness. (V2X:
Vehicle to X, V2I: Vehicle to Infrastructure, V2V: Vehicle to
Vehicle, V2N: Vehicle to Network)

1.2. Study of communication requirements
Through discussions with the TG and organizations andcompanies involved in each SIP-UC, the technical requirements(function sharing between road and vehicles, communication
functions,
etc.)
and
communication
requirements(communication sequences, message content, communication
performance, etc.) required to realize the use case were defined forTable 1: Examples of communication requirements
Functional classification

a. Merging/lane change assistanceUse-cases

Merging assistance by preliminary accelerationand deceleration

Merging assistance by targeting
the gap on the main lane

No.

a-1-1

a-1-2

Message nameLocation information

Location information

Location information

Control request

Negotiation request/update request

Communication method

V2I (I → V)

V2I (I → V)

V2I (I → V)

V2I (V → I)

V2I (I → V)

V2I (V → I)

Message destination

Non-specific vehicles

Non-specific vehicles

Non-specific vehicles

Roadsideinfrastructure

Specific vehicle

Specific vehicle

Target area (minimum range)

From 6 seconds before the merging point to thecenter between the 6 seconds before the merging
point and the merging point

From 6 seconds before the start ofmerging until the merging point

Within the control request range

Within the control request range

Number of transmission
vehicles per area

1 vehicle

1 vehicle

1 vehicle

1 vehicle

1 vehicle (xnumber of controls)

48 vehicles (number of control
vehicles, during congestion)

Required communication
distance

33.9 to 59.3 m(National Research Institute Specifications: 95 m)

67.8 to 118.6m

Connecting road: 67.8 to118.6 mMain lane: 112.5 to
270.0 m67.8 to 118.6m

Connecting road: 67.8 to 118.6 m
Main lane: 112.5 to 270.0 m

Connecting road: 67.8 to 118.6 m
Main lane: 112.5 to 270.0 m

Maximum relative velocity

Connecting road: 20 to 70 km/h

Connecting road: 20 to 70 km/h

Connecting road: 20 to
70 km/hMain lane: 20 to
120 km/hConnecting road:
20 to 70 km/h
Main lane: 20 to
120 km/hConnecting road: 20 to 70 km/h
Main lane: 20 to 120 km/h

Connecting road: 20 to 70 km/h
Main lane: 20 to 120 km/h

Maximum data size

1510byte estimate: 46 vehicles

2752byte estimate: 92 vehicles

5236byte estimate: 184
vehicles287byte369bytes

287bytePeriodic/Aperiodic

Periodic

Periodic

Periodic

Aperiodic

Aperiodic

Aperiodic

Cooperative merging assistance with vehicles on the main lane by roadside control
a-1-3

From 6 seconds before
Within the control
the start of merging until
request range
the merging point

Negotiation response/update response

Transmission interval

100ms

100ms

100ms

PAR per packet

PAR ≥ 99% (tentative)

PAR ≥ 99% (tentative)

PAR ≥ 99% (tentative)

PAR ≥ 99%
(tentative)

PAR ≥ 99% (tentative)

not specified
PAR ≥ 99% (tentative)

Maximum acceptable delay inradio area

Not specified

Not specified

Not specified

Assumed 100ms

Assumed 100ms

Assumed 100ms

PAR: Packet Arrival Rate

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Control server

VehicleCommunication
delay

Control

Processing delay

Collect

Base station DU/CU

Communication delay

Base station radio unit

Core network

Processing delay

Transmission network

Communication delay

Transmission

Judgment

Collect

Processing
delay

VehicleMNO telecom
infrastructures*Core network

Base station DU/CU

Communication delay

Transmission network

Base station radio unit

Processing delay

Issue 4: Feasibility of transmission
methods according to use cases,
acceptable number of vehicles
according to transmission methods,
and communication frequency
feasibility

Issue 1: Communication delay
during teleoperation

MNO telecom
infrastructures*Transmission

Detection

Cause ofdelay

Locationmanagement

Issue 3: Frequency of communicationof location information to determine
the vehicle sending the information

Collect

Issue 2: Communication volume
during remote monitoring andoperation(Video, etc.)

Processing delay

Series of processes from detection to control*MNO network (wide area network) is basically composed as multi-carrier.
1

Fig.1: Issues in realizing communication requirements© NEC Corporation 2021

Use Case a-1-3 (cooperative merging assistance)
Overview

V2I communication area
(1) Main lane vehicle location information

Merging vehicle

Roadside unit

(2) Control request(4) Negotiation request/(7) Update request(9) Update response

Main lanevehicleInfrastructure(3) Negotiation request(6) Update request(5) Negotiation response(8) Update response

Req. to keep/expand intervehicle gap or to change lane

V2I Communication Area

Fig.2: Communication model for merging assistance

SIP-UC in cooperation with the TG. Examples of communication
requirements for use-cases related to merging and lane change
support are shown in Table 1.
The results of the study were developed by the TG as a
Study report on communication scenarios and requirementsfor "SIP Use-cases for Cooperative Driving Automation".(2)1.3. Consideration of wide area network
For each SIP-UC using a wide-area network (d. Lookahead
information: trajectory change, f. information collection/
distribution by infrastructure, etc.), we organized the issues
facing the realization of communication requirements andtheoretically studied measures to deal with these issues. Fig. 1
Table 2: Proposed measures to address each issue
IssuesIssue 1: Communication delay
during teleoperation

Item to study—Reducedcommunicationdelay(V ⇔ N)

Issue 2: Communication volume
during remote monitoring andoperation (Peripheral Images, etc.)—Reducedcommunicationdelay(V to N)

Issue 3: Frequency ofcommunication of location
information to determine thevehicle sending the information
Issue 4: Feasibility of transmission
schemes according to use-cases,
acceptable number of vehicles
according to transmission
schemes, and communication
frequency feasibility—Reduction ofcommunication
frequency(V to N)

V: Vehicle, N: Network—Reduction ofcommunication
volume(N to V)

Policy Outline—Priority control of wirelesscommunications—Securing of dedicated frequency
bandwidth—Securing of end-to-end dedicatedbandwidth (e.g. slicing)—Priority control of wirelesscommunications—Securing uplink (V to N) communication
capacity—Securing of dedicated frequency
bandwidth—Securing of end-to-end dedicatedbandwidth (e.g. slicing)shows the issues in realizing the communication requirements,
and Table 2 shows proposed countermeasures for each issue.

1.4. Study of Short range communication
For each use case that utilizes short range (road-to-vehicle
and vehicle-to-vehicle) communication, we identified issues inrealizing communication requirements, verified communication
performance through cellular V2X communication simulations(5.9GHz band and 10 MHz bandwidths), and proposed
theoretical measures to address these issues. Two major usecases (1) cooperative merging assistance and (2) intersection
area were taken up for testing. Fig.2 shows an example of thecommunication model used in the simulation of cooperative
merging assistance communication.
Through communication simulations, issues in achieving
the communication requirements were clarified, and areassessment including countermeasures was then conducted.
[PAR %]
100

After taking
countermeasures

90
80
70
60

—Feasibility study for each transmission
method(Unicast, multicast, broadcast)

Before taking
countermeasures

50
40

Line-of-sight0

Non-line-of-sight
47[Distance m]116.7

Fig.3: Example of communication simulation results

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases[Reception level dBm]

[PAR %]

-60

100
90

PAR

-70

80
70

-80

60
50

-90

40
30

-100
-110
140

Reception Level
120

Measuringvehicle

100

80

60

Blocking
vehicle40

20

Transmitting
vehicle

20
10

RSSI
PAR

0
0 [Distance toblocking
vehicle m]

Fig.4: Example of test results on shadowing impact by a vehicle

Fig.3 shows, as an example, the results of the initial simulation
Corporation 2022
(packet arrival1 rate© NEC(PAR)
depending on the distance) and thatof the simulation after taking countermeasures (to aggregate
messages to be transmitted, to reduce impact of shadowing loss
due to vehicles) against assumed factors causing declining of
packet arrival rate, such as reception error on roadside unit byconflict of transmission/reception timing, as well as packet
conflict at timing of data transmission to vehicle due to hidden
node.
In addition, we conducted a communication performance
evaluation test using vehicles and cellular V2X communicators
on a test course to verify the effects of elements such as
shadowing and communication congestion, which require
verification in the field for wireless communication. From theperspective of communication performance, we evaluated (1)the impact of shadowing by vehicles and (2) the impact ofcommunication congestion depending on the communication
volume. Fig.4 shows the result the field operational tests (FOTs)of the impact of shadowing by a vehicle, as an example.
As a study of short range communication, the following
were confirmed from the results of communication simulations
and FOTs.
1) In order to realize a single use case, communications withvarious requirements are mixed and mutual influence occurs.
2) In order to realize multiple use-cases in the same location,communications with various requirements are mixed
and mutual influence occurs.
In actual operation, the wireless communication
environment (shadowing, multipath, etc.) and the driving
environment (number of vehicles, inter-vehicle gap, etc.)
cannot be uniquely specified or limited. Communication
taking place in an unexpected environment can lead tocommunication congestion and subsequent communication
delay or failure. It is therefore necessary to consider measures
to prevent such problems. Specifically, the following
measures should be considered: 1) reducing the impact ofcommunication congestion by controlling communication
congestion at not only the communication layer but also theapplication level, in accordance with the wirelesscommunication environment and the driving environment;2) reducing the impact of communication congestion by
allocating communication channels according to thecommunication area (by lanes, by direction of traffic, etc.)
and the content of communication.

