ASSESS D1.2 - ASSESS Project

Transcription

EUROPEAN COMMISSION
DG RTD
SEVENTH FRAMEWORK PROGRAMME
THEME 7
TRANSPORT - SST
SST.2008.4.1.1: Safety and security by design
GA No. 233942
ASSESS
Assessment of Integrated Vehicle Safety Systems for improved
vehicle safety
Deliverable No.
D1.2
Deliverable Title
Specifications for scenario definitions
Dissemination level
Public
Written By
Approved by
Marcus Wisch (BAST), Helen Fagerlind
(CHALMERS), Lisa Sulzberger (BOSCH), Mike
McCarthy (TRL), Mathieu Roynard (CEESAR),
Wesley Hulshof (TRL), Jean-Francois Boissou
(PSA), Swen Schaub (TRW), Ines Heinig
(CHALMERS), Matias Viström (CHALMERS)
Andreas Lüdeke (BASt), Thomas Unselt (DAI),
Carmen Rodarius (TNO), Andrés Aparicio (IDIADA)
Paul Lemmen (FTSS)
Accepted by EC
2011-03-11
Issue date
2010-12-21
Checked by
2010-10-28
2010-11-01
2010-12-21
ASSESS D1.2
Executive summary
The overall purpose of the ASSESS project is to develop robust test and assessment
methods and associated tools for integrated vehicle safety systems encompassing
assessment of driver behaviour, pre-crash and crash performance. ASSESS is focussing on
currently available pre-crash sensing systems (forward-directed collision avoidance and
mitigation systems) for passenger cars. It is intended that the information and methodology
developed will be applicable to a wider range of integrated vehicle safety systems.
The principle of the accident analysis was that it considered the accidents and casualties
independent of the detailed specifications of safety systems considered as examples for the
testing phases of the ASSESS project. The analysis therefore aimed to define the accident
scenarios based on frontal real world accident problems, not the accidents which could be
addressed by a specific safety system.
This report describes the work carried out in Task 1.2 which further defined the preliminary
accident scenarios identified in Task 1.1 and provides more detailed information on the
accident conditions. Variables such as driving speed, impact speed, crash offset, etc. were
examined in order to inform the development of test scenarios which represent real world
accident conditions. Other accident parameters such as environmental conditions were also
analysed; this information will be used in the WP2 socio-economic assessment to inform on
the proportion of the casualty groups which can be influenced by the system. Furthermore,
two rankings are examined to show the importance within the preliminary test scenarios for
initial collisions exclusively with four-wheelers and in addition for initial collisions with at least
two-wheeled vehicles.
The driving speed is chosen as core parameter for the assessment of the preliminary test
scenarios created by Work Package 4. With the help of the according analyses and for the
assessment advices could be given for the test scenarios.
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ASSESS D1.2
Abbreviations and definitions
ASSESS
BASt
Bosch
CDC
Chalmers
CEESAR
EDA
EuroFOT
FOT
GDV
GIDAS
I(V)SS
NDS
ONISR
OTS
PSA
PTW
SeMiFOT
STATS19
STRADA
SV
TRL
TRW
TTC
TV
VRU
EU-project: Assessment of Integrated Vehicle Safety Systems for
improved vehicle safety
Federal Highway Research Institute, Germany
Robert Bosch GmbH
Collision deformation classification
Chalmers University of Technology, Sweden
European Centre Studies of Safety and Risk Analysis, France
In-depth accident causation survey, France
EU-project: European Field Operational Test
Field Operational Test
German Insurance Association
German In-Depth-Accident Study
Integrated (Vehicle) Safety Systems
Naturalistic Driving Studies
National Interministerial Road Safety Observatory,
National accident database, France
On-The-Spot accident research, Great Britain
PSA Peugeot Citroën, France
Powered two-wheelers
The Sweden Michigan Field Operational Test, Sweden and USA
National accident data for reported road casualties in Great Britain
Swedish Traffic Accident Data Acquisition
Subject vehicle
Transport Research Laboratory, United Kingdom
TRW Automotive, Germany
Time to collision
Target vehicle
Vulnerable Road Users (pedestrians, bicycles, powered two-wheelers)
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ASSESS D1.2
Table of content
Executive summary ............................................................................................................... 2
Abbreviations and definitions ................................................................................................. 3
Table of content..................................................................................................................... 4
1
2
3
Introduction .................................................................................................................... 7
1.1
The EU project ASSESS ......................................................................................... 7
1.2
Background on previous work in Work Package 1 of ASSESS................................ 7
1.3
Objectives ............................................................................................................... 8
Compilation of data needs .............................................................................................. 9
2.1
Requirements for socio-economic assessment (WP2) ............................................ 9
2.2
Requirements for driver behaviour evaluation (WP3) .............................................. 9
2.3
Requirements for pre-crash system performance evaluation (WP4) ........................ 9
Extended National Database Analysis on General Level ...............................................10
3.1
Data sample ...........................................................................................................10
3.2
Casualty severity definitions ...................................................................................10
3.3
Extended national accident sample ........................................................................10
3.3.1
German Statistics ............................................................................................10
3.3.2
ONISR (France) ..............................................................................................11
3.3.3
Statistics Austria..............................................................................................11
3.3.4
STRADA (Sweden) .........................................................................................12
3.4
4
Results and discussion of extended national database analysis on general level ...13
3.4.1
Total casualties in the accident (by accident type)...........................................13
3.4.2
Ranking of accident scenarios.........................................................................16
Methodology and approach of accident analyses ..........................................................18
4.1
Descriptions of accident databases and naturalistic studies ...................................18
4.1.1
National databases .........................................................................................18
4.1.2
In-depth databases .........................................................................................18
4.1.3
Naturalistic Driving Study (NDS) .....................................................................20
4.1.4
Naturalistic Field Operational Test (N-FOT) ....................................................20
4.2
General data query ................................................................................................21
4.3
Preliminary test scenarios and accident types ........................................................22
4.3.1
Overview of preliminary test scenarios and assigned accident type codes ......22
4.3.2
Merging of accidents to accident type codes ...................................................25
4.3.3
Assigning and merging of accidents into test scenarios...................................25
4.3.4
Division into test scenario sublevels ................................................................27
4.4
Parameters, attributes and limitations ....................................................................28
4.4.1
Recommended parameters for detailed analysis .............................................28
4.4.2
Availability of selected parameters ..................................................................29
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ASSESS D1.2
5
Analyses and results of the available databases ...........................................................31
5.1
5.1.1
National databases (ONISR, STATS19) ..........................................................31
5.1.2
In-depth databases (GIDAS, EDA, OTS) .........................................................32
5.1.3
Naturalistic Field Operational Test (SeMiFOT) ................................................35
5.1.4
Naturalistic Driving Study (100-car-study) .......................................................37
5.2
Subject vehicle .......................................................................................................41
5.2.1
Driving speed ..................................................................................................41
5.2.2
Impact speed ..................................................................................................44
5.2.3
Overlap in crash situation ................................................................................46
5.2.4
Vehicle age .....................................................................................................48
5.3
Target vehicle.........................................................................................................48
5.3.1
Driving speed ..................................................................................................49
5.3.2
First impact location ........................................................................................50
5.3.3
Vehicle types ...................................................................................................52
5.4
Driver related information .......................................................................................55
5.4.1
Driver’s age .....................................................................................................55
5.4.2
Manoeuvres ....................................................................................................56
5.4.3
Distraction / Inattention....................................................................................57
5.4.4
Driver’s impairment .........................................................................................58
5.5
Environmental conditions .......................................................................................60
5.5.1
Weather conditions .........................................................................................60
5.5.2
Road surface conditions ..................................................................................61
5.5.3
Light conditions ...............................................................................................62
5.5.4
Daylight ...........................................................................................................63
5.6
6
Datasets .................................................................................................................31
Ranking of test scenarios (R1) ...............................................................................65
Assessment of proposed test scenarios ........................................................................67
6.1
Specifications of proposed Work Package 4 tests ..................................................67
6.2
Verification of parameters proposed for test scenarios ...........................................68
6.2.1
Test scenario A (rear-end collision) .................................................................69
6.2.2
Test scenario B (intersection conflict) ..............................................................73
6.2.3
Test scenario C (oncoming traffic collision) .....................................................75
6.2.4
Test scenario D (cut-in conflict) .......................................................................77
6.3
Overall ranking (R2) ...............................................................................................80
6.3.1
Establishment of ranking R2............................................................................80
6.3.2
Results of ranking R2 ......................................................................................82
6.4
Specification of test scenarios based on weighted data..........................................84
7
Discussion .....................................................................................................................85
8
Conclusions...................................................................................................................86
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9
Recommendations ........................................................................................................88
10
References ................................................................................................................89
11
Risk register...............................................................................................................90
12
Appendices ................................................................................................................91
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ASSESS D1.2
1 Introduction
1.1
The EU project ASSESS
The overall purpose of the ASSESS project [1] is to develop robust test and assessment
methods and associated tools for integrated vehicle safety systems (IVSS), encompassing
assessment of driver behaviour, pre-crash and crash performance. ASSESS is focussing on
available pre-crash sensing systems (forward-looking collision avoidance and mitigation
systems) fitted to current passenger cars. It is intended that the information and final
assessment methodology developed will be applicable to a wider range of integrated vehicle
safety systems.
Methodologies and procedures will be developed for driver behaviour evaluation (WP3), precrash system performance evaluation (WP4), crash performance evaluation and integrated
performance assessment (WP5) and a socio economic assessment (WP2).
Task 1.2 defines further the detailed specification of the accident scenarios from Task 1.1
and provides relevant information on test scenario parameters from Task 4.1. Therefore, the
accident parameters are investigated explicitly using detailed analyses of the available field
data. Test variables identified previously are quantified so that the driving speed, impact
speed, crash overlap, etc. can be finalised in the technical activities in Work Packages 3-5.
Additionally, available field data is prepared for the socio-economic analysis in Work
Package 2. Field Operational Test data (SeMiFOT, 100-car-study), national representative
accident data (England, Sweden, France, Germany and Austria) and in-depth accident
databases (GIDAS, OTS, EDA) are applied.
1.2
Background on previous work in Work Package 1 of ASSESS
The first step in the project is the definition of casualty relevant accident scenarios.
Therefore, test scenarios are developed based on accident types which currently result in the
greatest injury outcome that is measured by a combination of casualty severity and casualty
frequency.
Therefore, the first task in Work Package 1 was to examine how relevant scenarios had been
developed by previous projects and to obtain and analyse European accident data to define
preliminary accident scenarios which could then be taken by Work Packages 3 (Driver
Behavioural evaluation) and 4 (Pre-crash assessment) as the initial accident types on which
to base further analysis.
The principle of the accident analysis in Task 1.1 [2] was the consideration of accidents and
casualties independent of the safety system - so the real world accident problem. This is to
ensure that the procedures developed for ASSESS are focussed on the priority casualty
problems (system validation), not simply to develop assessment methodologies to
demonstrate the system effectiveness in design conditions (system verification).
The review of previous projects provided a large overview of activities concerning the
research in terms of integrated safety. The most relevant assessment methods for ASSESS
were identified as the approaches defined by APROSYS and PReVAL. Unfortunately, only
some of the previous projects performed detailed accident analysis which could be directly
transferred to ASSESS. Some of the work that was performed within eIMPACT, TRACE, and
eVALUE could be used for an overview of the accidents on the EU level.
In general pre-crash sensing systems may combine a wide range of functionalities. For
example, a system may or may not include brake assist, driver warning and/or restraint
activation. Activities in ASSESS are based on two currently “on the market” systems that
monitor the environment in front of the car and provide driver warnings, automatic brake
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ASSESS D1.2
activation and pre-crash restraint optimisation. Beyond these two examples, tests scenarios
developed can be used to assess the performance of any system vs. the priority cases
identified in the project.
The principle of the accident analysis was that it considered the accidents and casualties
independent of the detailed specifications of safety systems considered in ASSESS. The
analysis therefore aimed to define the preliminary accident scenarios based on frontal real
world accident problems, not the accidents which could be addressed by a specific safety
system.
Analysis was completed for a range of accident databases, including those which were
nationally representative (STATS19 and STRADA) and in-depth sources which provided
more detailed parameters to characterise the accident types (GIDAS and OTS). A common
analysis method was developed in order to compare the data from these sources with regard
to their different contents and organizations. The majority of the data was aligned in such a
way as to allow a comparison between these databases.
The results from the analyses were further ranked by valuations reflecting the cost assigned
to fatal, serious and slight casualties (injury costs = casualty frequency x casualty costs
weighting factors). This enabled the “total casualty outcome” of the accidents to be
assessed, thereby adjusting for accident types which occur less frequently but result in
greater number of more seriously injured casualties (and vice versa).
After a comparison between the data sources, the ranking of the preliminary accident
scenarios from the analysis were [3]:
Rank
1
2
3
4
Accident type
Type 1a: Driving accident - single vehicle
Type 6: Accidents in longitudinal traffic (6a and 6b included)
Type 2&3: Accidents with turning vehicle(s) or crossing paths in junction
Type 4: Accidents involving pedestrians
The analyses considered all type of road accidents involving at least one vehicle. However,
for the activities of Work Package 4 (pre-crash performance evaluation), no single vehicle
driving accidents (Type 1a) were considered as they describe mainly loss of control
situations which are out of the scope of the systems to be tested in this project. Accidents
involving vulnerable road users, such as pedestrians (Type 4) and cyclists, are not included
in the objectives of the ASSESS project.
The analysis has confirmed that the systems selected within ASSESS are relevant with
respect to the current casualty problems, with Type 6 and Type 2&3 accidents being relevant
to the ASSESS pre-crash systems.
From these first results, preliminary test scenarios were created in Work Package 4 [4] which
also considered information from international projects and organisations such as NHTSA.
Approximately 56 test scenarios have been created to form the basis of preliminary test
scenarios. This report examines detailed accident data to validate or provide evidence with
which to modify these test scenarios.
1.3
Objectives
The objective of this report is to confirm and/or refine the preliminary test scenarios proposed
by Work Package 4 on the basis of real-world road traffic accident analyses. The final test
scenarios shall be included in the test protocol.
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ASSESS D1.2
2 Compilation of data needs
2.1
Requirements for socio-economic assessment (WP2)
In ASSESS, Work Package 2 will deliver an integrated methodology framework for the socioeconomic impact of safety systems and will also provide a proposal on a methodological
approach to evaluate the legal and liability effects of Integrated Safety Systems (ISS).
The data needed for this work is:
• Frequencies of accident types (first-, two-digits analyses)
• For each accident type (two-digits) absolute numbers / share of:
Casualties (fatalities, seriously and slightly injured) per accident type
Conflict situation (vehicle speed, vehicle impact offset, impact angle,
crash avoidance manoeuvres)
Road, weather and light conditions
Accident seriousness (most harmful event)
Driver state / behaviour (drugs, alcohol, distraction, fatigue)
Age of the driver
Age of the vehicle
Road type
2.2
Requirements for driver behaviour evaluation (WP3)
In ASSESS, Work Package 3 will deliver a draft protocol for the assessment of behavioural
aspects, including the results of experimental studies.
Data needed for this work is:
• Finding one specific scenario category (for practical reasons), that is
producible and will be implemented in driving simulator test and track test.
(At the moment this is a rear-end collision on a motorway.)
• Field Operational Test (FOT) data for driver behaviour studies
• Catalogue of realistic situations that would startle or alarm drivers (e.g.
crosswind, rock fall, sounds etc.)
2.3
Requirements for pre-crash system performance evaluation (WP4)
In ASSESS, Work Package 4 will deliver a final proposed test and assessment protocol for
the pre-crash assessment of integrated safety systems. This test protocol will be suitable for
regulatory testing and consumer assessment.
In the detailed accident analysis the following actions shall be taken in order to ensure a
reasonable and realistic test programme:
• Confirmation of the relevance of the proposed set of preliminary test scenarios
(D4.1) and specifications in Europe
• Detailed accident information for Type 2&3 and Type 6 scenarios presented in
D1.1
• Ranking of the chosen scenarios and manoeuvres based on frequencies and
injury costs in real world accidents using the two-digit accident type coding
• Verification and/or update of the test scenarios in term of vehicle speeds,
impact angles etc.
• Proposal for reduction of the number of scenarios / manoeuvres to
approximately 50 tests.
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ASSESS D1.2
3 Extended National Database Analysis on General Level
In Deliverable 1.1 [2] of the ASSESS project the general analysis of national databases
Sweden and the UK were presented. These are “high” performing countries in terms of road
safety according to previous research [5]. However, to gain a larger view of the European
situation it was decided to add France and Austria which have an “intermediate” road safety
performance. This chapter presents additional analysis from these two countries and
additional national data from Germany for comparison with the analysis already presented in
Deliverable 1.1. This analysis is performed to confirm that results in term of accident scenario
relevance are relevant for a larger population of Europe. However in Deliverable 1.1 the
ranking of the scenarios was carried out in comparison with the two in-depth databases
GIDAS and OTS. In the analysis below the national database from Germany, France, Austria
and Sweden is taken into account. The national database of the UK (STATS19) is not taken
into account since it was difficult to transfer the accident types into selected accident
scenarios in ASSESS.
3.1
Data sample
The data selected for analysis was injury accidents which involved at least one passenger
car divided into the accident types described in section 4.2. However, some of the national
databases have constraints which are mentioned in the separate database sections below.
3.2
Casualty severity definitions
The casualty severity definitions used for the analysis were those defined by the respective
databases. The definitions of the databases are presented in Table 1, below.
Table 1: Casualty severity definitions
Database
Germany
France
ONISR
Statistics
Austria
Sweden
STRADA
3.3
3.3.1
Fatal
All persons who
died within 30 days
as a result of the
accident.
All persons who
died within 30 days
as a result of the
accident.
Death within 30
days of a road
accident
Death within 30
days of a road
accident
Severe
All persons who were immediately
taken to hospital for inpatient treatment
(of at least 24 hours)
All persons who were immediately
taken to hospital for inpatient treatment
(of at least 24 hours)
According to the police at the accident
scene
According to the police at the accident
scene
Slight
All other injured
persons (hospitalised
less than 24 hours or
not hospitalised)
All other injured
persons (hospitalised
less than 24 hours or
not hospitalised)
According to the
police at the accident
scene
According to the
police at the accident
scene
Extended national accident sample
German Statistics
Survey records for the statistics of road traffic accidents are the copies of the standard traffic
accident notices as used for the entire Federal Republic which are completed by the police
officers attending the accident. After its transfer to data recording media, the information
included in the accident notices is tabulated on a monthly and annual basis at the statistical
offices at the states (‘Länder’) according to a standard programme for the entire Federal
Republic. The state results are compiled to the federal result.
This analysis includes 1,399,353 people in 1,045,410 accidents involving at least one car
from the period 2005 to 2008 inclusive.
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ASSESS D1.2
Analysis constraints:
1) Accident types 6a&6b (Accidents in longitudinal traffic) as well as 7a&7b (Other
accidents) have been merged since it is not possible to assign all German road traffic
accidents to the predetermined accident type groups.
3.3.2
ONISR (France)
ONISR (National Interministerial Observatory of Road Safety) is the French national accident
database comprising details of accidents and casualties recorded by the Police and cover all
road accidents in France which involve personal injury. For the purposes of this analysis,
data from the period 2005 to 2008 inclusive was selected.
1) The ONISR database is not detailed enough to well understand accidents involving
more than two vehicles. As a consequence, the French analysis is based on
accidents with maximum two vehicles involved.
