To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamental and well-studied driving ones. However, previous studies have mainly focused on individual tasks, and car following tasks have typically relied on complete leader-follower information to attain optimal performance. To address this limitation, we propose a vision-based deep reinforcement learning (DRL) agent that can simultaneously perform lane keeping and car following maneuvers. To evaluate the performance of our DRL agent, we compare it with a baseline controller and use various performance metrics for quantitative analysis. Furthermore, we conduct a real-world evaluation to demonstrate the Sim2Real transfer capability of the trained DRL agent. To the best of our knowledge, our vision-based car following and lane keeping agent with Sim2Real transfer capability is the first of its kind. We have made the codes and the videos of the simulation and real-world evaluation accessible online11Code and videos are available on: https://github.com/DailyL/Sim2Realautonomous_vehicle.


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    Titel :

    Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer


    Beteiligte:
    Li, Dianzhao (Autor:in) / Okhrin, Ostap (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    2437152 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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