ISO/TS 21934-2:2024

Technical
Specification
ISO/TS 21934-2
First edition
Road vehicles — Prospective
2024-10
safety performance assessment
of pre-crash technology by virtual
simulation —
Part 2:
Guidelines and requirements for
application
Véhicules routiers — Évaluation prospective de la performance
sécuritaire des systèmes de pré-accident par simulation
numérique —
Partie 2: Lignes directrices et exigences pour la mise en œuvre
Reference number
© ISO 2024
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ii
Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms. 6
4.1 Symbols .6
4.2 Abbreviations .7
5 Overview: A general description of the process for prospective safety performance
assessment of pre-crash technology by virtual simulation . 8
5.1 General approach and structure .8
5.2 Input data .8
6 Evaluation objective .13
6.1 Process for identification of the evaluation objective . 13
6.2 Definition of a precise research question .14
6.3 Identification of relevant scenario categories .14
6.4 Metrics in prospective safety performance assessment by simulation .14
6.4.1 Introduction to metrics .14
6.4.2 Selective compilation of metrics to determine safety critical events .16
6.4.3 Selective compilation of collision related metrics .18
6.5 Selection of metric . 20
7 Baseline .21
7.1 Baseline approaches .21
7.1.1 General .21
7.1.2 Approach A . 23
7.1.3 Approach B . 23
7.1.4 Approach C . 23
7.1.5 Requirements .24
7.1.6 Example research questions . 25
7.2 Example for minimum required information for establishing a baseline . 25
8 Virtual simulation .26
8.1 Framework . 26
8.2 Models .27
8.2.1 Scope of section .27
8.2.2 Simulation control block .27
8.2.3 Vehicle surroundings block. 28
8.2.4 Sensor/perception input generation . 33
8.2.5 Vehicle under test block . 34
8.2.6 Collision block .42
8.3 Example for minimum required information for treatment simulation .42
9 Assessment of safety performance.44
9.1 Calculation of safety performance . 44
9.2 Example for minimum required information for safety performance assessment . 44
10 Documentation .44
11 Validation and verification .48
Annex A (informative) Example for documentation of input data .55
Annex B (informative) Comparison of simulation tools .59
Annex C (informative) Examples for documentation of a study . 61

iii
Bibliography .81

iv
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
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The procedures used to develop this document and those intended for its further maintenance are described
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This document was prepared by Technical Committee ISO/TC 22, Road vehicles, Subcommittee SC 36, Safety
and impact testing.
A list of all parts in the ISO 21934 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html. .

v
Introduction
Active safety and advanced driver assistance systems (ADAS), collectively referred to in this document as
active safety technologies, as well as automated driving technology have recently been introduced into the
market. Their development raises questions about the extent to which these technologies prevent crashes
and their ensuing injuries. These questions are of relevance to stakeholders such as vehicle manufacturers
and suppliers, road authorities, research organisations and academia, politicians, insurance companies as
well as consumer organisations.
The answers to these questions are derived from assessing the technology in terms of road traffic safety.
There is a number of assessment methodologies in use (see ISO/TR 12353-4). In general, the current
methodologies are divided into two types: retrospective assessments and prospective assessments.
Retrospective methods determine the technology’s safety effect after its market introduction based
on accident data. A precondition for these methods is that sufficient accident cases with and without the
technology have been recorded for a comparison in a certain vehicle subgroup or class. Prospective methods,
on the other hand, predict the technology’s safety effect before its market introduction.
This document focuses on the prospective assessment of traffic safety for vehicle-integrated technologies
acting in the pre-crash phase by means of virtual simulation.
The safety performance of a technology is determined by comparing data from the baseline and treatment
simulations. The baseline for the assessment is the simulation without the vehicle-integrated technology
while the treatment is the simulation with the technology.
The assessment method that is described in this document is limited to vehicle-integrated technology and
does not consider technologies operating off-board. The virtual simulation method per se is not limited to
a certain vehicle type. Furthermore, the assessment approach discussed in this document focuses on crash
avoidance and the technology’s contribution to the mitigation of the consequences. Safety technologies that
act in the in-crash phase or the post-crash phase are not explicitly addressed by the method, although the
output from prospective assessments of crash avoidance technologies can be considered as an important
input to determine the consequences of these technologies.
In general, the assessment of active safety technologies requires consideration of the interaction with
surrounding traffic as well as the driver of the vehicle under test. Consequently, for a comprehensive
assessment, the technology’s safety performance must be analysed in a multitude of scenarios to cover
all relevant circumstances that affect the critical situation. The virtual simulation approach allows for
running large numbers of cases and offers a promising combination of flexibility, reproducibility and
experimental control in the assessment of safety performance. The need for virtual simulations in the
prospective assessment of safety technologies is generally recognized. This will have a positive impact on
the comparability of results by virtual assessment.
The state of the art with respect to prospective safety performance assessment is described in
ISO/TR 21934-1, which builds the foundation of this document.

