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 (Summary)May 2021
The University of Tokyo
Doshisha University
1

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
sector(5) Effect on industry andsociety

Advice(6) International
cooperation(1) Contribution to UN SDGs

(2) Diffusion simulation

Impact assessment(7) Advisory Committee

Overall configuration of study3

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>
To organize 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>
To organize 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 goals
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 privately owned, a modal
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.
→ To introduce an economic framework in which external
diseconomies are internalized.
→ Other initiatives are needed, such as improved connectivity
between AD vehicles and other modes of transit.

10

2 Simulation of AD vehicle diffusion＜Outline＞• To establish a diffusion simulation model of AD vehicles to use the simulation results
as common data for various impact assessments in this project.
• Two types of simulations are conducted:••

Dynamic model : Simulation to estimate the diffusion of AD vehicles up to SAE Level 4,
where the timing of technology realization is foreseeable to some extent,
Static model: Simulation to estimate the diffusion of AD vehicles assuming a situation where
driverless AD vehicles (equivalent to SAE levels 4 or 5) are realized.

＜Method＞A）Dynamic Model:Simulation of AD vehicle diffusion up to SAE Level 4••••

To estimate the number of vehicles owned in the future based on the future estimates of GDP
and population.
To set a market launch date for AD vehicles in each automated driving category.
To estimate the percentage of vehicles in each automated driving category among new
vehicles based on “cosumer’s choice pertaining to automated driving technology" and "costreduction by experience curve effect”.
To estimate the number of vehicles owned and travel distance for each automated driving
categoryB）Static model: Simulation of impacts from introduction of driverless AD vehicles.
• To assume a society where driverless AD vehicles (equivalent to SAE levels 4 or 5) are
realized and a transportation system that utilizes these vehicles is available,• To estimate the use of each mode of transportation based on the “consumer's mode choicemodel “,• To estimate the number of vehicles owned and travel distance based on the use of eachtransportation mode.
11

Objectives of simulation• Simulation results are used as common data for various impact assessments in this project.
• Impacts from the following factors on market diffusion of AD vehicles can be evaluated:
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, at what price),
3.
Service levels in shared autonomous vehicle services（usage fee, waiting time）,
4.
Enhancement of societal acceptance.

Policy measures

OEM’s strategy forlaunching products
Service levels inshared autonomousvehicle services

Dynamic Model
Numbers of vehicles owned
Total travel distance

Each study
item in thisproject

Static model

Societal acceptance12A）Dynamic Model:Outline
1.

Classification of simulation
Category in this study
Privately owned vehicle

2.

Outline of simulation method

Outputs

To use results of online survey to model consumer’s technology-related choices

Mobility services

Ratio of each category of AD vehicle is set referring to privately owned vehicle

Logistics services

To use results from the study (4)-ⅱ. Reduction of costs and resolution of driver
shortage in logistics and transport services*

New vehicle registrations
Traffic volumes

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

Simulation period
2015

Numbers of vehicles owned

2020

2025

2030

2035

2040

2045

2050

3． Categories of AD vehicles considered in the dynamic models
Cate
gory

Highways

Generalroads

Compatible technologies and others

C0

SAE Lv. 1
or less

SAE Lv. 1 Level under C1.
or less

C1

SAE Lv. 1
Driverassistance

SAE Lv. 1 Equipped with all the following four devices
・Collision-damage-reducing brakes,
・Acceleration limiters for accidental
accelerations (due to driver error),・Lane-departure warning system,
・Car distance warning system.

C2

SAE Lv. 2
Partialautomation

SAE Lv. 1 In addition to C1:・On highways, lane keeping systems (LKAS)+ adaptive cruise control (ACC),
・Automatic lane changing on highway.

Cate
gory

Highways

General roads

Compatible technologies and others

C3

SAE Lv. 3
Conditional
automation

SAE Lv. 2

C4

SAE Lv. 4
Highautomation

C5

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

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

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

*The AD vehicles used to address the driver shortage in logistics and transportation services are equivalent to category C5.

13A）Dynamic Model:Outline
3. To subdivide the number of new vehicles
based on categories of AD vehicles andconventional vs. electric.
GDP
Population

1. To determine
the number ofvehicles that will
be required

Retention rate

2. To
determine thenew vehicle
registrations

AD vehicles
Option pricePrivately owned
vehicles(For Passenger)

Logistics andmobility
services

Sales volume
by category of
AD vehicle

To reflect thepercentage of new
privately owned
vehicles in each AD
vehicle category

Research results of”(4)-ii. Reductionof costs andresolution of driver
shortage in logistics
and transport
services "New vehicle
registrations

4. To estimate
the number ofvehicles owned
by categories ofAD vehicles andconventional vs.
electric

5. To estimate
the distancetraveled bycategories of AD
vehicles andconventional vs.
electric

Number ofvehicles owned

Total travel
distance

Subdivisions by vehicle types (private use vs. business use, categories of AD
vehicles, and conventional vs. electric)

14A）Dynamic Model: Distribution of AD vehicle categories
among new privately owned passenger cars
Distribution of AD vehicle categories among new privately owned (passenger) cars
Method for determining
Option price Determination of option price
Option pricecategory distribution of new(for the case of C5)
The
acceptance
rate
curve
for
each
purchases
category of AD vehicle is derived using theindividual data from the web questionnaire.

