Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles. In this work, we present a hierarchical pedestrian behavior model that generates high-level decisions through the use of behavior trees, in order to produce maneuvers executed by a low-level motion planner using an adapted Social Force model. A full implementation of our work is integrated into GeoScenario Server, a scenario definition and execution engine, extending its vehicle simulation capabilities with pedestrian simulation. The extended environment allows simulating test scenarios involving both vehicles and pedestrians to assist in the scenario-based testing process of autonomous vehicles. The presented hierarchical model is evaluated on two real-world data sets collected at separate locations with different road structures. Our model is shown to replicate the real-world pedestrians’ trajectories with a high degree of fidelity and a decision-making accuracy of 98% or better, given only high-level routing information for each pedestrian.


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    Titel :

    A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation


    Beteiligte:
    Larter, Scott (Autor:in) / Queiroz, Rodrigo (Autor:in) / Sedwards, Sean (Autor:in) / Sarkar, Atrisha (Autor:in) / Czarnecki, Krzysztof (Autor:in)


    Erscheinungsdatum :

    05.06.2022


    Format / Umfang :

    355128 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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