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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    Title :

    Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning


    Contributors:
    Bagwe, Gaurav (author) / Li, Jian (author) / Deng, Xiaoheng (author) / Yuan, Xiaoyong (author) / Zhang, Lan (author)


    Publication date :

    2022-12-01


    Size :

    551154 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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