2

A Study on V2X communication forachieving use-cases of cooperative driving automation (700MHz band ITS)

2.1. Overview
As in Section 1, among the SIP-UC formulated by the TF, 20
use-cases (see Table 3, with specific use-cases hereafter referred
to by those designations), targeting V2I and V2V wereconsidered. Based on study report on communication scenarios
and requirements for "SIP Use-cases for Cooperative Driving
Automation",(2) we verified whether the use-cases can besupported by 700 MHz band Intelligent Transport Systems(hereafter "700 MHz band ITS") and developed countermeasures
to solve relevant issues through desk study and simulations.

2.2. Desk study
Desk study evaluated communication quality from theperspective of linked budgets, transmission time constraints,
roadside unit installation constraints, and message sets.
In the evaluation of communication quality andtransmission time constraints, SIP-UC a-1-3, a-1-4, a-2, anda-3, in which interaction is required, did not meet thetransmission time constraint due to the number of transmitting
vehicles per area. In the use case of unmanned platooning offollowing vehicles by electronic towbar (g-1), thecommunication requirement specifies a transmission cycle of
20 ms in the case of emergency, but the 700 MHz band ITS
standard ARIB STD-T109 specifies a communication cycle of
100 ms, so the communication requirement was not met. In
other use-cases, the transmission requirement was met.
In the roadside unit installation constraint evaluation, we
examined the allocation of wireless transmission slots(hereafter "slot") for roadside units, and found possible toallocate the four slots required for SIP-UC. We therefore
confirmed that the slot allocation for roadside-vehiclecommunication and inter-roadside communication is
feasible even if SIP-UC is added to the 700 MHz band ITS
service.

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(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

In the message set evaluation, we examined the possibilityof adding SIP-UC message sets to 700 MHz band ITS
messages, and confirmed that SIP-UC onboard unit message
sets can be added by utilizing the free field (optional area) of
ITS Connect TD-001.(3)system for 700 MHz band ITS, when onboard unit A and B are
located outside the carrier sense range and do not know eachother’s transmission timing, if both transmit at the same timing,
their messages will collide at the position where each transmission
arrives (hidden terminal problem).

2.3. Simulations

2.4. Issues and future actions

To confirm whether SIP-UC is compatible with the 700 MHz
band ITS, we constructed a simulation environment in whichradio waves transmitted from multiple roadside units and onboard
units interfere with each other. Fig.5 shows the SIP-UC study
model. The road model is a 300m-plane layout, which has thehighest density of roadside units in the existing model simulation(4),and includes the main lane and connecting routes of the expressway.
Table 3 shows the simulation results.
SIP-UC infrastructure-vehicle communication (a-1-1, a-1-2,
a-1-3, b-1-1, c-2-2, d-1 to d-5) and some SIP-UC vehicleinfrastructure communication (d-1 to d-4, f-2) were met thecommunication requirements of the TG. SIP-UC inter vehiclecommunication (a-1-4, a-2, a-3, c-1, c-2-1, c-3, e-1, g-1, g-2) andsome SIP-UC vehicle-infrastructure communication (a-1-3) could
not be met the communication requirements of the TG. The
reason for this is considered that under CSMA/CA, the radio

Based on the results of Sections 2.2 and 2.3, we identified
issues and proposed future actions for the use-cases that did notmeet the communication requirements of the TG.
The communication requirements of the TG are more
stringent than the service requirements because the details of theservice requirements have not yet been finalized. This shows thatin confirming these communication requirements in detail, it will
be necessary to further organize the service requirements forautomated driving, such as the applicable scenarios and means ofcollecting information for each SIP-UC. One future action is towork with related organizations that have studied each use case tofurther specify the service requirements and define practical andoptimal communication requirements.
For SIP-UC a-1-3, a-1-4, a-2, a-3 that require interaction,
since the current 700 MHz band ITS specification is a broadcast
protocol, supporting interaction is difficult. We therefore
propose the following possible two countermeasures.
Proposal 1: New communication method only
A new communication method will be used that includes
recognition of the surrounding situation (e.g., position andspeed information). In such cases, it is necessary to study
bandwidth and transmission (propagation). In addition,
relationships with existing safe driving support services need
to be considered (in the case of coexistence, the impact of costand installation is significant. Deployment and disseminationof vehicles and quality assurance are issues when transitioning
to a new system).
Proposal 2: 700 MHz band ITS + new communication method
This proposal is for the concept of "Basic Message (BM) +Advanced Message," in which basic recognition of thesurrounding situation (position, speed information, etc.) is
performed by the 700 MHz band ITS, and the subsequentinteractive sequence is performed by new communication
methods.
The above requires further discussion in the future.
The challenges for practical application of the system
include standardization of message sets, slot allocation
1
placement rules, and cybersecurity requirements. Future
actions to address this issue include formulating guidelines
and promoting standardization in consultation with relatedorganizations.
In the use case g-1, the issue is the 20 ms cycle transmission
during emergency braking. As a future action, the ARIB
STD-T109 standard should be revised, or the requirements should
be modified by further digging down into use case requirements.

SIP-UC (Expressway)
Roadside unit

Expressway: Roadside unit at key intersection: Roadside unit at intersection: SIP-UC (Expressway) Roadside unit

Fig.5: SIP-UC study model
Table 3: Simulation results
SIP-UC Namea-1-1. Merging assistance for accelerating/decelerating on merging lane
a-1-2. Merging assistance for aiming inter-vehicle gap on main laneb-1-1. Driving assistance by providing SPaT information (V2I)
c-1. Collision avoidance assistance for sudden stop or sudden deceleration in front
c-2-1. Driving assistance by providing intersection information (V2V)
c-2-2. Driving assistance by providing intersection information (V2I)
c-3. CollisionKYOCERA
avoidance assistanceby providing hazard warning information
Corporation
d-1. Driving assistance by providing notification of abnormal vehicles
d-2. Driving assistance by providing notification of wrong-way driving
d-3. Driving assistance by providing congestion information
d-4. Assistance for congestion at highway junction and exit pointd-5. Driving assistance by providing hazard warning information
e-1. Driving assistance by providing information on emergency vehicles
f-2. Information collection for traffic flow optimization
a-1-3. Cooperative merging assistance for mainline vehicles with roadside
infrastructure network
a-1-4. Merging assistance with inter-vehicle negotiation
a-2. Lane change assistance during congestion
a-3. Assistance for entering to priority road from non-priority roads during congestion
g-1. Electrified platoon without drivers
g-2. Adaptive cruise control (ACC) driving and platoon with driver using vehicles
equipped with ACC

Compatible or not with
700 MHz band ITS
✓
✓
✓
–
–
✓
–
✓
✓
✓
✓
✓
–
✓
–
(V2I is ✓)
–
–
–
–
–

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1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

2.5. Future prospects
2.5.1. Promote verification testing and social implementation of 700 MHz band ITS, which can beutilized over the long term
In this R&D project, we verified the applicability of SIP-UC
to the 700 MHz band ITS through theoretical studies andsimulations. Excepting SIP-UC a-1-3, a-1-4, a-2, and a-3, which
require interaction, most of the use-cases handle the same basic
information—such as vehicle position, speed, and direction—as
the 700 MHz band ITS services currently in use. In addition, the700 MHz band wave ability to reach far and travel aroundobstacles is said to be very advantageous for Japanese roadconditions, which face issues such as poor visibility, and forvehicle antenna design. It is therefore important to maximize
the use of the 700 MHz band ITS for cooperative automateddriving, while taking into account international trends. For thispurpose, it is important to define more detailed service
requirements and communication requirements for each SIPUC, taking into account various conditions such as roadconditions; to prioritize essential services for driving automatedvehicles according to the use case roadmap; and to steadily
implement these services in society. In addition, more advanced
communication is also required.

2.5.2. Consideration of a new communication method, assuming the period for realization of SIPUC that requires mutual communication
SIP-UC a-1-3, a-1-4, a-2, and a-3, which require mutualcommunication, are difficult to support using a broadcast systemdesigned for information distribution such as the 700 MHz band
ITS. A new communication method that enables bidirectional
communication will be necessary for such communication.
However, further study is needed to investigate thecommunication requirements and new communication
methods. As we continue to study this issue, it is important todiscuss new communication methods, including a proposal thatincorporates the concept of "basic message + advanced message."

3

Establish a roadmap for the timing of social
implementation of communication technologies necessary for the realization of an
automated driving society

3.1. Overview
In developing the roadmap, based on the plans alreadydeveloped by the relevant ministries, agencies, and organizations, aswell as case studies of their efforts, we established a timeline for therealization of each SIP-UC, and organized a use case deployment
plan that lists the realization timeline in chronological order. Then,considering the issues in realizing the communication requirementsof the use-cases described in Sections 1 and 2, we organized theitems necessary for the realization of the deployment plan. Based on
the above, we have compiled a roadmap in chronological order ofthe items to be implemented to realize the deployment plan.