2) One group was assigned “other accident type” since these accidents were too difficult
to assign to any accident type because the initial manoeuvre is unknown for one
vehicle or both. Some groups, for example pedestrian accidents, can be assigned to
Type 4 even if the manoeuvre is unknown. In Figure 1 these are included in the
numbers but in Table 4 to Table 5 “other accident type” is removed from the sample.
3) It was difficult to distinguish between Type 1b and the type 6 group why type 1b have
a high frequency and type 6 have a low one compared to other countries.
This analysis includes 511 962 people in 226 741 accidents involving at least one car and
maximum two vehicles from the period 2005 to 2008 inclusive (see Figure 1).
All accidents
2005-2008
Accidents involving at
least one car
Accidents involving at
least one car (maximum
2 vehicles involved)
324 079
260 668
226 741
accidents
accidents
accidents
968 488
Vehicles
504 671
vehicles
410 868
vehicles
735 353
people
661 124
people
511 962
people
Figure 1: ONISR accident data sample (2005-2008), numbers are including the group of other accident
type.
3.3.3
Statistics Austria
In Austria road traffic accidents with personal injuries are collected by police and reports,
commonly known as ÖSTAT accident reports, are filled in and provided to Statistics Austrian.
These data can be evaluated according to different types of accidents (as defined in Austria).
The Austrian accident type catalogue comprises ten main groups with distinct sub-groups
which describe the accident. In total, about 105 different accident types can be distinguished.
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ASSESS D1.2
Accidents are described as incidences which occur on public roads and include at least one
injured person. For the purposes of this analysis, data from the period 2002 to 2009 inclusive
was selected.
According to the accident type codes selected in ASSESS [3] the Austrian code consists of 3
digits, whereby the first digit represents the overall type (main type). The first and second
digits together represent the subtype and therefore, all digits completely describe the
accident.
1) Transforming the Austrian national data to the SafetyNet main types is feasible even
without detailed knowledge of the accident scene. However, Austrian national
statistics might not be easily transferred to SafetyNet accident subgroups. Difficulties
would arise, if all three code digits of the Austrian national statistics had to be
assigned to the SafetyNet coding scheme because of missing information regarding
the accident scene.
2) The national data provided by the project partner TU Graz (TUG) contain numbers of
‘unknown injured people’ that means ‘not known injury degree’. In other words, those
people had been injured but could not be assigned certainly either to be a slightly or
severely injured person. Fatalities are coded as fatal and gathered completely, since
these figures must be reported to the ministry. Thus, the numbers of ‘unknown injured
people’ were divided proportionally according to the known numbers of slightly and
severely injured people per accident type group and added finally. The corrected data
is shown in Table 6 to Table 7.
This analysis includes 546 972 people in 267 870 accidents involving at least one car from
the period 2002 to 2009 inclusive (see Figure 2).
All accidents
2002-2009
Accidents involving
at least one car
326 565
accidents
267 870
accidents
612 767
vehicles
407 494
cars involved
709 126
people
615 497
people
Figure 2: Statistics Austria accident data sample (2002-2009)
3.3.4
STRADA (Sweden)
The Swedish Traffic Accident Data Acquisition (STRADA) is an information system for road
accidents with personal injuries (see Figure 3). The system includes information from the
police and the emergency hospitals (71%, June 2009). Since 2003 the Swedish official
statistics are based on the police records stored in STRADA. The police report road
accidents involving at least one moving vehicle and a person sustained an injury. For this
analysis the national statistics which are compiled from the police records was used.
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ASSESS D1.2
1) The recoding of accident types in STRADA to the accident types chosen for the
ASSESS accident analysis is described in [2].
2) The national data provided contain numbers of ‘unknown injured people’ (n= 14 927).
These people have been divided proportionally to the severe, slight and uninjured
groups. The counts of fatally injured people are considered to be correct in Sweden
since these numbers each year are compared to the register of death causation from
the National Board of Health and Welfare. The corrected data is shown in Table 8 to
Table 9.
This analysis includes 137 936 people in 61 814 accidents involving at least one car from the
period 2005 to 2008 inclusive (see Figure 3).
All accidents 20052008
Accidents involving
at least one car
74 974
accidents
61 814
accidents
131 914
vehicles
87 555
cars involved
137 936
people
Figure 3: STRADA accident data sample (2005-2008)
3.4
Results and discussion of extended national database analysis on general
level
The purpose of the first analysis in WP1.1 was to rank the most frequent and severe accident
scenarios on a general level. For comparing the different datasets the following steps were
taken (see [2] for details on accident types and injury cost weighting factors):
1.
2.
3.
Accident type frequency according to the one digit code
Injury severity for all people in all involved vehicles
Weight of the accident frequency and injury severity by injury cost weighting factors
3.4.1
Total casualties in the accident (by accident type)
A comparison was made of the casualties in the accident, in order to find the most frequent
accident types based on injury severity. In Table 2 to Table 9 the count and percentage1 of
each dataset is presented. The figures in percentage are the relative number of the total sum
of each dataset.
In Germany the highest frequency of deaths happen in single vehicle accidents followed by
accidents in longitudinal traffic and accidents in junctions. For both severe and slight injuries
the accidents in junctions are the most frequent type.
1
Fields are marked orange in case of percentages between 3% - 10% and marked red for percentages >=10%.
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ASSESS D1.2
Table 2: Count of injury severities of involved persons by accident type in Germany (1,399,353 involved
persons in injury accidents involving at least one car 2005-2008)
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
fatal
s evere
s li ght
uninjured
Type 1a:
Type 1b:
Drivi ng
D ri ving
ac c ident - ac c i dent s i ngle
mul ti ple
vehi c l e
vehic les
4067
2368
41113
20002
106810
69664
Ty pe 2&3:
Ac c ident s wi th
t urni ng vehic l e(s )
or c ros s i ng pat hs
i n junc ti on (w/o
pedes t.)
2622
76234
487003
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
1380
19888
42650
Type 5:
Ac c i dents
wit h
park ed
vehi c les
83
4389
33902
Type 6a&6b:
Ac c ident s i n
l ongi tudinal
t raf fi c
3479
40443
351534
Type
7a&7b
Other
ac c i dent
905
14924
75893
Table 3: Percentage of injury severities of involved persons by accident type in Germany (1,399,353
involved persons in injury accidents involving at least one car 2005-2008)
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
fatal
s evere
s li ght
uninjured
Type 1a:
Type 1b:
D ri ving
Drivi ng
mul ti ple
vehi c l e
vehic les
0.3%
0.2%
2.9%
1.4%
7.6%
5.0%
0.0%
0.0%
Ty pe 2&3:
Ac c ident s wi th
i n junc ti on (w/o
pedes t.)
0.2%
5.4%
34.8%
0.0%
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
0.1%
1.4%
3.0%
0.0%
Type 5:
Ac c i dents
wit h
park ed
vehi c les
0.0%
0.3%
2.4%
0.0%
Type 6a&6b:
Ac c ident s i n
l ongi tudinal
t raf fi c
0.2%
2.9%
25.1%
0.0%
Type
7a&7b
Other
ac c i dent
0.1%
1.1%
5.4%
0.0%
In France, accidents where it was not possible to assign an accident type according to the
scheme used in ASSESS were removed. It is not expected that this influences meaningfully
the results. Finally, 444,024 people remain which were involved in accidents involving at
least one car and maximum two vehicles. The highest fatality rate based on frequency of
accident and injuries is found in single vehicle accidents and similar to Germany accidents in
junctions have the highest frequency in the severe and slight injury group.
Table 4: Count of injury severities of involved persons by accident type in France (440,024 involved
persons in injury accidents involving at least one car and maximum two vehicles, 2005-2008)
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
fatal
s evere
s li ght
uninjured
Type 1a:
Type 1b:
D ri ving
Drivi ng
mul ti ple
vehi c l e
vehic les
4656
2995
21826
16731
20151
15998
8211
19631
Ty pe 2&3:
Ac c ident s wi th
i n junc ti on (w/o
pedes t.)
1689
28967
62318
78262
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
1123
13258
18542
32360
Type 5:
Ac c i dents
wit h
park ed
vehi c les
56
961
2614
3478
Type 6a:
Ac c ident s i n
l ongi tudinal
t raf fi c s ame
direc tion
478
7034
20794
25200
Ty pe 6b:
Ac c ident s i n
l ongit udinal
t raf fic oppos it e
di rec tion
506
4327
8570
10630
Type 7a:
Type 7b:
Other
Other
ac c i dent - ac c ident s ingl e
mult ipl e
vehi c le
vehic l es
20
101
208
1533
185
2825
73
3713
Table 5: Percentage of injury severities of involved persons by accident type in France (440,024 involved
persons in injury accidents involving at least one car and maximum two vehicles, 2005-2008)
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
fatal
s evere
s li ght
uninjured
Type 1a:
Type 1b:
D ri ving
Drivi ng
mul ti ple
vehi c l e
vehic les
1.1%
0.7%
5.0%
3.8%
4.6%
3.6%
1.9%
4.5%
Ty pe 2&3:
Ac c ident s wi th
i n junc ti on (w/o
pedes t.)
0.4%
6.6%
14.2%
17.8%
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
0.3%
3.0%
4.2%
7.4%
Type 5:
Ac c i dents
wit h
park ed
vehi c les
0.0%
0.2%
0.6%
0.8%
Type 6a:
Ac c ident s i n
l ongi tudinal
t raf fi c s ame
direc tion
0.1%
1.6%
4.7%
5.7%
Ty pe 6b:
Ac c ident s i n
l ongit udinal
di rec tion
0.1%
1.0%
1.9%
2.4%
Type 7a:
Type 7b:
Other
Other
mult ipl e
vehi c le
vehic l es
0.0%
0.0%
0.0%
0.3%
0.0%
0.6%
0.0%
0.8%
The Austrian numbers show similar result as Germany and France which means that the
highest frequency of fatalities is involved in single vehicle accidents while accidents in
junctions have the highest frequency concerning serious and slight injuries.
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ASSESS D1.2
Table 6: Count of injury severities of involved persons by accident type in Austria (615,497 involved
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
fatal
Type 1a:
Type 1b:
Drivi ng
D ri ving
mul ti ple
vehi c l e
vehic les
1561
12
Ty pe 2&3:
Ac c ident s wi th
i n junc ti on (w/o
pedes t.)
567
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
652
Type 5:
Ac c i dents
wit h
park ed
vehi c les
22
Type 6a:
Ac c ident s i n
l ongi tudinal
t raf fi c s ame
direc tion
383
Ty pe 6b:
Ac c ident s i n
l ongit udinal
di rec tion
1438
Type 7a:
Type 7b:
Other
Other
mult ipl e
vehi c le
vehic l es
14
129
s evere
10283
191
14212
6734
459
4556
9513
99
794
s li ght
41614
1288
119328
21583
3341
87178
31707
573
5973
4363
1133
101930
27776
3386
86176
21030
94
5405
uninjured
Table 7: Percentage of injury severities of involved persons by accident type in Austria (615,497 involved
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
Type 1a:
Type 1b:
Drivi ng
D ri ving
mul ti ple
vehi c l e
vehic les
Ty pe 2&3:
Ac c ident s wi th
i n junc ti on (w/o
pedes t.)
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
Type 5:
Ac c i dents
wit h
park ed
vehi c les
Type 6a:
Ac c ident s i n
l ongi tudinal
t raf fi c s ame
direc tion
Ty pe 6b:
Ac c ident s i n
l ongit udinal
di rec tion
Type 7a:
Type 7b:
Other
Other
mult ipl e
vehi c le
vehic l es
fatal
0.3%
0.0%
0.1%
0.1%
0.0%
0.1%
0.2%
0.0%
0.0%
s evere
1.7%
0.0%
2.3%
1.1%
0.1%
0.7%
1.5%
0.0%
0.1%
s li ght
6.8%
0.2%
19.4%
3.5%
0.5%
14.2%
5.2%
0.1%
1.0%
uninjured
0.7%
0.2%
16.6%
4.5%
0.6%
14.0%
3.4%
0.0%
0.9%
Sweden confirms the previous reported figures concerning the frequency of fatalities and
slight injuries. The numbers show that single vehicle accidents also have the highest
frequency in the single vehicle accidents but numbers for accidents in junctions are similar.
Table 8: Count of injury severities of involved persons by accident type in Sweden (137,936 involved
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
fatal
Type 1a:
Type 1b:
D ri ving
Drivi ng
mul ti ple
vehi c l e
vehic les
441
Ty pe 2&3:
Ac c ident s wi th
i n junc ti on (w/o
pedes t.)
216
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
149
Type 5:
Ac c i dents
wit h
park ed
vehi c les
13
Type 6a:
Ac c ident s i n
l ongi tudinal
t raf fi c s ame
direc tion
43
Ty pe 6b:
Ac c ident s i n
l ongit udinal
di rec tion
433
Type 7a:
Type 7b:
Other
Other
mult ipl e
vehi c le
vehic l es
74
s evere
4049
3739
1291
212
1762
1900
926
s li ght
20187
26369
5114
1755
21038
6397
7262
1168
12433
3442
615
11608
2440
2861
uninjured
Table 9: Percentage of injury severities of involved persons by accident type in Sweden (137,936 involved
Injury
s everity of
invol ved
pers ons in
ac c i dents
wi th injuri es
Type 1a:
Type 1b:
D ri ving
Drivi ng
mul ti ple
vehi c l e
vehic les
Ty pe 2&3:
Ac c ident s wi th
i n junc ti on (w/o
pedes t.)
Type 4:
Ac c ident s
i nvol ving
pedes tria
ns
Type 5:
Ac c i dents
wit h
park ed
vehi c les
Type 6a:
Ac c ident s i n
l ongi tudinal
t raf fi c s ame
direc tion
Ty pe 6b:
Ac c ident s i n
l ongit udinal
di rec tion
Type 7a:
Type 7b:
Other
Other
mult ipl e
vehi c le
vehic l es
fatal
0.3%
0.2%
0.1%
0.0%
0.0%
0.3%
s evere
2.9%
2.7%
0.9%
0.2%
1.3%
1.4%
0.7%
s li ght
14.6%
19.1%
3.7%
1.3%
15.3%
4.6%
5.3%
0.8%
9.0%
2.5%
0.4%
8.4%
1.8%
2.1%
uninjured
0.1%
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ASSESS D1.2
3.4.2
Ranking of accident scenarios
The ranking of the accident scenarios is performed by applying three weighting factors using
injury costs; one for each personal injury severity. That is for fatalities x 1 + severely injured
person x 0.11 + slightly injured person x 0.011 [2]. After the calculation the frequency of each
scenario is used to rank the scenarios based on the population of the country (see Table 10)
which finally presents a weighted average of the ranking; (DE1a x 0.50)+(FR1a x
0.39)+(AT1a x 0.05)+(SE1a x 0.06).
Table 10: Values used to obtain a weighted average of the frequency of accident types for all countries.
Germany
France
Austria
Sweden
TOTAL
Population in Mio.
82.3
64.3
8.3
9.2
164.1
Weight
0.50
0.39
0.05
0.06
1
In the analysis of the different sources there were some difficulties assigning accidents to
each type, therefore Type 6 and 7 respectively were merged. Concerning Type 1b
Table 11: Frequency and ranking of the accident types weighted by injury costs for injury accidents
Dat abas e
Germany
n=1,399,353
France
n=440,024
Austria
n=615,497
Sweden
n=137,936
Weighted average
Injury
s ev erity of
inv olv ed
persons in
acc idents
wit h injuries
frequency
ranking
frequency
ranking
frequency
ranking
frequency
ranking
frequency
ranking
Ty pe 1a:
Type 1b:
Driv ing
Driving
ac cident - acc ident single
multiple
vehic le
v ehicles
19%
10%
3
4
31%
21%
1
3
24%
0.4%
3
7
29%
2
24%
13%
2
4
Ty pe 2&3:
Acc idents wit h
turning vehicle(s)
or c ross ing paths
in junction (w/o
pedes t.)
32%
1
23%
2
26%
2
24%
3
28%
1
Ty pe 4:
Acc idents
inv olving
pedes tria
ns
8%
5
12%
4
12%
4
9%
4
10%
5
Type 5:
Ac cidents
with
park ed
v ehicles
2%
7
1%
7
1%
6
1%
6
1%
7
Ty pe 6a&6b:
Ac c idents in
longitudinal
traf fic
23%
2
11%
5
35%
1
31%
1
19%
3
Type 7a&b:
Other
ac cident single &
multiple
vehicle
7%
6
1%
6
2%
5
7%
5
4%
6
The ranking of the scenarios differ to the one presented in Deliverable 1.1 [2] where the
following ranking was presented based on both national and in-depth data.
Rank
1
2
3
4
Accident type
Type 1a: Driving accident - single vehicle
Type 6: Accidents in longitudinal traffic (6a and 6b included)
Type 2&3: Accidents with turning vehicle(s) or crossing paths in junction
Type 4: Accidents involving pedestrians
The top three scenarios are the same in the ranking presented in Table 11 but the order is
different with accidents in junction ranked highest followed by single vehicle accidents and
accidents in longitudinal traffic.
The accident scenario description chosen for ASSESS [cp. 3] bases on the first conflict
which led to the accident rather than the final accident configuration. All national databases
do not use this approach in assigning the accident types. For Type 1b this issue becomes
very obvious. A driving accident is explained by a driver who loses control over the vehicle
which makes it a driving accident Type 1. The outcome of this loss of control can either be
e.g. a run off the road which assign the accident 1a – single vehicle accident or e.g. the
vehicle collides with another vehicle either in same or opposite directions which assign the
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ASSESS D1.2
accident 1b – driving accident with multiple vehicles. The numbers for Type 6 in Sweden and
Austria are very high and for 1b data is missing or very low respectively. Looking at Germany
and France these numbers differ. If it can be assumed that most of the cases in Type 1b
have an accident configuration of Type 6 the ranking changes again (see Table 12).
Table 12: Ranking of the accident scenarios when Type 1b has been merged with Type 6
D atabas e
Germany
n=1,399,353
France
n=440,024
Austria
n=615,497
Sweden
n=137,936
Weighted average
I njury
s ev erity of
i nvol v ed
persons in
acc i dents
with i njuri es
frequency
ranking
frequency
ranking
frequency
ranking
frequency
ranking
frequency
ranking
Type 1b:
D ri vi ng
ac ci dent multi pl e
v ehic l es
Type 1a:
D riv ing
acc i dent - Merged
s ingl e
with Type
v ehi cl e
6a&6B
19%
3
31%
2
24%
3
29%
2
24%
3
Type 2&3:
Ac c idents wi th
turni ng v ehic l e(s )
or cros s ing paths
i n junc tion (w/o
pedes t.)
32%
2
23%
3
26%
2
24%
3
28%
2
Type 4:
Ac ci dents
inv ol vi ng
pedes tria
ns
8%
4
12%
4
12%
4
9%
4
10%
4
Type 5:
Acc i dents
wi th
park ed
v ehic l es
2%
6
1%
6
1%
6
1%
6
1%
6
Type 1b,
6a&6b:
Type 7a&b:
Other
Ac ci dents
ac ci dent wi th mul tipl e si ngle &
vehi cl es (non multi pl e
of 2- 5 or 7)
vehic l e
33%
7%
1
5
32%
1%
1
5
35%
2%
1
5
31%
7%
1
5
33%
4%
1
5
When this assumption is applied the ranking of the different accident scenarios becomes
very similar regarding the different data sources and the weighted average shows that
accidents in longitudinal traffic (and driving accidents with multiple vehicles) are ranked
highest followed by accidents in junctions and single vehicle accidents. Again, the top three
scenarios remain the same.
