DE102023201784A1 - Adaptive real-data-based simulation of a centrally coordinated traffic area - Google Patents

Abstract

The invention relates to a method with the steps: providing (S1) from a scenario library a data set with information about a real traffic scenario from a traffic area such as a parking garage with a central coordination unit for guiding automated vehicles and its own sensor network, executing (S2) a simulation with virtual counterparts of guided automated vehicles and non-coordinated road users with a simulated virtual test vehicle, determining (S3) a dissimilarity metric between the data set and trajectories determined in the simulation, and if the dissimilarity metric is greater than a limit value: repeating (S4) the simulation while replacing the real movement data and/or behavior models of non-coordinated road users with virtual agent models; if necessary, adapting (S5) the scenario library with synthetic sensor information from the repeated simulation.

Description

The invention relates to a method for carrying out a traffic simulation for testing a vehicle component.

In the development and validation of automatic driving control systems, simulation-based methods are used and tested, particularly with regard to the operating areas that are critical for the automated driving control function. For example, through scenario-based development and testing, it is possible to estimate how safely the later real vehicle will operate in the open world (possibly within a limited operating area) by using the digital twin of the automated vehicle during early development phases.

This generally applies to (highly) automated vehicles that are approved and designed for general and public road traffic. Typically, the individual vehicles have their own individual implementations and designs (but obeying standardized protocols and norms) of automatic driving control systems. Another technical implementation for carrying out a highly automated journey of a vehicle, for which the same considerations regarding the use of simulations with a very large number of parameter variations, hardware-in-the-loop tests, behavioral simulations of other road users, etc. apply, can in principle be carried out in locally demarcated traffic areas, such as parking garages, industrial production areas, logistics areas and the like, where automated vehicles are used, but they are not dependent on their own sensors and driving control systems as in general free road traffic, but a networked system of sensors transmits information to a central coordination unit, so that the coordination unit is able to collectively coordinate automated vehicles using a central driving control system and specify their movements. In parking garages, centrally specified parking space allocations can be used to improve the use of parking space and to optimize the parking, re-parking or re-parking process for each vehicle. In logistics areas, not only optimized goods control but also optimized path guidance for automated logistics vehicles can be carried out. Since uncoordinated road users are generally to be expected in such locally defined traffic areas with special sensors and a central coordination unit, such as pedestrians, uncoordinated vehicles, etc., and technical uncertainties can exist, such as the latency of the calculations for specifying the respective trajectories or different software versions that need to be checked, a simulation with a large number of parameter variations within a maximum expected parameter space is also recommended here in order to be able to guarantee the safety and expediency of the journeys of the automated vehicles.

So-called "adaptive replay to sim" methods are known in the state of the art. In such methods, real scenarios are recorded and information about them is made available for a simulation in order to be able to carry out tests of driving control systems (or their sensors) based on realistic data. The advantage of transferring the data into a simulation is the possibility of safe virtual testing, as well as the possibility of deliberately changing the data from reality.

The EN 10 2019 206 908 B4 relates to a method for training at least one algorithm for a control unit of a motor vehicle, wherein the control unit is provided for implementing an automated or autonomous driving function by intervening in units of the motor vehicle on the basis of input data using the at least one algorithm, wherein the algorithm is trained by a self-learning neural network, comprising the following steps: a) providing a computer program product module for the automated or autonomous driving function, wherein the computer program product module contains the algorithm to be trained and the self-learning neural network, b) embedding the trained computer program product module in the control unit of the motor vehicle, c) driving the motor vehicle in a real traffic environment by a human driver, wherein the journey determines a driven trajectory, d) feeding data from an environmental sensor system and a motor vehicle sensor system to the control unit and calculating a virtual trajectory by the algorithm, e) deriving a metric from a comparison of the driven trajectory and the virtual trajectory and storing the data from the environmental sensor system and the motor vehicle sensors in a memory if certain metric criteria are met for a traffic situation, f) providing information about the traffic situation to a traffic simulation, g) analyzing the traffic situation through the traffic simulation, whereby a virtual image of the traffic situation is created using the map data, the environmental sensors and the motor vehicle sensors, whereby the data of the traffic situation are varied by means of the traffic simulation, and h) training the algorithm by varying the traffic situation. The traffic simulation is described as carries out a simulation of the traffic situation from the perspective of the traffic environment, wherein behavior and technology models are provided for each individual road user in the traffic simulation so that a future traffic situation is simulated based on a variation of the behavior and technology models provided and wherein past environmental and motor vehicle data are also varied in the traffic simulation.