vi
Technical Specification ISO/TS 21934-2:2024(en)
Road vehicles — Prospective safety performance assessment
of pre-crash technology by virtual simulation —
Part 2:
Guidelines and requirements for application
1 Scope
This document specifies methods, guidelines and their application for prospective safety performance
assessment of pre-crash technologies in road vehicles by virtual simulation. The purpose of the document
is to provide prerequisites for the procedures to achieve comparable results among different safety
performance assessments and tools.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
ISO 8855, Road vehicles — Vehicle dynamics and road-holding ability — Vocabulary
ISO 12353-1, Road vehicles — Traffic accident analysis — Part 1: Vocabulary
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 12353-1 and the following apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
baseline
set of data to which the performance of the technology under study is compared when performing
prospective assessments of performance of technologies
3.2
simulation block
grouping of at least two simulation models that are related to each other in terms of topic
3.3
collision
road vehicle accident event in which a vehicle strikes, or is struck by, another vehicle, road user or obstacle
(on or off the road), with ensuing damage and/or injury
Note 1 to entry: In simulation, a collision is typically detected once the volumes of two objects overlap in an infinitely
small manner.
[SOURCE: ISO 6813:1998, 3.3, modified — Note 1 to entry added.]

3.4
conflict
situation in which at least two road users are involved and which leads to a collision in the near future if no
actions is taken
Note 1 to entry: The definition of conflict is based on Reference [8].
3.5
data point
set of one or more discrete measurements on a single member of a unit of interest
EXAMPLE Vehicle mass and velocity.
3.6
data series
description of multiple data points that are linked via another type of information
EXAMPLE The velocity over time measured during the simulation for the centre of gravity of the vehicle under test.
Note 1 to entry: Typically, the type of information that links the data points is time.
3.7
deterministic simulation model
model that produces the same result when simulated twice with the same inputs and parameter values
3.8
distribution-based pre-simulation model
model that includes a distribution for at least one of the simulation parameters defined by the pre-
simulation model
Note 1 to entry: Most pre-simulation models are distribution-based. The following are examples of distribution-based
simulation models:
EXAMPLE 1 A simulation parameter (e.g. the road friction coefficient or a system parameter such as maximum
intervention deceleration) is sampled at specific values within a range and a simulation is run for each of these
parameter values.
EXAMPLE 2 Like EXAMPLE 1, but with a nonuniform distribution for the simulation parameter (e.g. driver brake
reaction time). A simulation parameter, such as Monte Carlo, is sampled for each simulation run from a probability
distribution (either defined on a closed mathematical form, such as a lognormal distribution, or defined as an
empirical, numerical distribution).
EXAMPLE 3 Like EXAMPLE 2, but instead of a Monte Carlo-style draw, only one simulation is carried out per
simulation parameter’s bin in an empirical distribution. The simulation results are weighted accordingly in a post-
processing step.
Note 2 to entry: If a pre-simulation model is not distribution-based, it transforms parameters provided by the simulation
framework user to other parameters needed by the simulation model (e.g., simple unit conversions or calculating model
update matrices for a linear vehicle model from parameters specifying vehicle mass, tire stiffnesses, etc).
3.9
driver
vehicle occupant in actual control of a vehicle, or who was in control before that control was lost or taken
over by a technology
3.10
event
state change at a certain point in time