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ASSESS D1.2
4 Methodology and approach of accident analyses
This chapter gives detailed information about the databases used. Furthermore, the
methodology and the approach for the analyses within ASSESS are explained considering
the requirements of the other Work Packages and the specific characteristics of the sources.
4.1
4.1.1
Descriptions of accident databases and naturalistic studies
National databases
ONISR:
ONISR (the National Interministerial Road Safety Observatory) is the French national
accident database comprising details of accidents and casualties recorded by the Police and
cover all road accidents in France which involve personal injury (one or more vehicles
involved)2.
STATS19:
Stats19 is the national accident database for road accidents in Great Britain. Since 1949,
police throughout Great Britain have recorded details of road accidents that involve personal
injury using a single, regularly reviewed reporting system. Included are all road accidents in
Great Britain (England, Scotland & Wales) involving human death or personal injury notified
to the police within 30 days of occurrence, and in which one or more vehicles were involved.
The basic details of the people, vehicles and roads involved in these accidents are recorded,
and since 2005, factors which contributed to accident causation are also included. The data
sample used in this task was based on Stats19 data from 2005-2008 inclusive.
4.1.2
In-depth databases
GIDAS:
GIDAS (German In-Depth Accident Study) is the largest and most comprehensive in-depth
road traffic accident study in Germany. Since mid 1999, the GIDAS project investigates
about 2,000 accidents in the areas of Hanover and Dresden per year and records up to
3,000 variables per crash. The project is supported by the Federal Highway Research
Institute (BASt) and the German Association for Research in Automobile Technology (FAT).
In GIDAS road traffic accidents involving personal injury are investigated according to a
statistical sampling process using the ‘on the scene’ approach. The data scopes to technical
vehicle data, crash information, road design, active and passive safety systems, accident
scene details and cause of the accident. Furthermore, surveyed facts are impact contact
points of passengers or vulnerable road users, environmental conditions, information on
traffic control and related to other parties (road users) involved. Additionally, vehicles are
measured more in detail, further medical information is gathered and an extensive crash
reconstruction is performed. The detailed documentation of the accidents is performed by
survey teams consisting of technical and medical staff.
In order to avoid biases in the database related to all accidents involving personal injury in
Germany, the data collected in the study is compared to the official accident statistics and
adapted by annually-calculated weighting factors.
2
http://www.securiteroutiere.gouv.fr/article.php3?id_article=3289
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ASSESS D1.2
EDA:
EDA (In-depth accident causation survey) is conducted in France by CEESAR and provides
in-depth data from “on the spot” accident investigations since 1992 for the LAB (Laboratory
of Accidentology, Biomechanics and human behaviour).
The main aim of the EDA database is to provide detailed information on accident causation
and casualties (damages/injuries). The further objective is to open to questions and
hypotheses to be investigated through different methods (statistical accident analysis,
experimentations and observations).
The EDA database currently contains information on approximately 1,077 accidents,
involving 1,618 drivers. It corresponds to approximately 60 accidents per year across three
areas in France (region of Amiens, region of Evreux and region of Bondoufle (South of
Paris)). These accident investigation zones were chosen based on existing hospital facilities,
urban and rural road networks, a comprehensive emergency service system, a high accident
potential and proximity to CEESAR headquarters.
The survey strategy is based on collecting as much information as possible on the accident
sequence, at the scene of the accident itself. This data collection is three-fold: driver, vehicle
and infrastructure. Data collected includes accident identification data, the circumstances of
the accident, road and environmental parameters, the accident situation, vehicle active and
passive safety information, participant descriptive and behavioural details (inc. cars,
motorcycles & pedestrians/ cyclists), information on accident causation, injury causation and
human factors. Moreover photographic and audio files and kinematic reconstruction data are
also available. Each person involved in the accident (drivers, passengers, witnesses) is
interviewed on the spot, or in the hospital emergency service.
The French EDA is not aimed at constituting a representative database (this being the role
devoted to national databases) but more an illustrative investigation of the precise processes
governing different types of accident mechanisms, according to drivers, layout, vehicles, and
the different interactions they put forward in line of the road system operating. The purpose
of such an investigation being to bring qualitative data allowing to better understand the
processes involved in the accidents patterns put forward at a macro-accidentology level.
The database contains accidents with at least one passenger car involved. As with all indepth accident databases, the EDA database contains a certain number of biases. Only one
team of investigators worked on each site, resulting in a lack of accidents during off-duty and
holiday periods. It probably exists a geographical bias with a notable lack of large city and
motorway traffic accidents between 1992 and 2004.
OTS:
The OTS study collected in-depth accident data in the UK between 2000 and 2010 and
established an in-depth database that could be used to improve the understanding of the
causes and consequences of road traffic accidents, and thus aid the government in reducing
road accident casualties.
OTS involved two data collection teams: TRL covering the Thames Valley area, and the
Vehicle Safety Research Centre (VSRC), attached to Loughborough University and covering
Nottinghamshire. Expert investigators from these teams attended the scenes of accidents,
usually within 15 minutes of an incident occurring, using dedicated response vehicles and
equipment. In total, the teams made in-depth investigations of about 500 crashes per year,
and recorded 3,116 parameters for each accident. Accidents of all injury severities are
included in the database, including non-injury accidents.
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ASSESS D1.2
In contrast to other accident studies which are based on evidence gathered after incidents, or
based on secondary evidence, OTS investigations allowed ‘perishable’ accident data to be
gathered. These included trace marks on the highway, pedestrian contact marks on vehicles,
the final resting places of the vehicles involved, weather at the time of the incident, visibility
and traffic conditions. Medical data from participating hospitals and questionnaires were also
collected.
4.1.3
Naturalistic Driving Study (NDS)
The central focuses in Naturalistic Driving Studies (NDS) are the explanatory factors
associated with crashes and the possibility to predict involvement in crashes. NDS represent
something as rare as a new paradigm in accident- and road safety research. [6]
In this study an analysis was made of the 100-car-study data. The data sets published online
were used, which includes manually reviewed and coded events. Candidate events were
found in the data by searching for peaks in longitudinal or lateral acceleration for instance,
then the video clips were reviewed by analysts and information coded in the database.
Hence, even though available data does not include the video clips, the event classification is
to be considered reliable. The data includes two main sets; crashes and near-crashes, which
both were included in the analysis. An important question for this type of analysis is whether
near-crashes are usable as surrogates for crashes. This is often assumed (and would be
convenient as the number of crashes is quite low), but there are signs that indicate that this
might not be the case. Generally, for crashes, the severity levels are low, why the division
into slightly and fatally injured is not feasible for this data.
4.1.4
Naturalistic Field Operational Test (N-FOT)
Naturalistic Driving Studies and Field Operational Tests are merging methodologically. To
recapitulate, Naturalistic Driving Studies tend to focus on crash-explanatory factors, and
Field Operational Tests generally focus on evaluation of systems or functions. [2, 6]
The Sweden Michigan Field Operational Test (SeMiFOT) was a joint project between 15
partners in Sweden and the United States which started January 2008 and ended December
2009. SeMiFOT as a project mainly aimed at developing tools in the methodological chain
needed to perform a FOT rather than collecting representative data which will be done in
further projects as e.g. euroFOT. This method combines elements from both Naturalistic
Driving Studies and Field Operational Tests [2, 6].
Data was collected for each vehicle across a period of about one year. SeMiFOT included a
test fleet consisting of 18 vehicles – 11 cars and 7 trucks – running in Sweden. In total about
40 drivers were included. Data from the vehicle Controller Area Network (CAN), external
accelerometers and GPS were recorded. Six channels of video were recorded: forward (two
cameras with 90° field of view mounted with some overlap giving about 160° field of view
forward), face, cabin, rear camera (cars only) and blind spot camera (trucks only). The face
camera was part of the eye-tracking system.
The data set is organised in trips (ignition on/off) and mainly consists of ‘baseline data’ which
means normal driving including all events. Critical or crash relevant events are not yet
predefined and available as for the 100 car study (see above). Additional information to
weather, temperature, road type etc. is available.
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ASSESS D1.2
4.2
General data query
In order to define important accident types to be taken forward by the ASSESS project, it was
necessary to obtain an appropriate sample of accident data. Therefore, Task 1.1 had already
created a solid basis for the accident analyses and ranked them on a general level. To
specify the accident data in both selected types (longitudinal and junction accident types), a
general data query was developed:
•
•
•
•
•
•
Accident type groups 2&3 and 6 [GDV, 3]
Injury accidents involving at least one passenger car
At least one vehicle impact
Casualty severities of all involved people are known.
At least one passenger car had an initial front-impact.
First opponent vehicle has at least 4 wheels (limited to analysis of test specifications).
The general data query was used to define the set of accidents used to investigate the
conditions of the proposed test scenarios. This examined the parameters at the accident
level to determine the applicability of the test scenarios.
Furthermore, the OTS analysis considered the accident parameters (such as the driving and
impact speed of the subject and target vehicles, collision overlap etc.) against the highest
accident severity (two groups). Here, the accidents are distributed into accidents with ‘at
least fatalities/seriously injured people’ or ‘maximal slightly injured people’ so that the
relevance of the test speeds could be evaluated.
In addition to the detailed examination of the test scenario parameters, two ranking exercises
were carried out. These rankings took account of the total injury outcome of the accident.
The accident data used for the first ranking analysis used the same criteria as the general
data query. However for the ranking analysis the data was examined on the casualty level
such that all casualties involved in the selected accidents were considered. The second
ranking was based on the same criteria as the general data query with the exception that the
first opponent vehicle could also be a two-wheeled vehicle. The ranking procedure is
described in sections 5.6 and 6.3.
In ASSESS, the term of “Vulnerable Road Users” (VRU) includes pedestrians, bicycles,
mopeds and motorcycles (all kinds of two-wheelers). VRU were excluded in the detailed
analysis as initial collision partners concerning the specifications for the test scenarios (see
chapter 5). However, bicycles and powered two-wheelers were included as initial collision
partners in the overall ranking of the test scenarios (see chapter 6).
The analyses (performed in Task 1.1) considered all types of accidents occurring on the road
with at least one vehicle involved. However, for the activities of Work Package 4 (pre-crash
evaluation) accidents were omitted which are out of the scope of the systems investigated in
this project. Thus, no accidents involving vulnerable road users (first impact) and no single
vehicle driving accidents (Type 1a) were considered. Hence, the analysis [2] has confirmed
that the systems selected within ASSESS are relevant with respect to the current casualty
problems, with Type 2&3 and Type 6 accidents being relevant to the pre-crash systems
regarded. In Figure 4 the choice of the accident types in Task 1.1 and Task 1.2 is
summarized.
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Figure 4: Overview of used accident types in Task 1.1 and Task 1.2
The accidents classification by the first digit of the accident type is not sufficient for the
assignment to the test scenarios. Therefore, the two-digit-accident type codes have been
chosen which provide more detailed information. The final assignments of these type codes
are shown [see also appendix] in Figure 5 and are discussed in section 4.3.
4.3
4.3.1
Preliminary test scenarios and accident types
Overview of preliminary test scenarios and assigned accident type codes
Based on the initial accident types presented in D1.1 [2], Work Package 4 transferred these
to test scenarios [4].
The scenarios and their specific parameters shall be based on the most relevant
accidents/conflict situations as specified and ranked by Work Package 1 considering the
‘general data query’ (see section 4.2.). Table 13 shows the currently developed test
scenarios A-D and the available sublevels A1-A3 and D1-D2. Specifications for the sublevels
are given in section 6.1 and are used to assess their relevance within the particular test
scenarios.
Table 13: Test scenarios and their sublevels
Test scenario
A
Rear-end collision
B
Intersection conflict
C
Oncoming traffic conflict
D
Cut-in conflict
A1
A2
A3
Available sublevels
Slower lead vehicle
Decelerating lead vehicle
Stopped lead vehicle
D1
D2
Oncoming cut-in
Lane change cut-in
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Furthermore, the test scenarios proposed by Work Package 4 include up to three different
kinds of driver reactions to a warning signal given in a critical manoeuvre: fast, slow or no
reaction.
The assignments of the respective accidents/accident type codes to the test scenarios are shown
in Table 14 and in Figure 5. These determinations also are the basis for the analyses described
in chapter 5 and the ranking R1 that is accomplished in section 5.6.
Table 14: Assignment accident type codes for test specifics and ranking R1
Test scenario
A
B
C
D
Type 2
20, 23
21*, 27, 28*
21*, 28*
Assigned accident type codes
Type 3
Type 6
60, 61, 62
30, 31, 32, 33, 35
66, 68
63, 64
* 0.5 weighting factor
The databases used have different coding and elicitation methods. Therefore, few accidents
couldn’t be assigned to the accident type codes due to these mismatching issues which are
discussed in the following sections.
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Figure 5: Determined Assignment of accident type codes (SafetyNet, GDV) to Test Scenarios A-D
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4.3.2
Merging of accidents to accident type codes
The initial conflict situation which leads to the accident is described with the accident type.
Two-digit-accident type codes are necessary for determining the number of accidents which
can be assigned to the test scenarios.
ONISR:
In ONISR database, it’s impossible to assign accident type codes. Indeed,
data are not enough detailed to produce the individual accident type codes.
EDA:
In EDA database, the accident type codes were assigned according to
selected accident type codes in the methodology. The whole bank of data was
filtered with general data query to select relevant accidents (subject vehicle is
a passenger car and its first impact is frontal…). The accident type codes were
assigned according to manoeuvres of target and subject vehicles,
infrastructure (crossing or not) and CEESAR’s accident type codes.
OTS:
The accidents in OTS, same as in STATS19, cannot be easily merged into the
same accident type codes used in SafetyNet. OTS uses a similar system of
“conflicts” which can be used to define the test scenarios, but are not detailed
enough to produce the individual SafetyNet accident codes.
GIDAS:
For each accident in GIDAS a 3-digit accident type code based on the
‘Accident Classification System’ by GDV (German Insurance Association) is
given. With this classification the initial conflict situation is described. The
‘Accident Classification System’ by GDV equals the codes used in SafetyNet.
The 2-digit accident type code is generated by truncating the last digit, e.g.
‘631’ becomes ‘63’.
4.3.3
Assigning and merging of accidents into test scenarios
The test scenarios given by Work Package 4 are evaluated concerning their relevance in real
world accidents.
GIDAS:
In order to determine the occurrence frequencies of the specified test scenarios by Work
Package 4 the assignment to real world accidents is necessary. By considering the two-digit
accident type codes the scenarios A-D are mapped to accidents. An overview about the
assignments is given in Table 14 and Figure 5 and could be transferred to this analysis.
Some two-digit accident type codes occur with a low frequency in the accident dataset used
(accidents with type 2, 3 or 6 involving cars not colliding initially with a VRU) and are not
considered in the assignment to the test scenarios. These accidents are excluded from the
analyses.
Some two-digit accident type codes cannot be mapped to one test scenario exclusively.
Thus, these accidents are assigned to all test scenarios for which the initial conflict situation
is convenient. In order to avoid multiple considerations and counts of these accidents a
weighting factor is introduced. These factors are the reciprocal of the number of test scenario
assignments and sum up to 1. That is, the two-digit accident type code ‘21’ can be assigned
to the test scenarios B and D. Accidents of Type ‘21’ are mapped to both test scenario B and
D and obtain a factor of 0.5.
The accident type describes the initial conflict situation which leads to the accident. Some
few assignments might be inappropriate based on the two-digit accident type code. The
mapping can be inadequate if the initial conflict situation would lead to another accident
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situation than expected. This change of the crash outcome might be caused by a different
driver’s reaction and is not coded in the accident type. In general, a change of the entire
accident situation is not expected due to changed driver’s reaction.
Example:
A driver approaches an intersection on the privileged carriageway and
tries to avoid an assumed side crash with another vehicle coming from
the right side. By evading this car the driver subsequently collides with
another vehicle in the oncoming traffic. The initial conflict situation is a
conflict between vehicles at an intersection (Type 321) which should be
coded in the data. The evading manoeuvre leads to a collision with the
oncoming traffic which could be described by Type 661.
OTS:
For OTS, data from October 2000 to March 2010 inclusive was considered. The general data
query was used to select the accident data sample used for the analysis. The OTS data was
matched to the test scenarios (A, B, C, D) by assigning OTS conflict codes to each test
scenario.
It should be noted that this matching process was not based on the accident type code level,
but involved matching the test scenario group to the most relevant OTS conflict codes.
Therefore, this is the best match that could be achieved, and although the OTS codes are
similar to the accident type codes used by GIDAS, these could not be exactly matched.
STATS19:
For Stats19, data from 2005 to 2008 inclusive was used. As with the OTS data, the general
data query was used to select the accident data sample used for the analysis. The Stats19
data was matched to the test scenarios (A, B, C, D) by assigning accident circumstances and
vehicle manoeuvres for each accident type code.
Again, the matching between the Stats19 data and the accident type codes and test
scenarios was the closest match that could be achieved based on the manoeuvres contained
in Stats19. The Stats19 data was presented at the test scenario level because it was known
that the matching to the accident type code was not exact. Any errors were minimised by
considering the results only at the test scenario level.
EDA:
For EDA, data from 1992 to march 2010 inclusive was used. Data was assigned to the test
scenarios (A, B, C, D) by merging corresponding accident type codes defined in ASSESS
methodology. For accident type codes 21 and 28 common to test scenarios B and D, they
are weighted with factor 0.5.
ONISR:
For ONISR, data from 2005 to 2008 inclusive was used. Data available in ONISR, as
mentioned above, are not detailed enough to produce the individual accident type codes but
they are sufficient to allow assigning test scenarios into groups A-D. The whole bank of data
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ASSESS D1.2
was filtered with general data query to select relevant accidents (subject vehicle is a
passenger car and its first impact is frontal). To assign each test scenario information about
the type of the collision/impact, infrastructure (crossing or not) and the initial manoeuvre were
used. Accidents corresponding to accident type codes 21 and 28 are common for both, test
scenarios B and D that are weighted by a factor of 0.5.
Table 15: Allocation of ONISR data to test scenarios
Test scenario
A
B
C
D
4.3.4
Description of test scenario
Rear-end collision
Intersection conflict + Merging accident type codes 21 and 28 without
distinction. Inclusion in test scenarios B and D with a weight factor of 0.5
Oncoming traffic conflict
Cut-in conflict + Merging accident type codes 21 and 28 without distinction.
Inclusion in test scenarios B and D with a weight factor of 0.5
Division into test scenario sublevels
The data given by the two-digit accident type codes do not provide enough information to
assign the accidents to the test scenario sublevels. Therefore, additional accident related
parameters were considered to achieve the assignments. This section describes the
analysts’ procedures for the in-depth databases EDA, GIDAS and OTS as well as for the
French national database ONISR. Further detailed information about the test scenario
sublevels are given in section 6.1.