Also from the publication “ A Needle in a Haystack - How to Derive Relevant Scenarios for Testing Automated Driving Systems in Urban Areas” by Nico Weber, Dr.-Ing. Christoph Thiem and Prof. Dr.-Ing. Ulrich Konigorski of the preprint for “30th Aachen Colloquium Sustainable Mobility 2021 “ it is known to use rigid scenarios for traffic simulation and to make them more flexible. Based on an “Adaptive-Replay-to-Sim” approach, a simulation-based toolchain is presented for the development and testing of vehicles equipped with automatic driving control systems in urban environments. Multimodal interactions between different road users are considered and, among other things, made available for feeding back into a scenario database.

For testing and/or validating driver assistance systems to support manual driving of vehicles such as passenger cars or trucks, as well as automation systems for highly automated and even autonomous vehicles (summarized under "automated driving control functions"; English: "Advanced Driver Assistance Systems" or systems for "Autonomous Driving", abbreviated to ADAS/AD), the use of scenario libraries is also known. These ideally contain descriptions of a large number of (ideally as widely as possible) varying scenarios, so that a vehicle does not encounter fundamentally untested situations in later regular operation. In such scenario libraries, scenario descriptions are typically stored in a machine-readable and automatically storable form. Scenario description languages are usually used for this, which use special documentation and program languages. An example of this is the established scenario description language of the "ASAM e.V." association called 'OpenSCENARlO®' for use in scenario libraries. There are also scenario libraries with data in machine-readable formats that also have a human-understandable context, such as “ADScene” and “SafetyPool”.

In order to be able to use such a scenario library to investigate the behavior of a driving control system or its sensor unit in a reactive manner to the environment of a road user under consideration, in particular an automated vehicle (typically called a “vehicle under test”), the journey of this selected real road user under consideration can be simulated, while the behavior of the other real road users and the environmental parameters can be taken from the scenario library and in this case are based on real data.

However, because the data on the behavior of the other real road users and on the environmental parameters reflect facts that have come about because the other road users adapt their reactions to the behavior of the real road user under consideration (who is replaced in the simulation by the "vehicle under test" as a virtual counterpart), the behavior of the other road users recorded in the simulation is only valid according to their stored data if the original behavior of the real road user under consideration and that of its virtual counterpart in the simulation (the simulated "vehicle under test") are essentially the same. If the simulated behavior of the road user as a virtual "vehicle under test" deviates too much from the behavior of the real road user under consideration at the time, the integrity of the degree of realism of the simulation environment is limited and in some cases therefore unsuitable for testing a driving control system or its sensors or a sensor unit for the simulated and thus virtual "vehicle under test".

The object of the invention is to provide the most efficient possible simulation of a locally delimited traffic area with, in particular, a stationary sensor network and a central coordination unit for coordinating automated vehicles in the presence of non-centrally coordinated road users.

The invention results from the features of the independent claims. Advantageous further developments and embodiments are the subject of the dependent claims.

