Autonomous vehicle systems require sensor data to make crucial driving and traffic management decisions. Reliable data as well as computational resources become critical. In this paper, we develop a Vehicular Edge Computing Scheduling Pipeline for connected and autonomous vehicles (CAVs) exploring scheduling optimization, pipeline design and vehicle to edge interactions. Through our pipeline, the data, generated by on-board sensors, is used towards various edge serviceable tasks. Due to the limited view of a vehicle, sensor data from one vehicle cannot be used to perceive road and traffic condition of a larger area. To address this problem, our pipeline facilitates data transfer and fusion for cooperative object detection of multiple vehicles. Through real-world experiments, we evaluate the performance and robustness of our pipeline on different device architectures and under different scenarios. We demonstrate that our pipeline achieves a real-time deadline capable edge to vehicle interaction via vehicle-edge data transfer and on-edge computation.


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    Titel :

    Vehicular Edge Computing for Multi-Vehicle Perception


    Beteiligte:
    Tang, Sihai (Autor:in) / Gu, Zhaochen (Autor:in) / Fu, Song (Autor:in) / Yang, Qing (Autor:in)


    Erscheinungsdatum :

    01.04.2021


    Format / Umfang :

    306911 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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