ONISR / EDA:
ONISR: Not available
A1, A2, A3
D1
EDA: available but to many small numbers to be relevant/useful
ONISR: Considered as Cut-in (head-on). Oncoming target vehicle (for
accident types 21 and 28, they are weighted with factor 0.5)
EDA: accident types 21 and 28 (weighted with factor 0.5)
ONISR: Considered as Cut-in (rear-end). Lane changing target vehicle
D2
EDA: accident types 63 and 64
GIDAS:
A1
A2
A3
D1
D2
Accidents of type 20, 23, 60, 61 or 62
+
(Driving speed of target vehicle=impact speed of target vehicle OR no
braking deceleration of target vehicle) AND driving speed of target vehicle <
driving speed of subject vehicle
+
Impact speed of target vehicle < driving speed of target vehicle OR braking
deceleration of target vehicle > 0
+
Impact and driving speed of target vehicle = 0
Accidents of type 21 or 28 (weighted with factor 0.5)
Accidents of types 63 or 64
For OTS, the allocation of accidents to the test scenario sublevel was achieved by
considering the driving speeds for the target vehicle for A1/A2/A3 and the OTS conflict codes
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for D1 and D2. For the subcategories of test scenario A, it was not possible to determine
whether the target vehicle was decelerating or travelling at a constant slower speed than the
subject vehicle. This was because the deceleration is not recorded. Therefore, subcategories
A1 and A2 were merged. Accidents were allocated to A3 if the target vehicle was stationary
(see Table 16).
Table 16: Allocation of accidents to test scenario sublevel (OTS)
Test scenario sublevel
A1
A2
A3
D1
D2
4.4
Sublevel allocation criteria
Considered if target vehicle driving speed >0
Considered if target vehicle driving speed=0
Considered as Cut-in (head-on) from OTS conflict codes.
Normally vehicle travelling in other direction making an
overtaking manoeuvre
Considered as Cut-in (rear-end) from OTS conflict codes.
Normally target vehicle travelling in same direction
overtaking subject vehicle.
Parameters, attributes and limitations
4.4.1
Recommended parameters for detailed analysis
The testing carried out in ASSESS considers currently available forward facing, pre-crash
sensing systems and the analyses are focusing on the recommended parameters by
Deliverable D1.1 [2] which are shown in Table 17. In summary the parameters are classified
into seven categories.
Table 17: Recommendations for Task 1.2 of D1.1
Categories
Vehicle
Driver behaviour
Road layout
Environmental
conditions
Type of vehicle /
target / object
Collision deformation
classification (CDC)
Time to collision (TTC)
Parameters
Driving speed
Closing speed to opponent in normal driving
Impact speed
Relative distance to leading vehicle in normal driving
Relative angle when driving
Collision angle
Impact location
Acceleration (absolute and relative)
Position
Secondary Task
Manoeuvres
Reaction on warnings
Not further specified
a) Weather conditions
b) Road conditions
c) Light conditions (including sun position, e.g. glaring light)
a)
b)
c)
d)
e)
f)
g)
h)
i)
a)
b)
c)
Indicator of system performance (e.g. to determine the minimal time
systems will obtain to react during the tests)
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This rough compilation of parameters derived from initial demands on the analyses of Task
1.2 to assess the test scenarios which are defined by Work Package 4. Hence, the available
databases had to be checked accordingly to their specifications and the availability/reliability
of the data.
4.4.2
Availability of selected parameters
A first check of the databases for their availability of the recommended parameters from D1.1
showed necessary restrictions to the analyses. Due to the fact that not all accident
databases could completely provide sufficient and reliable data, the following parameters
were omitted:
•
•
Category Vehicle:
o Relative distance to leading vehicle in normal driving
o Relative angle in normal driving
o Collision angle
o Acceleration (absolute and relative)
o Position
Category Time to collision (TTC).
Furthermore, the ‘collision deformation classification (CDC)’ was restricted to the information
about the damaged car side of the target vehicle considering the general data query (see
section 4.2) that focuses on frontal crashes of the subject vehicles.
Issues on the ascertainment of the ‘closing speed’ arose while comparing information of the
different databases and discussing the requirements for the project-related pre-crash
evaluation (Work Package 4). Some correlations between the driving speed, the ‘closing
speed’ and the impact speed are shown in Figure 6.
Figure 6: Driving speed, Impact speed
The difference in driving speeds for the subject and target vehicle can be used for estimating
the ‘closing speed’ in longitudinal scenarios. However, the accuracy of this variable depends
strongly on both the accuracy and the time at which the driving speed was assessed. It is
considered that because of their uncertain accuracy, these variables (including closing
speed) should be used for indication only.
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Table 18 shows an overview of the availability of the recommended parameters (see 4.4.1)
for the databases used. The data quality is described by the subjective score: 1 (good), 2
(moderate), 3 (poor) and 9 (not available).
Table 18: Available data quality of selected parameters (1…good, 2…moderate, 3…poor, 9...not available)
Vehicle
Driving speed
‘Closing speed’
Impact speed
Dist. to leading veh.
First impact location
Overlap in crash
Driver behaviour
Secondary Task
Manoeuvres
Warning reactions
Road layout
Road type
Environmental
conditions
Weather conditions
Road conditions
Light conditions
Type of opponent
GIDAS
OTS
EDA
STATS19
ONISR
SeMiFOT
100-car
2
2
2
9
1
2
2/3
2/3
2/3
9
1
1
2
2
2
9
1
1
9
9
9
9
1
9
9
9
9
9
1
9
1
1
2/3
1
9
9
1
1
1
1
9
9
3
2
9
2
2
9
2
2
9
2
2
9
3
9
9
1
1
1
1
1
9
1
1
1
1
1
1
3
1
1
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
During the analyses by the Work Package partners, several iterative analyses and
arrangements of parameters were necessary to both ensure a high data quality and to fulfil
the aims of this Task. However, some divergences between the different data sources
remain. These are discussed in the detailed analyses in chapter 5 and are summarised in the
attached document [see appendix on document for data truth/quality]. One example for that
is the quality of the calculation/reconstruction method used to define the driving speeds
which differs between the databases GIDAS, OTS and EDA. The specifications for the final
parameters selected are described in the ‘codebook’ which is attached in the appendix. In
particular, the in-depth databases only contain a small number of fatalities which decreases
with restrictions to the specific data query. Furthermore, the information about an injured
person might be recorded as seriously injured at the scene but subsequently become fatal.
This could have implications for the later ranking.
The data quality in NDS/FOT studies is generally very good compared to accident studies.
Data is recorded directly from the cars and add-on equipment such as radars and what
cannot be achieved from the car can often be observed from video with quite high accuracy,
weather for instance. Accident studies often base speed information for example on
estimations and reconstructions, and observations are made in retrospect. The limitations of
a NDS lie on another level. Where accident studies choose to study only those cars involved
in accidents of a certain severity, NDS studies record everything what happens to a small
group of cars. Hence, there is a large amount of data not including crash relevant data,
where accident studies include only relevant data. Accidents rarely occur in NDS studies and
are not comparable to those in accident studies when it comes to severity.
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5 Analyses and results of the available databases
National databases used for this analysis are ONISR (France) and STATS19 (Great Britain).
The three available in-depth databases used for this analysis are GIDAS (Germany), OTS
(Great Britain) and EDA (France). Furthermore, results are presented from SeMiFOT and the
100-car-study.
This chapter starts with an introduction and description of the available databases. Following,
main results and the obtained knowledge are shown on the basis of comparing diagrams and
tables in the sections 5.2 to 5.5. Finally, the ranking R1 is presented and discussed in the
section 5.6. The previous section 4.4.2 already described main issues and limitations of the
data sources used.
The sections 5.2 - 5.5 base on the number of passenger cars, the ranking R1 in section 5.6
bases on the number of accidents.
5.1
Datasets
This section describes and quantifies the datasets and studies used for the detailed analysis.
Overviews are given about the number of available accidents, successful assignments to the
test scenarios and the number of involved persons for each database. At first national
databases are presented, followed by the in-depth databases and finally the naturalistic
studies. Due to difficulties with the comparison of accident databases (national statistics or
on-the-spot or retrospective databases) and the naturalistic studies the sections about
SeMiFOT (see 5.1.3) and about the 100-car-study (see 5.1.4) are introduced and directly
evaluated.
5.1.1
National databases (ONISR, STATS19)
ONISR:
Data sample of 01/01/2007 - 31/12/2008
Figure 7: ONISR (2007-2008)
Decimals appear because of weighting factor 0.5 in B and D
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STATS19:
For the Stats19 data a further constraint on the general data query was that only two vehicle
accidents were considered. Thus, any accidents which involved three or more vehicles were
excluded at the second step in Figure 8.
Figure 8: STATS19 (2005-2008)
5.1.2
In-depth databases (GIDAS, EDA, OTS)
GIDAS:
Accidents with personal injury are documented in detail in the German In-Depth Accident
Study (GIDAS) in two investigation regions of Germany. In order to minimize biases in the
database to all accidents with personal injury in Germany, the data collected in the study is
compared to the official accident statistics and adapted by annually-calculated weighting
factors*. For data analyses within the ASSESS project weighting factors are calculated
based on information about accident location, accident severity and accident type.
For the following analyses a data sample of 2001-2007 is used. The query described in
section 4.2 is applied on the GIDAS dataset. In figure 7 the numbers of accidents and
casualties used in the analyses are shown.
The information about the subject vehicle from GIDAS is based on cars involved in accidents
which can be mapped to test scenarios A-D. In GIDAS the number of cars is very small
having an accident on a motorway which can be assigned to test scenario B (intersection
conflict) or C (oncoming traffic). Therefore, no information about cars on motorways is
provided for test scenarios B and C.
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Figure 9: GIDAS (2001-2007)
EDA:
Figure 10: EDA (1992-03/2010)
Decimals appear because of weighting factor 0.5 in B and D.
Albeit, the EDA database contains 1,077 in-depth accidents, 306 relevant accidents have
been selected according to the ASSESS data query. Splitting these 306 accidents into
accident type codes lead on to obtain samples statistically not significant (for some of them
accident counts are less than 30). For this reason, French data are merged and analysed
only on test scenario level (A, B, C, D) avoiding every sub levels like road type in the
analysis.
The French data resulting from this analysis are provided to highlight the French tendencies
and specificities and to complete the German and the English approach and provide a better
overview of the European accidentology through three European in-depth data sources.
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OTS:
Figure 11: OTS (2000-2010)
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5.1.3
Naturalistic Field Operational Test (SeMiFOT)
Since SeMiFOT data are not representative a different approach as used for analysis of
accident data bases had to be chosen in order to provide relevant information for scenario
definition in Task 1.2. Proposed scenario parameters as defined in deliverable D4.1 [4] were
used to find and count all those scenarios present in the data sets to get a first impression of
how representative they are in normal driving. In a second step all found scenarios were
checked for type and criticality by watching the corresponding video records. As a last step
the proportion of different scenario types could be analyzed and compared to the proportion
of accident types in order to see how likely a critical scenario of one type ends up in a crash
of the same type. (This last step could not be done here as no crashes were present in the
data of SeMiFOT.)
In order to find rear-end scenarios as pre-defined in D4.1 the following query was used:
• cars only
• trips where
o SV speed >45 and <55 [km/h]
o Relative speed <-10 and >-15 [m/s]
o Distance to TV < 28 [m] (based on TTC < 2,6 s)
Only trips were used where information for speed(s) and distance was available. Data were
checked from 9 different drivers.
Results:
•
•
Total driving time used for analysis:
o 532 h, 1881 trips
Percentage of events (time where conditions below were fulfilled over total
time analysed):
o 18,28 % SV speed >45 and <55 [km/h]
o 0,085 % SV speed >45 and <55 [km/h] AND relative speed <-10 and
>-15 [m/s]
o 0,0018 % SV speed >45 and <55 [km/h] AND relative speed <-10 and
>-15 [m/s] AND distance to TV < 28 [m]
Video check for event type and criticality:
A total number of 15 events (5 different drivers) were found:
•
•
•
•
3 rear end events (according to scenario A):
o 1 slower lead vehicle (tractor) on rural road
o 1 slower lead vehicle (in middle line, late overtaking via left line) on
highway
o 1 stopped lead vehicle/ traffic jam on highway
12 intersection events (according to scenario B, all urban/rural):
o 7 Decelerating lead vehicle which turns left at intersection
o 3 Decelerating lead vehicle which turns right at intersection
o 1 Decelerating lead vehicle before roundabout
o 1 Stopped lead vehicle at traffic light
0 oncoming traffic (according to scenario C):
0 cut-in (according to scenario D):
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Figure 12: Decelerating lead vehicle which turns left at intersection
Additional parameter checked:
•
•
•
•
•
Input parameter for finding events:
o Driving speed/closing speed (relative speed)
o Distance to leading vehicle (based on TTC)
No crashes: no impact speed, no impact location etc., no offset
Environmental conditions: daylight (14), night, no road lights (1), dry road, no
influence by sunlight
Road type: urban/rural (13), highway (2)
Driver Behaviour:
o Reaction warnings: no warnings
o Manoeuvres: no, (gas pedal release,) brake pedal press
o Distraction/inattention: no secondary tasks
Though contribution of SeMiFOT data analysis to scenarios specification in Task 1.2 is
limited, a general approach for using FOT data is shown which will be appropriate when
representative FOT data are available in the nearest future.
By now however, the amount of data collected within the SeMiFOT project is too small to be
a contributor to this kind of analysis. The events are too few and the general severity level is
too low, which makes it difficult using SeMiFOT data as complement to accident studies in
this analysis. The analysis of 100-car study data below demonstrates on American data what
ideally in this case should have been conducted on a European FOT/NDS database.
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5.1.4
Naturalistic Driving Study (100-car-study)
In 100-car data, data reductionists have coded the events with respect to the initial conflict
(Event Nature), crash or near-crash configuration (Event Type) and in relation to junction
among others. These three variables were used to define the ASSESS test scenarios A-D in
the 100-car study data sets.
The four ASSESS test scenarios were interpreted as follows:
Table 19: Classification of 100-car-study variables to test scenarios
ASSESS test scenario
100-car study variable
A (Rear-end collisions)
Event Nature
Event Type
Event Nature
B (Junctions)
Relation to junction
C (Oncoming)
Event Nature
D (Cut-in)
Event Nature
Relation to junction
Categories included in
sample
Conflict with lead vehicle
Rear-end striking
Conflict with vehicle turning
across another vehicle path,
opposite direction
same direction
into another vehicle path,
opposite direction
same direction
Conflict with vehicle moving
through intersection
Intersection
Intersection Related
Conflict with oncoming
vehicle
same direction
Non junction
As variables and categories in ASSESS and 100-car study database not fully match, an
interpretation had to be made. Time series data (such as speed) also required a point in time
to be defined at which the value should be read. This time often is the start of the event, as
defined by the 100-car study reductionists, also referred to as ‘start of the precipitating
event’; e.g. the point in time at which the state of environment or action that led to the nearcrash or crash occurred (e.g. in typical rear-end crash: the time when lead vehicle stars
braking).
Availability and interpretation of ASSESS variables:
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Table 20: Availability of corresponding variables
ASSESS Variable
Driving speed
Closing speed to opponent when critical
situation is recognized
Impact speed
Initial distance when critical situation is
recognized
Offset in crash situation
Average distance to lead vehicle when
normal driving
Vehicle age
Driving speed (target vehicle)
First impact location (target vehicle)
Vehicle type (target vehicle)
Distraction/inattention
Manoeuvres
Reaction warnings
Impairment
Age of the driver
Weather conditions
Road surface conditions
Light conditions
Daylight
Posted speed
Availability/ corresponding variable
Speed at event start
Range rate at event start
Speed at longitudinal acceleration peak in
relation to crash
Range at event start
Not available
Not available
Not available
Not available
Driving speed (subject vehicle) minus
Closing speed at event start
Not available
Vehicle 2 type (if vehicle 2 is the first conflict
partner)
Distraction
Driver reaction
Not available
Driver impairments
Age
Weather
Surface condition
Not available
Lighting
Not available
The query for the four accident (incident) types resulted in the following number of matches:
Table 21: Analyses on matching events
Scenario
Rear-end
Junctions
On-coming
Cut-in
Matching events
14 crashes
370 near-crashes
2 crashes
74 near-crashes
0 crashes
25 near-crashes
0 crashes
3 near-crashes
Further analyzed
x
x
x
x
Speeds, distances etc
Rear-end collisions was the only accident category from ASSESS selection that had
sufficient representation in the 100-car crash database in order to make any analysis. The
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sample of crashes was rather small but still indicated a possible difference between crashes
and near-crashes. Generally, speeds for crashes were much lower than for near-crashes
(mean driving speed 14 km/h for crashes, 52 km/h for near-crashes, and mean closing speed
3 km/h vs. 25 km/h). Speed and distance information was possible to gain for scenarios A, B
and C for the near-crashes, for scenario D the sample was too small for further analysis.
Driver behaviour
Distraction in 100-car study was coded with some 60 categories and with the possibility to
code up to three different distracters for the same event.
Distracters appear to have been present to some extent in all events; in 93 % of rear-end
crashes, 58 % of rear-end near-crashes, 39 % of junction near-crashes and in 48 % of oncoming near-crashes. Distraction inside the vehicle appeared to be the dominant category for
all scenarios.
Coded distracters for rear-end
crashes
Distribution of distracters rear-end
crashes
Inside vehicle
No distracters
Outside
vehicle
One distracter
Two distracters
Coded distracters for rear-end
near -crashes
Not further
specified
Distribution of distracters rear end near -crashes
No distracters
Inside vehicle
One distractor
Two distractors
Three
distractors
Outside
vehicle
Not further
specified
Figure 13: Rear-end near-crashes
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Coded distracters for junction near crashes
Distribution of distracters
junction near -crashes
No distracters
Inside vehicle
One distractor
Outside
vehicle
Three
distractors
Not further
specified
Figure 14: Junction near-crashes
Coded distracters for oncoming
near -crashes
Distribution of distracters
oncoming near-crashes
No distracters
One distractor
Inside vehicle
Two distractors
Figure 15: Oncoming near-crashes
As may be expected avoidance manoeuvres were more apparent in near-crashes than
crashes; in all rear-end near-crashes the driver did react by steering or braking (or a
combination), whereas 36 % reacted in the crashes. Braking manoeuvres were dominant for
rear-end and junction near-crashes, while steering was more common for the on-coming
scenario.
Coded impairments belonged primarily to fatigue and other (mainly distraction) categories for
rear-end and junction scenarios; rear-end crashes 13 % fatigue and 63 % other, rear-end
near-crashes 14 % fatigue and 39 % other, junction near-crashes 9 % fatigue and 22 %
other. Impairments however, as it is defined in the codebook [see appendix], does not match
well with the 100-car study variable, and as distraction is one category of impairments there
seems to be a double coding with the distraction variable.
The chosen approach, using start of precipitating event as reference point in time was the
best approximation possible without reviewing video data in each case, which was not
available for 100-car study.
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5.2
Subject vehicle
The information about the subject vehicle is based on cars, meeting the demands for a
subject vehicle (see section 4.2).
In GIDAS there are 3,267 cars, meeting the demands for a subject vehicle, involved in the
2,804 accidents which can be assigned to test scenarios A-D. Again, it is to emphasize that
in order to minimize biases in the database to all accidents with personal injury in Germany,
the data collected is adapted to the official accident statistics by annually-calculated
weighting factors. That is, in this chapter the following data about e.g. the number of cars and
speeds are GIDAS-internal weighted. This also means that they are different from the
weighting factors used e.g. in section 5.6 which refer to the injury costs being applied to
generate the ranking of the test scenarios. Finally, to test scenario ‘A’ 1,367 passenger cars
can be assigned. Furthermore 1,202 cars can be assigned to test scenario ‘B’, 279 cars to
test scenario ‘C’ and 419 cars to test scenario ‘D’. The number of cars which can be used for
verifying the test scenarios varies dependent on the variable which is analyzed. E.g. the
driving speed cannot be determined as often as the impact speed.