• - Bereitstellen aus einer Szenarienbibliothek: Einen Datensatz mit Informationen über ein reales Verkehrsszenario aus einem lokal abgegrenzten Verkehrsraum mit einer zentralen Koordinationseinheit zur Koordinierung von automatisierten Fahrzeugen durch Vorgabe von Trajektorien für die automatisierten Fahrzeuge auf Basis von Sensorinformationen eines stationären Sensornetzes des Verkehrsraums, wobei der Datensatz vom Sensornetz erfasste und/oder durch die Koordinationseinheit ermittelte Trajektorieninformationen der durch die Koordinationseinheit geführten automatisierten Fahrzeuge im Verkehrsszenario, sowie mittels des Sensornetzes erfasste reale Bewegungsdaten und/oder Verhaltensmodelle jeweils von nicht-koordinierten Verkehrsteilnehmern im Verkehrsszenario, umfasst,
• - Ausführen eines ersten Durchlaufs einer Simulation mit virtuellen Gegenstücken der geführten automatisierten Fahrzeuge und der nicht-koordinierten Verkehrsteilnehmer so, dass alle Trajektorieninformationen und Bewegungsdaten der virtuellen Gegenstücke gleich denen aus dem Datensatz sind mit Ausnahme zumindest eines zu ersetzenden aus den geführten automatisierten Fahrzeugen, das jeweils als virtuelles Testfahrzeug simuliert wird, wobei in der Simulation zumindest eine Randbedingung gegenüber dem Datensatz verändert wird,
• - Ermitteln einer Unähnlichkeitsmetrik zwischen den jeweiligen Trajektorieninformationen des Datensatzes und den in der Simulation ermittelten neuen Trajektorien eines oder mehrerer der geführten automatisierten Fahrzeuge, insbesondere des jeweiligen virtuellen Testfahrzeugs, und Vergleichen der Unähnlichkeitsmetrik mit einem vorgegebenen Grenzwert, und wenn die Unähnlichkeitsmetrik größer als der Grenzwert ist:
• - Wiederholen der Simulation unter Ersetzung der realen Bewegungsdaten und/oder Verhaltensmodelle von nicht-koordinierten Verkehrsteilnehmern durch virtuelle Agentenmodelle und Erzeugen von zusätzlichen oder vollständig die realen Sensorinformationen ersetzenden synthetischen Sensorinformationen für das in der Simulation abgebildete Sensornetz auf Basis von ermittelten Verhalten der Agentenmodellen, sodass die Koordinationseinheit in der wiederholten Simulation abhängig von den synthetischen Sensorinformationen die Koordinierung zumindest eines Teils, insbesondere aller, der automatisierten Fahrzeuge durchführt; und
• - gegebenenfalls Anpassen der Szenarienbibliothek mit den synthetischen Sensorinformationen der wiederholten Simulation.

A first aspect of the invention relates to a method, in particular a computer-implemented method, for carrying out a traffic simulation, comprising the steps:

• - Provision from a scenario library: A data set with information about a real traffic scenario from a locally defined traffic area with a central coordination unit for coordinating automated vehicles by specifying trajectories for the automated driving vehicles based on sensor information from a stationary sensor network of the traffic area, wherein the data set comprises trajectory information recorded by the sensor network and/or determined by the coordination unit of the automated vehicles guided by the coordination unit in the traffic scenario, as well as real movement data and/or behavior models recorded by the sensor network of non-coordinated road users in the traffic scenario,
• - Carrying out a first run of a simulation with virtual counterparts of the guided automated vehicles and the non-coordinated road users in such a way that all trajectory information and movement data of the virtual counterparts are the same as those from the data set with the exception of at least one of the guided automated vehicles to be replaced, which is simulated as a virtual test vehicle, wherein in the simulation at least one boundary condition is changed compared to the data set,
• - Determining a dissimilarity metric between the respective trajectory information of the data set and the new trajectories of one or more of the guided automated vehicles, in particular the respective virtual test vehicle, determined in the simulation and comparing the dissimilarity metric with a predetermined limit value, and if the dissimilarity metric is greater than the limit value:
• - Repeating the simulation by replacing the real movement data and/or behavior models of non-coordinated road users with virtual agent models and generating additional synthetic sensor information that completely replaces the real sensor information for the sensor network depicted in the simulation on the basis of determined behavior of the agent models, so that the coordination unit in the repeated simulation carries out the coordination of at least some, in particular all, of the automated vehicles depending on the synthetic sensor information; and
• - if necessary, adapt the scenario library with the synthetic sensor information from the repeated simulation.

