ITARDA 23th Work Shop Report

2020
23rd Presentation Session for Traffic Accident Investigations, Analysis, and Research

Using PCM on Micro Data for Autonomous Vehicles—A Collaboration with VUFO in Germany—
Toru Kiuchi
Senior Researcher, Research Division

Summary:
ITARDA has had the opportunity to interact with many institutes for traffic accident research outside of Japan.
In this paper, our collaborations with VUFO is introduced. Its managing director, Mr. Liers was invited to givea lecture ITARDA’s 25th Anniversary Forum.

ITARDA has shared the most technical exchanges withVUFO. The PCM (Pre-Crash-Matrix) format developed by VUFO is a format that provides chronological dataon the movements of vehicles for 5 seconds prior to a collision, including such data for road environments,etc. ITARDA had the opportunity to participate in the workshops in which VUFO provided guidance thatenabled ITARDA to produce PCM from its own micro data. ITARDA then used PCM to analyze accidents on
expressways caused by abrupt lane changes, the data from which was then utilized as back data to show thevalidity of scenarios and parameters used in the SAKURA project on autonomous vehicles in Japan. ITARDA
plans to continue further use of PCM into the future.

1ITARDA 23th Work Shop Report

1.

Introduction
Until now, ITARDA has served as a deputy

member representing Japan in IRTAD conferences
held every year in April at the OECD headquarters(Paris) at which it has reported on its research
results. However, in recent years, ITARDA has also
been involved in many exchanges with research
institutes outside of Japan via initiatives such as its
Fig. 1 - Research institutes ITARDA has visited in recent years

new micro database construction project. (Fig. 1)

In this report, our collaborations with VUFO is introduced. , ITARDA has shared the most exchanges withVUFO among all such research institutes.

2.

ITARDA and VUFO

2-1. Overview
While ITARDA was established in 1992, VUFO was established in 2006 and both institutes use “Institute forTraffic Accident Research” in their English names. Although there is a large difference in the quantity of traffic
accident case studies performed by both institutes annually, the contents of such surveys are generally the same innature. (Table 1)Table 1 - Comparison of traffic accident case surveys performed by ITARDA and VUFONameYearestablished
Organization
typeTraffic accident
case studies

1992
Public interest incorporated foundation

2006
Limited liability company (GmBH)

Micro data (since 1993)

GIDAS data (since 1999)(German In-Depth Accident Study)

Annual number of studies: Approx. 200(Tsukuba Traffic Accident Investigation Office +Tokyo Accident Investigation Office)
1993: Establishment of Tsukuba Traffic Accident
Investigation Office
2016: Establishment of Tokyo Traffic Accident
Investigation Office

Annual number of studies:2,000(of those by VUFO: 1,000)
On-call 24 hours for fieldsurveys

No field surveys in recent years

2-2. Details of main exchanges between ITARDA and VUFO
(1) 25th Anniversary Forum
ITARDA’s 25th Anniversary Forum was held as its 20thPresentation Session in October of 2017 at the Bellesalle event hall inTokyo’s Kanda area. At this event, ITARDA invited the Managing

2Fig. 2 - Photo from Mr. Liers’ special lecture

ITARDA 23th Work Shop Report

Director of VUFO, Mr. Henrik Liers, to give a special lecture entitled “Traffic Accident Research in Germany”.
This invitation was made possible by a visit to VUFO in the fall of the previous year and a meeting with him at theOECD/IRTAD meeting held in the spring of that same year.
(2) PCM Workshop
At the request of ITARDA, a workshop was held at
VUFO in October of 2019 that consisted of VUFO experts andresearchers from ITARDA and Japan’s National ResearchInstitute of Police Science. By receiving detailed guidance foreach step of PCM creation (see “3. PCM”) using micro data,
ITARDA was able to garner numerous results to aid in its
future enhanced use of PCM.

3.

Fig. 3 - Photo of workshop held at VUFO

PCM

3-1. Overview of PCM
The PCM (Pre-Crash-Matrix) developed by VUFO uses latitude and longitude coordinates to show thechronological movement of participants for 5 seconds prior to the traffic accident collision, as well as for the traffic
infrastructure (lanes, road surface markings, etc.) and surrounding objects.
Global Data
Participant Data

Pre-Crash-Matrix®

0 sec

0 sec

Dynamics Data

(GIDAS-PCM)-5 secTraffic
Infrastructure data

-5 secObjects Data

Fig. 4 - Data used to form the PCMThe usage example shown here was an attempt by VUFO to examine specifications for the detection function
and braking function of a damage mitigation brake system using a PCM (Fig. 5) for a collision accident that actuallyoccurred in Germany. As PCM is capable of being used in such a manner, it is expected that PCM will beproduced

from micro data by Japanese automobile manufacturers as a means of obtaining data that is extremely useful in thedevelopment of preventive safety technology and autonomous vehicles technology. (Fig. 5 & Fig. 6)

Collision mitigation

Collision

Fig. 5 - PCM simulation for actual accident

Collision avoidanceFig. 6 - Examination of system specifications using PCM

3ITARDA 23th Work Shop Report

3-2. Creation of PCM by ITARDAAt ITARDA, a PCM is being created using micro data such as vehicle surveys, road surveys, and interviewsof parties concerned. Particularly in the case of vehicle surveys, it is possible to accurately reproduce dynamic datafrom 5 seconds prior to a collision by reading vehicle data using Bosch's CDR and GTS and also by utilizing thevideo of the accident provided by a dashcam. Furthermore, even in the case of road surveys, by obtaining 3D point
cloud data for the road using a 3D scanner, it is possible to create a more detailed accident reconstruction simulation(PC-CRASH) and create a PCM based on that. (Fig. 7)
Start of survey
Accident
occurs

Vehicle survey

Interviews

Road survey
Accident site information

EDR information

Equipment: Bosch CDR
Information obtained:
Operational data from 5
seconds prior, collision speed,ΔV, TTF, etc.