① Option price in year ｙ

Price of C5⑤ Option price in year ｙ＋１④Decrease in option price due to increased
production

Price of C4
C5 acceptance rate curve

Price of C3

C4 acceptance rate curve
C3 acceptance rate curve

Price of C2

C2 acceptance rate curve
Proportionof C5

Proportionof C4

Proportionof C3

Proportionof C2

Proportionof C1+C0

1.0

Acceptancerate②Determination of category distribution of new-vehicle purchases:
Among automated vehicle categories for which prices lie at or below the price a
consumers is willing to pay, the highest-ranking automated vehicle category is
assumed to be purchased.

Proportional distributionoflogisticsPercentage
servicesby AD vehicle categories
Percentage
Percentage of Percentage ofPercentage
Numberof new
vehicles

of C5

of C4

of C3

C2

C0+C1

Cumulative production volume③ Production increase

Transport services:
Ratio of each category of AD vehicle is
set referring to privately owned vehicle.

Number of AD vehicles introduced as a
response to the driver shortage in logistics

15A）Dynamic Model: Market release times andinitial option prices by category of AD vehicle• The year of market launch, the initial option price, and the cost reduction(progress ratio) due to the experience curve effect are set as follows.
*Progress ratio: Production cost ratio when cumulative production volume has doubled

Privately owner car（For passenger）

Year of market launch
AD vehicle
category

Transportation services

Privately
owned carRide shareForpassengerFor
passenger

Logistics services

Initial option
price (10,000
yen)

C2

2021

2022

2019

2019

41

C3

2023

2023

2020

2020

70

C4

2025

2025

2025

2025

81

2030

2030

93

C5

2030

5 years after C4

Determined based on “Public andPrivate ITS Initiative / Roadmap
2019” and “Action Plan for realize theAutomated Driving Version 2.0”
(Panel on Business Strategies inAutomated Driving, March 30, 2018)

2030

Determined in reference to
Xavier Mosquet et. Al,“Revolution in the Driver’s
Seat: The Road toAutonomous Vehicles”（Boston Consulting Group,
2015）

progressratio*

0.9

Determined based on
experience curves forautomated braking
systems16

A）Dynamic Model: Base case simulation results (numberof vehicles owned)
The number of vehicles owned is calculated as a base case tocompare the effects of various measures.

Future population estimates: National population estimates for the medium birth and death rates, the National Institute of Population and Social
Security Research, 2017
Future estimates of GDP: Case III in "Current Status and Outlook of Finances for National Pension and Welfare Pension - Results of Fiscal Verificationin 2019“, Ministry of Health, Labour and Welfare, August 27, 2019

Ten thousand
8,000

Number of vehicles owned by type of vehicle

6,000
4,000
2,000
0
2015

Standard普通貨物

sized truck

2020

Small小型貨物
sized truck

2025

Standard普通乗合
sized bus

2030

Small小型乗合
sized bus

2035

Standard普通乗用
sized car

2040

Small小型乗用
sized carNumber of passenger cars owned bycategory of AD vehicle

Ten thousand

8,000

2045

Mini
軽貨物
truck

2050

Mini
軽乗用
car

・According to this estimation, the share of manualdriving vehicles (category C2 or lower) in the total
number of passenger cars owned will still be morethan 50% in 2050.
・In trucks, AD vehicles to address the driver
shortage (C5) will account for 26% of the number ofvehicles owned. Driver shortage will be a major factor
in the diffusion of AD vehicles. These results are
obtained using the estimates of ４ーⅱ.
Ten thousand

Number of trucks by category of AD vehicle

1,800
1,600
1,400
1,200

6,000

1,000
4,000

800
600

2,000

400
200

0
2015

2020

2025
C0
C1

2030
C2

2035
C3
C4

2040
C5

2045

2050

0
2015

2020

2025

2030

2035

2040

2045

C0
C1
C2
C3
C4
C5
17
Source: The numbers of vehicles owned in 2015 are provided by the Automobile Inspection & Registration Information Association. The others are
estimated values.

2050

A）Dynamic Model: Base case simulation results(New vehicle registrations)ThousandThis estimation shows that new vehicle
registrations peak in 2025, starting todecrease after that due to decrease ofpassenger vehicles resulting fromdepopulation.

New vehicle registrations
by type of vehicle

6,000
5,000

4,000
3,000
2,000

New trucks keep a constant level due to
GDP growth.

1,000
0
2015
Standard普通貨物
sized truck

2020

2025

2030

2035

Small小型貨物
sized truck

Standard普通乗合
sized bus

Small小型乗合Standard普通乗用

sized bus

2040

sized car

2045

Small小型乗用
sized car

2050

Mini
軽貨物
truck

New passenger vehicle registrations by category of AD vehicle

Thousand

Mini
軽乗用
car

Thousand New truck registrations by category of AD vehicle
1,200

5,000

1,000

4,000

800

3,000

600

2,000

400

1,000

200

0

0
2015

2020

2025
C0

C1

2030
C2

2035
C3

C4

2040
C5

2045

2050

2015

2020

2025
C0

C1

2030
C2

2035
C3

C4

2040

2045

2050

C5

Source: New vehicle registrations are calculated from the numbers of vehicles owned.The numbers of new vehicles by categories of AD vehicles in 2015 are obtained using data
provided by MLIT. The others are estimated values.