In one step of the method, the vehicle component to be tested is tested by executing the traffic simulation. The vehicle component to be tested includes in particular a corresponding sensor and/or a trajectory planner such as a driving control computer. The vehicle component to be tested is either integrated into the simulation as a physical device using a hardware-in-the-loop method, or is virtually mapped and also simulated. This results in improved testing of the vehicle component to be tested compared to conventional testing methods.

The scenario library provides a data set with information about a locally defined traffic area. Such a locally defined traffic area, for example a parking garage optimized for autonomous vehicles or a logistics area with a large number of work machines, has the advantage over a free traffic area in the general public traffic area of being able to centrally coordinate and specify the paths to be taken by vehicles in order to coordinate the road users with each other, as well as specifying optimal trajectories for the individual road users, for example an optimal parking process in a parking garage.

In such a locally delimited traffic area, the central coordination unit takes on the function of a driving control computer, but in comparison to driving control systems of individual automated vehicles, the coordination unit takes over the control of a large number of automated vehicles synchronously. This means that the control electronics and sensors are no longer necessarily located in the respective vehicle itself. This applies in particular to the real counterpart to the virtual test vehicle from the real traffic scenario. These are in particular vehicles with external intelligence and perception that move in the same controlled environment (parking garage / logistics area). However, it can be assumed that in such a locally delimited traffic area, not all road users are automated vehicles controlled by the coordination unit. Rather, there are also non-coordinated road users, for example pedestrians, vehicles controlled by people directly or from a distance, or other vehicles not controlled by the central coordination unit, i.e. non-coordinated.

However, due to the design and structure of the sensor network, the data set of the scenario library contains both trajectory information about the movements of the automated vehicles controlled by the central coordination unit as well as movement data and/or behavior models of the non-coordinated road users such as the pedestrians mentioned above. In an adaptive replay-to-sim process, the movement data of the data set is therefore suitable for being transferred to a simulation in order to create realistic scenarios from the locally delimited traffic area in the simulation. However, one or more of the automated vehicles controlled by the central coordination unit are replaced in the simulation by a respective virtual test vehicle. Automated vehicles controlled by the coordination unit that are not replaced become a specific subtype of non-coordinated road users in this sense. The behavior models can be determined from the data set in order to use machine learning methods to build catalogs or database scenarios, to classify the scenarios that have occurred, and to establish statistical distributions for the behavior of the non-coordinated road users.

The virtual counterpart to the real vehicle to be replaced is simulated under changed boundary conditions, which is why a changed planned trajectory of the virtual vehicle can be determined by the simulated coordination unit. This is the basis for determining the dissimilarity metric, which is used to check whether the trajectory of the virtual test vehicle in the simulation is sufficiently similar to the trajectory of the underlying real vehicle. Such a deviation between the respective trajectory information can arise because changed boundary conditions are simulated in the first run of the simulation, such as changed behavior of the non-coordinated road users or changed latencies of the coordination unit.

Once the dissimilarity metric has been determined, its value is compared with a limit value. If the limit value is exceeded by the dissimilarity metric, there are already non-negligible deviations between the trajectories of at least one virtual test vehicle and the respective associated real vehicle in the previous journey in the real traffic scenario.

Therefore, the other non-coordinated road users identified by their real recorded trajectories are replaced by agent models in the simulation. Such agent models are able to actively react to the behavior of the virtual test vehicle during the simulation, which is not possible when using the recorded trajectories of the real road users, because the latter are used statically in the simulation and only the virtual test vehicle would then be able to carry out reactions in the simulation. However, if agent models are used, there is a mutual reaction between the virtual test vehicle and the other road users implemented as agent models.