3.2. Considerations of use case deployment plans
In considering the use case deployment plan, thecommunication methods (V2I, V2V, and V2N) applicable toeach use case were assumed and studied through the TF
discussions. Fundamentally, the use-cases are to be realized ascooperative automated driving, but since some of them may beinitially used first for driving safety support, they are divided
into cooperative automated driving and driving safety support
in the deployment plan. Based on the above, we have establisheda timeline for the realization of each use case and organized ause case deployment plan that lists the realization timeline inchronological order, based on plans already established byrelated government agencies and organizations, as well as
examples of initiatives being taken by various organizations.
Fig.6-8 show the use case deployment plans for each
communication method.

3.3. Implementation items for realization of use case
deployment plans
Based on the issues in realizing the communication
requirements of the use-cases described in Sections 1 and 2, wehave organized the items necessary to realize the deployment
plans. In realizing the use case deployment plans, we haveorganized the items to be implemented into the following two
categories.
(A) Clarification of the direction of various issues throughexamination and discussion among the entities
concerned (details shown in Fig.9).
(B) Practical application of new communication methods (e.g.,
application of 5.9GHz band) (details shown in Fig.10)
For the compilation of A), Issue Survey Report ofAdvanced ITS and Automated Driving Using Cellular
Communications Technologies by ITS Info-communications
Forum Cellular System the TG(5) were referred to.

3.4. Development of the roadmap
Based upon study results up to this point, we can conclude
that in order to realize all 25 SIP-UC: new communication
methods for V2I and V2V are needed around 2040; newcommunication methods need to be in place around 2030
(when the automated driving begin to be adopted) so as to beinstalled in 30% of cooperative automated vehicles by theassumed plan target of 2040; and the 700 MHz band ITS, an
existing ITS wireless communication system, is expected to be
utilized for the use-cases that are starting relatively sooner.
Taking these points into account, the items required for therealization of the plan to expand the use-cases were broken
down and a roadmap of communication requirements forcooperative automated driving necessary for the realization of
an automated driving society was developed. The roadmap isshown in Fig.11.

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1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

Deployment of each use case and expected communication requirements foreach communication method: V2I
2025-

2030-

2035-

2040-

▼: Assumptions regarding use case start date●: Assumption of the development plan for related infrastructure, etc.
(Bold = assumed from the descriptions in the roadmap by the relevant ministries andagencies, etc.; normal = assumed by project participants)

b-1-1. Driving assistance by using traffic signal information (V2I)
Some of the use cases are already in service through ITS connect (red light alert, guidance for preparing to start waiting at a traffic light).
▼ Assumed from existing service offerings (assumed by project participants)

c-2-2. Driving assistance based on intersection information (V2I)
Some of the use cases are already in service through ITS connect (turn right alert)▼ Public-Private ITS Initiative/Roadmaps: "Deployment of Driverless Automated Driving Mobility Services in Limited Areas to More Than 40 Locations by
FY2025"▼ Assuming that b-1-1 and c-2-2 are necessary to realize automated driving mobility services in limited areas, and that the service will start around 2025 (assumed
by project participants)

b-1-1. Driving assistance by using traffic signal information (V2I)
c-2-2. Driving assistance based on intersection information (V2I)

Cooperative automated driving

Driving Safety Support and Cooperative Automated Driving

Driving Safety Support▼ Assumed from existing service offerings (assumed by project participants)● Locations where mobility services are
deployed
40 locations (Public-Private ITS
Initiative/Roadmaps)● 100 locations where mobility
services are deployed assumed byproject participants)● Further expansion of locations where
mobility services are deployed(contractor assumption)▼ Public-Private ITS Initiative/Roadmap "Realization of Level 4 Automated Trucks on Expressways Around FY2025"▼ Assumed to aim to realize the service of main line merging assistance to realize the goals of public-private ITS Initiative/Roadmaps (contractor assumption).

Merging/lane change assistance(V2I)
a-1-1, a-1-2▼ Merging support Day 3 system
Automated driving adaptation
rate of 30% or higher (FromJAMA materials)
a-1-3. Cooperative merging
assistance with vehicles on themain lane by roadside control● Adaptation of automated vehicles(L3 and above) reaches about 30%(Assumed by project participants)

Fig.6: Use case deployment plan (V2I)

Deployment of each use case and expected communication requirements foreach communication method: V2V
2025-

2030-

2035-

2040-

▼: Assumptions regarding use case start date●: Assumption of the development plan for related infrastructure, etc.
(Bold = assumed from the descriptions in the roadmap by the relevant ministries andagencies, etc.; normal = assumed by project participants)▼ Assumed from existing service offerings (assumed
by project participants)

Driving Safety Support

c-2-1. Driving assistance based on intersection information (V2V)
Some of the use cases are already in service through ITS connect (turn right alert)▼ Assumed from existing service offerings (assumed by project participants)

e-1(1). Driving assistance based on emergency vehicle information
Some of the use cases are already in service through ITS connect (emergency vehicle notification)c-1. Collision avoidance assistance when a vehicle ahead stops or decelerates suddenly
Collision avoidance assistance by using hazard
information▼ Assumed from existing service offerings for driving safety (assumed by project participants)

c-2-1. Driving assistance based on intersection information (V2V)1

e-1(1). Driving assistance based on emergency vehicle information▼ Public-Private ITS Initiative/Roadmaps: "Goal for 2030: Realize a safe and convenient digital transport society that supports
enriched quality of life of citizens, getting ahead of the rest of the world."▼ Assumed that c-1 services will be realized to achieve the goals of the public-private ITS roadmap (assumed by project participants)

c-1. Collision avoidance assistance when a vehicle ahead stops or decelerates suddenly

Cooperative automated driving

Driving Safety Support and Cooperative Automated Driving

c-3.

▼ Assumed to be realized later than c-1 because lane change assistance is also
included in the plan (assumed by project participants)

c-3. Collision avoidance assistance by using hazard
information▼ Merging support Day 4 system
Automated driving adaptation rate ofaround 50% (JAMA document based)▼ Commercialization of platooning technology (envisioned in METI’s "RoAD to the L4")
▼ Public-Private Roadmap: "Realization of Level 4 Automated Trucks on Expressways Around
FY2025"▼ Similar services have already been tested in FOTs Assumed that the service is implemented as soon as
possible basically after deployment of FOTs output or verification of technology (assumed by project
participants)

Merging/lane change
assistance(V2V)
a-1-4, a-2, a-3● Adaptation of automatedvehicles (L3 and above)
reaches about 50%(Assumed by project
participants)

Platooning/adaptive cruise control (V2V)
g-1. g-2● Addition of priority lane on
certain areas of
Osaka-Tokyo major expressways(Assumed by project participants)● Addition of priority lanes on the
Osaka-Tokyo major expressways(Assumed by project participants)● Expansion of priority lanes on
major expressways in Honshu(Assumed by project participants)

Fig.7: Use case deployment plan (V2V)

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
1) Research on Communication Methods to Realize Cooperative Automated Driving Use-cases

Deployment of each use case and expected communication requirements foreach communication method: V2N
2025-

2030-

2035-

2040-

▼: Assumptions regarding use case start date●: Assumption of the development plan for related infrastructure, etc.
(Bold = assumed from the descriptions in the roadmap by the relevant ministries and agencies,
etc.; normal = assumed by project participants)▼ Assumed that automated vehicles willtake time to become widely available
and that services will be provided to support driving safety for the time being
Assumed to start around 2025 based on SIP R&D trends (assumed by project participants)

b-1-2. Driving assistance by using traffic signal information (V2N)▼ Assumption that services will begin in 2025, as rapid and widespread deployment can be expected, and early start-up is expected tobe effective (assumed by project participants).

Lookahead information: trajectory change (V2N)
d-1, d-2, d-3, d-4, d-5e-1(2). Driving assistance based on emergency vehicle information▼ Assumed from existing service offerings (assumed by project participants)

f-1. Request for rescue (e-Call)
Some of the use cases have already in service through Help Net▼ Start of service through OEM telematics service (collection of vehicle and driving information)▼ Assumed from existing service offerings (assumed by project participants)

f-2. Collection of information to optimize the traffic flow
f-4. Distribution of dynamic map information▼ Need to verify technology
through field operational
tests for f-3.