The OTS data used only contains few accidents which could be assigned to the test scenario
D, however D is presented here.
5.2.1
Driving speed
In the test scenario specifications for pre-crash sensing safety systems it is necessary to
define initial speeds. Knowing these initial speeds in real world road traffic accidents and
transferring them into to the tests provides a good representation of the real conditions prior
to accident situation. The mean average and several percentiles are given separately for the
test scenarios and road types. As it can be seen in Figure 16 for GIDAS and in Figure 17 for
OTS these values give an overview about the initial speeds in real accidents.
140
100
80
mean average
60
5th
50th (median)
40
75th
95th
20
A
B
C
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
0
urban road
Driving speed (km/h)
120
D
Test scenarios
Figure 16: GIDAS – Driving speeds (absolute numbers: mean averages and percentiles)
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In GIDAS (see Figure 16) the driving speed is defined as the speed of the vehicle before a
critical situation was recognized. The driving speed is reconstructed after the accident data
collection is completed. The quality of the driving speed differ dependant on procedure used
for estimating it. For some accidents the initial speed is reconstructed by using computer
programs or by hand calculations, for other accidents the speed is estimated and for further
accidents the tachograph disk or EDR data is evaluated. It is necessary to consider the data
extraction for an appropriate interpretation of the results based on accident data. Hence the
calculated mean average and percentiles are subject to fluctuations.
Compared to all subject vehicles (see section 5.2) involved in accidents which can be
assigned to a test scenario the number of cars with information about the driving speeds is
reduced. For some cars the driving speed could not be reconstructed and it is therefore
unknown. Hence the data basis for calculating the mean average and percentiles of the
driving speeds is decreased. The results shown in Figure 16 are based on 2,677 cars.
The calculation of the mean average and percentiles are based for test scenario ‘A’ on 1,082
cars, for test scenario ‘B’ on 1,041 cars, for test scenario ‘C’ on 204 cars and for test
scenario ‘D’ on 350 cars.
Additionally the test scenarios are subdivided into three different road types.
Independent on test scenarios the mean average of the driving speed of the subject vehicle
is about 40km/h on urban roads. The 75th percentile of the driving speed on urban road is
also similar for the different test scenarios and is around 50km/h. This means that 75% of the
analyzed cars have a driving speed of around 50 km/h or less.
In all test scenarios the driving speed increases from urban to rural roads. The mean
average of the driving speed on rural roads is for all test scenarios around 60km/h. Only for
test scenario C the driving speed (mean average is almost 70km/h) on rural roads is slightly
increased compared to the other scenarios.
On motorways the mean average of the driving speed is clearly increased compared to the
mean averages on other road types. The mean average of the driving speed is above
110km/h in test scenario A and D.
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140
120
100
80
mean average
60
5th
50th (median)
40
75th
95th
20
A
B
C
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
urban road
0
D
Test scenarios
Figure 17: OTS - Driving speeds (absolute numbers: mean averages and percentiles)
Analysis of the OTS data for the driving speed of the subject vehicle shows the expected
trends for greater driving speeds on rural roads compared with urban roads, and the greatest
driving speeds on motorways. The proximity of the median and mean values indicates that
the data is not significantly skewed toward extreme driving speeds. It should be noted that
the driving speed is estimated from a range of sources: reconstruction, witness accounts.
The consistency of the variable and its collection with respect to the point immediately prior
to the precipitating event (the action of manoeuvre which made the accident unavoidable) is
considered indicative.
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5.2.2
Impact speed
In particular, data on impact speeds are of relevance for the socio-economic assessment of
accident mitigation effects (Work Package 2) on which test results of the pre-crash system
performance evaluation will provide information (Work Package 4).
As for the driving speed the mean average and several percentiles are given separately for
the test scenarios and road types. An overview about the impact speeds in real accidents is
shown in Figure 18 and in Figure 19.
140
Impact speed (km/h)
120
100
80
mean average
60
5th
50th (median)
40
75th
95th
20
A
B
C
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
urban road
0
D
Test scenarios
Figure 18: GIDAS – Impact speeds (absolute numbers: mean averages and percentiles)
In GIDAS the impact speed is defined as the speed of vehicle at the time of collision. As the
driving speed the impact speed is reconstructed after the accident data collection is
completed. For the reconstruction of the impact speed the similar tools are used as for the
reconstruction of the driving speed. It is necessary to consider the data extraction for an
appropriate interpretation of the results based on accident data. Hence the calculated mean
average and percentiles are subject to fluctuations.
Compared to all subject vehicles (see section 5.2) involved in accidents which can be
assigned to a test scenario the number of cars with information about the impact speeds is
reduced. For some cars the impact speed could not be reconstructed and it is therefore
unknown. Hence, the data basis for calculating the mean average and percentiles of impact
speeds is decreased. The results shown in Figure 18 are based on 3,106 cars.
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As for driving speeds the mean average and percentiles of the impact speed of the subject
vehicle is similar on urban roads independent on test scenarios. The mean average of the
driving speeds on urban roads is around 30km/h and the 75th percentile varies around
40km/h.
The mean average of the driving speed on rural roads is for test scenarios above 40km/h. A
detailed look at the 75th percentiles shows that the impact speed on rural roads is below
70km/h for 75% of the cars assigned to test scenarios B, C or D. For test scenario A the 75th
percentile is slightly below 60km/h. The comparison between cars having an accident on
motorways assigned to test scenario A and cars assigned to scenario D shows that impact
speeds are increased clearly in scenario D.
80
70
Impact speed (km/h)
60
50
40
mean average
5th
30
50th (median)
20
75th
95th
10
A
B
C
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
urban road
motorway
rural road
urban road
0
D
Test scenarios
Figure 19: OTS - Impact speeds (absolute numbers: mean averages and percentiles)
For the impact speeds, the OTS data shows that these follow the same trends identified for
the analysis of subject vehicle driving speed.
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5.2.3
Overlap in crash situation
Real world accidents between two vehicles do not often occur geometrically ideal either in
longitudinal direction or in a right angle to each other. In particular for the assessment of
rear-end collisions (test scenario A), the overlap in crash situation (longitudinal direction) is a
quite challenging factor for the current pre-crash systems regarded. Obviously, the basis
information for the test specifications of Work Package 4 (pre-crash evaluation) is needed
some time steps before the inevitable conflict occurs. The data provided in this analysis
varies as information on the overlap of real world accidents is only approximately available
for the crash point in time.
The information about overlaps in crash situations is given for the test scenarios A-D. The
classification into road types is not provided for the overlap since it is not expected that the
road type has any influence. The distribution of the gathered overlap data is shown in Figure
20 for GIDAS and for OTS in Figure 21.
100%
90%
80%
Frequency
70%
60%
76-100%
50%
51-75%
26-50%
40%
<=25%
30%
20%
10%
0%
A
B
C
D
Test scenarios
Figure 20: GIDAS – Overlap of the subject vehicle in crash situation
The degree of overlap in GIDAS expresses the percentage of impact structure overlap by the
collision opponent. In frontal and rear-end collisions the original vehicle width equals 100%
and in side collisions the original vehicle length equals 100%. The overlap is determined
based on measurements of the vehicles’ damages.
The number of cars with information about the overlap in crash situation is reduced
compared to all subject vehicles (see section 5.2) involved in accidents, which can be
assigned to a test scenario. For some cars the overlap could not be determined and it is
therefore unknown. Hence, the data basis for the distribution about overlap in crash is
decreased. The results shown in Figure 20 are based on 3,221 cars.
In test scenario A the overlap is bigger than 75% for more than half of the analyzed cars. For
nearly half of the cars assigned to test scenario B the overlap is bigger than 75%. But the
distribution changes for test scenario C (oncoming traffic). Here more than 60% of the
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ASSESS D1.2
analyzed cars have an overlap in crash of 50% or less. Still more than a third of the cars
have an overlap in crash of 25% or less. Nearly half of the subject vehicles involved in
accidents assigned to test scenario D (cut-in) have an overlap in crash of 50% or less.
100%
90%
80%
Frequency
70%
60%
76-100%
50%
51-75%
40%
26-50%
30%
<=25%
20%
10%
0%
A
B
C
D
Test scenarios
Figure 21: OTS – Overlap of the subject vehicle in crash situation
Figure 21 shows the distribution of the OTS dataset. Crashes with unknown overlap are
omitted assuming that these cases can be distributed over the known ones.
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5.2.4
Vehicle age
The average ages of vehicles involved in accidents are shown in Figure 22. The information
gathered allows a comparison between the three databases GIDAS, OTS and EDA. No
further sub-division is required for an overall comparison.
8
7
Vehicle age in years
6
5
4
3
2
1
0
GIDAS
OTS
EDA
Databases
Figure 22: Average subject vehicle ages in GIDAS, OTS and EDA
In GIDAS the vehicle age is not available for all subject vehicles (see section 5.2) which can
be assigned to a test scenario. Therefore, the data basis for the mean average of the subject
vehicle’s age (see section 5.2), is reduced to 2,967 cars.
Based on GIDAS it can be concluded that cars involved in accidents, which can be assigned
to a test scenario, are in average between 7 and 8 years old.
For OTS, the average vehicle age for the subject vehicle was 7.3 years. This value is very
similar to the average value from the GIDAS dataset.
In EDA, whatever the test scenario, the subject vehicle involved has an average age of 6.1
years. Furthermore in ONISR, the subject vehicle involved has an average age of 8.9 years.
5.3
Target vehicle
Information about the target vehicles is based on vehicles colliding with a subject vehicle in
accidents which can be mapped to the test scenarios A-D.
In GIDAS each target vehicle is assigned to one subject vehicle. Therefore, the same
number of datasets is available for analyses about the target vehicle as for the analyses
about the subject vehicle (3,267 datasets). In GIDAS very few cars have an accident on a
motorway which can be assigned to test scenario B (intersection conflict) or C (oncoming
traffic). Therefore, no information about cars on motorways is provided for test scenarios B
and C. Furthermore, data shown are GIDAS-internal weighted.
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5.3.1
Driving speed
The driving speed of the target vehicle is also a very sensitive parameter. It demonstrates a
big challenge for the functionality of the pre-crash sensing systems regarded. In particular,
the initial speed contributes to the calculation of the time to collision (TTC) and might have a
large influence on the accident outcome. In order to map the real world accident situation to
test scenarios, this section provides analysis of the target vehicle’s driving speed.
The mean average and median of the target vehicle’s driving speed before crash is given
separately for the assigned test scenarios and road types. In Figure 23 the target vehicle’s
driving speed is shown for both databases GIDAS and OTS.
90
80
70
60
GIDAS mean average
50
GIDAS 50th (median)
40
OTS mean average
30
OTS 50th (median)
20
10
A
B
C
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
urban roads
0
D
Test scenarios
Figure 23: GIDAS / OTS - Driving speeds of target vehicles (mean averages and median)
In GIDAS the driving speed of the target vehicle is determined with the same procedures as
the driving speed of the subject vehicle. Therefore, the results about the driving speed of the
target vehicle should be interpreted in the same way than the results about the driving speed
of the subject vehicle.
Compared to all target vehicles (see section 5.3) involved in accidents which can be
assigned to a test scenario the number of cars with information about the driving speeds is
reduced. For some vehicles the driving speed could not be reconstructed and it is therefore
unknown. Hence, the data basis for calculating the mean average and median of the driving
speeds is decreased. The results shown in Figure 23 are based on 2,807 vehicles.
vehicles, for test scenario ‘B’ on 1,048 vehicles, for test scenario ‘C’ on 208 vehicles and for
test scenario ‘D’ on 365 vehicles.
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Based on the median of the target vehicles’ driving speed for test scenario A (rear-end
collision) it can be concluded that at least half of the analyzed target vehicles on urban and
rural roads stand still before crash. On motorways the driving speed of the target vehicles is
clearly increased.
For test scenario B (intersection) it can be derived (based on the mean average) that the
speed of the target vehicle is around 30km/h before crash on urban and rural roads.
Dependant on the road type the target vehicle’s driving speed differs in accidents assigned to
test scenario C (oncoming traffic). On urban roads the target vehicle’s driving speed is in
average around 45 km/h. The speed on rural roads is clearly increased by about 20km/h.
In test scenario D (cut-in) a similar effect can be seen as in scenario A. The driving speeds of
the target vehicles are very similar on urban and rural roads. On motorways the target
vehicle’s driving speed is clearly increased. Half of the target vehicles are driven with a
speed of about 30km/h or less before crash. On motorways the driving speed of the target
vehicle is in average clearly higher. Half of the analyzed target vehicles have a speed of
80km/h before crash.
As explained in the previous sections the analysis of the EDA dataset is restricted to the test
scenarios and doesn’t include a sub-division into the different road types. The results of the
driving speeds of target vehicles of EDA are shown in Table 22.
Intersection accidents (test scenario B) present the lowest mean average and median
speeds for target vehicles (respectively 23 km/h and 15 km/h). Additionally, accidents
assigned to Cut-in conflicts (test scenario D) show comparatively slow rates with a driving
speed median of 18 km/h. Oncoming traffic accidents (test scenario C) present the highest
average and median speeds for target vehicles with around 70 km/h.
Table 22: EDA – driving speeds of target vehicles for test scenarios
Test scenarios
A
B
C
D
Mean average
50th (median)
26.5
35.0
22.7
15.0
69.8
69.0
28.7
18.0
In general, the driving speeds of target vehicles are lower than the driving speeds of subject
vehicles. Moreover there are significant differences between the test scenarios.
5.3.2
This section provides information about the first impact location restricted to the left side,
right side, front or rear of the target vehicle. Further test scenario specification details can be
derived from this distribution that is given for the different test scenarios. The classification
into road types is not provided for the first impact location since it is not expected that the
road type has any influence. The distribution of the gathered impact location data is shown in
Figure 24 for GIDAS, in Figure 25 for OTS and in Figure 26 for EDA.
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Figure 24: GIDAS – First impact location of the target vehicle
In GIDAS the main deformed vehicle area caused by initial crash is given for the first impact
location. For some cars the impact location could not be determined and it is therefore
unknown. Hence, the data basis for the distribution about impact location is decreased. The
results shown in Figure 24 are based on 3,262 cars.
Based on GIDAS data in test scenario A (rear-end collision) the target vehicle is (as
expected) mainly hit at the rear. The target vehicle’s first impact location is test scenario B
(intersection) is in nearly 70% side. The analyzed target vehicles often have a front impact
with the subject vehicle accidents that can be assigned to test scenario C (oncoming traffic).
In test scenario D (cut-in) the target vehicle collides almost as often with its side as with its
front.
Figure 25: OTS – First impact location of the target vehicle
With reference to the OTS values (see Figure 25), it can be seen that the general trends are
comparable with GIDAS. Crashes with an unknown first impact location are omitted
assuming that these cases are few and can be distributed over the known ones.
Looking at the EDA dataset and its results of the target vehicles first impact location
accordance with the expectations can be seen in Figure 26. Known values are used to
continue since only 1% of the information is unknown for test scenario B and 3% for D. For
rear-end collisions (test scenario A), more than 80% of first impacts on target vehicles are
located in the rear and approximately 20% on the left side. In most of the intersection
accidents (test scenario B), subject vehicles are in conflict with turning off target vehicles.
That is why, 84% of first impacts are side impacts (equal distribution between left and right
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sides), 15% are front impacts and only 1% of them are rear impacts. For oncoming traffic
accidents (test scenario C), nearly 90% of the first impact are frontal, 10% are side impacts
(equal distribution between left and right sides) and only 1% are rear impacts. In cut-in
accidents (test scenario D) 55% of first impacts are frontal and 45% are right side impacts.
Figure 26: EDA – First impact location of the target vehicle
In ONISR, results are presented only for known values (less than 2% of unknown information
for each test scenario) in Figure 27. For rear-end collisions (test scenario A), more than 75%
of first impacts on target vehicles are located in the rear and approximately 20% on the front.
In most of the intersection accidents (test scenario B), 69% of first impacts are front impacts,
24% are side impacts and 7% of them are rear impacts. For oncoming traffic accidents (test
scenario C), nearly 90% of the first impact are frontal, 10% are side impacts (equal
distribution between left and right sides) and 5% are rear impacts. In cut-in accidents (test
scenario D) nearly 90% of first impacts are frontal. Note that there are some differences
compared to the other databases, in particular for test scenarios A, B and D. It could be
explained by the fact that the frontal collisions included are partly front-side impacts which
cannot be corrected due to a lack of sufficient information. This applies analogue for rear-end
impacts.
Figure 27: ONISR – First impact location of the target vehicle
5.3.3
Vehicle types
Regarding vehicles equipped with pre-crash sensing systems the technical detection of a
target vehicle is influenced by its type. This section provides information about the target
vehicle types in order to show system relevant information out of the accident databases and
hence, to support the pre-crash evaluation (Work Package 4).
The distributions of the different vehicle types are given in Table 23 to Table 26 separated for
the test scenarios A-D and related to the road types: urban roads, rural roads and
motorways.
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The vehicle types are classified into four categories:
• Passenger cars
• Larger vehicles
(Involving Light utility vehicles/minibuses, Light and heavy trucks, buses)
• Others (Other, remaining vehicles)
• Unknown (Unknown type of target vehicle)
In GIDAS the type of the target vehicle is known for all accidents which can be assigned to a
test scenario. The number of vehicles on which the distributions about the vehicle type is
based, is equal to the number which is given at the beginning of section 5.3.
Based on GIDAS and OTS analyses it can be concluded that subject vehicle mainly collides
with another passenger car. Only on motorways a collision with another opponent is a bit
increased.
In ONISR the type of the opposing vehicle is known for all accidents which can be assigned
to the test scenarios. Passenger cars are the main target vehicle type with at least 80%.
Table 23: Test scenario A - Opposing vehicle types for urban (U), rural roads (R) and motorways (M)
A
U
R
M
Passenger cars
GIDAS OTS ONISR
95%
84% 93%
91%
87% 87%
84%
88% 77%
Large vehicles
Others
GIDAS OTS ONISR GIDAS OTS ONISR
4%
8%
7%
1%
8%
9%
13%
1%
16%
10% 23%
-
Unknown
GIDAS OTS ONISR
8%
4%
2%
-
Table 24: Test scenario B - Opposing vehicle types for urban (U), rural roads (R) and motorways (M)
B
U
R
M
Passenger cars
GIDAS OTS ONISR
94%
92% 93%
91%
89% 91%
-
Large vehicles
Others
5%
7%
7%
1%
1%
9%
10%
9%
1%
-
Unknown
GIDAS OTS ONISR
-
Table 25: Test scenario C - Opposing vehicle types for urban (U), rural roads (R) and motorways (M)
C
U
R
M
Passenger cars
GIDAS OTS ONISR
87%
92% 90%
90%
86%
-
Large vehicles
Others
13%
6%
10%
9%
14%
1%
-
Unknown
GIDAS OTS ONISR
-
Table 26: Test scenario D - Opposing vehicle types for urban (U), rural roads (R) and motorways (M)
D
U
R
M
Passenger cars
GIDAS OTS ONISR
94%
89% 93%
88%
90% 89%
79%
-
Large vehicles
Others
5%
9%
7%
1%
12%
9%
11%
21%
-
Unknown
GIDAS OTS ONISR
2%
1%
-
The distributions of the target vehicle types in the French EDA dataset are shown in Table
27. All type of the opposing vehicle is known for all accidents. The category ‘passenger cars’
is the main opposing vehicle type with at least 80% for all test scenarios. However, two
scenarios can be highlighted. Regarding the accidents assigned to test scenario the involved
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target vehicles are larger than a passenger car in 15% of test scenario A and in 20% of test
scenario C.