How many agent models should replace the real recorded movement paths of the other road users in the simulation is best decided based on how close or potentially reactive the other road users are to the virtual test vehicle in the current traffic situation. The simplest decision to make is to replace all road users surrounding the virtual test vehicle in the simulation with agent models, but this comes at the expense of the computing power required to run the traffic simulation.

The agent-based trajectories are used in particular to generate new synthetic data for the sensor network or directly new trajectory data for the central coordination unit. If the agent-based trajectories are used to generate new synthetic data for one or more sensors of the sensor network to be tested or the respective perception channel thereof, 3D graphic data generated in real time is preferably used, which is generated in particular by a 3D engine and fed into the respective perception channel, i.e. as sensor information.

Whether the scenario library is adjusted depends in particular on whether the dissimilarity metric was smaller than the threshold or not.

It is an advantageous effect of the invention that X-in-the-loop tests of the perception train become more realistic and adaptive by using original movement paths and the sensor information consistent with them (as opposed to completely synthetic) for as long as possible. The agent-based simulation, in turn, advantageously supports the simulation-based approach with its advantages that the enormous effort (personnel/time/financial) for running scenarios by employees of an automobile manufacturer for testing the perception channels can be reduced by the method according to the invention if existing scenarios can be made more flexible in the proposed form and used for testing the perception channel. There is therefore no danger for test drivers or other road users in the event of a problem with the perception technology to be developed or tested or the associated perception train. Simple automated parameter variation and test evaluation are thus possible.

According to an advantageous embodiment, the boundary conditions changed in the first run of the simulation comprise at least one of the following: Changed algorithms of the coordination tion unit for specifying trajectories for the guided automated vehicles, changed behavior or behavior patterns or other parameterization of the behavior models of the non-coordinated road users, changed latencies in the transmission of the commands transmitted from the coordination unit to the guided automated vehicles, changed latencies regarding the calculation of the commands determined by the coordination unit for the guided automated vehicles, changed perception by the stationary sensor network.

According to a further advantageous embodiment, the dissimilarity metric comprises a geometric distance between trajectories from the trajectory information, wherein the dissimilarity metric comprises a sum of respective geometric distances when the dissimilarity metric is related to several of the guided automated vehicles.

Such a geometric distance can be determined, for example, by the area between the tracks of the virtual test vehicle (the simulated “vehicle under test”) and the real counterpart as a measure of the geometric distance to each other.

According to a further advantageous embodiment, the locally delimited traffic space is one of the following: a parking lot, a parking garage, a logistics zone.

According to a further advantageous embodiment, the sensor information includes all trajectory information of the real automated vehicles guided by the coordination unit as well as all real movement data of the non-coordinated road users, each from a bird's eye view.

For this purpose, the data set preferably already contains the corresponding, comprehensive information from a bird's eye view. This is particularly the case if the sensor network monitors the locally defined traffic area, so that the data is either available directly from a bird's eye view or can be reconstructed into one. For example, the sensor network in the locally defined traffic area has ceiling cameras or a so-called "indoor GPS" in order to be able to track the temporal position sequence and thus the trajectories of at least the guided automated vehicles and, ideally, also of the non-coordinated road users.

According to a further advantageous embodiment, the sensor information comprises at least some trajectory information of the real automated vehicles guided by the coordination unit and/or at least some real movement data of the non-coordinated road users, each from a first-person perspective.

If, for example, a parking garage is the demarcated traffic area under consideration, it can also be assumed that vehicles approved and intended for general public traffic will drive in this traffic area. However, depending on their intended level of automation, such vehicles already have a large number of environmental sensors themselves, the information from which is typically recorded from the ego perspective, but which can be used to support coordination by the coordination unit.

According to a further advantageous embodiment, the sensor information comprises camera data.

According to a further advantageous embodiment, the additional synthetic sensor information for the sensor network, which completely replaces the real sensor information, comprises synthetic real-time graphic data which is generated by a 3D engine and fed into the respective associated perception channel of the sensor network.

According to a further advantageous embodiment, the non-coordinated road users comprise at least one pedestrian.