▼ Assumed to be realized later than other use cases due to the need for technical verification forrealization (assumed by project participants)

f-3. Update and automatic generation of maps

▼ On-going study under SIP for providing SPaT information to automated vehicles via V2N▼ Assumed by project participants (discussion needed on when to start use cases for automated driving)b-1-2. Driving assistance by using traffic signal information (V2N)
Lookahead information: trajectory change(V2N) d-1, d-2, d-3, d-4, d-5

Cooperative automated driving

Driving Safety Support and Cooperative Automated Driving

Driving Safety Support▼ Assumed to start around 2025 based on SIP R&D trends (assumed by project participants)▼FOTs conducted under SIP▼ Assumed to be realized as early as possibly based on SIP R&D trend (assumed by project participants)

e-1(2). Driving assistance based on emergency vehicle information
f-1. Request for rescue (e-Call)
f-2. Collection of information to optimize the traffic flow
f-4. Distribution of dynamic map information
f-3. Update and automatic generation of maps
▼ Practical application of mobility services vehicles using remote monitoring (envisioned in METI’s "RoAD to the L4")
▼ Public-Private ITS Initiative/Roadmaps: "Deployment of Automated Driving Mobility Services in Limited Areas to More Than 40 Locations by FY2025"▼ Field operational tests of similar services▼ Assumed to implement the services as early as possible basically after deployment of FOTs output or verification of the services (assumed by project
participants)

h-1. Operation and management of mobility service cars
● 40 locations where mobility services ● 100 locations where mobility services areare deployed
deployed assumed by project participants)

Fig.8: Use case deployment plan (V2N)

A) Activities
practicalof use casesof use casesA)toward
Activities
towardapplication
practical application•••

Negotiation use cases are expected to begin after 2040, when automated• Negotiation use cases are expected to begin after 2040, when automatedvehicles will be in full use.
vehicles will be in full use.
It is difficult to realize negotiation use cases with the current 700 MHz• It is difficult to realize negotiation use cases with the current 700 MHz
band ITS.
band ITS.
On the other hand, the 5.9 GHz band is mainstream internationally.
• On the other hand, the 5.9 GHz band is mainstream internationally.
New communication methods need to be put to practical use.
New communication methods need to be put to practical use.

Approach

Implementation
Item

Fig.9: Undertakings toward practical application of use-cases

Implementation
Item

Implementation
Item

Implementation
ItemA-1) Formulation of security and privacy measuresA-1) Formulation of security and privacy measuresA-2) Establishment of policy on shared use of onboard unitA-2) Establishment of policy on shared use of onboard unitA-3) Formulation of policy on generational changes inA-3) Formulation of policy on generational changes incommunication methods
communication methodsA-4) Establishment of business modelA-4) Establishment of business modelA-5) Secure communication qualityA-5) Secure communication qualityA-6) Clarification of division of responsibilitiesA-6) Clarification of division of responsibilities

Approach•

In regard to practical use cases for V2I/V2V/V2N,• In regard to practical use cases for V2I/V2V/V2N,
the ITS Information and Telecommunication Systems Promotion Council
the ITS Information and Telecommunication Systems Promotion Council
and the study the TF have pointed out various issues (discussion points)
and the study the TF have pointed out various issues (discussion points)
via the study the TF
via the study the TF
These stakeholders need to cover a wide range of issues (discussion points)• These stakeholders need to cover a wide range of issues (discussion points)
through examination and discussion among the concerned entities,
through examination and discussion among the concerned entities,
making clear the direction that should be taken.
making clear the direction that should be taken.
3

Approach

Approach•B) Activities
thetoward
practicalof new of newB)toward
Activities
theapplication
practical application
communication
methods methods
communicationB-1) Frequency utilization studyB-1) Frequency utilization studyB-2) Standardization and specificationsB-2) Standardization and specificationsB-3) Equipment development and commercializationB-3) Equipment development and commercialization

Fig.10: Activities toward practical application of new communication
methods

1© NEC Corporation 2022
© NEC Corporation 2022
1

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2025 -

2020 2022 (R4)USA

2023 (R5)

Start of deployment of V2N services in 5G (DG MOVE)(R e fe re nc e ) Inte rna tio na l tre nds

Development of V2X
safety evaluation criteria(EuroNCAP)(EuroNCAP)

2040 -

Transition from 5.9 GHz band DSRC
to C-V2X completed

-V2X)

(FCC)

Europe2035 -

2024 (R6)

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

Study of protocols

2030 -

Study of advancement of ITS-G5

V2X becomes a
subject ofassessment(EuroNCAP)R2R communication network construction using RSUs
(Czech implementation plan in C

Start of mediation use case
deployment(DG MOVE)-Roads)Fully automated driving realizedon all roads after 2040
(DG MOVE)(C2CCC)ITS -G5 Acceleration of RSU implementation (C-Roads)Intelligent connected vehicle

China

PA/CA: 50 % of all automobile sales
HA level automobile market introduction
Percentage of new vehicles equippedwith C-V2 terminals : 50 %Formulation of IEEE 802.11bd

International

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

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

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

Frequency reorganization action plan +30

MHz for next -generation mobility

AssumedCommunication
technology

700 MHz band ITS

New communication technology (application of 5.9 GHz, for example)A) Appro a c h to the pra c tic a l a pplic a tio n o f the us e c a s e sStudy of specifications forsecurity and privacy measures

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

Security scheme study

Study of policies for sharing inA-2) Formulation ofpolicies for sharing in vehicle devices

Issuance of specifications for security and privacy measures
Security scheme formation-vehicle devices

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

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

B ) Appro a c h to the pra c tic a l a pplic a tio n c o m m unic a tio n te c hno lo g ie s
(5.9 GHz band)B-1) Frequency
utilization

Organization of concept of division of responsibility

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

Communication
standard formulation

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

OBU draft study

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

Study of OBU common specifications

OBU connectivity confirmation scheme
formulation

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

Equipping of new 5.9 GHz OBU

Adoptionrate 30%

Adoptionrate 50%

Deployment of new 5.9 GHz RSU

B-3) Equipment
development andpractical application

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

V2I(mediation)

V2V(Negotiation)

Fig.11: Proposed Roadmap

4Conclusion

Through the verification of the compatibility of theexisting wireless communication cellular V2X system, andthe planning and verification of measures to solve any issues
that arose, a direction has been determined for the realizationof the issues facing SIP-UC.
Through the verification of the compatibility of theexisting wireless communication 700MHz band ITS, and theplanning and verification of measures to solve any issues that
arose, it has been shown that the long-term utilization of the
700 MHz band ITS is an effective means to realize SIP-UC.
The roadmap formulated based on the above was compiled
based on discussions among academic experts, relevant ministries
and agencies, and members of industry associations (automotive
industry, electronics industry) in the TF, as well as technologicaldevelopment trends and practical application trends incommunications as of October 2021 to March 2022. The practicalapplication of new communication methods (e.g., application of
5.9GHz band) necessary to realize cooperative automated driving
is assumed to take place around 2030, and efforts for frequency

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utilization, standardization and specifications, and equipment
development and practical application should be made toward
the practical application of these new methods. In addition, forthe practical application of the use-cases themselves, it isnecessary to discuss and clarify the direction of various related
issues, such as cybersecurity and privacy measures for theprovided services, policies for the shared use of onboard unit,
policies for the generational changes of equipment, businessmodels, ensuring communication quality, and sharing of
responsibility.
It is expected that, starting from the roadmap and usecases, each of the parties concerned, including vehicles andinfrastructure as well as telecommunication, will play theirrespective roles in the study, and that deepening discussion
and cooperation across industries will promote efforts
toward the realization of cooperative automated driving.
[References]
(1) Collaborative Automated Driving Communication Method Consideration The
TF: SIP Use-cases for Cooperative Driving Automation-Activity Report of Task
Force on V2X Communication for Cooperative Driving Automation in FY2019-,
https://en.sip-adus.go.jp/rd/rddata/usecase.pdf, 2022.
(2) ITS Info-communications Forum: Study report on communication scenarios andrequirements for "SIP Use-cases for Cooperative Driving Automation" ITS FORUM
RC-017 Version 1.0, ITS Info-communications Forum, https://itsforum.gr.jp/
Public/E3Schedule/p40/ITS_FORUM_RC-017eng_v10s.pdf，2022.
(3) Inter-vehicle Communication Message Specifications ITS Connect TD-001 ver.
1.1, 2018, https://www.itsconnect-pc.org/en/_img/about/TD-001_210310.pdf.
(4) Land Mobile Communications Committee, Information and Communications
Technology Subcommittee, Information and Communications Council Report
No. 2029, July 28, 2009, on "Technical Conditions for ITS Radio System,"referring specifically "Technical Conditions for Advanced 700 MHz Band
Intelligent Transport Systems."(5) ITS Info-communications Forum Cellular System The TG: Issue Survey Reportof Advanced ITS and Automated Driving Using Cellular Communications
Technologies, Supplementary Material: Issue Survey for SIP Use-cases for
Cooperative Driving Automation, ITS Information and Telecommunication
Systems Promotion Council, https://itsforum.gr.jp/Public/J7Database/p69/
Cellular_system_202205.pdf, 2022.

