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 FRAMEwork (VECFrame) for connected and autonomous vehicles (CAVs) exploring containerization, indirect communication, and edge-enabled cooperative object detection. Through our framework, 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, VECFrame facilitates data transfer and fusion and cooperative object detection from multiple vehicles. Through real-world experiments, we evaluate the performance and robustness of our framework on different device architectures and under different scenarios. We demonstrate that our framework achieves a more accurate perception of traffic condition via vehicle-edge data transfer and on-edge computation.


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    Title :

    VECFrame: A Vehicular Edge Computing Framework for Connected Autonomous Vehicles


    Contributors:
    Tang, Sihai (author) / Chen, Bruce (author) / Iwen, Harold (author) / Hirsch, Jason (author) / Fu, Song (author) / Yang, Qing (author) / Palacharla, Paparao (author) / Wang, Nannan (author) / Wang, Xi (author) / Shi, Weisong (author)


    Publication date :

    2021-09-01


    Size :

    10883999 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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