In various examples, a traffic model including one or more traffic scenarios may be generated and/or updated based on using human feedback. Human feedback may be provided indicating a preference for various traffic scenarios to identify which scenarios in a model are more realistic. A reward model may capture the preference information and rank the realism of one or more traffic scenarios.


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    Titel :

    REINFORCEMENT LEARNING FOR TRAFFIC SIMULATION


    Beteiligte:
    CAO YULONG (Autor:in) / XIAO CHAOWEI (Autor:in) / PAVONE MARCO (Autor:in) / IVANOVIC BORIS (Autor:in)

    Erscheinungsdatum :

    23.01.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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