[Contacts]
NEC Corporation, Cross Industry Business Initiative Division 5-7-1 Shiba, Minato
City, Tokyo 108-8001, Tel: 03-3454-1111 (main line), Contact: Satoshi KimuraIntelligent Transport Systems R&D Division, Systems R&D Group, Corporate R&D
Group, Kyocera Corporation, 3-7-1 Minatomirai, Nishi-ku, Yokohama-shi,
Kanagawa 220-0012, 045-605-7189, Contact: Masato Ogawa

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Establishment and Utilization of Traffic Environment Data

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

2) Development of New Technologies, V2X
and Others, for Communication
Kinya Asano, Shoichi Nakabayashi (Oki Electric Industry Co., Ltd.), Satoshi Kimura, Masahiro Ohtsuka (NEC Corporation)(Abstract) In this study, based on the road map , which describes specific requirements for wireless communication
technologies applied to the cooperative automated driving use cases developed through R&Ds in the 2nd phase of
SIP-adus, we proposed a specification of wireless devices including communication protocol requirements for
implementation of the V2X system with radio wave in 5.9GHz band, in order to accelerate to solve relevant issues andto proceed study for the realization of cooperative automated driving.
Keywords: cooperative automated driving , V2X, cellular V2X (C-V2X), DSRC, communication protocol

1

Scope of Study

1.1. General Concept
We have studied feasibility of communication technologies
such as requirements for wireless communication technology
applied to the cooperative automated driving use cases(1)(hereafter the Use cases) through the R&D project named as"Research on Communication Methods to Realize Cooperative
Automated Driving Use Cases" as part of the 2nd phase of SIPadus. As a result of this study, including expectation tocommunication technologies to be developed for the future,
the project proposed the use cases and the road map (hereafter
the Roadmap) defining requirements for each wirelesscommunication technology.
In this study, based on output from the above mentioned
feasibility study and the Roadmap development, the wirelessdevice specification was prepared mentioning communication
protocols required for the implementation of V2X system withradio wave in 5.9 GHz band (hereafter V2X system in 5.9 GHzband) in order to advance solving related issues and study. (V2X:
Vehicle to X, V2I: Vehicle to Infrastructure, V2V: Vehicle to
Vehicle)Summary of communication specifications
International
standardization
trends research

Research aimed atdeveloping
communication
protocol proposals

Analysis and research of standardization and institutionalization trends incountries and regions

Organization of operation methods

Preliminary research of communication requirements
Examination of communication requirements via simulation

Developing a communication protocol proposal
Designingcommunication
protocol proposals

Developing a message set proposal

Stakeholder
meeting

1.2. Actions and Objectives
The activities shown in Figure 1 were carried out withmedium and long term perspectives in mind.
a) International standardization trends research
We researched and analyzed standardization andinstitutionalization trends related to the V2X system in 5.9
GHz band communication protocol, message sets, as well as
relevant communication specifications in countries andregions including the U.S., Europe and China.
b) Research aimed at developing communication protocol
proposals
The V2X system with 5.9 GHz band communication
requirements for the technological research ofcommunication protocols was studied based on thecommunication requirements and message sets discussed
through ITS Info-communication Forum, as well as on the
result of the Roadmap development.(2)c) Designing communication protocol proposals
Based on the evaluation results of the communication
simulation, we designed the communication protocol for theV2X system in the 5.9 GHz band, which was concluded ascommunication protocol proposal mentioning communication
procedures and protocol stack, along with message sets.
d) Proposing wireless device specifications
Taking into account functions required for practicalimplementation and cooperation with existing wireless systems,
we developed a specification for on-board unit and roadside

Simulation-based communication performance assessment

Examination of wireless device specifications
Proposing wirelessdevice specifications

FOTs on test courses, etc.
Clarification of issues and its solutions for implementation

Fig.1: Activities for study

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2) Development of New Technologies, V2X and Others, for Communication

unit using V2X system in 5.9 GHz band then clarified issues for
implementation and counter measures to solve the issues.
Further, we carried out verification and discussions
concerning evaluation results and issues at the meeting
structure as shown in Fig.2 with stakeholders when necessary
to appropriately apply these results to the study.
Observers(Cabinet Oﬃce, Ministry ofInternal Aﬀairs andCommunications, NEDO)

Stakeholder meeting(Organizers: OKI, NEC)Japan Automobile
Manufacturers Association
Electronics subcommittee(Mobile Communication
Expert Group)

ITS Info-communications Forum
Advanced ITS Info-communication
System Committee(Radio System TG)(Related organizations)Fig.2: Stakeholder meeting structure

2International standardization trends
research

For the Use cases,(1) we prepared the Roadmap defining
specific action items related to the wireless communication
technologies. The result of this process was utilized to research
and analyze the latest and consistent trend of standardizationand institutionalization in terms of V2X system in 5.9 GHz
band communication protocol (communication procedures
and stacks etc.) adapted to traffic circumstances in foreigncountries and regions including the US, Europe, and China,also message sets and relevant communication specifications
are involved. The output of the range of these efforts wasapplied to the studies described in Chapter 3, 4, and 5. Please
see Fig.3 for specifications of standardization organizations inregions subject to the study.

3

Research aimed at developing
communication protocol proposals

The communication requirements for the V2X system inthe 5.9 GHz band required for technological study ofcommunication protocol were studied as shown below, taking
into account cooperation with the driving safety support system
in 700 MHz band. This was conducted based on the results of
the International standardization trends research, the study
report on communication scenarios and requirements
developed by the Radio System Technology Task Group of
Advanced ITS Info-communication System Committee in ITS
info-communication Forum (hereafter Radio System TG (2)),
and the result of the Roadmap development. The results of thisresearch was used for studies described in Chapter 4 and 5.
(1) Communication channel allocation
The Use cases were broken up to groups based oncommunication medias (V2I, V2V) applied in the Use casesand communication contents (broadcasting, negotiation),then the channel allocation proposal for each group wasdeveloped following the estimation of communication
volumes based on case studies under specific circumstances.
The grouping criteria for the Use cases is in Fig.4.
(2) Communication congestion control methods at upper
layersBased on the results of study on specifications in SAE,ETSI and other organizations and the communication
channel allocation proposal, the study of communication
congestion control methods at upper layers was conducted.
Overview of communication congestion control methods
at upper layers are shown in Fig.5.
(3) Traffic flow simulation-based effectiveness evaluation
Based on the results of research for (1) and (2), we ran
ETSI (Europe)

SAE (U.S.)CCSA (China)

Application・Facility[BSM, etc.]
SAE J2735[CAM/DENM]ETSI EN302 637[BSM, etc.]
YD/T 3709[WSMP]IEEE 1609.3[GeoNetwork]ETSI EN302 636-5[DSRC] ETSIEN302 636-4-1[DSMP]
YD/T 3707[C-V2X] ETSITS102 636-4-3[DSRC]
EN302 663

[C-V2X]
EN303 613

[C-V2X]
YD/T 3340[C-V2X]
TS103 574

[C-V2X]

ProfileTotal[DSRC]SAE J2945

Transport
network

Access[C-V2X]SAE J3161[DSRC]IEEE 1609.4[C-V2X]
3GPP TS23.285

IEEE 802.11p

Operation
methodCongestion
control

Cybersecurity[DSRC]ETSI TS302 665[C-V2X]ETSI TS103 723[C-V2X]
YD/T 3400

IEEE 802.11p[DSRC]SAE J2945/1[C-V2X]SAE J3161/1[Security]
IEEE 1609.2
IEEE 1609.2.1
SAE SS V2X 001[DSRC]
TS102 687

[Security]
TS102 940, 941, 942, 943
TS103 097

Refer to (3GPP C-V2X)[Security]
GB/T 40856

Fig.3: Standardization organization specifications, etc. in regions subject to study
SAE: Society of Automotive Engineers
ETSI: European Telecommunication Standards Institute
CCSA: China Communication Standards Association

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V2I

V2V

a-1-1. Merging assistance by preliminary acceleration and deceleration
a-1-2. Merging assistance by targeting the gap on the main laneb-1-1. Driving assistance by using traffic signal information(V2I)
c-2-2. Driving assistance based on intersection information (V2I)

Negotiation

Vehicle broadcasts every time position, speed, etc.
c-2-1. Driving assistance based on intersection information (V2V)
e-1. Driving assistance based on emergency vehicle information

Vehicle broadcasts alert warning when taking sudden hard breaking

Emergency

BroadcastContinuous

Vehicle receives broadcast from roadside units

c-1.
c-3.

Automated vehicle negotiates for merging withusing V2I communication
a-1-3. Cooperative merging assistance with vehicles on the main laneby roadside control

Collision avoidance assistance when a vehicle ahead stops or
decelerates suddenly
Collision avoidance assistance by using hazard information

Automated vehicle negotiates for merging using V2V
communication

a-1-4. Merging assistance based on negotiations between vehicles
a-2. Lane change assistance when the traffic is heavy
a-3. Entry assistance from non-priority roads to priority roads during traffic congestion

Fig.4: Use case grouping criteria

Radio communication environment
management(Radio resource occupancy, etc.)

Driving environment management(Vehicle speed, traffic density, etc.)

Transmission timing control andpriority control for communication
congestion reduction

Fig.5: Communication congestion control methods for upper layers

traffic flow simulation to evaluate the effectiveness of theV2X system in 5.9 GHz for the Use cases, including bothsituations where the intelligent transportation systems in 700
MHz band was and was not being used.
This traffic flow simulation re-creates the Use cases based oncase studies under specific conditions and evaluates differencesin vehicle behavior (deceleration, etc.) due to differences amongcommunication specifications (communication timing, etc.). A
model showing vehicle behaviors during traffic flow simulationis shown in Fig. 6.

