With the rise of simulation in the pursuit of safe autonomous vehicles (AVs) there is a need to be able to work with scenario data in a way that is unrestrictive in its inputs, allows fast reactive simulation of scenarios and enables powerful insight into the interactions and risks present in the data. We propose Scenario Gym 11https://github.com/driskai/scenario_gym as a universal autonomous driving tool to address these needs. Scenario Gym is an opensource, lightweight simulator that allows fast execution of unconfined, complex scenarios containing a range of road users with the ability to gain rich insight via customised metrics and includes a framework for designing intelligent agents for reactive simulation.


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    Titel :

    Scenario Gym: A Scenario-Centric Lightweight Simulator


    Beteiligte:


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    1002775 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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