Second Phase of Cross-Ministerial Strategic Innovation
Promotion Program— Innovation of Automated Driving for Universal Services(System and Service Expansion)

Study of the Impact of AutomatedDriving on Reducing Traffic Accidents
and on Others

Report (Summery)
March 2020
The University of Tokyo
Doshisha University

Objective of study
Research and development plan for Second Phase of Cross-Ministerial Strategic
Innovation Promotion Program — Innovation of Automated Driving for Universal
Services (System and Service Expansion)
Commercial development and increased diffusion of automated driving (AD) vehicles will
help to reduce traffic accidents, alleviate traffic congestion, ensure mobility forvulnerable road users, resolve the driver shortage and reduce costs in logistics andtransport services, and resolve other social problems. The aim is to achieve a society inwhich everyone is able to enjoy a high-quality life.

Quantification and monetaryvaluation of impact (benefits
and potential risks)

Focus on differences in
impact caused bygovernmental policies and/or
manufactures’ launching
methods

Basic references forfostering social acceptanceUse in corporate
management andpolicymaking
2(3) Effect on road transport(4) Effect on traffic services
sectorAdvice(6) International
cooperation(1) Contribution to UN SDGs

(2) Diffusion simulation

Impact assessment(7) Advisory Committee

Overall configuration of study(5) Effect on industry andsociety3

Study items(1) Relevance of AD to SDGs
(2) Simulation of AD vehicle diffusion(3) Effect on road transport
i. Estimation of effectiveness in reducing traffic accidents
ii. Estimation of reduction of traffic congestion and reduction of CO2 emissions(4) Effect on traffic services sector
i. Ensuring mobility for vulnerable road users and in depopulated areas and other locations withpoor access to transport
ii. Reduction of costs and resolution of driver shortage in logistics and transport services
iii. Change in ownership and usage of vehicle, and the structure of consumers’ choice(5) Effect on industry and society
i. Effect on whole automobile industry due to change in vehicle ownership structure and othereffectsii. Contribution to growth of the total factor productivity of the Japanese economy(6) Formation of organization for international cooperation(7) Convening of Advisory Committee41. Relevance of AD to SDGs
<Outline>
Organizing the relevance of AD to the UN's Sustainable
Development Goals to establish a clear vision of how
self-driving cars can help us build sustainable societies.
<Method>
Organizing the relevance based on the discussion
within the Advisory Committee on the Relevance of AD
to SDGs.

5

Advisory Committee on the Relevance of AD to SDGs
1）Meeting dates and venues:
April 5, 2019, 1:00 - 3:30 PM, at Kambaikan meeting room of the Institute forTechnology, Enterprise, and Competitiveness, Doshisha University(Individual interview with Dr. Hiroto Inoi, member of the advisory committee, wasconducted on
April 23, 2019, 1:00 - 2:30 PM, at Kambaikan meeting room of the Institute forTechnology, Enterprise, and Competitiveness, Doshisha University)
2） Advisory Committee members (without honorifics)
Name

Affiliation and Position

Chairman
Masanobu Kii

Professor, Faculty of Engineering and Design, Kagawa University

MemberTateo Arimoto

Visiting Professor, National Graduate Institute for Policy Studies (GRIPS) / Principal
Fellow, Japan Science and Technology Agency (JST),

Hiroto Inoi

Associate Professor, Faculty of Sustainable Design, Department of Civil Design
and Engineering, University of Toyama

Yasuhiro Shiomi

Associate Professor, College of Science and Engineering, Department ofEnvironmental Systems Engineering, Ritsumeikan University

Keii Gi

Senior Researcher, Systems Analysis Group, Research Institute of Innovative
Technology for the Earth (RITE)

Keisuke Matsuhashi

Head, Center for Social and Environmental Systems Research（Environmental
Policy Section), National Institute for Environmental Studies

6Classifying impact (1)1) Direct and indirect impact
Direct impact： Effects exerted by AD, through the changes in mobility
they enable, on SDG goals and targets.
The Global Mobility Report 2017 prepared by Sustainable Mobility
for All defines four sectors of sustainable mobility: universal access,efficiency, safety, and green mobility. Following this guidance, we
considered potential impacts on each of the four sectors—universal
access, system efficiency, safety, and green mobility—as well
as synergies and tradeoffs arising between them.
Indirect impact (ripple effects): Effects exerted by AD—through thedirect impact they have on SDG goals and targets—on other SDG goals
and targets. We identify three sectors: (1) Increased income andreduction of disparities, (2) improved sanitation, and (3) expanded
educational opportunities.