4

Event
occurrence

Precedingvehicles(Event detection
and message
transmission
under conditions)

Designing communication protocol
proposals

In response to the results of the research described in
Chapters 2 and 3, we developed a communication protocol
for the V2X system in the 5.9 GHz band shown below basedon status of institutionalization in Japan related to traffic
environment and frequency allocation, etc.
(1) Developing communication protocol proposals
Based on the results of research described in Chapters 2
and 3, and on the results of simulation-based communication
performance evaluations described below, we researchedcommunication content, procedures, layer configuration,requirements, etc. for each Use case and developed
communication protocol proposals. Table 1 shows examples
of communication protocols for reference standards.

Following vehicles(receive messages)

Transmit message

Time
Sensing
distance○
Direction of travel

Application requirements (transmission
timing, priority, communications quality,
etc.)

×

×

○
×
Dottedline
GrayData received
Data not received
In case of data received at
the second transmission of data
General vehicle trajectory

Fig.6: Model of vehicle behaviors during traffic flow simulation(2) Developing a message set proposal
A message set proposal was prepared considering status ofstandardization and its studies in and outside Japan,(2) thecommunication requirements and the communication channel
allocation mentioned in Chapter 3 etc. For this, possibilities ofreuse/ communalization of messages were also included in theconsideration as another perspective.
(3) Simulation-based communication performance evaluation
Through a system-level communication performance
evaluation conducted using a computer simulation, we
studied the impact of deploying V2X systems when thecommunication protocol proposal and message set proposal
we developed as described in (1), (2) above were implemented.
Based on the expectations and assumptions used for thecommunication channel allocation and others in Chapter 3,
we defined an evaluation scenario, such as combination ofuse cases, traffic road models (road scale, installation ofintersections etc.) and traffic flow models (driving speed,inter-vehicle gap etc.) to study radio propagation models andcongestion/interference models, then built up an evaluation
model. Fig.7 shows examples of radio propagation models.

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Table 1: Examples of communication protocols for reference standards
Communication control flow
Layer

U.S.
DSRC

Protocol stack

Europe
C-V2X

DSRC

U.S.

C-V2X

Europe

DSRC

C-V2X

DSRC

C-V2X

Application

Request message generation upon event detection

EEBL, FCW, etc. (DSRC: SAE J2945/1, etc., C-V2X: SAE
J3161/1, etc.)

Co-operative Awareness, Road Hazard Warning, etc. (ETSI
TS 102-637-1, etc.)

Corresponds toupper layers(layers 5-7)

・Collect required data, generate message
・ Congestion control (bandwidth control (*1))

Communication services: BSM Exchange, etc. (DSRC: SAE
J2945/1, etc., C-V2X: SAE J3161/1, etc.), Message: BSM, etc.
(SAE J2735, etc.)

Communication services: CA Basic Service, DEN Basic
Service, etc. (ETSI TS 102-894-1, etc.), Message: CAM,
DENM ,etc. (ETSI TS 102-894-2, etc.)

Transport layer(layer 4)

Adds data that specifies upper layer services

WSMP Transport Protocols (IEEE 1609.3)

Basic Transport Protocol (ETSI EN 302 636-5)

Network layer(Layer 3)

No substantialfunctions

WSMP Networking Protocols (IEEE 1609.3)

GeoNetworking (ETSI EN 302 636-4)

Adds data that specifies
network addresses

Adds data that specifies upper layer protocols

LLC (IEEE 1609.3)

PDCP (ETSI TS 136 323)

LLC (ISO/IEC 8802-2)

PDCP (ETSI TS 136 323)-

RLC (ETSI TS 136 322)-

RLC (ETSI TS 136 322)

Performs switching when multiple channels exist

Channel Coordination(IEEE 1609.4)

-

-

-

・ Adds data that specifies sender link
・ Send timing control
・ Congestion control (priority control (*2))

MAC (IEEE 802.11)

MAC (ETSI TS 136 321)

MAC (IEEE 802.11)

MAC (ETSI TS 136 321)

Physical layer(layer 1)

Sends after encoding and modulating

PHY (IEEE 802.11)

PHY (ETSI TS 136 201)

PHY (IEEE 802.11)

PHY (ETSI TS 136 201)

Cybersecurity
layer

Adds data (signature) to guarantee senderauthenticity and message integrity

Splits packets
Data link layer(layer 2)

Electronic signature(IEEE 1609.2)(*1) Output control available through U.S. DSRC (*2) Bandwidth control and output control available through Europe DSRC

Category
Metric property
ShadowingMultipath fading
Shadowing loss

Overview

Study model
ITU-R P.1411 (assuming urban environment)
Fading due to surrounding
Log-normal distribution
environment changes
Standard deviation at 5.9 GHz: 3.68 dB
Fading by symbol
3GPP Extended Vehicular A model (EVA)
Attenuation when there are ・ 3GPP TR 38.901 Blockage model B(Model using knife-edge diffraction)
large vehicles between
・ Attenuation is a fixed value based on blockage by one
transmitter and receiveradjacent large vehicle(One second of space between vehicles at 40 km/h and 5.9
GHz: 10 dB)
Attenuation due to communications range

Roadside unit (transmitter)

Blocking vehicle

Vehicle (receiver)*Occurs in situations where the number of adjacent vehicles,
buildings, etc. changes

Fig.7: Examples of radio propagation models for communication performance evaluation systems

5

Proposing wireless device specifications

We examined wireless device (on-board devices androadside units) specifications for the V2X system in the 5.9
GHz and developed a proposal for wireless device
specifications. Based on the results of studies described in
Chapters 2, 3, and 4 concerning specifications and functions
for each communication layer, we conducted the study
taking into account the possible cooperation with existing
wireless systems. Examples of items to study for wirelessdevice specifications are shown in Table 2.
Further, we carried out the FOTs (Field Operational
Tests) on test courses under the condition that multiple use
cases co-exist, then analyzed the result of this communication
performance evaluation with multiple channels in comparing
with the result of evaluation with simulation in Chapter 4.

6Conclusion

For the realization of cooperative automated driving, wedefined a wireless devices specification including communication
protocols to be required for implementation of the V2X systemin the 5.9 GHz band, as we need to accelerate to study and solve
issues for this implementation.
Studies aimed at developing communication protocols
involved examining communication requirements for the V2X
system in the 5.9 GHz band (communication content,
communication channels, communication congestion control,etc.) based on international trends (related institutions andstandards). In designing a communication protocol proposal
and wireless device specifications proposal, based oncommunication simulation evaluation results that took into
account radio propagation characteristics and the impact ofinterference between communication channels, among otherfactors, we studied communication protocol proposal including
communication procedures and protocol stacks, and also

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Table 2: Examples of items to study for wireless device specifications
V2X communication function, characteristics, andinterface details

Transmitter characteristics

Antenna power

Overview

Coding Rate:

Antenna power permissible deviation
Leakage power during carrier off
Transmitter spurious

Transmission timing accuracy
Adjacent channel rejection
Non-adjacent channel rejection
Secondary emitted radio wave strength
Receiving maximum input power
CCA requirements

Antenna

Antenna structure
Antenna gain
Antenna polarization
Antenna installation

Communication control section functions, characteristics, interface

Modulation accuracy

Layer 7 functions

Layer 7 communication control

Data format

Transmit/receive procedure:

Layer 7
Service interface

Parameters

Protocol data unit

Overview

Transmit spectrum mask

Functions

Service primitive

Layer 1 communication control

Modulation method

Frequency permissible deviation-

Layer 1Service interface

Layer 1 functions

ChannelReceiver sensitivity

Receivercharacteristics

General requirements and technical requirements for wireless device

Generalrequirements

Data rate

V2X communication function, characteristics, and interface details

-

:

:

-

Functions

Service primitive

Parameters

Security primitive
Protocol data unit

FunctionsParameters
Data format

Transmit/receive procedure

Functions

Layer 1
Layer management
Entity functions

Layer management
Service interface

Management information base
(MIB)
:

:
Functions

Layer 7

Management information base
(MIB)

Security layer functions
Communication sequence
Use cases
a-1-1Interface ofservice in use
Transmission parameters

Other functions:

Transmission requirements:
Communication sequence

Use cases
g-2

Interface ofservice in use
Transmission parameters

Congestion control-

Between antenna section andtransmission/receiving section

Physical interface--

Between transmission/receiving section
and communication control section

Physical interface--

Physical interface--

Logical interface--

Between application and layer 7

message set proposal such as communalization and expandability
when coexisting multiple use cases.
With the wireless device specification including
communication protocol developed in this study, it is expectedthat all relevant research institutes and companies align andwork together for the realization of cooperative automateddriving.

[References]
(1) SIP Use Cases for Cooperative Driving Automation-Activity Report of Task
Force on V2X Communication for Cooperative Driving Automation in FY2019,2020.
(2) Study report on communication scenarios and requirements for "SIP Use Cases
for Cooperative Driving Automation" ITS FORUM RC-017 Version 1.0, ITS
Info-communication Forum, 2022.

