VECFrame: A Vehicular Edge Computing Framework for Connected Autonomous Vehicles

Abstract

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.