Current sensing and control of self-driving vehicles based on full-view recognition is hard to keep a high-frequency with a fast moving vehicle, as increasingly complex computation is employed to cope with the variations of driving environment. This work, however, explores a light-weight sensing-planning framework for autonomous driving. Taking the advantage that a vehicle moves along a smooth path, we only locate several sampling lines in the view to scan the road, vehicles and environments continuously, which generates a fraction of full video data. We have applied semantic segmentation to the streaming road profiles without redundant data computing. In this paper, we plan vehicle path/motion based on this minimum data set that contains essential information for driving. Based on the lane, headway length, and vehicle motion detected from road/motion profiles, a path and speed of ego-vehicle as well as the interaction with surrounding vehicles are computed. This sensing-planning scheme based on spatially sparse yet temporally dense data can ensure a fast response to events, which yields smooth driving in busy traffic flow.


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    Titel :

    Planning Autonomous Driving with Compact Road Profiles


    Beteiligte:
    Wang, Zheyuan (Autor:in) / Cheng, Guo (Autor:in) / Zheng, Jiang Yu (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    2495510 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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