[Contacts]
Oki Electric Industry Co., Ltd., DX Business Center, 1-16-8, Chuou, Warabi, Saitama
335-8510, 048-431-7211 (main line), Contact: Kinya Asano
NEC Corporation, Cross Industry Business Initiative Division 5-7-1 Shiba, Minato
City, Tokyo 108-8001, 03-3454-1111 (main line), Contact: Satoshi Kimura

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Establishment and Utilization of Traffic Environment Data

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

3) Research and Development Concerning the Collection
and Transmission of Mid-Scale Network Information
Tomohiko Saito, Shota Taki (NTT Communications Corporation)(Abstract) There are a number of issues that will need to be addressed in order to make automated driving ubiquitousin the future. One of these is the fact that automated vehicles cannot themselves perform adequate sensing incomplex environments such as intersections, forcing these vehicles to stop or slow down in front of intersections inorder to safely proceed through intersections, potentially interrupting the flow of traffic. As part of research anddevelopment conducted in fiscal 2019 and fiscal 2020, preliminary research and field tests were conducted involving
core technologies concerning the collection of data on objects such as vehicles and pedestrians from sensors, etc.
in the vicinity of intersections, as well as data distribution control technologies for automated vehicles. In fiscal 2021,
steps were taken to support a broader range of data such as emergency vehicle data and traffic signal schedule
information, as well as steps to support information distribution in mid-scale network for a variety of use cases.
Meanwhile, tests were run to gauge server load in mid-scale network area and data transmission efficiency, in addition
to assessing the usefulness of information distribution system in mid-scale network, with the goal of practicalapplication of these technologies. The results of this research and tests led to the creation of the Mid-Scale Network
Server Implementation Guidelines for business operators considering V2N information distribution in mid-scale
network.
Keywords: V2N, V2X, Information distribution system in mid-scale network, emergency vehicle information, SPaT information,
PULL/PUSH data delivery

1

Overview of this Research andDevelopment

There are a number of issues that will need to be addressedin order to put automated driving into practical use in the
future. One of these is the fact that automated vehicles cannot
themselves perform adequate sensing in complex environments
such as intersections, forcing these vehicles to stop or slow
down in front of intersections in order to safely proceed throughintersections, potentially interrupting the flow of traffic.

Safely enter and passthrough intersection

Avoid vehicles parked onroads (lane changing)

Bypass upcoming intersections(route changing)Fig.1: Vision and goals for this R&D

In this study, as shown in Fig.1, to resolve these issues, wehave established use cases for assisting easier entrance tointersections, avoiding cars parked on streets, and assisting tochange route to avoid upcoming intersections. Based on the use
cases, the target information in blind spots from multiple
information sources was collected and integrated, anddistributed to the small-scale network area surrounding anintersection around which the information was collected and to
mid-scale network area which covers multiple intersections
blocks or municipal(s). These are the research and development
we have carried out since fiscal 2019 to contribute to passage
assistance at intersections and lane-change assistance before
entering to intersections. In fiscal 2019, preliminary research
was done for core technologies to efficiently collect, integrate,and distribute multiple object data from roadside infrastructure,and others. Then the Field Operational Tests (FOTs) wereconducted in fiscal 2020. Based on these results, specifications
for a common interface, as well as data distribution methods,
integration methods, and edge server installation standards
were established in the interest of building a "system to assist
automated vehicles."These efforts shed light on the fact that practical applicationof these technologies would require addressing a broad range ofdata encompassing more than just object data to support
automated driving, as well as identifying issues for different use
cases.(1)SIP 2nd Phase: Automated Driving for Universal Services - Final Results Report (2018-2022)

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information

2

Fiscal 2021 R&D activities andresults

As part of fiscal 2021 R&D activities aimed at solving
problems with a view to implement the above technologies insocieties, research was conducted with respect to the expansion
and application of technologies concerning the distribution ofdata to automated vehicle at mid-scale network area. In
addition, through FOTs with user participation conducted incoordination with the large scale FOTs carried out in the Tokyo
waterfront area, a more detailed research was performed withthe goal of practical use of the technologies, while examining
the adaptability of the whole system of mid-scale network
information distribution system.

2.1. Research into the expansion and application of
distribution-related technologies
With respect to information distribution in mid-scale
network, it is necessary to examine the need for immediacy andthe effective distribution area for each kind of information
based on the use cases, and then to formulate distribution
methods. In light of this and given the characteristics of
distribution systems (PULL/PUSH), these FOTs have centeredon distribution systems, communications protocols,
distribution areas, and distribution frequencies for each
information to be distributed.
(1) Research concerning distribution methods for use cases
With regard to information to be distributed as part of theseR&D efforts, the use cases we assumed and the immediacy thatwill be required for each of the use cases are presented in Table
1. Also the adequate distribution method in light of the
distribution system characteristics was examined as shown in
Table 2.
Table 1: Use cases and other conditions for distributed information
Information

Needs for real-time
distribution

Assumed UC

Rainfallinformation

Handover to manual driving & underpass
avoidance (route changing)

Low

Lane-level roadtraffic
information

Avoid medium and wide range congestion/
restrictions (route/path changing)/detect jam
endpoints and slow down in advance

Low

Emergency
vehicleinformation

Detect emergency vehicles approaching andslow down/stop

High

SPaT
information

Dilemma avoidance/assistance when traffic
lights are at blind angles/ adjust to best route/speed

For lane-level road data and rainfall information, because of
the low update frequency, the PULL system was deemed to be
the most suitable as a vehicle can acquire information when
they need it. The PUSH system, on the other hand, was deemed
to be the most suitable for emergency vehicle information and
SPaT (Signal Phasing and Timing) information as theinformation can be distributed from the server to the vehicle,
satisfying the need for immediate information processing.
Due to the likelihood of data volume increasing with the
PUSH system as information is sent to individual vehicles at ahigher frequency, this R&D focused on studying emergency
vehicle data and SPaT information, which was distributed by
two methods: distance-based and intersection-based.
(2) Communications protocols used for information distribution in mid-scale network
MQTT was selected as the communications protocol used
for distribution in mid-scale network. The MQTT protocol,
which uses a Publish/Subscribe model and is simple, lightweight,
and fast, with an extensive library, was made to send and receive
short messages frequently among numerous devices. It is alsoeasy to sort transmissions differently depending on the Topic,
and has seen extensive use in connection with IoT devices.
These and other factors give MQTT more advantages than otherprotocols. MQTT was selected as it was deemed the best fit forthese FOTs. Its QoS controls the assurance of message delivery
between client and broker depending on its level setting. Fig.2
shows a system configuration diagram for mid-scale network
servers using MQTT, while Fig.3 shows a software chart.
Cloud server
DB server
Distributed Information DB

Externallylinked server
Externally linked
serverLegend: Uplink

Table 2: Characteristics of data suited to distribution via each system
Distribution system

Best information use caseBest information
type

Needs to be distributed
in real-time

PULL system
Distribute information
based on user request

When the receiver chooses
and uses needed data frombroad ranging and profuse
data

Static data
Semi-static data
Semi-dynamic
data

Low

PUSH system
Distribute information
to target vehicle from
distribution source

When wanting to
distribute information
upon event occurrence orin short intervals

DynamicdataHighMid-scale network
application server
Mid-scaleMid-scaleMid-scaleMid-scaleMid-scaleMid-scaleMid-scale
network
network
network
network
network
network
network
applicationapplication
application
application
application
application
Mid-scaleMid-scale…× Vehicle countnetwork
network
application
application: Downlink

Distance-based
distribution only
Vehicle

MQTT serverDatasourceserver

MQTT broker

Fig.2: Mid-scale network server system configuration diagram
V2X system application
Mid-scale network server application
Spring Framework

Medium/highVehicleinformation DB〜

Mid-scale network server application

Externally linked application

Spring Framework

Spring Framework

MQTT client:
Eclipse Paho 1.0.2
VM:
Amazon Corretto 8 8.262.10.1
OS: Ubuntu 18.04.2 LTS

DB:MemSQL
6.8.17

MQTT
broker:
EMQX
4.1.2

Fig.3: Mid-scale network server software configuration diagram

Mid-scale network server application configuration(3) examination
For PUSH distribution system, based on the location

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information

information of a vehicle, we implemented a server in mid-scale
network to distribute information narrowed down to that ofvicinity of the vehicle. As required and searched informationdiffers for each vehicle, we had an application launch for eachvehicle. Using this configuration, in order to reduce the delay
between information update and distribution, information was
distributed from the server every second (parameters can bechanged) and MQTT Topics were given unique values for eachvehicle. Application installation was performed using Java’s
Spring Framework.
(a) Emergency vehicle information
Emergency vehicles require responding in real time when
they approach from behind or from out of view, but do notaffect vehicle travel when they are far away. We consequently
studied using PUSH distributions for emergency vehicle
information within a certain radius around the vehicle, as
shown in Fig.4.
➢ Distribution frequency : 1-second interval➢ Distribution radius: 300m
[Emergency vehicle information]
300m
Informationreceiving vehicles