3.11
injury risk function
description of the probability of an injury or fatality in relation to collision attributes
Note 1 to entry: The most frequently used injury risk functions describe the probability of an injury of a specific
severity in relation to collision severity, for example impact velocity or change of velocity during collision.
3.12
in-simulation model
model that is part of the simulation framework and is updated for each time-step in the simulation
3.13
model
covers at least one (physical) domain (e.g. mechanical or electronic) and can consist of different process
steps and calculations
Note 1 to entry: A simulation model can also be a container to a collection of simulation models.
3.14
non-deterministic simulation model
model that can produce different results between simulations even when inputs and parameters are
constant
Note 1 to entry: Non-deterministic simulation models are often probabilistic, including some form of random draw
occurring during the simulation. A probabilistic model becomes deterministic if the random seeds are assigned
fixed values.
3.15
penetration rate
number of vehicles of a certain type equipped with the technology under assessment compared to the total
number of vehicles of that type in a certain geographic area
3.16
probability distribution
function that describes the probabilities of outcomes of a random event
Note 1 to entry: A probability distribution of a sufficiently large sample size can be used to make inferences about the
distribution of a population.
3.17
pre-crash phase
time phase immediately prior to the crash
Note 1 to entry: This phase ends with the contact between participants or objects involved in the crash.
Note 2 to entry: In this document, the pre-crash phase covers normal driving and critical situations up to the point of
contact.
3.18
pre-simulation model
model that is part of the simulation framework outside of the simulation over time
Note 1 to entry: Such models are used to determine and set parameters for the in-simulation models.
3.19
projection
estimation of time or space changes for a population or target area based on the results of a smaller sample
of input data
Note 1 to entry: A projection can be conducted either in time or space or in both dimensions. The time projection is an
estimation of (future) changes for a population based on the results of a reference period. The space projection is an
estimation of changes for a target area based on the results of a smaller sample/subset of input data.

3.20
prospective assessment
predictive assessment of the future performance of given technologies before their deployment into a vehicle
population
3.21
real-world data
data collected in a non-virtual situation and environment
3.22
representative
sample that is an available subset of a population
Note 1 to entry: The sample is representative of the population for a set of features if their statistical characteristics
(e.g. proportion, distribution) match those of the entire population.
3.23
research question
question that a research project is designed to answer
Note 1 to entry: A research question defines the scope of a prospective safety performance assessment by simulation.
3.24
retrospective assessment
assessment of the past performance of given technologies after their deployment into a vehicle population
3.25
safety critical event
SCE
conflict or series of related conflicts that involves the subject vehicle either alone or in combination with
another vehicle, pedal cyclist, pedestrian, object or road edge
Note 1 to entry: This document describes the range of conflict types that may comprise an SCE and an SCE may be
composed of a single conflict type or multiple simultaneous or sequential conflict types. Conflicts should be non-
intentional and non-premeditated (unplanned) by at least one conflict partner.
[SOURCE: ISO/TR 21974-1:2018, 3.13]
3.26
safety performance
quantified capability of a technology to achieve an improvement in road traffic safety
3.27
scenario
description of the traffic, infrastructure and environmental conditions (e.g. weather and lighting conditions)
for the simulation that consists of a time sequence of scenes
Note 1 to entry: A scenario is limited in terms of time and space.
Note 2 to entry: A scene describes a snapshot that encompasses the mobile and immobile elements of the traffic,
infrastructure and environmental conditions, the self-representation of all actors and observers and the relations
between these elements.
3.28
scenario category
selection of scenarios that share one or more characteristics
3.29
severity
estimate of the extent of harm to one or more individuals or of property damage that can occur in a potential
collision
3.30
simulation
enactment of a situation with artificial conditions, typically performed by updating models over discrete
time steps
3.31
simulation framework
aggregate of all components in a simulation including all simulation blocks and models
Note 1 to entry: Process steps outside the simulation (e.g. post processing) are not part of the simulation framework.
3.32
traffic agent
anyone who uses a road including sidewalk and other adjacent spaces
3.33
technology
collection of vehicle-implemented techniques, processes and systems capable of temporarily or permanently
taking control of the vehicle and from which the expected safety benefit is predicted in the prospective
assessment
3.34
test
use of quantitative measures to evaluate technology under a set of specified conditions, with reference to
values that represent an acceptable outcome
3.35
treatment
use of a specific technology to affect the course of events in a scenario to avoid or mitigate crashes when
performing prospective assessments of performance of technologies
Note 1 to entry: Treatment simulations provide data on the performance of the technology under assessment to
compare with baseline data.
Note 2 to entry: See 3.1.
3.36
validation
confirmation, through the provision of objective evidence, that the requirements for a specific intended use
or application have been fulfilled
Note 1 to entry: For the prospective safety performance assessment, it is important that the results of the virtual
assessment are reliable (i.e. the results are reproducible under the same conditions) and trustable (i.e. the results are
consistent with the real-world safety performance of the technology).
3.37
vehicle under test
VuT
vehicle that is focused on in the safety performance assessment
Note 1 to entry: In the treatment, this vehicle is equipped with the technology under assessment.
3.38
verification
confirmation through the provision of objective evidence that (internal) requirements of the safety
performance assessment process and tool (including methods and models) have been fulfilled