According to a further advantageous embodiment, one of the following maneuvers is simulated in the simulation for the virtual test vehicle: parking, reversing, vehicle handover on a logistics area.

A further aspect of the invention relates to a device for data processing, comprising means for carrying out the steps of the method as described above and below.

A further aspect of the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method as described above and below.

Advantages and preferred developments of the proposed device or the proposed computer program product result from an analogous and analogous transfer of the statements made above in connection with the proposed method.

Further advantages, features and details emerge from the following description, in which - if necessary with reference to the drawing - at least one embodiment is described in detail. Identical, similar and/or functionally identical parts are provided with the same reference numerals.

• 1: Ein Verfahren gemäß einem Ausführungsbeispiel der Erfindung.

It shows:

• 1 : A method according to an embodiment of the invention.

The representations in the figure are schematic and not to scale.

1 shows a method for carrying out a traffic simulation of a parking garage with a central coordination unit for coordinating automated vehicles. First, a data set with information about a real traffic scenario from the parking garage with a central coordination unit for coordinating automated vehicles is provided S1 from a scenario library by specifying trajectories for the automated vehicles based on sensor information from a stationary camera sensor network of the parking garage, wherein the data set includes trajectory information of the automated vehicles guided by the coordination unit in the traffic scenario recorded by the sensor network and determined by the coordination unit, as well as real movement data and/or behavior models of non-coordinated road users such as pedestrians in the traffic scenario recorded by the sensor network. Furthermore, a first run of a simulation with virtual counterparts of the non-coordinated road users is carried out S2 in such a way that all movement data of the virtual counterparts are the same as those from the data set. The guided automated vehicles are simulated as respective virtual test vehicles. In the simulation, at least one boundary condition is changed compared to the data set, whereby the boundary conditions changed in the first run of the simulation include at least one of the following: changed algorithms of the coordination unit, changed behavior or behavior patterns or other parameterization of the behavior models of the non-coordinated road users, changed latencies in the transmission of the commands transmitted by the coordination unit to the guided automated vehicles, changed latencies regarding the calculation of the commands determined by the coordination unit for the guided automated vehicles, changed perception by the stationary sensor network. Furthermore, a dissimilarity metric is determined S3 between the respective trajectory information of the data set and the new trajectories of one or more of the guided automated vehicles, in particular of the respective virtual test vehicle, determined in the simulation and the dissimilarity metric is compared with a predetermined limit value, and if the dissimilarity metric is greater than the limit value, the simulation is repeated S4 by replacing the real movement data and/or behavior models of non-coordinated road users with virtual agent models and generating additional synthetic sensor information or synthetic sensor information that completely replaces the real sensor information for the sensor network depicted in the simulation on the basis of the determined behavior of the agent models, so that the coordination unit in the repeated simulation carries out the coordination of at least some, in particular all, of the automated vehicles depending on the synthetic sensor information; If the dissimilarity metric has exceeded the limit value and new synthetic data has thus been generated for the sensor network, the scenario library is adapted S5 with the synthetic sensor information from the repeated simulation.

Although the invention has been illustrated and explained in detail by preferred embodiments, the invention is not limited by the disclosed examples and other variations can be derived therefrom by the person skilled in the art without departing from the scope of the invention. It is therefore clear that a multitude of possible variations exist. It is also clear that embodiments mentioned by way of example really only represent examples that are not to be understood in any way as a limitation of the scope of protection, the possible applications or the configuration of the invention. Rather, the preceding description and the description of the figures enable the person skilled in the art to implement the exemplary embodiments in concrete terms, whereby the person skilled in the art, with knowledge of the disclosed inventive concept, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without departing from the scope of protection defined by the claims and their legal equivalents, such as further explanations in the description.

List of reference symbols

S1

Provide

S2

Carry out

S3

Determine

S4

Repeat

S5

Adjust

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions.