18B）Static Model: Outline
To estimate how car ownership and usage will be in a society where
driverless automated vehicles (equivalent to SAE levels 4 or 5) are
implemented.
The following six types of transportation modes are assumed to beavailable to consumer, assuming that car-sharing/ride-sharing bydriverless automated taxies is possible,
Transportation Modes

Parkinglot

1. Privately owned manual vehicle

Destination
Parkinglot

2. Privately owned self-driving vehicle
Destination

Automatic parking

3. Single ride self-driving taxi

Destination

Waiting

Privately owned manual vehicle
Privately owned self-driving vehicle

4. Share ride self-driving taxi

Single ride self-driving taxi

Waiting

Share ride self-driving taxi

5. Public Transportation

Public Transportation

Destination

StationStationWalking / Biking

Destination

Single ride self-driving taxies:Fully automated taxies.
Share ride self-driving taxies:Fully automated taxies. The fare is set lower thanks
to riding with other people.
The manual vehicles are equipped with SAE Level 2 driving assistance functions.

6. Walking and/or Biking
Destination

To estimate how the followings will change depending on the price of AD vehicles, the level of service of and usage fees for self-driving taxies,
and consumers’ expectations for using AD vehicles, under the assumption that consumers' trip behavior (purpose, frequency, and travel
distance) will remain the same as in 2015.
-Consumers' transportation mode choice,- Ownership and travel distance of private passenger cars and self-driving taxies.
Option prices for AD vehicles
Service level of self-driving taxies
Expectations for the use of AD vehicles(4)-iii Research results of"Change in ownership and usageof vehicle, and the structure of
consumers’ choice”

Number of trips by trip
purpose, travel distance,
weekdays or holidays,
transportation modes.

Note: Estimated in this PJ. using microdata of National Urban Traffic Characteristics Survey (FY2010 and FY2015) provided by MLIT.

Number of vehicles
owned and the total
travel distance
Number of self-driving
taxies and the total travel
distance

19B）Static Model: Simulation results of the number ofvehicles owned and travevel distance
The following 7 cases are set using 2015 as the base year and the number of vehicles owned and travel distance are estimated.
Expectations of AD vehicles have a greater impact on the number of vehicles owned and travel distance than the option price of AD vehicles or theservice level of self-driving taxies.
Cases

Settings

2015

Base year

Without AD vehicles, single ride self-driving taxies and share ride self-driving taxies

Case 1

Standard Case

AD vehicle option price : 1,000,000 yen; Ffare of self-driving taxies: 25 yen/km; Average time to dispatch forself-driving taxies: 10 minutes; Score of expectation for using AD vehicles: 0.63;
Average waiting time for public transportation: 17 minutes

Case 2
Case 3
Case 4

Price drop for AD vehicles

AD vehicle option price 1,000,000 yen→ 200,000 yen

Price drop for Self-Driving Taxies

Fare of self-driving taxi 25 yen/km → 15 yen/km

Price increase for self-driving taxies

Fare of self-driving taxi 25 yen/km → 60 yen/km

Case 5

Increase in waiting time for self-driving taxies

Average time to dispatch for a self-driving taxi: 10 minutes → 20 minutes

Case 6
Case 7

Increased expectations for use of AD vehicles

Score of expectation for use of AD vehicles 0.63 → 1.00

Reduction of waiting time for public transportation

Average waiting time for public transportation 17 min → 6 min

Note: The price for share ride self-driving taxies is set at 77% of that of single ride self-driving taxies

Number of passenger car vehicles
2015年
Case1

2015年

Case2

Case2

Case3

Case3

Case4

Case4

Travel distance per day (vehicle km)

Case1

Case5

Case5

Case6

Case6

Case7
0

Privately
owned
個人所有手動運転車
manual vehicle20

40

Privately
owned self個人所有自動運転車
driving vehicle

60

Million80

Singleride self-riving Shareride self-driving
自動運転タクシー
乗合自動運転タクシー
taxi
taxi

Case7

Million0

Privately owned
個人所有手動運転車
manual vehicle

500

個人所有自動運転車
Privately owned selfdriving vehicle

1,000

1,500

ride self-driving
Singleride self-riving Share
自動運転タクシー
乗合自動運転タクシーMilliontaxi
taxi

Notes:
1. The number of vehicles owned and travel distance per day in 2015 are provided by the Automobile Inspection & Registration Information Association and MLIT, respectively.
2. Estimated in this PJ. using Microdata of National Urban Traffic Characteristics Survey (FY2010 and FY2015) provided by MLIT.

20

B）Static Model: Consumer’s expectations
for AD vehicles
Consumer’s expectations for fully-automated vehicle in the following table are investigated and scored.
・Definitely No (corresponds to “0”)・Rather No (to “1”)・Neither (to “ 2”)The total of applied scores is divided by 52 (full marks).
・Rather Yes (to “3”)The following figure shows the relative frequency distribution of the・Definitely Yes (to “4”)adjusted total score. The average is 0.63.

1Resolution or alleviation of traffic congestion

2

Reduction of traffic accidents

3

Environmental load reduction

4

Assistance of transportation of elderly persons, or others

5

Alternatives to public transport in underpopulated areas（N=5,102）

Increase of opportunity of going out for shopping,amusement, pleasure trip, and others
Increase of opportunity of personal interaction with
7
friends, acquaintances, family, and relatives

6

8

Reduction of private car driver’s burden

9Effective utilization of travel time

10 To allow drivers to call the car from another place
11 Unnecessary to use parking place on–the –go

Resolution of shortage of professional driver (for trucks,
buses and taxis)
Stimulation of economy and enhancement of international
13
competitiveness resulting from new industries
12

Adjusted total score regarding expectation for fully automatedvehicle21

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 SIPadus estimates the reduction of the number of accidents andcasualties due to the diffusion of AD vehicles. We estimate themonetary value of reduced traffic accidents.