4 Symbols and abbreviated terms
4.1 Symbols
E energy equivalent speed
EES
E kinetic energy dissipated by the vehicle during the contact phase by deformation
def
I severity of (injury) type i in the baseline simulation
Baseline,i
I severity of (injury) type i in the treatment simulation
Treatment,i
P crash rate
CR
P probability of being killed in a crash
K
P probability of being killed or severely injured in a crash
KSI
P victim rate
VR
S safety performance
S safety performance of the injury severity, i
i
d distance of a cyclist to a reference point (position or another vehicle)
Cyclist
d distance to the lane
DLC
d relative distance between two objects
Rel
d distance of the VuT to a reference point (position or another vehicle)
VUT
f occurrence frequency of a scenario with injury level i in the baseline
Baseline,i
f occurrence frequency of a scenario with injury level i in the treatment
Treatment,i
m mass
n number of collisions either in the baseline or treatment simulation
coll
n number of victims either in the baseline or treatment simulation
vict
N number of simulations either in the baseline or treatment simulation
sim
t time
t time of the collision
collision
t time headway
THW
t time to line crossing
TLC
t time to collision
TTC
Δt time step
x longitudinal position
y lateral position
v velocity
v relative velocity between two objects
Rel
v velocity of the vehicle under test
VUT
4.2 Abbreviations
AIS abbreviated injury scale
ADAS advanced driver assistance system
AEB autonomous emergency braking
COG centre of gravity
CSV comma-separated values
CVNB car-to-vulnerable road user near-side bicycle
DLC distance to line crossing
DP data point
DS data series
EDR event data recorder
EES energy equivalent speed
FCW forward collision warning
FE finite element
FoV field of view
FOT field operational test
HIL hardware in the loop
I2V infrastructure to vehicle
IRF injury risk function
KSI killed or severely injured
MAIS maximum abbreviated injury scale
NCAP new car assessment program
ND normal driving (not safety critical)
NDS naturalistic driving study
PD probability distribution
PET post encroachment time
THW time headway
TLC time to line crossing
TTC time to collision
V2X vehicle to x (vehicle, pedestrian, cyclist and/or infrastructure) communication
VR victim rate
VRU vulnerable road user
V&V validation and verification
VuT vehicle under test
V2V vehicle to vehicle
XML extensible markup language
5 Overview: A general description of the process for prospective safety performance
assessment of pre-crash technology by virtual simulation
5.1 General approach and structure
To estimate the performance of technologies designed to avoid or mitigate crashes, the analysis of a high
number of scenarios is needed. The general process for prospective safety performance assessment of pre-
crash technology by virtual simulation is described in Figure 1 and builds up on ISO/TR 21934-1.
Figure 1 — Overview of the prospective assessment of traffic safety process for vehicle-integrated
technology by means of virtual simulation
The process does not provide any development guidelines or assessment results in terms of functional
safety (the ISO 26262 series) or safety of the intended functionality (ISO 21448). These topics are covered
by other ISO documents. Furthermore, no recommendations are given with respect to the usage of certain
input data sources or simulation tools. No methodology advises are given for the scaling up and projection of
the simulation results which is often conducted in conjunction with this type of assessment.
5.2 Input data
Input data are required in all parts of the prospective safety performance assessment by simulation. An
overview of the information type that is required for the simulation models and process steps and their
relevance is given in Table 1. Information about possible data sources is given in ISO/TR 21934-1. The
technical format and the type of the input, as well as the definition of minimum required information is
crucial for safety performance assessments.

