The recent success of Artificial Intelligence (AI) has enabled autonomous driving with better perception capabilities. However, on-ramp merging remains one of the main challenging scenarios for reliable autonomous driving. Within the limited onboard sensing range, a merging vehicle can hardly observe and predict the main road conditions properly, restricting appropriate merging maneuvers. In this poster, we outline ongoing research ideas for reliable and autonomous on-ramp merging assisted by vehicular communications. By jointly leveraging the basic safety messages (BSM) from neighboring vehicles and the surveillance images, a merging vehicle can perform reliable driving via robust multimodal reinforcement learning. Some experimental results are provided to evaluate our idea under the Simulation of Urban MObility (SUMO) platform.


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    Titel :

    Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning


    Beteiligte:
    Bagwe, Gaurav (Autor:in) / Li, Jian (Autor:in) / Deng, Xiaoheng (Autor:in) / Yuan, Xiaoyong (Autor:in) / Zhang, Lan (Autor:in)


    Erscheinungsdatum :

    01.12.2022


    Format / Umfang :

    551154 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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