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Scenario based methods for testing and validation of automated driving systems in virtual test environments are gaining importance and becoming an important component for verification and validation processes of automated driving systems. The high system complexity of such systems and the high testing costs lead to an exponential increase of test efforts for real world testing. Recent research works have shown that it is necessary to drive and test billions of kilometers to ensure the safety and reliability of automated driving systems. This amount of test kilometers is far from possible and achievable for any test procedure regarding the time and costs. Using different methods and procedures it is possible to reduce the number of scenarios, which should be tested to approve the safety and reliability of automated driving systems.


The scenario space consisting of critical and non-critical scenarios and the test effort can be reduced to an infinite and comprehensible amount of relevant scenarios for the system under test. Using scenario and simulation based approaches this effort can be efficiently reduced concerning the costs and time. The biggest challenges hereby are the detection and selection of a suitable scenarios and simulation environments or platforms for the system under test. Besides, suitable safety metrics are essential for the detection, evaluation, and reduction of relevant scenarios for testing of automated driving systems.


Current scientific work offers various strategies and approaches for generating relevant scenarios for automated driving systems. All of them have their advantages and disadvantages related to the used virtual environment, vehicle model, traffic model, and the integration complexity. This paper presents a survey through different approaches, methods, and safety metrics for scenario generation and evaluation for testing and validation of automated driving systems. The reader should get a state of the art overview on scenario based approaches of automated driving systems.



Dipl.-Ing. Demin Nalic, University of Technology Graz, AUSTRIA; Dipl.-Ing. Tomislav Mihalj, TU Graz, AUSTRIA; Dipl.-Ing. Maximilian Bäumler, TU Dresden, GERMANY; Dipl.-Ing. Matthias Lehmann, TU Dresden, GERMANY; Prof. Dr.-Ing. Arno Eichberger, TU Graz, AUSTRIA

Scenario Based Testing of Automated Driving Systems: A Literature Survey

F2020-ACM-096 • Paper • FISITA Web Congress 2020 • Automated and Connected Mobility (ACM)

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