Table 1 — Overview on relevance of information for the stages of the prospective safety
performance assessment
Vehicle surrounding Vehicle under test
Simu-
Environ-
lation con- Collision
Infra- Technol-
mental Traffic Vehicle Driver
trol
structure ogy
conditions
Definition of eval-
(X) (X) X (X) X X
uation objective
Establishing the
X X (X) X X X
baseline
Virtual simulation
with and without X (X) (X) X X X X (X)
technology
Estimation of the
safet y per for- (X) (X) X X X X
mance
V&V X X X X X X X
NOTE “X” means the information is always relevant; “(X)” means the relevance of the information depends on the research
question.
A combination of different information and/or different information sources can be applied to derive the
required input information. Input data can serve four main purposes:
— generate (in the sense of digitizing, replicating, reconstructing or sampling) baseline scenarios (either
completely or in part);
— development and parameterisation of simulation models (e.g. driver, vehicle, technology);
— V&V of the prospective safety performance study as a whole, of models or created crashes (and other
created traffic situations);
— scaling up and projection.
The input type is either a single DP, a series of DPs or a PD (see 3.5 and 3.16). Independent of the input type,
the quality shall be ensured by checking the validity of the data as well as its source. The assessment result
is sensitive to the complex interplay of input data and its usage along the process chain. The following three
aspects shall be considered:
1) Ensure data quality and representativeness for prospective safety performance assessment.
The simulations consist of models that intend to replicate and predict relationships between variables of
interest of a real-world system as accurately as possible. However, these mathematical models are not real-
world systems and are therefore inherently subject to uncertainties. Biases of the input data influence the
descriptive and predictive power of the models. To assure an unconfounded and valid safety performance
assessment, input data are checked for their validity and, if possible, quantified by confidence intervals. The
generalizability of the estimated safety performance depends on the representativity of the simulation and
its models. Specifically, since the models rely on the input data, the representativity of input data for the
defined evaluation objective shall be checked and reported.
2) Guidelines for checking data quality criteria.
Due to the complexity and diversity of traffic, it is not possible to provide a universal and generally accepted
method to quantify the above-mentioned quality criteria (including representativity) for input data to the
assessment process. A qualitative decision process for the grading of used input sources should be applied in
the assessment when available.
3) Transparent communication of the used input data sources.
For a transparent assessment, the used data source shall be reported in the documentation (see Clause 10).
This includes results of the data quality assessment according to item 2) of this subclause.

To allow external stakeholders to generally understand the assessment and decide whether the assessment
is reasonable, the following approach should be used:
First, information shall be provided on the input data for establishing the baseline (see Clause 7), which can
either be one single source or a combination of multiple sources. The following aspect shall be stated:
— characteristics of the data source(s) used for establishing the baseline scenarios:
— data on crashes
— data on critical events (i.e. near-crash data)
— data without crashes (i.e. non-critical driving data)
In addition to describing the used database(s), any selection process (inclusion and exclusion criteria) for
the study shall be described as well. If the scenarios have been provided by a third party, a reference to the
project/report that provides information about the origin of scenarios shall be mentioned.
The following are examples for this first step in documenting the source of data (for baseline approaches,
see Clause 7):
— using baseline approach A, digitized crashes from crash database “nn1” of the time frame 2010 to 2017
were used;
— using baseline approach C2, crash database “nn1” was used for input when designing driver model “x”.
FOT dataset “nn2” was used to design the traffic model “y”. Then, these models were used to create
crashes for the baseline in a setup where the digitized maps in “nn3” was used to represent the streets.
Additional information about the used data source can be added optionally, but this is not required for the
first item.
Secondly, information that is relevant for understanding the complete simulation process shall be provided.
This concerns all data that have been used within the process. To provide a comprehensive overview, a
description of the data collection method, their representativeness with respect to the evaluation objective,
and if different datasets are used, how well they match each other should be presented. In general, references
should be used wherever possible to facilitate documentation. These references shall be accessible to any
organization or person that has an interest in the results of the assessment from a professional point of view.
The interest is driven by the needs of a person or organization to assess the correctness of the assessment
result(s).
For the development and/or parameterization of models and scenarios, different data should be used than
the data used for the V&V. If the same data has been used in development and/or parameterization, it should
be made clear why the data can also be applied to the V&V.
Table 2 should be filled for documentation.