＜Method＞• The number of deaths and injuries provided by the above-mentioned
research is estimated only for the primary and secondary parties anddoes not include the third party including fellow passengers. For thisreason, the damage caused to fellow passengers is estimated in acomplementary manner using Japan Traffic Accident Database (macrodata) provided by ITARDA.
• Monetary and non-monetary losses to traffic accident victims andnon-monetary losses to traffic accident perpetrators are valued inmonetary terms using the number of traffic accident casualties andthe economic loss per victim listed on p.25.
22

Procedural flow for estimating the impact oftraffic-accident reduction
Traffic-accident simulations conducted in above mentioned anotherresearch conducted by the Japan Automobile Research Institutepredicts changes in the frequency of traffic accidents and the number oftraffic-accident victims thanks to diffusion of AD vehicles.
The results are utilized in this research project to estimate the impact of
reduced traffic accidents due to increasing diffusion of AD vehicles
in monetary terms.
Travel distancesof privately owned
vehicle, mobility
service, or logistics
servicesby type of vehicle
and category ofAD

Traffic-accident simulations—conducted within the program“Visualize the effects ofreducing traffic accidents
through automated driving
and driving assistance” .
Frequencies of traffic
accidents by type of
accident; Number ofvictims by severity of bodily
injury.

Monetary estimation ofeffectiveness in
reducing traffic
accidents due toincreasing diffusion of
AD vehicles.

23

Results of web-survey regarding perpetrator’s
non-monetary losses
Estimated WTP of annual usage fee (Median)Two types of web-survey are conducted to make monetary
estimation of effectiveness in reducing traffic accidents: survey
for virtual perpetrators and one for virtual victims.

円

30,00025,00020,00015,00010,0005,000
0Virtual 削減 加害者調査
Virtual 削減
Virtual 削減 被害者調査
Virtual 削減 加害者調査
被害者調査
victims
perpetrators
victims
perpetrators
率 50%
率 90%
率 50%
率 90%
90％
90％
50％
50％

Survey for virtual perpetrators : To evaluate the willingness topay (WTP) of annual usage fee for the device that reduces theprobability of causing accident where a driver of other party is
killed in an accident between 4-wheel vehicles, by 50 % (90%)
under the following assumptions:
1) Probability of causing fatal accident between 4-wheel
vehicles in a year is a 1/200,000,
2) Percentage of fault of respondents is 100%.
Survey for virtual victims : To evaluate the willingness to pay(WTP) for the annual usage fee of the device that reduces theprobability of encountering accident between 4-wheel vehicles
where each respondent is killed, by 50 % (90%) under thefollowing assumptions:1)Probability of encountering fatal accident between 4-wheel
vehicles in a year is a 1/200,000,
2)Percentage of fault of respondents is 0%.

Total
N=2,557

Virtual perpetrators
N=1,285

Virtual victims
N=1,272
Device that reduces the probabilities of causing accidents where other party is killed

Q:Which device do you feel more valuable under thecondition that you and/or your family do not bear any
monetary burden at all, device that reduces the probability of
causing accident where other party is killed, or one that
reduces the probability of encountering accident where you
are killed?

Device that reduces the probabilities of encountering accidents where you are killed
Feel valuable with both to the same degree

Note: The results is obtained using clean-up data after deleting misunderstanding data using a delete pattern.

24Economic loss per victim
Economic loss per victim
Perpetrator’sVictim's economic loss
Victim's degree ofphysical injury

Monetary
loss

Death

non-monetary loss

Nonmonetary

Unit: Ten thousand yen

Total loss

loss

Accident between motor

Accident between motor

vehicle and pedestrianvehiclesPrimary

SecondaryPrimary

Secondary

party

partｙ

party

partｙ

3,292

53,69556,98752,191

5,799

43,493

14,498906

10,191

11,097

9,906

1,101

8,255

2,752

153

100

253

97

11

81

27

Serious injury（Injury
with residual
disability）
Slight injury（Injury
without residual
disability）

Note 1: Victim’s economic loss is calculated using Table 3.3-1 and Table 5.2-1 in "Research Report on the Economic Analysis of Damage and Losses
from Traffic Accidents," March 2017, Cabinet Office Director General for Policy on Cohesive Society (hereafter, Cabinet Office, 2017).
Note 2: The amount of non-monetary loss of the perpetrator is assumed to be 1.08 times that of the victim when the percentage of fault is 1, basedon the results of web-survey. The amount of non-monetary loss is assumed to change proportionally to the percentage of fault. The amount of
non-monetary loss for the perpetrator in the table is calculated assuming that the ratio of fault of first party to that of secondary party is 9 to 1 in
the case of accident between motor vehicle and pedetrian,and 7.5 to 2.5 in the case of accident between motor vehicles, respectively.
Note 3: While the Cabinet Office (2017) categorized the victims' degree of physical injury from the accident into "death," "injuries with residual
disability," and "injuries without residual disability," the number of casualties estimated by above-mentioned another research is classified
differently as "death," "serious injuries," and "slight injuries. Therefore, we place "injury with residual disability" and "injuries without residual
disability" as equivalent to "serious injury" and slight injury", respectively, and conduct the calculations on the next page.
25

Estimation results of effectiveness in reducing traffic
accidents
Economic losses due to road traffic accidents

100 million
億円
yen

Traffic accidents between motor vehicles and pedestrians (while crossing pedestrian crosswalks
at Intersections or areas outside of intersections )20,00015,000
7,542