Cited patent literature

• DE 102019206908 B4 [0005]

Cited non-patent literature

• A Needle in a Haystack - How to Derive Relevant Scenarios for Testing Automated Driving Systems in Urban Areas” by Nico Weber, Dr.-Ing. Christoph Thiem and Prof. Dr.-Ing. Ulrich Konigorski of the preprint for “30th Aachen Colloquium Sustainable Mobility 2021 [0006]

Claims (10)

Method for carrying out a traffic simulation for testing a vehicle component, comprising the steps: - Providing (S1) from a scenario library: a data set with information about a real traffic scenario from a locally delimited traffic area with a central coordination unit for coordinating automated vehicles by specifying trajectories for the automated vehicles based on sensor information from a stationary sensor network of the traffic area, wherein the data set comprises trajectory information of the automated vehicles guided by the coordination unit in the traffic scenario recorded by the sensor network and/or specified by the coordination unit, as well as real movement data and/or behavior models of non-coordinated road users in the traffic scenario recorded by means of the sensor network, - Carrying out (S2) a first run of a simulation with virtual counterparts of the guided automated vehicles and the non-coordinated road users in such a way that all trajectory information and movement data of the virtual counterparts are the same as those from the data set with the exception of at least one to be replaced from the guided automated vehicles, each of which is simulated as a virtual test vehicle with a vehicle component to be tested, wherein in the simulation at least one boundary condition is changed compared to the data set, - determining (S3) a dissimilarity metric between the respective trajectory information of the data set and the new trajectories of one or more of the guided automated vehicles, in particular the respective virtual test vehicle, determined in the simulation, and comparing the dissimilarity metric with a predetermined limit value, and if the dissimilarity metric is greater than the limit value: - repeating (S4) the simulation by replacing the real movement data and/or behavior models of non-coordinated road users with virtual agent models and generating additional synthetic sensor information or synthetic sensor information that completely replaces the real sensor information for the sensor network depicted in the simulation on the basis of the determined behavior of the agent models, so that the coordination unit in the repeated simulation carries out the coordination of at least some, in particular all, of the automated vehicles depending on the synthetic sensor information; and - if necessary, adapting (S5) the scenario library with the synthetic sensor information of the repeated simulation, and testing the vehicle component to be tested. Procedure according to Claim 1 , wherein the boundary conditions changed in the first run of the simulation include at least one of the following: changed algorithms of the coordination unit, changed behavior or behavior patterns or other parameterization of the behavior models of the non-coordinated road users, changed latencies in the transmission of the commands transmitted by the coordination unit to the guided automated vehicles, changed latencies regarding the calculation of the commands determined by the coordination unit for the guided automated vehicles, changed perception by the stationary sensor network. A method according to any preceding claim, wherein the dissimilarity metric comprises a geometric distance between trajectories from the trajectory information, the dissimilarity metric comprising a sum of respective geometric distances when the dissimilarity metric is related to a plurality of the guided automated vehicles. Method according to one of the preceding claims, wherein the locally delimited traffic space is one of the following: a parking lot, a parking garage, a logistics zone. Method according to one of the preceding claims, wherein the sensor information comprises all trajectory information of the real automated vehicles guided by the coordination unit as well as all real movement data of the non-coordinated road users, each from a bird's eye view. Procedure according to Claim 5 , wherein the sensor information comprises at least some trajectory information of the real automated vehicles guided by the coordination unit and/or at least some real movement data of the non-coordinated road users, each from an ego perspective. Method according to one of the preceding claims, wherein the sensor information comprises camera data. Procedure according to Claim 7 , wherein the additional synthetic sensor information for the sensor network, which completely replaces the real sensor information, comprises synthetic real-time graphics data generated by a 3D engine and stored in the respective associated per reception channel of the sensor network. Method according to one of the preceding claims, wherein the non-coordinated road users comprise at least one pedestrian. Method according to one of the preceding claims, wherein in the simulation one of the following maneuvers is simulated for the virtual test vehicle: parking, reversing, vehicle handover in a logistics zone.

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