Table 27: EDA - Opposing vehicle types for test scenarios
Test scenarios
A
B
C
D
Passenger cars
85%
94%
80%
94%
Large vehicles
15%
6%
20%
6%
Others
0%
0%
0%
0%
Unknown
0%
0%
0%
0%
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5.4
Driver related information
Information about the driver’s reactions and the behaviours in critical situations are highly
desirable features for pre-crash scenario definition (Work Package 4) and for the driver
behaviour evaluation (Work Package 3). Since driver behaviour will also have an impact on
system effectiveness of the vehicle safety system, driver related information is also of
relevance for the socio-economic assessment (Work Package 2). Driver related information
extracted from the accident databases is based on passenger cars involved in accidents
which can be mapped to the test scenarios A-D. The driver of the subject vehicle might be
influenced mainly by the pre-crash sensing safety system. Therefore, in this section only
information to the driver of the subject vehicle is shown.
Each subject vehicle is occupied with a driver. Therefore in GIDAS the same number of
datasets is available for analyses about the driver than for the analyses about the subject
vehicle (3,267 datasets). Again, data shown are GIDAS-internal weighted.
For driver related information distributions about specified attributes are given. The
distributions are based on 1,367 drivers for test scenario ‘A’, on 1,202 drivers for test
scenario ‘B’, on 279 drivers for test scenario ‘C’ and on 419 drivers for test scenario ‘D’. The
distributions also include the share of cases with missing or unknown values.
5.4.1
Driver’s age
The driver’s age might be a contributing factor of the reaction time and kind related to the
warnings and actions of an active safety system. The distributions about the driver’s age are
shown in Figure 28 for the different test scenarios.
100%
90%
80%
70%
unknown age
60%
> 75 years
50%
66 - 75 years
46 - 65 years
40%
26 - 45 years
30%
18 - 25 years
20%
< 18 years
10%
0%
GIDAS OTS ONISRGIDAS OTS ONISRGIDAS OTS ONISRGIDAS OTS ONISR
A
B
C
D
Figure 28: GIDAS / OTS / EDA – Driver’s age groups
The GIDAS data shows very similar distributions about the driver’s age for the different test
scenarios. About one quarter of the analyzed drivers of the subject vehicles are between 18
and 25 years old. About one third of the driver is between 26 and 45 years old. Almost
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another quarter of the subject vehicle’s driver is between 46 and 65 years old. The rest of the
drivers are older than 65 or the age is unknown.
The data for OTS are broadly similar to the other databases considering the amount of
unknown driver’s ages.
The ONISR results show very similar distributions about the driver’s age for the different test
scenarios. Approximately 25% of subject vehicle drivers are between 18 and 25 years old
and more than 63% are younger than 45 years. Almost one quarter of subject vehicle drivers
are between 46 and 65 years old.
The results of the EDA database aren’t demonstrated in the figure above but show that
approximately 2 out of 3 subject vehicle drivers are younger than 45 years, visible for all test
scenarios. For elderly drivers (age over 66 years), two main test scenarios are striking: rearend collisions (test scenario A) and cut-in conflicts (test scenario D). Assumed advanced
drivers (ages of 46-65 years) seem to be more involved in intersection accidents (test
scenario B) and oncoming traffic accidents (test scenario C). Young drivers with an age up to
25 years are slightly over represented in cut-in accidents (test scenario D).
5.4.2
Manoeuvres
This section provides information about the driver’s manoeuvres. The gathered manoeuvre
data mainly refers to the pre-crash phase as a driver’s reaction to avoid the crash. The
continuance of the actions can’t be identified clearly for each case which also varies within
the different databases used. The distribution about manoeuvres subdivided in test scenarios
is given in Figure 29.
100%
90%
80%
70%
60%
Unknown
None
50%
Accelerating
40%
Steering & Braking
Braking
30%
Steering
20%
10%
0%
GIDAS OTS
A
EDA GIDAS OTS
B
EDA GIDAS OTS
C
EDA GIDAS OTS
EDA
D
Figure 29: GIDAS / OTS / EDA – Distribution of manoeuvres in test scenarios A-D
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ASSESS D1.2
The information about manoeuvres as given in Figure 29 is not exactly coded in GIDAS as
needed for this statistic. For the distribution a combination of several GIDAS variables is
used. The braking deceleration based on reconstruction and drivers’ answers on questions
about braking and steering reactions are used for generating the diagram. Because of using
results from drivers’ questionnaires the reliability of the distribution about manoeuvres before
crash can only provide a rough tendency with a minor reliability.
Based on GIDAS data it can be concluded that the driver only reacts very rarely by steering
before crash. Only in test scenario D (cut-in) the share of steering is slightly increased. The
most common reaction before crash is braking.
The results from OTS and GIDAS cannot be compared directly. In GIDAS ‘braking’ means
that the cars decelerate. This is not necessarily equal to the information that a driver braked
before the crash. The shown GIDAS data is extracted from reconstruction (braking
deceleration value of the car) and from driver interviews retrospectively. Thus, the attribute
‘braking’ is assigned to an accident if the reconstructed braking deceleration is diverse from 0
or the driver’s braking action is known. This difference between the analyses of GIDAS and
OTS might possibly explain the smaller share of ‘no reaction before crash’ in all test
scenarios in GIDAS compared to OTS.
Furthermore, OTS data are separated in an additional category ‘accelerating&steering’. Due
to the overall comparison this group is divided into two parts with the same size which are
distributed to ‘accelerating’ and ‘steering’.
Summarizing the results of the EDA analysis more than 67% of drivers have performed a
manoeuvre to avoid the collision. In most of the cases, drivers brake or combine braking and
steering (68% for test scenario A, 66% for test scenario B, 47% for test scenario C and 56%
for test scenario D). We notice that for test scenario C we have 30% of unknown values
about driver’s manoeuvre. This is due to information not available for them (drivers don’t
remember or driver isn’t interviewed.). This lack of data could also be linked with a higher
severity level for test scenario C (30% of severe or fatal injuries) compared to the others
(less than 9% of severe or fatal injuries).
5.4.3
Distraction / Inattention
To gain further knowledge on the driver’s behaviour before the crash subject vehicle driver’s
information about possibly distraction or inattention is summarized in this section. In general,
it is quite difficult to determine if distraction or inattention have had an influence on the
accident genesis.
Data of GIDAS is not available so far due to fact that the elicitation of the driver’s distraction
or inattention has been introduced recently in the database. Thus, the number of cases with
the necessary information quality is too small for reliable statements and omitted in this
analysis.
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ASSESS D1.2
100%
90%
Unknown
80%
70%
No distraction
60%
50%
Distraction not
otherwise specified
40%
30%
Event from outside
of the car
20%
Event inside the car
(Secondary Task)
10%
0%
OTS
EDA
OTS
A
EDA
B
OTS
EDA
OTS
C
EDA
D
Figure 30: OTS / EDA – Distraction / inattention of drivers in test scenarios A-D
One found striking fact in the EDA dataset is that for all test scenarios except test scenario D
the Secondary Task (event inside the car) is the main factor for distraction/inattention if it was
reported.
The French national database ONISR also includes information about the potential
distraction of drivers but only distributes into ‘Distraction not otherwise specified’ and ‘No
distraction’. Results of this analysis are shown in Table 28. In more than 90% of the cases
regarded, subject vehicle drivers have no distraction. For Test scenarios B, C and D 5% of
drivers are distracted before the crash. This ratio is higher for test scenario A with 10% of
drivers distracted.
Table 28: ONISR – Frequencies of driver’s distraction/inattention for test scenarios A-D
Distraction
Distraction not otherwise specified
No
5.4.4
A
10%
90%
B
5%
95%
C
5%
95%
D
4%
96%
Driver’s impairment
This section provides additional driver’s related information in terms of the possibly driver’s
impairment.
Again this information cannot be extracted from GIDAS data. The collection of driver’s
impairment has been introduced recently in this database. Thus, the number of cases with
the necessary information quality is too small for reliable statements and omitted in this
analysis.
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ASSESS D1.2
100%
90%
80%
No impairment
Frequencies
70%
60%
Multiple impairments
50%
Others
40%
Fatigue
30%
20%
Alcohol
10%
Drugs
0%
OTS
ONISR
A
OTS
ONISR
B
OTS
ONISR
C
OTS
ONISR
D
Figure 31: OTS / ONISR – Driver’s impairment in test scenarios A-D
In France drugs are tested on drivers only in fatal accidents. So drug impairment in France
could be underrepresented. This remark is relevant for ONISR and EDA data.
In ONISR we have one variable to code: fatigue, alcohol, drug and distraction without
possibility to combine these factors. So it’s possible to have a bias for multiple impairments.
Moreover we haven’t available data to detect them and to know which of them are
underrepresented. Alcohol is the main driver’s impairment, followed by fatigue and drugs. For
test scenario A more than 10% of the drivers have at least one impairment. This ratio is
about 17% for test scenario C versus less than 5% for test scenarios B and D.
Because of the general small number of available accidents in the EDA database and the
high numbers of unknown driver’s impairment, data is only shown as additional information in
Table 29. Focusing on the known data of this analysis, most of the drivers were in a good
state (‘None’). Alcohol could be identified as the main contributing impairment of the driver
for all test scenarios and especially in rear-end collisions (test scenario A).
Table 29: EDA – Driver’s impairment in test scenarios A-D
Test scenarios
A
B
C
D
Drugs
0%
1%
1%
3%
Alcohol
21%
4%
6%
8%
Driver’s impairment
Fatigue
Others
0%
0%
0%
0%
0%
0%
3%
0%
None
56%
65%
49%
67%
Unknown
23%
30%
43%
19%
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ASSESS D1.2
5.5
Environmental conditions
The following presented information about environmental conditions is based on passenger
cars involved in accidents which can be mapped to test scenario A-D. In particular, data on
environmental conditions are of relevance for the socio-economic assessment since test
results of the pre-crash system performance (Work Package 4) have to be verified against
data on real-world accident conditions.
The classification into road types is not provided for the distributions of the weather (see
5.5.1), road surface (see 5.5.2) and light conditions (see 5.5.3) since it is not expected that
these correlations contribute significantly. The information of the gathered environmental
conditions is shown in this section.
In contrast, the distributions about the daylight (see section 5.5.4) might differ for different
road types. Hence, sub-divisions are additionally provided for the road types urban roads,
rural roads and motorways.
The information about the environmental conditions in GIDAS is provided for each subject
vehicle and the shares of unknown cases are reported separately. Hence, in GIDAS for the
distributions about environmental conditions 3,267 datasets are used. Again, data shown are
GIDAS-internal weighted. The distributions are based on 1,367 drivers for test scenario ‘A’,
on 1,202 drivers for test scenario ‘B’, on 279 drivers for test scenario ‘C’ and on 419 drivers
for test scenario ‘D’.
5.5.1
Weather conditions
100%
90%
80%
70%
Unknown
60%
Snowy
50%
Foggy
Rainy
40%
Cloudy
30%
Fine
20%
10%
A
B
C
ONISR
EDA
OTS
GIDAS
ONISR
EDA
OTS
GIDAS
ONISR
EDA
OTS
GIDAS
ONISR
EDA
OTS
GIDAS
0%
D
Figure 32: GIDAS / OTS / EDA / ONISR - Weather conditions
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ASSESS D1.2
The distributions about the weather conditions based on GIDAS data are very similar
between the different test scenarios. Nearly 40% of the subject vehicles are involved in
accidents and fine weather conditions. Another 40% are involved in accidents when it is
cloudy. Only 10% of the subject vehicles are involved in accidents under rainy conditions.
Looking at the French EDA dataset in more than half of all accidents the weather conditions
are fine. In particular, test scenario A (rear-end collisions) heads this fact with 82% of all
concerned cases. Regarding the further scenarios a tendency can be identified towards
assumed worse weather conditions like rainy and cloudy. These conditions could influence
the performance of the pre-crash systems regarded meaningfully and with that the system’s
benefit. Focusing on rainy weather conditions test scenarios B-D (12-17%) show at least
doubled increased values than in test scenario A (6%). A further striking effect can be shown
for test scenario C (oncoming traffic conflicts) where foggy weather conditions seem to play a
decisive role compared to the other test scenarios.
In ONISR, the weather condition distributions are similar within the different test scenarios. It
has to be noticed that no cloudy conditions are recorded which is impossible. It seems the
police would consider cloudy conditions as fine conditions if there was no influence on the
accident. In general, weather conditions are fine in more than 77% of cases, bad conditions
(rainy, snowy, foggy) in around 18% and approximately 5% unknown. Test scenarios A, B
and D heads fine weather conditions with more than 79% and approximately 16% of bad
conditions for all concerned cases. Focusing on bad weather conditions (rainy, snowy,
foggy), test scenario C have the higher ratio with 22% of all concerned cases (only 71% of
fine conditions).
5.5.2
100%
90%
80%
70%
Unknown
60%
Ice
50%
Snow
40%
Wet
30%
Dry
20%
10%
0%
GIDAS OTS ONISRGIDAS OTS ONISRGIDAS OTS ONISRGIDAS OTS ONISR
A
B
C
D
Figure 33: GIDAS / OTS / ONISR - Road surface conditions
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ASSESS D1.2
As for the weather conditions the road surface conditions in GIDAS accidents do not differ
clearly between the test scenarios for the subject vehicles. Mainly the subject vehicles are
involved in accidents happening on dry roads.
In ONISR, results on road surface conditions are comparable with weather conditions. Thus,
test scenarios A, B and D have similar distributions for road surface conditions with
approximately 21% of bad road surface conditions (wet, snow, ice) and 30% for test scenario
C. Information about the road surface conditions are not available for less than 3% of all
concerned cases.
Because of the general small number of available accidents in the EDA database and the
high numbers of unknown road surface conditions, data is only shown as additional
information in Table 30. Focusing on the known data of this analysis, most of the road
surfaces were in a good state and dry. In particular, test scenario C (oncoming traffic
conflicts) seems to be effected gravely by worse conditions like wet and icy roads.
Table 30: EDA – Road surface conditions in test scenarios A-D
Test scenarios
Dry
53%
38%
21%
44%
A
B
C
D
5.5.3
Wet
18%
15%
26%
17%
Snow
0%
2%
1%
6%
Ice
0%
0%
4%
0%
Unknown
29%
45%
48%
33%
Light conditions
100%
90%
80%
Light conditions
70%
60%
Unknown
50%
No influence
40%
Dazzling sun
30%
20%
10%
0%
GIDAS OTS
A
EDA GIDAS OTS
B
EDA GIDAS OTS
C
EDA GIDAS OTS
EDA
D
Figure 34: GIDAS / OTS / EDA - Light conditions
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ASSESS D1.2
Based on GIDAS and OTS it can be concluded that there are only very few subject vehicles
involved in accidents in which dazzle is a contributing factor. But it has to be considered that
it is difficult to determine after the accident whether the driver was constrained by dazzling
light. It might be possible that the influence of dazzling light is underestimated in the data.
The lighting conditions presented in the EDA dataset show high frequencies (at least 80%) of
no contributing influence of light throughout all test scenarios. Dazzling sun has influence on
the accident genesis in roughly every tenth accident which could be assigned to the test
scenarios.
The French national database ONISR provides concordant information. At least 99% of the
accidents assigned to the test scenarios show no contributing influence by glaring sun.
5.5.4
Daylight
Depending on the road type the available lighting might differ. Therefore, the distributions
about daylight are additionally subdivided into road types. The information about daylight is
presented in Figure 35 to Figure 37. The daylight classification distinguishes three main
categories: darkness, twilight and daylight. Within the category darkness this analysis
distinguishes between ‘darkness w/o artificial lighting’, ‘darkness w/ artificial lighting’ and
‘darkness (not other specified)’.
100%
90%
80%
70%
Daylight
Darkness w/o artificial ligthing
60%
Darkness w artificial ligthing
50%
Darkness (not other specified)
40%
30%
Twilight
20%
Daylight
10%
A
B
C
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
urban roads
0%
D
Test scenarios
Figure 35: GIDAS - Daylight
As for weather and road surface conditions the distributions are similar for the test scenarios
in GIDAS. Mainly the subject vehicles are involved in accidents which occur in daylight.
Dependant on the road type the characteristics of accidents in the dark differ between urban
and rural or between urban roads and motorways. This difference is caused by infrastructural
circumstances. In urban areas the roads are usually lighted. On motorways or rural roads
there is mainly no artificial lighting.
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ASSESS D1.2
100%
90%
80%
70%
Daylight
60%
50%
40%
30%
Twilight
20%
Daylight
10%
A
B
C
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
urban roads
0%
D
Test scenarios
Figure 36: OTS - Daylight
100%
90%
80%
70%
50%
40%
30%
Twilight
20%
Daylight
10%
A
B
C
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
urban roads
motorway
rural roads
0%
urban roads
Daylight
60%
D
Test scenarios
Figure 37: ONISR - Daylight
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ASSESS D1.2
In ONISR, the subject vehicles are mainly involved in accidents which occur in daylight
(approx. 70% of cases). Dependent on the road type the characteristics of accidents in the
dark differ between urban and rural or motorways. This difference is caused by infrastructural
circumstances. In urban areas the roads are usually lighted. On motorways or rural roads
there is mainly no artificial lighting. For approximately 5% of all cases in all test scenarios,
accidents occur in twilight conditions. Accidents in motorways occur in daylight only in 62%
of cases for all test scenarios.
The analysis of the EDA dataset is restricted to the test scenarios and doesn’t include a subdivision into the different road types. Therefore, the results are shown separately in Table 31.
In France, there is no surprise regarding to the accident lighting conditions. Indeed, most of
them are during daylight. Nevertheless, it can be noticed that for accidents at intersections
(test scenario C) around 25% of them happen when it is dark and when there is no artificial
lights.
Table 31: EDA – Daylight in test scenarios A-D
Daylight
Test scenarios
Daylight
Twilight
Darkness
(not specified)
79%
78%
69%
72%
0%
4%
3%
3%
0%
0%
0%
0%
A
B
C
D
5.6
Darkness
with artificial
lighting
9%
9%
5%
19%
Darkness
w/o artificial
lighting
12%
9%
23%
6%
Ranking of test scenarios (R1)
In addition to the detailed examination of the test scenario parameters, the relative
importance of each test scenario was considered by taking into account both the severity and
the frequency for each casualty severity. This was achieved by weighting all the casualties in
the accident with respect to the injury severity. Thus the rankings took account of the total
injury outcome of the accident.
Ranking 1 (R1) used the same criteria as the general data query to select those accidents
considered. Among other things this refers to initial contacts with vehicles with 4+wheels. As
introduced in the deliverable D1.1 [2] the calculation of the number of the weighted accidents
follows the formula:
Number of slightly injured people· 0.011 +
Number of seriously injured people · 0.11 + Number of fatalities · 1.
The results of these calculations are presented in Table 32 (rounded numbers).
Table 32: Ranking R1 – numbers and percentages of weighted accidents for test scenarios A-D
GIDAS
A
B
C
D
N
38
42
22
14
OTS
%
33
36
19
12
N
12
15
38
15
%
15
19
48
19
STATS19
N
%
703
15
1506
33
1711
38
619
14
EDA
N
4
26
29
1
ONISR
%
6
44
48
2
N
455
692
2101
65
%
14
21
63
2
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ASSESS D1.2
Transferring these numbers into percentages for each database leads to the distributions
that can be seen in Figure 38.
