A common approach in testing automated and autonomous driving systems (ADS) consists in running the ADS in a simulator where driving and environmental conditions are specified in terms of scenarios. An important aspect in ADS testing is to cover different driving situations in which the autonomous car must perform different types of maneuvers. In this paper, we consider the path planner of our industry partner; the path planner is responsible for deciding the path that must be followed by the autonomous car. A path is characterized by the driving characteristics (as forward acceleration, lateral acceleration, curvature, and so on) that are needed, at each time point, to implement it. For different driving characteristics, a good test suite should contain a scenario for which the path planner chooses a path that requires the application of the selected driving characteristics for a non-negligible period of time: this means that the characteristics are relevant in that path. With such a test suite, engineers can observe the different types of decision taken by the path planner, and so possibly better assess its correctness. In the paper, we introduce the notion of patterns of driving characteristics, to characterize their interaction (i.e., simultaneous or not) and measure their duration. Exploiting this definition, we propose two search-based approaches (for single and pairs of driving characteristics) to find scenarios in which such patterns occur and their duration is maximized. Experimental results show that the approaches are effective in finding scenarios for which the path planner generates paths where the different driving characteristics occur in terms of the specified pattern.


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    Titel :

    Targeting Patterns of Driving Characteristics in Testing Autonomous Driving Systems


    Beteiligte:
    Arcaini, Paolo (Autor:in) / Zhang, Xiao-Yi (Autor:in) / Ishikawa, Fuyuki (Autor:in)

    Erscheinungsdatum :

    2021-04-04



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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