Information on driving conditions

Equipment: Z+F Imager5016
Information obtained:
3D data of accident site, roadconfiguration, coordinates ofsigns, etc.

Information obtained:
Driving route, driving speed,
circumstances when driving,etc.

Vehicle control history
Equipment: Bosch GTS,
Denso DST-i
Information obtained:
ASV operation, brake control,etc.

Participant data
Dynamics data
Traffic infrastructure dataImages of accident conditions
Equipment: Dashcam
Information obtained:
Images of accident, speed,
positional information, etc.

Accidents are reconstructedusing the following (PC-CRASH):
 Various information regardingvehicles in the accident"
 3D data of site Interviews
Fig. 7 - How a PCM is created by ITARDA

Objects data

PCM created from
PC-CRASH dataShown below is the accident reconstruction (PC-CRASH) created using such a procedure and the PCM createdutilizing that reproduction. This PCM is the result of the workshop held at VUFO.

Fig. 8 - Accident reconstruction using PC-CRASH

4.

Fig. 9 - PCM created by ITARDA

Example of utilization by the SAKURA project
Since the year before last, the Ministry of Economy, Trade and Industry (METI) has been promoting a researchand development project (SAKURA project) aimed at construction of safety evaluation technology for autonomousvehicles systems as one of its "Research and Development/Demonstration Project for Implementation of an
Advanced Autonomous Driving System in Society". As part of this project, the Japan Automobile Manufacturers
Association (JAMA) and the Japan Automobile Research Institute (JARI) have developed a process to ensure the4

ITARDA 23th Work Shop Report

safety of autonomous vehicles systems by taking into account all foreseeable scenarios on expressways.
Scenarios for autonomous vehicles traveling on expressways were classified into four types of expressway
components (thru traffic lanes, merging lanes, interchanges, on/off ramps) and two autonomous vehicle driving
states (remaining in same lane, changing lanes), with a total of 32 scenarios being defined according to the positions
and movements of surrounding vehicles.
Shown below are four scenarios in which an autonomous vehicle in the thru traffic lane continues driving inthe same lane. The PCM created by ITARDA is based on the scenario indicated by the image shown in the red box
below in which another vehicle abruptly changes lanes in front of an autonomous vehicle that remains traveling in
the thru traffic lane. (Fig. 10)
Cutting inby other vehicle

Cutting outby other vehicle

Acceleration
by other vehicle

Deceleration
by other vehicle

Fig. 10 - Example scenario in which an autonomous vehicle in the thru traffic lane of an expressway remains in the same lane

Furthermore, in this project, observation values for various parameters in the scenario that do not lead to a
traffic accident on the actual expressway will be collected by actually driving on expressways and recording thestates of other peripherally traveling vehicles. These values will then be used to define the foreseeable range ofcontrol for autonomous vehicles.
The figure to the right shows the actual parameters for
ITARDA’s PCM for a traffic accident caused by an abrupt
lane change. In this project, these parameters are used toOther vehicle
speed

Othervehicle

Inter-vehiculardistance
Autonomousvehicleaccording to the speed of the autonomous vehicle. Byplotting parameter values for ITARDA’s PCM in each

Lateralposition

graphs below showing examples of parameters organized

Lateralspeed

plot the foreseeable range and observation value, with

Own vehicle speed

Fig. 11 - Parameters for ITARDA’s PCM

graph, the validity of the assumed scenario and parameters
could be confirmed as they were included in the foreseeable range. (Fig. 12)

Own vehicle speed (km/h)

Lateral speed (m/s)

Lateral position (m)Observationvalue(n=790)

Own vehicle speed (m)Relative speed (m/s)

Foreseeable range

Own vehicle speed (km/h)

Own vehicle speed (km/h)

Own vehicle speed (km/h)

Fig. 12 - Foreseeable range/observation value and PCM plotting for each parameter (abrupt lane change)

5

ITARDA 23th Work Shop Report

Currently, only one ITARDA’s PCM is being employed on an expressway, with further increases expected inthe future.

5.

Conclusion
Below is a summary of the contents covered up to the previous section.
Through technical collaborations with our longtime exchange partner, VUFO, ITARDA was able to createa PCM as developed by VUFO using our micro data.
ITARDA’S PCM is being used as part of the SAKURA project to provide data effective in showing thevalidity of assumed scenarios and we expect that the number of PCMs using our micro data will continue
to increase.
We plan to continue and strengthen our collaborations with VUFO to enhance the efficiency of the PCM
creation process.

<References/Sources>(1) VUFO、2019 年、Pre-Crash-Matrix (PCM)

Format Specification v5.0(2) 経済産業省、2020 年、⾃動⾛⾏ビジネス検討会「⾃動⾛⾏の実現に向けた取組報告と⽅針」ｖ4.0(End)6