100%
90%
80%
70%
60%
A
50%
B
40%
C
30%
D
20%
10%
0%
GIDAS
OTS
STATS19
EDA
ONISR
Figure 38: Ranking R1 – Distribution in percentage of weighted accidents for test scenarios A-D
Clear differences are noticeable within the distributions of the test scenarios and the
databases. By moving these proportions to a ranking results into Table 33 which gives rank 1
to the test scenario with the highest percentage.
Table 33: Overview of Ranking R1 for databases and test scenarios A-D
GIDAS
2
1
3
4
A
B
C
D
OTS
4
3
1
2
STATS19
3
2
1
4
EDA
3
2
1
4
ONISR
3
2
1
4
When separating these results into the groups in-depth databases and national databases
further conclusions can be made. For that purpose Table 34 shows the mean averages of
the ranked results R1 which allow further interpretations of the data. It’s mentionable that in
both groups the results of the ranking R1 are the same. The most important identified test
scenario is C – oncoming traffic collisions followed by intersection (B), rear-end (A) and cut-in
collisions (D).
Table 34: Mean averages of ranked results R1
A
B
C
D
In-depth databases
Mean
Rank
3
3
2
2
1.7
1
3.3
4
National databases
Mean
Rank
3
3
2
2
1
1
4
4
All databases
Mean
Rank
3
3
2
2
1.4
1
3.6
4
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ASSESS D1.2
6 Assessment of proposed test scenarios
Work Package 4 (pre-crash system performance evaluation) has created 4 test scenarios
with approximately 20 manoeuvres and different driver reactions which lead to 56 tests [4].
This chapter describes the assessment of these tests on the basis of the real world accident
analyses done in the previous chapter 5.
Therefore, section 6.1 explains the relevant available specifications that will be verified in
section 6.2 by the accident database analyses for each test scenario separately. Following,
section 6.3 introduces the ranking R2 and hence demonstrates the meanings of the different
test scenarios considering injury costs. The last section 6.4 describes why the use of
weighted speed data is omitted in this assessment.
6.1
Specifications of proposed Work Package 4 tests
The proposed test matrix is separated into four main categories. These are rear-end
collisions (A), intersection conflicts (B), oncoming traffic collisions (C) and cut-in conflicts (D).
In addition, there are subcategories A1, A2, A3, D1 and D2 which are introduced in section
4.3.1 and described in detail in Table 35. The test scenarios B and C aren’t subdivided with
regard to different initial speed set-ups since there is no big scenario internal change
between the movements of the subject and the target vehicle. Nevertheless, there are the
sublevels B1 and B2 for open respectively obstructed view.
Table 35: Features of sublevels/subcategories A1, A2, A3, D1 and D2
Sublevel
Main feature
A1
Target vehicle is slower
than the subject vehicle.
Target vehicle moves with a
constant driving speed.
A2
Target vehicle decelerates
e.g. to stop.
A3
Target vehicle has stopped
when the subject vehicle’s
system detected it.
Sketch – example (SV/TV … subject/target vehicle)
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ASSESS D1.2
D1
Target vehicle approaches
from oncoming direction
D2
Target vehicle changes the
lane which is occupied
The proposed specifications are summarized in Deliverable D4.1 [4]. Besides the initial
positioning of the cars, the main parameter for the test scenarios is the driving speed of both
the subject and the target vehicle. Furthermore, parameters for the target vehicle are
determined with regard to the braking deceleration, initial distance to the subject vehicle, to
the lateral overlap and the different driver reactions. Table 36 demonstrates the driving
speeds proposed by Work Package 4 that are assessed in section 6.2.
Table 36: Proposed driving speeds by Work Package 4 for the test scenarios
Test scenario
Sublevel
A1
(Slower lead vehicle)
A
(rear-end)
A2
(Decelerating vehicle)
A3
(Stopped lead vehicle)
B
(intersection)
B1/B2
(Urban scenarios)
C
(oncoming)
C1
(Rural scenario)
D
(cut-in)
D1
(Oncoming)
D2
(Lane change)
6.2
subject vehicle
target vehicle
50
100
50
80
50
80
50
50
40
64
50
10
20
50
80
0
0
10
50
40
64
10
80
40
Verification of parameters proposed for test scenarios
The real world accident analyses cannot provide statements about all of the proposed
parameters. This is due to the fact that in-depth databases are restricted with regard to
information about the pre-crash phase. Furthermore, national databases used do not provide
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ASSESS D1.2
information about speeds. Hence, the in-depth databases GIDAS and OTS are used in
particular and the verification focuses on the core parameter driving speed. Further
indications concerning the offset prior to the crash could be gained by a further expert
analysis of the overlaps in the crash time point (see section 5.2.3). Again (see also section
4.4.2), it is to emphasize that relative speeds (closing speeds) are not presented in this
section (except for A1 and A3). This is due to the lack of known point of time at which the
driving speed was identified and the fact that the accident data is merged to the test
scenarios and hence doesn’t allow clear assignments with respect to the subject and target
vehicle on accident level.
As in chapter 5 the recorded speed values (absolute numbers) of the involved vehicles are
used which mirror the real world. The alternative use of driving speed data weighted by injury
costs is considered to be inappropriate and is discussed in section 6.4.
As explained in section 4.3 the assignment of the road traffic accidents of the in-depth
databases to the subcategories A1, A2, A3, D1 and D2 was transferred as accurate as
possible.
For OTS (UK), this allocation was achieved by considering the driving speeds for the target
vehicle and the OTS conflict codes. For the subcategories of test scenario A, it was not
possible to determine whether the target vehicle was decelerating or travelling at a constant
slower speed than the subject vehicle. This was because the deceleration is not recorded.
Therefore, subcategories A1 and A2 were merged. Accidents were allocated to A3 if the
target vehicle was stationary.
For GIDAS (Germany), the allocation of most of the accidents to all subcategories could be
achieved. Test scenario A was additionally split to a subcategory ‘Others’ that includes e.g.
cars with unknown data necessary for assigning to A1-A3. Cars belonging to the subcategory
‘Others’ are not considered anymore. For the analysis the same dataset like in sections 5.2.1
and 5.3.1 was used. This means, first impacts with any kind of vulnerable road users are
excluded. In general higher detected speeds can be associated with the infrastructure in
Germany and the speed limit free sections on motorways.
Furthermore this section contains information of the driving speeds for some specific
percentiles also to provide different system challenging levels. The 75th percentile was
chosen as the primary reference value for the assessment by Work Package 1 since then
75% of the concerned cases are addressed.
6.2.1
Test scenario A (rear-end collision)
In the rear-end scenarios, conflicts are presented for both the entire test scenario and
separated for the sublevels involving different movement speeds of the target vehicle.
The following Figure 39 gives an overview about the driving speeds of the subject vehicle for
test scenario A and its sublevels. The additional column ‘A1+A2’ stands for the combination
of the sublevels A1 and A2 and is used to show data from the OTS database since it cannot
differentiate clearly between them. Summarizing the results for the entire test scenario A
both databases show driving speed similarities of the subject vehicle with approximately
50km/h. While this characteristic stays nearly conform in the OTS database for the sublevels
A1-A3 GIDAS differs meaningful within them with driving speeds from 85 km/h to 50km/h.
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ASSESS D1.2
100
90
Driving speed (km/h) of SV
80
70
60
GIDAS (mean average)
50
GIDAS (median)
OTS (mean average)
40
OTS (median)
30
20
10
0
A
A1
A1+A2
A2
A3
Test scenario A
Figure 39: Driving speeds of subject vehicle in GIDAS and OTS for test scenario A
Figure 40 gives an overview about the driving speeds of the target vehicle for test scenario A
and its sublevels. For the entire test scenario an average speed can be ascertained for a
range from 0 to 20km/h with regard to the noticeable divergences between the mean
averages and medians. Focusing on the sublevels A1 and A2 demonstrates target vehicle
speeds in the range from 25km/h to nearly 50km/h. As expected A3 shows for both
databases standstill of the target vehicle because of the definition of this sublevel.
60
Driving speed (km/h) of TV
50
40
GIDAS (median)
30
OTS (mean average)
OTS (median)
20
10
0
A
A1
A1+A2
A2
A3
Test scenario A
Figure 40: Driving speeds of target vehicle in GIDAS and OTS for test scenario A
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ASSESS D1.2
More specifications are necessary to assess the proposed driving speed parameters for each
sublevel of test scenario A (rear-end collisions). Regarding the 50th (median), 75th and 95th
percentiles of the gathered driving speeds lead to further possibilities of interpreting the data.
Driving speeds are demonstrated exclusively in the following charts for the test scenarios A1
and A2 by the GIDAS database since OTS cannot differentiate clearly between them.
Test scenario A1:
To assess the test scenario A1 only the GIDAS database could provide detailed information.
Figure 41 shows the percentile-distribution for test scenario A1 (slower lead vehicle with a
constant speed) for both subject vehicle (SV) and target vehicle (TV). The concerned cases
are merged over all road types due to the limited number of crashes. Higher speeds up to
approximately 140km/h can be identified that are associated with the infrastructure in
Germany and the speed limit free sections on motorways. When picking the 75th percentile
as primary reference value subject vehicles move with driving speeds of 120km/h and target
vehicles of 80km/h.
160
Driving speed km/h
140
120
100
80
GIDAS A1 SV
60
GIDAS A1 TV
40
20
0
50th
75th
95th
Figure 41: Driving speed (percentiles) for test scenario A1 from GIDAS
The accident analyses propose driving speeds up to 120 km/h (75th percentile) for the subject
vehicle in test scenario A1. Due to the technical feasibility in testing (testability) speeds of
more than 100 km/h are considered to be unsafe and therefore have to be adapted.
Table 37: Suggested test driving speeds for test scenario A1 (ASSESS D4.1, 2010).
Test scenario
Sublevel
A
(rear-end)
A1
(Slower lead vehicle)
subject vehicle
target vehicle
50
100
10
20
In addition to the driving speed data for test scenario A1 the relative (closing) speed values
can be estimated and are shown in Figure 43 since the target vehicle goes with a lower,
constant driving speed. Comparing these values with the suggestions out of Table 37 closing
speeds are proposed from 40km/h (median) to 60km/h (75th percentile). It has to be
mentioned that no further considerations about energy conversions are considered.
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Relative (closing) speed (km/h) for A1
ASSESS D1.2
100
90
80
70
60
50
40
30
20
10
0
mean average 50th (median)
75th
95th
Figure 42: Relative (closing) speed for test scenario A1 from GIDAS
Test scenario A2:
For the assessment of test scenario A2 again GIDAS is used exclusively. The driving speed
percentiles for test scenario A2 are shown in Figure 43. The relative (closing) speed cannot
be estimated as the driving speed difference of the subject and target vehicle because of the
lack of known point of time at which the driving speeds were identified. When picking the 75th
percentile subject vehicles move with driving speeds of 75km/h and target vehicles of
60km/h. For each percentile the differences between subject and target vehicles’ driving
speeds are small and cover a range from minimal 5km/h to maximal 25km/h.
140
Driving speed km/h
120
100
80
GIDAS A2 SV
60
GIDAS A2 TV
40
20
0
50th
75th
95th
Figure 43: Driving speed (percentiles) for test scenario A2 from GIDAS
Comparing the proposed speeds (see Table 38) with the 50th and 75th percentile of the
driving speeds the subject vehicle speed can be confirmed. The accident analyses propose a
slightly lower target vehicle driving speed. When using the shown driving speeds it has to be
considered that subject vehicles and target vehicles are not linked directly on accident level.
The values are calculated either for all subject vehicles or all target vehicles.
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ASSESS D1.2
Test scenario
Sublevel
A
(rear-end)
A2
(Decelerating vehicle)
subject vehicle
target vehicle
50
80
50
80
Test scenario A3:
The assessment of test scenario A3 is done on the basis of both in-depth databases GIDAS
and OTS. The driving speed data is presented with the percentile-distributions in Figure 44.
The analysis results show for all percentiles comparable subject vehicle driving speeds
between both databases. For the 75th percentile the speeds can be determined in a range
from 50km/h to 65km/h.
Figure 44: Driving speed (percentiles) for test scenario A3 from GIDAS and OTS
As for test scenario A1 relative (closing) speed information can be identified for test scenario
A3 since the driving speed of the target vehicle is known to be zero and hence the wanted
data corresponds to the driving speed of the subject vehicle. Comparing the speed
information with the proposed values of Table 39 reveal similar results and hence can be
confirmed.
Test scenario
Sublevel
A
(rear-end)
A3
(Stopped lead vehicle)
6.2.2
subject vehicle
target vehicle
50
80
0
0
Test scenario B (intersection conflict)
In the intersection scenario, conflicts are presented for both the entire test scenario and
separated for road types although there is only an urban scenario proposed by Work
Package 4. This is supported by the proportion of urban (81%) and rural roads (19%) basing
on GIDAS and their meaningful driving speed differences.
The following Figure 45 gives an overview about the driving speeds of the subject vehicle for
test scenario B. Additionally to the proposed specifications the division is done into both road
types urban and rural roads. Regarding the entire test scenario B the speed results are close
to each other and cover a range from 40km/h to approximately 50km/h. The detected delta
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ASSESS D1.2
range of 10km/h can be recovered in the driving speeds distributed into urban and rural
roads though on different levels.
70
60
50
40
GIDAS (median)
OTS (mean average)
30
OTS (median)
20
10
0
B
B (urban roads)
B (rural roads)
Test scenario B
Figure 45: Driving speeds of subject vehicle in GIDAS and OTS for B and road type classification
The target vehicles in test scenario B are considered in Figure 46 which gives an overview
about the driving speeds and the distribution into the road types urban and rural roads. For
the entire test scenario B a very small driving speed range from 30km/h to 35km/h can be
observed which changes significantly when regarding the rural roads.
40
35
30
25
GIDAS (median)
20
OTS (mean average)
15
OTS (median)
10
5
0
B
B (urban roads)
B (rural roads)
Test scenario B
Figure 46: Driving speeds of target vehicle in GIDAS and OTS for B and road type classification
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ASSESS D1.2
Regarding the 50th (median), 75th and 95th percentiles of the gathered driving speeds lead to
Figure 47 for GIDAS and OTS for both subject vehicle (SV) and target vehicle (TV). The
distributions contain in each case all road types and be alike for the different percentiles in a
total range from approximately 40km/h up to 100km/h.
Figure 47: Driving speed (percentiles) for test scenario B from GIDAS and OTS
Considering the different point of views concerning the road types the proposed subject
vehicle driving speed (see Table 40) is covered by 50th percentile but should be increased to
65km/h (75th percentile) from the accidentology’s point of view.
Table 40: Suggested test driving speeds for test scenario B (ASSESS D4.1, 2010).
Test scenario
Sublevel
B
(intersection)
B1/B2
(Urban scenarios)
6.2.3
subject vehicle
target vehicle
50
50
10
50
Test scenario C (oncoming traffic collision)
In the oncoming traffic scenarios, conflicts are presented for both the entire test scenario and
separated for road types although there is only a rural scenario proposed by Work Package
4. This is supported by the proportion of urban (46%) and rural roads (54%) basing on
GIDAS and their meaningful driving speed differences.
Figure 48 gives an overview about the driving speeds of the subject vehicle for test scenario
C. Additionally to the proposed specifications the division is done into both road types urban
and rural roads. Regarding the entire test scenario C the speed results are close to each
other and cover a range from 55km/h to approximately 60km/h. The detected delta range of
5km/h can be recovered in the driving speeds distributed into urban and rural roads though
on different levels.
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ASSESS D1.2
80
70
60
50
GIDAS (median)
40
OTS (mean average)
30
OTS (median)
20
10
0
C
C (urban roads)
C (rural roads)
Test scenario C
Figure 48: Driving speeds of subject vehicle in GIDAS and OTS for test scenario C and road types
The target vehicles in test scenario C are considered in Figure 49 which gives an overview
about their driving speeds and the distribution into the accident scenes urban and rural
roads. For the entire test scenario C a driving speed range from 50km/h to 60km/h is
analysed. Furthermore, this detected delta range from approximately 5km/h - 10km/h can be
recovered in the road type classification data.
70
60
50
40
GIDAS (median)
OTS (mean average)
30
OTS (median)
20
10
0
C
C (urban roads)
C (rural roads)
Test scenario C
Figure 49: Driving speeds of target vehicle in GIDAS and OTS for test scenario C and road types
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ASSESS D1.2
Regarding the 50th (median), 75th and 95th percentiles of the gathered driving speeds lead to
Figure 50 for GIDAS and OTS for both subject vehicles (SV) and target vehicles (TV). The
distributions contain in each case all road types and be alike for the different percentiles in a
total range from approximately 50km/h up to 110km/h.
Figure 50: Driving speed (percentiles) for test scenario C from GIDAS and OTS
Comparing these real-world accident data with the proposed driving speeds (see Table 41)
confirms the assumption of same speeds for both subject and target vehicles. The 75th
percentile covers both proposals of 40km/h respectively 64km/h but also gives an indication
for higher driving speeds up to 80km/h.
Table 41: Suggested test driving speeds for test scenario C (ASSESS D4.1, 2010).
Test scenario
Sublevel
C
(oncoming)
C1
(Rural scenario)
6.2.4
subject vehicle
target vehicle
40
64
40
64
Test scenario D (cut-in conflict)
In the cut-in scenarios, both conflicts in the opposite direction (D1) and in the same direction
(D2) are taken into account. For test scenarios D1 the accident scenario in Figure 51 was
included in the analysis. A criterion concerning the placement of the first impact was used to
determine if the vehicle was the subject or the target vehicle, respectively. The vehicle with a
first impact in the front was considered as the subject vehicle. If both vehicles had a first
impact in the front the accident conflict was used twice e.g. both vehicles driving speed
counted both as a subject vehicle and as the target vehicle. This approach make the
numbers for the target driving speeds reasonable high and is difficult to use for comparison
with the proposed test driving speeds. Note that the described method above was only used
to analyse the driving speeds but for ranking of the scenario a weighting factor of 0.5 was
used on each accident because the same scenario is included in the intersection scenarios
(B). As mentioned in previous scenarios the closing speed for this scenario is not available.
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ASSESS D1.2
Figure 51: Example of an accident scenario
For cut-in test scenarios (D) driving speeds presented in Table 42 were suggested from
Deliverable 4.1 [4].
Table 42: Suggested test driving speeds for test scenario D1 and D2 (ASSESS D4.1, 2010).
subject vehicle
target vehicle
Test scenario
Sublevel
D
(cut-in)
D1
(oncoming)
D2
(lane change)
50
10
80
40
Figure 52 gives an overview about the driving speeds of the subject vehicle for test scenario
D and its subcategories. For scenario D1 the median for GIDAS shows that the proposed
test speed are in line with the real-world data, however the OTS data shows somewhat
higher values. For scenario D2 the real-world data from the two sources diverge. OTS shows
here lower driving speeds than the proposed test speeds where GIDAS shows higher speeds
up to approximately 100km/h that could be associated with the infrastructure in Germany and
the speed limit free sections on motorways.
110
100
90
80
70
60
GIDAS (median)
50
OTS (mean average)
40
OTS (median)
30
20
10
0
D
D1
D2
Test scenario D
Figure 52: Driving speeds of subject vehicle in GIDAS and OTS for test scenario D
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ASSESS D1.2
Figure 53 gives an overview about the driving speeds of the target vehicle for test scenario D
and its subcategories. As discussed in the beginning of the chapter the target vehicle might
show higher values than expected due to the method used. However, the GIDAS values are
closer to the proposed speed for D1 while the OTS numbers are very high. On the other
hand OTS show matching numbers for D2 while GIDAS show an average of 60km/h.
