LiDAR based Cooperative Sensing in Vehicular Edge Computing

With rapid development of vehicular sensing and mobile communication technologies, cooperative sensing becomes an emerging paradigm of future intelligent transportation systems (ITSs). This paper investigates a LiDAR based cooperative sensing scenario in Vehicular Edge Computing (VEC). Specifically, we present the system architecture, in which vehicles with on-board LiDAR are able to detect objects via local processing of the sensed point-cloud data, and the outputs can be further shared via vehicle-to-vehicle (V2V) /vehicle-to-infrastructure (V2I) communications and fused in edge nodes. Then, we formulate the Edge Assisted Task Offloading (EATO) problem by considering the heterogeneous computation and communication capacities of vehicles and edge nodes, aiming at minimizing the average delay of the cooperative sensing tasks. Further, we propose a Multi-Armed Bandit (MAB)-based algorithm to make task offloading decisions adaptively. Finally, we implement the system prototype and give a comprehensive performance evaluation, which demonstrates the effectiveness of the proposed algorithm.