7Classifying impact (2)2) Short-term and long-term impact
Short-term impact refers to factors influencing SDG goals and targets
that will emerge when SAE level 1 or level 2 diffuse into society. Thisshould be more or less achieved by 2030, the target year for achieving
SDGs.
Long-term impact refers to factors influencing SDG goals and targets
that will emerge when SAE level 3 or level 4 ADs with sufficiently
extended ODD, or SAE level 5 ADs, diffuse into society. This will bedifficult to achieve by 2030.
3) Three categories of ADs for consideration of long-term impacts
For consideration of long-term impacts, it is useful to identify three
distinct categories of applications for ADs: (i) Logistics / mobility
services, (ii) Privately owned vehicles, and (iii) shared mobility.

8Conclusions (1)• Through both direct impacts—including associatedsynergies—and indirect impacts, AD will make wideranging contributions toward achieving SDG targets
and targets.
• On the other hand, there are several
potential negative impacts, and tradeoffs may arise
as described in the following examples:
1) Societies may suffer a decrease in resilience at thetime of disaster, which may have negative impacts
for targets 11 and 13.
→It is essential to promote the development ofmanagement technologies for AD vehicles.
9Conclusions（2）2）If passengers’ AD cars are mainly by privately owned, amodal shift toward automobile transit and urban sprawl is
expected.
If shared mobility becomes widely adopted, a modal shift towardautomobile transit in urban transportation is expected.
These developments would have negative impacts for goals 3, 7,
8, 9, and 11.
→ Introduce an economic framework in which external
diseconomies are internalized.
→ Need other initiatives, such as improved connectivity between
AD vehicles and other modes of transit.

10

2 Simulation of AD vehicle diffusion<Outline>• Establish a simulation model to use the simulation
results as common data for various impact
assessments in this project.
<Method>• Build element models such as car ownership models
and consumer preference models.
• Construction of a system combining element models.
• Trial simulation targeting privately-owned passenger
cars.

11

Objectives of simulation• Simulation results are used as common data for various impact assessments inthis project.
• Evaluate the impact of the following factors on market diffusion of AD vehicles:
1. Policy measures（economic incentives, mandatory installation of
automated-driving devices, introduction of new types of driver licenses
with relaxed conditions on license holders)
2. OEM’s strategy for launching products (when to launch into markets, atwhat price)3.

Enhancement of societal acceptance

Simulations of market
diffusion of AD vehicles

Study items inthis project
Numbers of vehicles owned
Traffic volumes

Policymeasures

Strategy forlaunchingproducts

Societal
acceptance12Various classification schemes used in our
simulations

13

Categories of vehicles and vehicle-based services,
and Temporal extent of simulations
1.

Categories of vehicles and vehicle-based services
Category in this study•
Privately owned vehicle•

Outline of method for determining results

Outputs

Use results of online surveys to model consumer’s
technology-related choices
Incorporate results from the study (4)-iii. Change inownership and usage of vehicle, and the structure of
consumers’ choice to model the transition from privately
owned vehicle to mobility services

Numbers of vehicle
owned
Numbers of
new vehicle
registrations
Traffic volumes

Mobility services
Use results from the study (4)-ⅱ. Reduction of costs andresolution of driver shortage in logistics and transport services(University of Tokyo)

Logistics services

2. Temporal extent of simulations

Simulation period: 2015 - 2050, data output at 5-year intervals

2015

2020

2025

2030

2035

2040

2045

2050

2015 is the most recent year for which basic
data are available for statistics including theRoad Traffic Census