70
60
50
40
GIDAS (median)
OTS (mean average)
30
OTS (median)
20
10
0
D
D1
D2
Test scenario D
Figure 53: Driving speeds of target vehicle in GIDAS and OTS for test scenario D
Test scenario D1:
Figure 54 shows the percentile-distribution for test scenario D1 for both subject vehicle (SV)
and target vehicle (TV) from the GIDAS and OTS databases. The 95th percentile is not
available from the OTS database. If 75% of the cases from real-world data should be
covered by the test proposed there need to be an increase of the test driving speeds. GIDAS
shows values around 60km/h while OTS shows values around 80km/h. However, with the
proposed subject vehicle driving speeds of 50km/h almost 50% of the real-world cases will
be addressed. However, the target vehicle’s driving speed cannot be verified regarding the
75th percentile and should be increased slightly.
Figure 54: Driving speed (percentiles) for test scenario D1 from GIDAS and OTS
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ASSESS D1.2
Test scenario D2:
Figure 55 shows the percentile-distribution for test scenario D2 for both subject vehicle (SV)
and target vehicle (TV) from the GIDAS and OTS database. The proposed subject vehicle
test scenario speed of 80km/h is verified by OTS on a 75th percentile level. GIDAS shows a
much higher level on both 75th (~130km/h) and 50th (~100km/h) percentile levels which is
caused by the fact that these cases mainly occur on German motorways.
Figure 55: Driving speed (percentiles) for test scenario D2 from GIDAS and OTS
6.3
6.3.1
Overall ranking (R2)
Establishment of ranking R2
In Ranking R1 only conflicts with at least four-wheeled vehicles are considered based on the
initial choice of accident type codes that focuses on conflicts vehicle vs. vehicle and keeps
the project relevance (assessment of currently available systems). Out of this data query of
Ranking R1 test specifications can be derived in terms of e.g. speeds and occurrence
frequencies. In Figure 56 the establishment of this ranking R1 and a further ranking R2 are
shown.
The ranking of the test scenarios is done by the use of injury cost-related weighting factors
that reflects their importance.
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ASSESS D1.2
Figure 56: Issues on ranking with VRU (Vulnerable Road Users)
The ranking R2 aims to extend R1 in order to gain an entire view of the actual road traffic
crash occurrence related to currently and continuatively available pre-crash sensing systems.
Therefore, initial contacts with two-wheeled vehicles are included additionally to obtain an
overall ranking. These two-wheelers were added as an initial target vehicle because current
systems may detect such targets, despite the fact that they are not guaranteed to do so.
Hence, further accident type codes have to be taken into account and to be merged to the
test scenarios A-D. Related to the initial general data query (see section 4.2) the choice of
accident types is restricted to accident types of groups 2&3 and 6. The determined
assignments can be seen in Table 43. Remaining accident type codes referring to crashes
that can’t be assigned to the test scenarios defined or are omitted due to a very small
number of cases. Finally, ranking R2 considers conflicts related to the test scenarios with all
kind of vehicles with two or more wheels, however no pedestrians.
Table 43: Assignment accident type codes for R2 (additionally considered accident types are bold)
Test scenario
A
B
C
D
Assigned accident type codes
Type 2
Type 3
20, 23
21*, 22*, 24*, 27, 28*
30, 31, 32, 33, 34, 35, 37
21*, 22*, 24*, 28*
Type 6
60, 61, 62
66, 68
63, 64
* 0.5 weighting factor
The additional accident type codes are presented as sketches with short explanations in
Figure 57.
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ASSESS D1.2
Figure 57: Determined Assignment of accident type codes with VRU to Test Scenarios B and D
6.3.2
Results of ranking R2
This section presents the results of the analyses of the ranking R2 that includes further
bicyclists and powered two-wheelers as initial collision partners. In Table 44 all numbers of
available accidents (weighted by injury severity and costs) of the in-depth databases GIDAS,
OTS and EDA as well as the national database ONISR are shown. The summations of the
number of sublevel accidents sometimes do not conform with the value of the main levels
which is caused by unsuccessful clear assignments of the accidents to the sublevels.
Besides, EDA data are not separated into the sublevels of test scenario A because of the
small number of available cases.
Table 44: Ranking R2 – numbers of weighted accidents for test scenarios A-D and their sublevels
GIDAS
A
A1
A2
A3
B
C
D
D1
D2
N
45
9
12
13
79
26
19
13
6
OTS
%
27
47
15
11
-
N
14
5
2
20
39
18
10
8
EDA
%
15
22
43
20
-
N
5
30
29
1
1
0
ONISR
%
8
46
44
2
-
N
799
1,376
2,714
182
174
8
%
16
27
54
4
-
Transferring these numbers into percentages for each database leads to the distributions
that can be seen in Figure 58.
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ASSESS D1.2
100%
90%
80%
70%
60%
A
50%
B
40%
C
30%
D
20%
10%
0%
GIDAS
OTS
EDA
ONISR
Figure 58: Ranking R2 – Distribution in percentage of weighted accidents for test scenarios A-D
As in ranking R1 clear differences are noticeable within the distributions of the test scenarios
and the databases. By moving these proportions to a ranking results into the left side of
Table 45 which gives rank 1 to the test scenario with the highest percentage. In the same
table a further ranking was applied on the right side to the sublevels internally for each test
scenario. This local sublevel ranking for test scenario A was only achievable for the
databases GIDAS and OTS.
Table 45: Ranking R2 for test scenarios A-D and their sublevels (weighted accidents)
A
A1
A2
A3
B
C
D
D1
D2
GIDAS
2
1
3
4
-
Main level – global
OTS
EDA
4
3
2
1
1
2
3
4
-
ONISR
3
2
1
4
-
GIDAS
3
2
1
1
2
Sublevel - local
OTS
EDA
1
2
1
1
2
2
ONISR
1
2
Summarizing the results of the main levels (test scenarios A-D) leads to Table 46 in which an
overall ranking is created by use of the mean averages of the ranked positions. The most
important identified test scenario is B – intersection conflicts followed by oncoming traffic
conflicts (C), rear-end (A) and cut-in collisions (D).
Table 46: Mean averages of ranked results R2 for main levels
A
B
C
D
All databases
Mean
Rank
3
3
1.5
1
1.75
2
3.75
4
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ASSESS D1.2
Compared to ranking R1 there is a change of the positions 1 and 2 in this ranking R2 which
is mainly caused by the additional high number of intersection accidents involving twowheelers as initial collision partners.
6.4
Specification of test scenarios based on weighted data
The assessment of test scenarios is on the one hand based on absolute numbers of
accidents and on the other hand on accident numbers weighted with the injury severity. In
section 6.2 the proposed parameters concerning driving speeds are verified for each test
scenario. The mean averages and medians are calculated based on the driving speeds in
accidents assigned to a specified test scenario without considering injury severities.
However the overall ranking in section 6.3 is based on accidents weighted with the injury
severity. The use of weighted data results in a stronger consideration of severe accidents.
The driving speed analysis was conducted using absolute numbers. Analysing weighted
driving speeds was considered so that the decision on test specification could be made with
reference to driving speeds which result in more severe accident outcomes.
However, there are many factors which influence the injury outcome and although higher
driving speeds are associated with greater injury, many other factors are much more relevant
for injury severity. The injury severity of car occupants is e.g. additionally associated with the
crash causation, occupants’ seat belt usage and the configuration of the crash (front, rear or
side). Therefore, it was considered that driving speed was not a reliable predictor of injury
severity and it was more representative to analyse absolute speeds.
Furthermore a high driving speed does not lead automatically to a high impact speed or a
high change of speed in crash which is known to be associated with the injury severity of car
occupants. One initial situation with high driving speed can lead to completely different crash
situations. For example, a driver brakes strongly before having a crash which results in a
small impact speed. The same initial situation results into a high impact speed if the driver
would react delayed and/or brake softly. Hence, from theoretical point of view the driving
speed does not describe the crash situation reliable. The driving speed can only give an
indication for the crash situation.
Based on these considerations it is expected that analyzing driving speeds based on
absolute numbers is the better option.
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ASSESS D1.2
7 Discussion
The accidentology work conducted so far has provided important input to defining relevant
test conditions. However, the reliability of some of the more important data, such as driving
speeds for example, is subject to a degree of uncertainty. It is known that driving speed is not
always accurate because this is gathered from a variety of sources and differs between the
databases used. That is, driving speeds are e.g. ideally reconstructed after the accident data
collection is completed or gathered by police evidence, witness and driver reports.
Furthermore the time at which the driving speed is taken is not well defined and is likely not
to be the same time point in the accident event for the vehicles involved, meaning that the
information is subject to an unknown degree of recording error. However, this information is
the best that is available with which to provide information on the initial speeds of the
involved vehicles to inform the test scenario specification.
In this report, nationally representative databases with police reported accidents have been
used along with in-depth database where professional accident investigators have coded the
accidents.
Because of the detailed query, the numbers considered for in-depth databases can be very
small; especially for fatal accidents. As such, in some sections of the accident analyses in
this report, some values have had to be omitted due to the very small sample size.
The ‘closing speed’ (‘relative speed’) has to be handled carefully because of its difficult
calculation out of the in-depth accident data. E.g. in rear-end collisions, it’s not possible to
simply extract this information out of the difference of the driving speeds of both subject and
target vehicle. Some main issues here are the different points of time (hence quick change of
position and situation) for the detected or reconstructed speeds, driver reactions and the
allocation of the vehicles in the same traffic accident which is not considered in this report.
This report also contains first results of a Naturalistic Field Operational Test (NFOT) and a
Naturalistic Driving Study (NDS). To catch accidents however, the study needs to be large in
scale, as demonstrated in 100-car study where 3,200,000 vehicle kilometres resulted in
approximately 80 crashes. A small study, like SeMiFOT, will most likely not record more than
a couple of real crashes. In the 100-car-study, the number of incidents seemed roughly to
relate to near-crashes and crashes as 100 to 10 to 1. To make best use of a study the nearcrashes would need to be used as surrogates for crashes. The speed and radar data were
sampled with high frequencies and are expected to be accurate. However, again the difficulty
in the analysis is to choose the right point in time to identify e.g. driving speeds. As many
other variables, driver behaviour variables in the 100-car-study are based on manual review
of video data. Actions at the time of an abnormal situation were viewed, and relatively easily
detected by the video reviewer. But as there were no interviews with the drivers conducted,
the database has no information about the driver’s mental state, emotions, stress level or
skills. This is information typically gained from interviews. Furthermore, information about
kinds and levels of injuries are missing. In addition, the test population may not always be
representatively selected; in 100-car-study an overrepresentation of people more likely to
crash were chosen, and in SeMiFOT the test persons were solely employees of each vehicle
manufacturer participating in the project. The same is valid for the subject cars in the study;
the composition is constant but may not be representative.
The performance of sensors (e.g. radar, lidar) and therewith the performance of the systems
could be effected by different environmental conditions. Hence, a consideration for the
assessment of the system’s benefit might be meaningful.
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ASSESS D1.2
8 Conclusions
The choice of the final accident types was conducted by considering the opportunity to be
influenced by the pre-crash sensing systems. These important accident types (Type 6 and
Type 2&3) were analysed in greater detail so that the test specification could be made as
representative of the real world accidents as possible. Based on these accident types,
preliminary test scenarios were proposed based on the first stage of the accidentology in this
project. These were:
o Test Scenario A: Rear-end collision
o Test Scenario B: Intersection conflict
o Test Scenario C: Oncoming traffic collision
o Test Scenario D: Cut–in conflict
Sub-levels were added to the test scenarios to account for stationary or decelerating target
vehicles (in test scenario A), for obstructed views in test scenario B and for cut-in accidents
involving vehicles travelling in the same direction and opposite direction (test scenario D).
The analysis of the driving speed data for subject and target vehicles in GIDAS and OTS was
compared to the test specifications proposed by Work Package 4, as presented below:
(Ranges of driving speeds due to deviations between GIDAS and OTS)
Table 47: Comparison of the proposed driving speeds with the analysed accident data
Driving speed (km/h) OTS and GIDAS (rounded)
Test scenario
Subject vehicle
Sublevel
50th
%ile
75th
%ile
95th
%ile
85
120
150
50
75
130
50
55-65
90-105
50
60
95
55
70-80
100
50
45-65
60-80
85
80
40-100
60-130
90-160
WP 4.1
data
A
(rear-end)
A1
(Slower lead
vehicle)
A2
(Decelerating
vehicle)
A3
(Stopped lead
vehicle)
B
(intersection)
B1
C
(oncoming)
C1
D
(cut-in)
D1
(oncoming)
D2
(lane change)
Target vehicle
50
100
50
50
40
64
95th
%ile
20
20
80
90
45
60
100
0
0
0
30
50
70
50
50
80
75th
%ile
10
50
80
WP 4.1 50th
data
%ile
80
0
0
10
50
40
50-55 70-80
105
10
30-65 50-75
75
40
40-60 50-80 100-120
64
This shows that the proposed specification for the driving speeds is verified by the in-depth
accident data from OTS and GIDAS:
• For A1 the subject vehicle speeds proposed cover between 50% and 75% of the
driving speeds from accidents. The target vehicle speeds represent less than 50% of
driving speeds from accidents, although the test specification proposed is a more
stringent test because of the faster closing speed.
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ASSESS D1.2
•
•
•
•
•
•
For A2 the subject vehicle speeds proposed cover 75% of the driving speeds from
accidents. The target vehicle speeds represent closer to 95% of driving speeds from
accidents
For A3 the subject vehicle speeds proposed cover close to 95% of the driving speeds
from accidents.
For B the subject vehicle speeds proposed cover 50% of the driving speeds from
accidents. The target vehicle speeds represent approximately 75% of driving speeds
from accidents
For C the subject vehicle speeds proposed cover more than 50% but less than 75%
of the driving speeds from accidents. The target vehicle speeds represent closer to
75% of driving speeds from accidents
For D1 the subject vehicle speeds proposed cover 50% of the driving speeds from
accidents. The target vehicle speeds represent much less than 50% of driving speeds
from accidents. The specification therefore represents a faster closing speed more
associated with higher severity accidents.
For D2 the subject and target vehicle speeds proposed cover less than 50% of the
driving speeds from GIDAS accidents, but for OTS this approaches 95% of driving
speeds for the subject vehicle and 50% for the target vehicle.
As well as the driving speeds, a range of information has been presented for other
parameters which, while not directly applicable to the derivation of the test scenarios, provide
information for the benefit estimate. The test performance will provide system information in
simplified conditions, but before this can be applied to the target casualty population to
estimate the system effectiveness, some additional adjustments will be made for other
parameters which might influence the system, such as the prevailing environmental
conditions etc.
In parallel with the main testing activities in the project, the future tasks will include carrying
out the socio-economic benefit assessment to estimate casualty benefits. This will provide a
more accurate benefit estimate than previous predictive studies because the effectiveness
estimate applied to the target population will include objective data from both the system
performance (from the Work Package 4 and 5 testing) and from the driver response and/or
behavioural adaptation (Work Package 3).
FOT data are potentially useful information to complement accident data, as the level of
details is much higher e.g. for analysing speeds or driver reactions. Data from the European
7th Framework project euroFOT (large-scale European Field Operational Test) could be a
good complement to European accident databases. However the data collection is currently
ongoing, thus there are no data available to analyse yet.
The analyses in ASSESS have shown that it is important to stress the fact that accidents
data need to be harmonised and comparable on both national and in-depth level to be able
to have a clear view of the accident situation in the EU.
87/91
ASSESS D1.2
9 Recommendations
The presented information about the environmental conditions in accidents (that could be
assigned to the test scenarios) gives various statements. The socio-economic assessment
(WP2) could use these data to consider the possibly effected performance of sensors (e.g.
radar, lidar) and therewith the performance of the systems.
The driver behaviour evaluation (WP3) is looking for a scenario to implement in the driving
simulator and on the track test. This could be a rear-end scenario because of the high
accident data quality and the expected manageable testability. Field Operational Test (FOT)
data would be highly interesting for the driver behaviour studies but they are not available so
far in a satisfying amount and tailored for the test scenarios regarded. The catalogue of
realistic situations that would startle or alarm drivers (by e.g. crosswind, rock fall, sounds
etc.) can’t be provided here. This is because of the lack of detailed information in the
accident databases used. However, this information could be available in future by Field
Operational Test data.
The driving speed was chosen as the core parameter to assess the preliminary test
scenarios proposed by the pre-crash system performance evaluation (WP4). Furthermore,
the presented overlap information in crashes could give indications about the offset of the
vehicles prior to the crash. This is not performed here and would need further researches as
well as expert experiences. By now, test scenarios B (intersection) and C (oncoming traffic)
are not arranged to be split into scenarios with different road types. However, the analyses of
these test scenarios related to urban and rural roads could show meaningful differences of
the driving speeds of the subject and target vehicles. Thus, this information could also be
used to refine the available test scenarios.
88/91
ASSESS D1.2
10 References
[1]
Assessment of Integrated Vehicle Safety Systems for improved vehicle safety, EU
FP7 project ASSESS, SST.2008.4.1.1: 233942, Available at http://www.assessproject.eu
[2]
M McCarthy, H Fagerlind, I Heinig, T Langner, S Heinrich, L Sulzberger, S Schaub,
Preliminary Test Scenarios, Deliverable D1.1 of the EU FP7 project ASSESS,
SST.2008.4.1.1: 233942, 2009
[3]
S Reed, A Morris, Glossary of data variables for fatal and accident causation
databases, Deliverable D5.5 of the EU FP6 project SafetyNet contract no
SI2.395465/506723, 2008
[4]
O Bartels, T Langner, A Aparicio, P Lemmen, C Rodarius, Action plan pre-crash
evaluation, Deliverable D4.1 of the EU FP7 project ASSESS, SST.2008.4.1.1:
233942, 2010
[5]
I Wilmink, P Rämä, G Lind, Th Benz, H Peters, Socio-economic impact assessment
of stand-alone and co-operative intelligent vehicle safety systems (IVSS) in Europe,
Deliverable D4 of the EU FP project eIMPACT
[6]
T Victor, J Bärgman, M Hjälmdahl, K Kircher, E Svanberg, S Hurtig, H Gellerman, F
Moeschlin, Sweden-Michigan Naturalistic Field Operational Test (SeMiFOT), Phase
1: Final Report, Safer Report 2010:02, Project C3 SeMiFOT
89/91
ASSESS D1.2
11 Risk register
Risk
No.
WP1.21
WP1.22
3
What is the risk
Representativeness of the in-depth
accident data of GIDAS and OTS for
Europe
Use of French EDA data
Level
of risk3
2
2
Solutions to overcome the risk
Regard to text annotations
Additional (belated) weighting
Consideration of the development of
other countries to industrialised
countries which presents the target
groups of the regarded systems
Data is known not be representative for
France and therefore handled specially
(pay attention to annotations in the
respective sections)
Risk level: 1 = high risk, 2 = medium risk, 3 = Low risk
90/91
ASSESS D1.2
12 Appendices
•
Codebook for the accident analyses in WP1 (ASSESS)
•
Truth / quality of in-depth data in accident analyses in WP1 (ASSESS)
91/91

ASSESS D1.2 - ASSESS Project

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