To facilitate studies in Deep Reinforcement Learning (DRL) and autonomous vehicles, we present the CarAware framework1 for detailed multi-agent vehicle simulations, which works together with the open-source traffic simulator CARLA. This framework aims to fill the gap identified in currently available CARLA DRL frameworks, often focused on the perception and control of a single vehicle. The new framework provides baselines for training DRL agents in scenarios with multiple connected autonomous vehicles (CAVs), focusing on their sensors’ data fusion for objects’ localization and identification. These features and tools allow studying many different DRL strategies and algorithms, applied for multi-vehicle sensors’ data fusion and interpretation.1https://github.com/tulioaraujoMG/CarAware


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    Titel :

    CarAware: A Deep Reinforcement Learning Platform for Multiple Autonomous Vehicles Based on CARLA Simulation Framework


    Beteiligte:


    Erscheinungsdatum :

    2023-06-14


    Format / Umfang :

    2036145 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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