Table 2 — Information on input data for documentation
If input data are used for
Type of
development or V&V of
Details on the input
Input data are input data Details on the input data
simulation models:
data (collection and
used for. (indicating (representativity)
processing)
Parameter
purpose)
Model
of the model
— Baseline Model Depends on List with type Depending on input Depending on input data,
(dig it izat ion name (see the model of data: data, see Table 3 see Table 3
of traffic Clause 8)
see Table 3
situations or
traffic specific
layers)
— Develop sim.
models
— V&V
Information should be provided in a table that informs (per aspect of the model) which data sources have
been used, how the data collection and processing was done and how representative the data are. Examples
for reporting on used data sources are given in Annex A.
For different types of input data, different aspects are relevant to document. An overview of the items that
shall be reported at least per input type is given in Table 3.

Table 3 — Items to be reported for different types of input data
Details on the input data (collection
Type of input data Details on the input data (representativity)
and processing)
Experiment data (FOT/ — Data collection method (used tools, — Amount of logged data (km driven and/or
NDS/static FOT, driving vehicles data logging equipment driven time)
simulator or study in con- etc.)
— Region and time of data collection
trolled field)
— Data analysis/aggregation
— Primary purpose of the study
processes
— Number of involved vehicles and test
— Considered filter criteria in the
persons (+ demographics)
study (e.g. for specific events
like near-crashes incl. trigger
— Limitations in the access to the data
conditions)
Crash data (reconstructed, — Data recording process — Number of accident cases
digitized accident data as
— Digitizing process (tools, — Overall milage of the considered
provided in detail crash
assumptions for non-measurable population (if it can be determined)
databases)
indicators)
— Covered types of accidents
— Applied weighting procedures (if
— Region and time of data collection
applicable)
— Primary purpose of database and its
— Considered filter criteria in
operator
the data collection (e.g. certain
accident types)
— Limitations in the access to the data
Aggregated crash data (i.e. — Data recording process (police — Number of accident cases
crashes are not reported in- report, insurance data, on-spot
— Overall milage of the considered
dividually as in e.g. national analysis incl. sketch, post-crash
population (if it can be determined)
accident statistics) measurement, EDR etc.)
— Covered types of accidents
— Applied weighting procedures (if
applicable)
— Region and time of data collection
— Considered filter criteria in the
— Primary purpose of database and its
data collection (e.g. just personal
operator
injury)
— Limitations in the access to the data
Environment(al) data — Data collection method — Amount of data
(infrastructure data, traffic
— Conducted analysis/aggregation — Region and time of data collection
flow data, weather data)
steps
— Primary purpose of the database and its
— Considered filter criteria in the operator
database
— Reference if available
— Limitations in the access to the data
Other (literature review, — Data collection method — Type of publication
scientific article, reports,
— Data analysis/aggregation — Information about the publication (author,
studies etc.)
processes year, location, publisher etc.)
— Description which and where — Primary purpose of study and its operator
information of the document has
— Limitations in the access to the data
been used
— In case of study, number of persons
— Description of the conducted
included and randomization procedure
study/analysis
— Considered filter criteria in the
study (if applicable)
Any information that is required to do the assessment since it has a quantifiable impact on the traffic safety
indicators builds the minimum required information. This minimum required information strongly depends
on the assessment scope. If the required minimum information is not available and cannot be derived from
alternative information, the assessment cannot be conducted.
As an approach for deriving the minimum required information, the following steps should be considered:
— identify all potential inputs that can influence the results of the virtual simulation and evaluate them
regarding their relevance for the given evaluation aspect (e.g. information about road friction becomes
relevant once a technology intervenes in the vehicle dynamics and different weather conditions should
be considered);
— identify systematically the required input information for the models that are applied in the simulation
(e.g. road friction for the vehicle model). A list of models with example input information is given in 8.2;
— check the derived minimum required information set for plausibility based on the available input data.
Critical aspects are:
— No information is available for a required input. In this case, it should be checked in the following
order: whether the input can be substituted or derived by other input(s) for which info
...