7,593

7,332

10,0005,000
0100 million
yen

Effectiveness in reducing
交通事故低減効果
traffic accidents
5,863

8,596

8,683

8,381

2015

2020

2025

Economic losses to
１当被害者の経済的損失
primary
parties as victims
Economic losses to
２当同乗者の経済的損失
secondary parties’ fellow
passengers

5,211

5,475

5,136

4,927

6,718

5,956

6,235

5,854

5,636

2030

2035

2040

2045

2050

Economic losses to primary
１当同乗者の経済的損失
parties’ fellow passengers

Economic losses to secondary
２当被害者の経済的損失
parties as victims

None-monetary losses to
１当加害者の非金銭的損失
primary parties asperpetrators

None-monetary losses to secondary
２当加害者の非金銭的損失
parties as perpetrators

Traffic accidents between motor vehicles (Right-turn, left-turn, rightangle, frontal, and rear-end)

Effectiveness in reducing
traffic accidents

Note 1: In the above-mentioned another research, the numberof traffic accident casualties every five years is calculated
assuming the composition ratio of each year's automobile's
automatic driving category is eqaul top that estimated in thisproject., while the traffic distance is fixed at the level of 2015.
The numbers in the graph were calculated using the numberof casualties estimated by this way and the amount of
economic loss per victim on the previous page. The amount ofchange from 2015 to each year indicates the monetaryequivalent of the effect of automation on reducing traffic
accidents.
Note 2: The amount of non-monetary loss to the perpetrator
is calculated from the total damage of the driver and thefellow passengers of the other vehicle in the accident in thecase of an accident between motor vehicles. For example, if
the perpetrator is a primary party and the driver and fellow
passengers of the secondary party's vehicle are killed, theperpetrator's non-monetary loss would be 434,930 thousand
yen*2. On the other hand, in the case of an accident between
motor vehicle and pedestrian, a car is considered to be theprimary party and a pedestrian is considered to be thesecondary party. The non-monetary loss to the driver (theprimary party) is calculated from the damage of one
pedestrian (the secondary party).
Norte 3: In the above-mentioned another research, vehicles in
the C0 category are assumed to be without driving-support
devices including collision-damage-reducing brakes.

Economic losses to primaryparties as victims

Economic losses to primaryparties’ fellow passengers

Economic losses tosecondary parties’ fellow
passengers

Economic losses to secondary
parties as victims

None-monetary losses toprimary parties asperpetrators

None-monetary losses to secondary
parties as perpetrators

Note 4: Scope of parties
Primary party: Bus (small-sized and standard-sized), passenger
car (standard-sized, small-sized , and mini), truck (standardsized, small-sized , and mini), special-purpose vehicle, andmotorcycle.
Secondary party:Bus (small-sized and standard-sized),
passenger car (standard-sized, small-sized , and mini), truck(standard-sized, small-sized , and mini), special-purpose
26
vehicle, motorcycle, bicycle, and pedestrian.

３-ⅱ. Estimation of reduction of traffic
congestion and CO2 emission<Outline>• To estimate the reduction of traffic congestion and CO2 emissions by ADs diffusion<Methods>• Same methods established during the SIP-1st phase is applied for estimation at expressways.
• Almost sixty percent of traffic congestion on expressways nationwide are occurring at sag
sections.
• ADs are supposed to contribute reduction of traffic congestion and CO2 emission because
they promote vehicles’ movement with proper headway and speed leading improvements ofroad traffic capacities.
• Based on the above idea, traffic simulation is used for estimation at two representing sections,a section with three-lane expressway and a section with two-lane expressway. Three ADs
diffusion rate, 20%, 50% and 90% are applied.
• For ordinary roads, based on the same idea as above mentioned, existing research results oftraffic simulation at the road section with signals (*) are referred.
• Then total amounts of reduction of traffic congestion and CO2 emissions nationwide arecalculated.

(*) Study report on international acceptance establishment of CO2 reduction effect evaluation method intransportation sector (Green automotive technology survey and research project in FY2013)27

Precondition for estimation
Item
Rate of reduction of traffic
congestion and CO2 emission

Content
Results of traffic simulation

Diffusion rate of ADs

20%, 50%, 90%

Road type

Expressways: Intercity expressways
Worst 30 sections, sag sections
Ordinary roads: Major national
highways and high-graded trunk roads

Vehicle type
Performance of ADs(Items giving impacts on
traffic congestion and CO2
emission)

Passenger vehicles
Maintaining of speed at sag sections/ Mitigation oflost time at signalized intersections
Effect by speed and inter-vehicle time
* Inter-vehicle time: 1.0s, 1.5s and 2.0s
→ 1.5s: Performance same as existing ACC vehicles (equivalent to SAE L1 and L2)
2.0s: Performance with longer inter-vehicle time (equivalent to SAE L3 or above)
1.0s: Minimum inter-vehicle time (as a reference)* AD’s loss time at signalized intersections is 0.2s shorter than ordinary vehicles’ one.
* ADs comply with regulated speed.
28

Traffic congestion and CO2 emission reduction
model by ADs diffusion
Passenger cars: ADs diffusion model
Output by TS

Vehicle-kms of ADs (passenger cars)
Distance

Road geometry, Network
Traffic regulation & control
Traffic demand
Traffic flow, vehicle, driver’s
characteristics etc.

Traffic simulation in considerationof ADs diffusiont
Vehicle behavior

Expressways: Sag bottleneck sections
Other roads: Indispensable freight-road network
Driving behavior of individual vehicles(average speed, travel time, drive
distance, stop time etc.)