14

Categories of AD vehicles considered in this study
Category

Highways

General roads

C0

SAE Lv.0

SAE Lv.0

No driving-support devices

C1

SAE Lv.1
Driver assistance

SAE Lv.1

・Collision-damage-reducing brakes
・Acceleration limiters for accidental accelerations (due todriver error)
・Lane-departure warning system
・Car distance warning system

C2

SAE Lv.2
Partial automation

SAE Lv.1

In addition to C1:・On highway, lane keeping systems (LKAS) + adaptive
cruise control (ACC) applicable to all vehicle speed
regimes, including low-speed motion and stopping
・Automatic lane changing on highway

C3

SAE Lv.3
Conditional
automation

SAE Lv.2

In addition to C2:
・Lv.3 on highways
・Lv.2 on general roads

C4

SAE Lv.4
High automation

C5

SAE Lv.4
SAE Lv.4 on major arteri In addition to C4:High automation
es and thoroughfares ・Lv.4 on major general roads
・Take-over requests (TORs) will not be issued

C6

Compatible technologies

SAE Lv.3
In addition to C3:
on major arteries and ・Lv.4 on highways
thoroughfares
・Lv.3 on major general roads
・On general roads, take-over requests (TORs) for driving
operations will be issued in response to system demand

SAE Lv.5
Fill automation

15

The logic of the simulations

16

Flowchart for AD vehicles market-diffusion simulations
For privately owned vehicles
Number of vehicles owned last year
Average travel distance
per individual vehicle

Numberof retained
vehiclesRetention rate

GDP
Population

1. Determine thenumber of vehicles
that will be required.

2. Determine thenumber of newvehicleregistrations

Subdivisions by vehicle
type (administrative
purpose, AD vehicle category, andconventional vs. electric)

3. Subdivide thenumber of newvehicle registrations
based on categories ofAD vehicles andconventional vs.
electric.

Number of new vehicle
registrations

4. Estimate thenumber of vehicles
owned by categories ofAD vehicles andconventional vs.
electric

5. Estimate thedistance traveled bycategories of AD
vehicles andconventional vs.
electric

Number of vehicles
owned

Travel distances

17

Acceptance curves for various categories of AD vehicles
Acceptance curves for each self-driving vehicle category are constructed based on individual responses to online
surveys.
Small passenger cars

Acceptance rate (%)

Acceptance rate (%)

Standard passenger cars

Option price (10,000Yen)
Option price (10,000Yen)

Acceptance rate (%)

Minicars

Option price (10,000Yen)

18

Distribution of AD vehicle categories among newpurchases of privately owned (passenger) cars
Determination of option price (forthe case of C5)

Method for determining category distribution of newpurchases

Option price

Option pricePrice of C6① Option price in year ｙ

Price of C5⑤Option price in year ｙ＋１④ Decrease in option price due to increased production

Price of C4
C6 acceptance curve
C5 acceptance curve

Price of C3

C4 acceptance curve
C3 acceptance curve

Price of C2

C2 acceptance curve
Proportion Proportion Proportion
of C6
of C5
of C4

Proportion Proportion Proportion
of C3
of C2
of C1+C0

Acceptancerate②Determination of category distribution of new-vehicle purchases:
Among AD vehicle categories for which prices lie ator below the price a consumers is willing to pay, thehighest-ranking AD vehicle category is purchased.

Cumulative production volume③Production increase19Market release times and initial option prices by AD
vehicle category (for passenger cars)• Market release times, initial option prices, and rates of pricereduction due to production experience (progress ratio) aredetermined as follows.
*Progress ratio: Production cost ratio when cumulative production volume has doubled

AD vehicle category Year released to market

Initial option price (10,000
yen)

C2

2019

41

C3

2020

70

C4

2025

81

2030

93

2035

107

C5
C6

After 5 years
After 10 years

Determined based on Public and Private
ITS Initiative / Roadmap 2019 and Action
Plan for realize the Automated Driving
Version 2.0 (Panel on Business Strategies inAutomated Driving, March 30, 2018)