Transmissionarea

Fig.4: Emergency vehicle information distribution frequency anddistribution area(b) SPaT information (distance-based method)
Drivers sometimes cannot make out a traffic light color due
to sunlight or obstruction, for example. For such situations, weconsidered having vehicles receive SPaT information in advanceas a means to support dilemma avoidance at intersections, planroutes efficiently, and achieve green waves. To reduce
communications traffic using the distance-based system, SPaT
information is transmitted within a rectangle around the
direction of the distributing vehicle’s travel. This is shown in
Fig.5.
➢ Distribution frequency: 1-second interval➢ Distribution radius: 1 square kilometer (parameters can be changed)[SPaT information]

Informationreceiving vehicles

1km

1km

intersection. As shown in Fig.6, vehicles designate intersections
on their own travel routes and divergent routes and continuously
receive SPaT information.
When a vehicle designates a new intersection, the updated
version of the SPaT information it has received. It also stops
receiving unneeded data about previous intersections.
➢ Distribution frequency: Whenever SPaT information is updated[SPaT information]

Informationreceiving vehicles

Fig.6: SPaT information distribution method (intersection-based system)

This system was achieved as follows. For SPaT information
distributed to a server in mid-scale network from a data source
server, an intersection ID that identifies each intersection is
published as a Topic. When a vehicle first subscribes, it
immediately distributes up-to-date information through
MQTT’s retain function. As long as a vehicle is subscribed, it
receives data every time data is updated.
Under this system, while vehicles need to designate theintersections for which they require data, server load can be
reduced as working servers do not have to process informationfor every vehicle. Moreover, as data is only transmitted upon
subscription and SPaT information update, distribution
frequency and communications traffic can be decreased.

2.2. Examination of system configuration feasibility
at the time of social implementation
As a step toward practical application of an information
distribution system in mid-scale network, we measured server
load in mid-scale network area and communications traffic,
and assessed the effectiveness of an information distribution
system in mid-scale network.
(1) Measuring server load for PUSH distributions
Using the configuration in Table 3, we measured the load
imposed on a server in mid-scale network when simulated
emergency vehicle information and SPaT information weresent to vehicles, based on the number of information-receiving
vehicles.
Table 3: Test server specifications

Transmission area

Fig.5: SPaT information distribution method (distance-based method)(c) SPaT information (intersection-based system)
The intersection-based system sends out PUSH
transmissions every time SPaT information is updated at anServer name

vCPU core countMemory (GB)DB server

4

16

Externally linked server

28Mid-scale network application server

856

MQTT server

4

16

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information

As described in Section 2.1 (3), when using the distancebased method for emergency vehicle information and the SPaT
information, one server application was launched for eachvehicle. However, under intense resource usage, it was notpossible to launch more than 70 server applications due to alack of enough CPU resources. Likely reasons for this are that
one application was launched for each vehicle, the Spring
Framework was running for each application, and transmissions
were processed every second for each application.
By contrast, the intersection-based method for SPaT
information has none of these issues, with a server load lower
than that of the distance-based method. There was also no
increased MQTT server load with the intersection-based
method. Table 4 shows CPU and memory usage when
distributing information to 70 vehicles.

(3) Examination of distribution system feasibility
As a step toward practical use of an information distribution
system in mid-scale network, we calculated costs of server in
mid-scale network that would be incurred to transmit a larger
amount of dada then examined feasibility of the system.
Based on calculations performed in Section 2.2.(1), we
calculated what the cost would be if they were to hypothetically
distribute information to vehicles using a Microsoft Azure
server. As shown in Table 6, we found that when using theintersection-based method the cost of using the distance-based
method per distributing vehicle could be kept to less than
1/100.

Table 4: CPU/ memory usage for information distribution from server in
mid-scale network

cost
of using the distance-based
method.
information
For Approx. 840 vehicles

Server in
mid-scale
network
CPU max
usage

Load test delay
verification(Average)

Server in
mid-scale
networkmemorymax usage

MQTTserver CPU
max usage

MQTTservermemorymax usage

Distribute to 70 vehicles simultaneously[Distribution methods]

Simulated
emergency vehicle
location
46.58%
58.14%
2.75%
2.42%
information/
also no increased
Distance-based MQTT server load with the intersectionmethod

based method. Table 4 shows CPU and memory usage when
SPaT information/
Distance-based
58.02%
8.77%
2.47%
distributing
information50.19%
to 70 vehicles.
methodSPaT information/
Distance-based
method

3.23%

2.38%

(2) Measuring communications traffic for PUSH distributions
We conducted a theoretical measurement of communications traffic discrepancies between the two methods when data
was distributed via the PUSH methods from a server in midscale network. We looked at SPaT information for which both
the distance-based and intersection-based methods were available.
As shown in Table 5, when the data distributing intersection
count was the same, the distance-based method distributedwith a higher frequency as data updating experienced no delay.
With the intersection-based method, distribution data volume
was reduced to less than 1/100 as data was only transmitted
when SPaT information was updated. Consequently, from theperspective of communications traffic, the intersection-based
method has the advantage.

Vehicle↓Mid-scale network area server

Table 5: Results of theoretical measurement of communications traffic for
SPaT information
Distribution
methodNumber ofintersection
per area

Distribution
frequency

SPaT information(Distance-based)

12(1km2）

1 second

12

120 seconds(Signal cycle length)

SPaT information(Intersection-based)

Table 6: Number of supportable vehicles and per-server cost calculation
Information

Azure D96ads v5
Supportable vehicles under
one configuration (one server)

Server cost per vehicle

Emergency vehicle
SPaT information(distance-based)

SPaT information(Intersection-based)

For Approx. 88,800 vehicles

188 yen/month

1.78 yen/month

2.3. Examination of functions needed for practicaluseThis section looks at functions and issues to be examinedwhen considering the social implementation of technologies
for distributing data to large areas, with a view to deploying
such technologies nationwide.
(1) Re-examination of requirements
Further reducing costs per information-receiving vehicle
will require re-examining requirements aimed at achieving
more efficient information distribution (distribution methods,
function allocation, etc.). The server load tests have shown that,for every type of data, distribution control per informationreceiving vehicle places considerable load on the server. As aresult, distribution methods need to undergo a re-examinationthat also factors in use cases. For example, this could mean to
apply the intersection-based method for SPaT information, orto distribute emergency vehicle information not to informationreceiving vehicles as shown in Fig.7 but to vehicles passing
within a certain size of area centered on an emergency vehicle.
➢ Distribution frequency: Whenever information source data is updated
Distribution radius cannot be➢ Distribution radius: 300 m (needs review)[Emergency vehicle location information]

set per vehicle, so is fixed

Information is not distributed to vehicles withno emergency vehicles in the vicinity

Transmission area
300m

DataCommunications Data distributed
receiving
per hourper hourvehicles928,800

Approx. 2.79 GB

7,740

Approx. 9.68 MB

258 (*)

Fig.7: Proposal method of information distribution to vehicles passing
within a certain size of area centered on an emergency vehicle*Average Vehicles per kilometer in Tokyo (fiscal 2022)

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2.Establishment and Utilization of Traffic Environment Data
(2) Technological Development Concerning the Transmission of Traffic Environment Data
3) Research and Development Concerning the Collection and Transmission of Mid-Scale Network Information(2) Creation of a system with a view to practical implementation
Creating an information distribution system in mid-scale
network area with a view to at-scale deployment will requireconsideration of overall system architecture. Further research
into availability and scalability is needed, including, forexample, the addition of functions for specifying available
servers from multiple distributing servers as shown in Fig.8,
further boosting efficiency through application optimization,and implementing non-functional requirements such as
redundancy and security.

[References]
(1) "Yuji Aburakawa: SIP 2nd Phase:Automated Driving for Universal

Services—Mid-TermResults Report (2018–2020), pp.43-49, September
2021, https://www.sip-adus.go.jp/file/rd03/chapter2_s.pdf (accessed
November 2022)[Contacts]
Business Solution Division, Business Solution Department 2, Group 8, NTT
Communications Corporation, 2-chome-3-1 Otemachi, Chiyoda City, Tokyo
100-8019, 050-3463-5532, Contact: Shota Taki

Data source server

Available server
designation function

Distribution
server (Z)Distribution server (A)Intersection ID& areaCL application

Distributed
information
CL application

Select Distribution server via available server designation function

Fig.8: Proposal for designating multiple distributing servers in order toenable the use of available servers(3) Establishment of server in mid-scale network implementation guidelines
Based on the results of this research and development, we
identified areas requiring consideration ahead of creating andstudying server in mid-scale networks, and established
implementation guidelines for business entities who are
considering to distribute information in mid-scale network to
vehicles through V2N system. Such business entities may
include OEM and content providers.

3 Conclusion
As part of research and development efforts in fiscal 2021
aimed at promoting the use and expansion of distribution
system in mid-scale network nationwide, we examined thefeasibility of expanding and applying distribution-related
technologies and conducted verifications by way of a large-scale
field operational tests.
In the future, we plan to accelerate efforts to put the resultsof this R&D into practical use and apply the knowledge gained
from these activities toward a range of fields through guidelines,
among other things.

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