Estimation of unit reduction of
traffic congestion along with the
diffusion of ADs(Expressways, Other roads)

Data for theextension of
estimation

Estimation of nationwide
traffic congestion reduction(Expressways, Other roads)v

Movingt

IdlingInformation of vehicle type composition
Information of vehicle emission factors

Accuracy

Estimation of unit reduction of
CO2 emission along with the
diffusion of ADs(Expressways, Other roads)

Data for theextension of
estimation

Estimation of nationwide CO2
emission reduction(Expressways, Other roads)

CO2 emission* Estimated by theCO2 emission model
developed by JARI

(Source) Study report on international acceptance establishment of CO2 reduction effect evaluation method in transportation sector (Green automotive technology survey andresearch project in FY2013)29

(Nationwide estimation: Expressways)
Traffic congestion reduction and CO2 emission reduction• The below graphs show the reduction rates for traffic congestion in
the worst 30 sections in case of inter-vehicle time of 1.5s.
(C1/ C2 in this study, equivalent to SAE L1/ L2 )

20%

50%

90%

Fig. Reduction rate of loss time

20%

50%

90%

Fig. Reduction rate of CO2 emission• “20%” and “50%” are corresponding to the diffusion rate at 2020
and 2025 calculated by the dynamic diffusion simulation.
30

(Nationwide estimation: Ordinary roads)
Traffic congestion reduction and CO2 emission reduction• The below graphs show the results in case of inter-vehicle time of 1.5s.
(C3 in this report, equivalent to SAE L1/ L2)• Stable traffic flow, prevention of capacity drop by less variety of departure loss
at signalized intersections and less factors causing traffic disturbance are
expected because ADs run at stable speed on ordinary roads.

20%

50%

90%

Fig. Reduction rate of loss time

20%

50%

90%

Fig. Reduction rate of CO2 emission31

４-ⅰ Ensuring mobility for vulnerable road users and indepopulated areas and other locations with poor accessto transport<Outline>• To summarize possibilities of new business and life
brought by AD technologies, particularly on remote
areas<Methods>• To implement interview surveys for staff of municipal
governments in remote areas on use cases of AD
technologies

32Use cases of ADs found by interview surveys• 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 improve administrative services level by AD technologies• Improvement of service frequency and expansion of service area
• Improvement of service quality→ Enhancement of service (Assist, Medical inspection, Escort, On-site check, Disaster
response)• Improved service for supported people and fellow passengers• Improvement of attention during on-site checks and disaster responses• Creation of new administration services• Realization of new services→ Automatic diagnosis identification (On-site check, Disaster response)• Identification of diagnosis, Emergency treatment33

4- ii Reduction of costs and resolution of driver shortage inlogistics and transport services<Outline>• To estimate how ADs introduction will contribute as a solution
against the on-going shortage of truck drivers<Methods>
Estimation of thenum. of truck drivers

Estimation of vehicle-kms of trucks which
can be covered by unmanned trucks
GDP assumption/
Estimation of vehiclekms by vehicle typeand business typeTo calculate demandand supply of truck
drivers in the future

Estimation of drivers’ shortage
mitigated by ADs
Possibility of ADs
introduction tologistics sector

Calculation based onassumed diffusion
scenarios

Economic impact estimation of
the mitigation of driver shortage

34

Assumed diffusion scenario of ADs
The following scenarios are assumed based on the Public-Private ITS
Initiative/Roadmaps.
A model estimating the number of driver shortage mitigation and its economic
impact is established in accordance with GDP assumption and diffusion
scenarios.
Scenario
1

Fully automated driving of trucks on somespecific sections of expressways

2025

2035

Commencement○Achievement

2

Fully automated driving of trucks on
expressways with 4 lanes and more

3

Unmanned autonomous driving delivery service
Commenin specific areas (remote areas)
cement4

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
○
Achieve
ment
〇

○
〇

35

Trial calculation of replacement of truck
drivers by ADs• Under the some assumption including the ADs diffusion
scenario No.2 and No.4, the gap between demand andsupply of truck drivers might be improved about 79% in
2035.
• In case goods trucks on expressways replaced by ADs,
about 63% of economic losses, which would be causedby truck driver shortage might be avoided in 2035.

36

(4)-iii. Change in ownership, usage of vehicle,
and consumers’ behavior in mode choice＜Outline＞• To estimate how consumers’ choice regarding theownership and usage of vehicle, and mobility will change byintroduction / diffusion of AD vehicles and MaaS.
＜Method＞• To construct a transport mode choice model from theresults of web questionnaire survey.
• We estimate how consumers’ behavior in the choice oftransport mode will change depending on the price of AD
vehicles, the level of service of self-driving taxies, and thelevel of expectation for using AD vehicles. However, we
assume that consumers' travel purpose, travel frequency,
and travel distance will remain the same as in 2015.
37

Mode choice model
Travel distancetravelDistance
1km
1km
3km
10km
3km
30km
10km
30km

Purpose:Commuting,Private use including shoppingand others

Travel cost of each mode

Modes
Privately owned manual
vehiclePrivately owned selfdriving vehicle

Mode choicemodel

Travel time of each mode

Single ride self-driving
taxi
Share ride self-driving
taxi

Social acceptance of AD
Regions:

Cities in the three major metropolitan
areas;
Cities around the three major
metropolitan areas;
Government-designated cities;
Core cities;
Other regions.

Public Transportation
Days:
Weekdays;
Holidays

Walking/ Biking

Note: Estimated in this PJ. using Microdata of National Urban Traffic Characteristics Survey (FY2015) provided by MLIT.