Determined in reference
to literatures

progress ratio*

0.9

Determined based on
experience curves forautomated braking
systems

20

Steps in the simulation procedure
STEP１

Present
time

・Establish simulation model・Establish assumptions and conditions

STEP２

・Revisit assumptions and conditions
・Investigate policies and other items to evaluate(4)-iii Change in ownership and usage of vehicle, andthe structure of consumers (modeling the transition
from privately owned vehicles to mobility services)

Market-diffusion simulations: STEP1

Market-diffusion simulations: STEP2

Number of vehicles owned / travel distance

Input
from WG

Reflect input

Number of vehicles owned / travel distanceStudy items in this project
Consideration of assumptions and conditions
STEP

2020/9 2020/10

2020/1

Transition from privately
owned vehicles to mobility
services

Change in vehicle
lifetimes due totransition to AD
vehicles / electricvehiclesTime and price ofmarket appearance ofAD vehicles

Selection of AD vehicle
categories by consumers

Transition toelectric vehicles

Scenario for introduction to cargotransport services and mobility
services

Change in travel
distance due totransition to AD
vehicles

STEP 1(Fiscalyear
2019)

Not considered

Vehicle lifetime
unchanged frompresent value

Determined inreference toliterature

Modeling of selectionof AD vehicle
categories by
consuｍers

Determined
independentl
y oftransition to
AD vehicles

Not considered

Travel distanceper vehicle
unchanged
from present
value

STEP 2(Fiscalyear
2020)

Use results from thestudy 4)-iii Change inownership and usageof vehicle, and thestructure of consumers

Investigatechange in vehicle li
fetimes due
to transition to AD
vehicles/
electric vehicles

Determined withmodifications
appropriate toJapan

Setting scenarios forchanges in acceptancerates

Investigate
therelationshipbetweentransition toself-driving
vehicles andtransition toelectric
vehiclesUse results from thestudy (4)-ⅱ. Reduction of
costs and resolution of driver
shortage in logistics andtransport
services (University ofTokyo)

Investigate changes in travel
distance pervehicle due totransition to
AD vehicles

21

3-i. Estimation of effectiveness in reducing traffic
accidents<Outline>• Another research, Visualize the effects of reducing traffic
accidents through Automated Driving and Driving Assistance,sponsored by SIP-adus estimates the reduction of the numberof accidents and casualties due to diffusion of AD vehicles. We
estimate the monetary value of the impact of reduced traffic
accidents.
<Method >• The monetary value for the reduction in non-monetary losses(psychological burden) of perpetrator” due to avoidance oftraffic accidents is estimated by means of web survey andeconomic experiments as part of this study. Economic
experiments was conducted in this fiscal year.

22Results of economic experiments• The participants in our experiments were 76
undergraduate students at Doshisha University.
• Non-monetary losses suffered by both victims andperpetrator are evaluated. Experiments consider
only fatalities.
• The most important finding from the experiments isthat the participants estimate non-monetary losses
suffered as a perpetrator equal to or grater thannon-monetary losses suffered as a victim.

23

3- ii. Estimation of reduction of traffic
congestion and reduction of CO2 emissions<Outline>• To estimate the reduction of traffic congestion and CO2 emissions bydiffusing ADs<Method>• Almost sixty percent of traffic congestion on expressways nationwide areoccurring at sag sections.
• ADs are supposed to contribute reduction of traffic congestion and CO2
because they promote vehicles’ movement with proper headway andvelocity.
• To prove the above idea, traffic simulation is used for two representing
sections, a section with three-lane expressway and a section with two-lane
expressway. AD diffusion rate are two cases, 20% and 90%.
• For ordinary roads, the idea is same as the above one and existing research
results are referred.
• Then total amounts of reduction of traffic congestion and CO2 emissions
nationwide are calculated.
24

Trial calculation of reduction of traffic congestion andCO2 emissions on expressways•AD diffusion rate is forecasted as 25% in 2035
and 34% in 2050.
•Trial calculation result for expressways is asfollows under many preconditions assumed thistime. (Of course, if the preconditions arechanged, result might be changed accordingly.)
Traffic congestion: 16% in 2035 and 17% in
2050 might be reduced compared to the present.
CO2 emission: 1.9% in 2035 and 2.2% in 2050
might be reduced compared to the present.

25

4-ⅰ Ensuring mobility for vulnerable road users and in depopulatedareas and other locations with poor access to transport<Outline>• To summarize possibilities of new business andlife brought by AD technologies, particularly on
remote areas<Method>• To implement interview surveys for staff ofmunicipal governments in remote areas on use
cases of AD technologies

26

Possible use cases of ADs in remote areas• Alternative of driving services• To substitute ADs for current driving services• Labor-saving (reduction of drivers’ work load)• Fully unmanned• Expansion of administrative services• To enhance administrative services in poor level by AD technologies• Improvement of service frequency and expansion of service area
• Improvement of service quality→ Enhancement of service (Assist, Medical inspection, Attendance, Check of infrastructure,
Disaster response)•

Improvement of those who are supported and occupants•

Check of infrastructure, Improvement of attention during response• Creation of new administration services• Realization of new services• Additionally needed technologies and expectation→ Automatic diagnosis identification (Check of infrastructure, Disaster response)•27

Identification of diagnosis, Emergency treatment

4- ii Reduction of costs and resolution of driver shortagein logistics and transport services<Outline>• To estimate how ADs introduction will contribute theon-going shortage of truck drivers<Method>
1. Estimation of future truck
drivers

2. Estimation of vehicle-km oftrucks which can be covered byunmanned trucks

To calculate demandand supply of truck
drives in the future

To calculate future vehiclekm by vehicle type, region,route

3. Trial calculation of replacement oftruck drivers by ADs
Trial calculation based on assumed
diffusion scenarios

28

Estimation method of truck drivers demand<Outline>•Firstly future freight demand is estimated, then number ofdrivers to satisfy those demand is calculated.
<Method>•Generated freight flows are calculated using MLIT’s future
traffic demand estimation committee’s method.
•Then, total freight vehicle-km are estimated using themean capacity tonnage model and the mean haul distancemodel.
•Future truck drivers demand are calculated with thepresent truck driver number and the change ratio under theassumption that change of vehicle-km and change of driverdemand are same.
29

Estimation method of truck drivers supply<Outline>• Number of truck drivers are forecasted using thestatistics on labor affair.
<Method>• The number of current truck drivers by age-grade iscalculated using the labor force survey and the basic
survey on wage structure.
• Future number of truck drivers every 5 years is
estimated by cohort method.

30

Assumed diffusion scenario of ADs
The following scenarios are assumed based on the Public-Private ITS
Initiative/Roadmaps 2019.

Scenario
1
2
3

Fully automated driving of trucks onsome specific sections of expressways

2025

2035

Commencement〇
Achievement

Fully automated driving of trucks on
expressways with 4 lanes and more
Unmanned autonomous driving delivery
service in specific areas (remote areas)

4

Unmanned autonomous driving delivery
service in specific areas (all areas except
urban and sub-urban areas)

5

Fully automated driving of trucks on
major ordinary highways

2040〇
Commencement〇
Achieve
ment
〇

〇
〇

Trial calculation of replacement of truck
drivers by ADs• Trial calculation result is as follows under many
preconditions assumed this time. (Of course, if thepreconditions are changed, result might be changed
accordingly.)
The gap between demand and supply of truck drivers
might be 180 thousand persons and 230 thousand
persons in 2035 and 2040 respectively.
In case there are proper provisions on roadways forunmanned trucks (i.e. expressways in near future andmajor highways in its following years), maximum 120
thousand persons and 200 thousand persons could besaved in 2035 and 2040 respectively.
32

4-iii. Change in ownership and usage of vehicle, and thestructure of consumers’ choice<Outline>• Estimate how the structure of consumers’ choice
regarding the ownership and usage of vehicle, andmobility will change by introduction / diffusion of AD
vehicles and MaaS.
<Method>• Investigation of construction of consumertransportation choice model and cooperation with AD
vehicle diffusion simulation• Preliminary survey targeting students was conducted.