38

Modal share after the spread of fully automated vehicles
Using 2015 as the base year, we set the following 7 cases and estimated the share of each mode.
Expectations of AD vehicles will have a greater impact on transportation mode choices than the option price of AD vehicles or thelevel of service of self-driving taxies.
Cases

Settings

2015

Base year

Case 1

Standard Case

Case 2
Case 3

Price drop for AD vehicles

AD vehicle option price : 1,000,000 yen; Ffare of self-driving taxies: 25 yen/km; Average time to dispatch for self-driving
taxies: 10 minutes; Score of expectation for using AD vehicles: 0.63;
Average waiting time for public transportation: 17 minutes
AD vehicle option price 1,000,000 yen→ 200,000 yen

Price drop for Self-Driving Taxies

Fare of self-driving taxi 25 yen/km → 15 yen/km

Case 4

Price increase for self-driving taxies

Fare of self-driving taxi 25 yen/km → 60 yen/km

Case 5

Increase in waiting time for self-driving
taxiesIncreased expectations for use of AD
vehiclesReduction of waiting time for public
transportation

Case 6
Case 7

Without AD vehicles, single ride self-driving taxies and share ride self-driving taxies

Average time to dispatch for a self-driving taxi: 10 minutes → 20 minutes
Score of expectation for use of AD vehicles 0.63 → 1.00
Average waiting time for public transportation 17 min → 6 minNote) The price for share ride self-driving taxies is set at 77% of that of single ride self-driving taxies.

Modal share

2015年
Case1
Case2
Case3
Case4
Case5
Case6
Case7
0%

10%

Privately
owned manual
個人所有手動運転車
vehicle20%

30%

Privately owned self個人所有自動運転車
driving vehicle

40%

50%

Single ride self-riving
自動運転タクシー
taxi

60%

70%

Share ride self-driving taxi
乗合自動運転タクシー

Note: Estimated in this PJ. using Microdata of National Urban Traffic Characteristics Survey (FY2015) provided by MLIT.

80%

Public
公共交通機関Transportation

90%

100%

Walking/ Biking
徒歩・自転車39

5. Effect on industry and society
ⅰ Impact on the automobile industry due to changes in
the automobile ownership structure and others＜Outline＞• Research on the impacts on the automobile industry andthe whole Japanese industry by the changes below:• Changes in automobile ownership due to the diffusion of AD
vehicles, car sharing/ride sharing and others.
• Changes in automobile components due to the shift toautomated driving and electrification.

＜Method＞• To estimates of changes in components as automobiles
become automated and electrified.
• Impacts on production value and employment in theautomobile industry and the whole Japanese industry are
estimated using the inter-industry relations table. Theinter-industry relations table is assumed to remainunchanged except for changes in components due to theshift to automated and electric vehicles.
40Classification
To classify vehicle base on category of AD and type of electrification
Passenger vehicles produced in the year of 2016 (I-O table year) are assumed to be without driving support devices including collision-damagereducing brakes, for simplification. Parts costs are estimated only for the automated categories C2 and above,
Parts costs are estimated for each electrification (HV, PHV, and EV).

■Categories of AD vehicles considered in this study
Cate
gory

Highways

Generalroads

C0*

SAE Lv. 1
or less

SAE Lv. 1
or less

Level under C1

C1*

SAE Lv. 1
Driverassistance

SAE Lv. 1

Equipped with all the following four
devices・Collision-damage-reducing brakes
・Acceleration limiters for accidental
accelerations (due to driver error)
・Lane-departure warning system
・Car distance warning system

SAE Lv. 2
Partialautomation

SAE Lv. 1

C2

Compatible technologies

In addition to C1:・On highways, lane keeping systems(LKAS) + adaptive cruise control(ACC)
・Automatic lane changing on highway■Electrification considered in this study

Cate
gory

Highways

General roads

Compatible technologies

C3

SAE Lv. 3
Conditional
automation

SAE Lv. 2

C4

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

C5

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

In addition to C2:・Lv. 3 on highways
・Lv. 2 on general roads*Out of scope of parts cost estimation.

Type

Description

ICE

Internal combustion engine vehicle

HV

Hybrid vehicle

PHV

Plug-in hybrid vehicles

EV

Electric vehicles

41New vehicle registrations: Simulation results of dynamic
diffusion simulation model (GDP medium-variant Case)• The number of new vehicle registrations by both category ofAD and type of electrification estimated in the dynamic modelof diffusion simulation is as follows:• The number of passenger car registrations decreases after
2025 due to the decrease of population,• No big change in the ratio of each category of AD after 2030.
New passenger car registrations by category of AD

New passenger car registrations by type of electrification

ThousandThousand

5,000

5,000
4,000

4,000

3,000

3,000

2,000

2,000

1,000

1,000

0
2015

2020

2025
C0

C1

2030
C2

2035
C3

C4

2040

2045

C5

Source: The number of C1 in 2015 is set based on the actual value obtained from
“Survey on Prevalence of ASV Technology ( as of Dec 26, 2018) “ issued by the MLIT.
The others are estimated values.