33

Preliminary survey• Preliminary survey targeting students was conducted
regarding structure of consumer choice among 4
modes: 1) owned manual driving car, 2) owned AD
vehicle, 3) 1-passenger self-driving taxi, 4) sharedself-driving taxi.
• Estimation methodology is in the middle ofreconsideration taking into account the opinions at
Advisory Committee on the Social Impact ofAutomated Driving Systems.

34

5 Effect on industry and society
ⅱ Contribution to growth of the total factor productivity of theJapanese economy＜Outline＞• Classify the route of AD enhancing the total factor
productivity (TFP) of Japanese economy. Then withineach route, organize how automatic driving
contributes to enhancing TFP by providing specificcases.
• Calculate the round number of how much the
diffusion of AD vehicles will contribute to theenhancement of TFP growth rate.
＜Method＞• This study will be conducted in FY 2020 on a full scale,
while literature survey and planning of the study wasstarted in FY2019. Results are partially described in
the report.
35

6. Formation of organization for international
cooperation• Japanese-German cooperation• The joint research was commenced based on the Joint
Declaration of Intent signed on January 12, 2017 by the Cabinet
Office of the Japanese government and the German Federal
Ministry of Education and Research (BMBF)• The first joint meeting was held in 7 th and 8 th October atGerman Aerospace Center, Berlin.
• Research contents of both sides were presented and then mutualinteresting research areas were shown and finally fields ofinformation sharing and joint research were discussed.

7. Convening of Advisory Committee• The meetings of the advisory committee were held on 26 th June 2019, 1st October
2019 and 3 rd February 2020.
Members of the Advisory Committee on the Social Impact of Automated Driving Systems
Name

Affiliation

Speciality

Masato Itohisa

Associate Professor, Faculty of Social Science, Hosei University

Technology management

Takeyoshi Imai
Keisuke Uehara

Professor, Graduate School of Law, Hosei University
Associate Professor, Faculty Environment and Information Studies, Keio University
Professor and Director, Advanced Mobility Research Center, Institute of Industrial Science, The
University of Tokyo

Criminal law
Iinformation technology

Shusuke Kakiuchi

Professor, Faculty of Law, Graduate Schools for Law and Politics, The University of Tokyo

Civil procedure law

Masanobu Kii

Professor, Faculty of Engineering and Design, Kagawa University

Yuto Kitamura

Associate Professor, Graduate School of Education, The University of Tokyo
Specially Appointed Associate Professor, Center for Embedded Computing Systems, Graduate
School of Informatics, Nagoya University
Professor, Interfaculty Initiative in Information Studies, Graduate School of Interdisciplinary
Information Studies, The University of Tokyo
Associate Professor, Department of Civil and Environmental Engineering, College of Scienceand Engineering, Ritsumeikan University
Associate Professor, Automated Vehicle Research Unit, Future Society Creation ResearchCore, Institute for Frontier Science Initiative, Kanazawa University
Professor, Faculty of Commerce and Director, Institute for Technology, Enterprise andCompetitiveness, Doshisha University
Professor, Graduate School of International Management, Yokohama City University
Professor, Department of Mechanical Systems Engineering, Tokyo University of Agriculture andTechnology
Professor, Faculty of Policy Studies and Director-General, Institute for Technology, Enterpriseand Competitiveness, Doshisha University
Professor, Department of Civil and Environmental Engineering, Faculty of Science andEngineering, Waseda University
Associate Professor, Center for the Study of Co Design, Osaka University○ Takashi Oguchi

Ryo Kurachi
Osamu Sakura
Yasuhiro Shiomi
Naoki Suganuma
Satoshi Taguchi☆ Akihiro Nakamura
Pongsathorn
Raksincharoensak
Hiroaki Miyoshi
Akinori Morimoto
Goro Yamazaki

Traffic control engineering

Urban and transportation
planning
Education
Cybersecurity
Science, Technology andSociety
Traffic engineering
Robotics engineering
Behavioral economics
Public economics
Machine dynamics andcontrolTechnology and public
policyUrban planning

37

Cultural anthropology