2050

0
2015

2020

2025
ICE

2030
HV

2035
PHV

2040

2045

2050

EV

Source: The ratios are set based on the report on diffusion strategy for nextgeneration vehicles prepared by Japanese Ministry of the Environment, May 200942

Estimated Impacts• To evaluated the impacts of changes in the number of passenger cars produced andshift to automated driving and electrification on the production value of thepassenger car sector, automobile parts / accessories sector, and all industries, using
the estimated production costs and prices of each category of AD.
• The production value of the passenger car sector will increase with the progress ofautonomous driving until 2025. It will decrease with the decrease in production
volume after 2025.
• Both the production value in the automobile parts and accessories sector will
decrease. It is thanks to the decrease in both the number of passenger cars
produced and demand of the internal combustion engine.
• In all industries, the production will increase due to the increased demand forautonomous driving parts, but it will eventually become negative compared to 2015
due to the decrease in production volume.

10%
5%
0%
-5%

2015 2020 2025 2030 2035 2040 2045 2050

-10%
-15%
-20%
-25%
Change of volume

Change of parts

All changes

2%

The production value change in theautomobile parts and accessories sector
compared with the level of 2015 and its
decomposition

0%2015 2020 2025 2030 2035 2040 2045 2050
-2%
-4%
-6%
-8%

The production value change in all industries
compared with the level of 2015 and its decomposition
ratio of change in production value

15%
ratio of change in production value

ratio of change in production value

The production value change of the passenger car sector
compared with the level of 2015 and its decomposition-10%
Change of volume

Change of parts

Source: Estimated using 2016 Updated Input-output Table provided by METI, and others

All changes

0.6%
0.4%
0.2%
0.0%
-0.2%

2015 2020 2025 2030 2035 2040 2045 2050

-0.4%
-0.6%

43

-0.8%
-1.0%
Change of volume

Change of parts

All Changes

5. Effect on industry and society
ⅱ Contribution to growth of total factor productivity ofJapanese economy＜ Outline ＞• The results of the estimated resolution of the truck driver
shortage calculated in "(4)-ii Reduction of costs and resolutionof driver shortage in logistics and transport services" areconverted into the effect of increases in labor productivity andtotal factor productivity.

44

Calculation methodology
Labor productivity growth＝TFP growth＋Capital equipment ratio growth＊（１－ Labor share）
Item
Labor
productivity

CapitalEquipment
ratio

Labor share

Calculation method
Total output/number of workers,
Total output: vehicle kilometer of travelled,
Number of workers:number of truck drivers

Notes
The values calculated in "(4)-ii
Reduction of costs and resolutionof driver shortage in logistics andtransport services are used for the
total output and the number ofworkers
Gross capital stock/Number of workers
Body price of new car
Standard-sized:6,000 JPY
Gross capital stock = Number of trucks owned ×
Small-sized:4,000 JPY(Body price ＋ Option price of AD vehicle
Mini:1,500 JPY
category (higher than C1）)
Option price of AD vehicle
The number of trucks is calculated in a dynamic category (higher than C1 ） for
model simulation.
trucks is assumed to be threetimes as much as that forpassenger cars
Calculated from Extended 2016 I-O Table
provided by METI.
Labor share: 0.713（Compensation of employees/ Gross value added）
45

Productivity improvement in the logistics
industry through automation
Private-use truck

Commercial-use truck
70.0%

63.8%

60.0%

30.0%

29.6%

44.1%

5.5%
24.0%

19.6%

0.0%
2035

50.0%
40.0%

37.5%
36.1%

30.0%

20.0%
10.0%

55.4%

60.0%

50.0%
40.0%

70.0%

2040

Contribution of the chage in capital equipment ratio
Residual including TFP growth

20.0%
10.0%
0.0%

26.8%

10.7%

2035

19.3%

2040

Contribution of the chage in capital equipment ratio
Residual including TFP growthNote: Growth rate compared to 201546

6. Research activities with international cooperation• Japanese-German cooperation• The joint research was commenced based on the Joint Declaration ofIntent signed on Jan. 12, 2017 by the Cabinet Office of the Japanese
government (CAO) and the German Federal Ministry of Education andResearch (BMBF)• The first joint meeting was held on Oct. 7 and 8, 2019 at German
Aerospace Center, Berlin.
• Research contents of both sides were presented, then mutualinteresting research areas were shown and finally fields of information
sharing and joint research were discussed.
• The second and third joint meetings were held on
Jun. 16 and 30, 2020 via online for information
sharing and discussion on mutual interesting
research area.
• The online symposium was held on Nov. 12, 2020.
• The activity progress was reported to CAO and
BMBF at the Steering Committee meeting on May
29, 2020 and at the Expert Workshop on Nov. 25,
47
2020.

7. Convening of Advisory Committee• The meetings of the advisory committee were held six times.

（7 th Mar 2019, 26 th Jun 2019, 1st Oct 2019, 3 rd Feb 2020, 28 th Oct 2020, 10 th May 2021）

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○ Takashi Oguchi
Shusuke Kakiuchi

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

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
Professor, Automated Vehicle Research Unit, Future Society Creation Research Core, Institutefor Frontier Science Initiative, Kanazawa University
Professor, Faculty of Commerce and Director, Institute for Technology, Enterprise andCompetitiveness, Doshisha University
Professor, Faculty of Economics, Chuo University
Professor, Department of Mechanical Systems Engineering, Tokyo University of Agriculture andTechnology
Professor, Faculty of Policy Studies, Institute for Technology, Enterprise and 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

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

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

48

Cultural anthropology

48This report documents the results of Cross-ministerial
Strategic Innovation Promotion Program (SIP) 2nd
Phase, Automated Driving for Universal Services (SIPadus, NEDO management number: JPNP18012) thatwas implemented by the Cabinet Office and wasserved by the New Energy and Industrial Technology
Development Organization (NEDO) as a secretariat.