Hybrid Sensing Data Fusion of Cooperative Perception for Autonomous Driving With Augmented Vehicular Reality

Abstract

Augmented vehicular reality (AVR) is one of the key technologies to realize intelligent transportation in the future, which can significantly improve traffic safety and transportation efficiency of autonomous driving. However, available computation and spectrum resources of vehicles are not well utilized to meet the requirements of cooperative perception of autonomous vehicles. In order to meet the needs of sharing sensing data to cooperatively perceive the surrounding environment, executing delay-sensitive and computationally intensive tasks of autonomous driving applications, we propose computation offloading and resource allocation optimization for AVR (CoroAVR) algorithm with hybrid sensing data fusion of cooperative perception to process the real-time data in fog-edge computing for maximizing system throughput and spectrum utilization on the premise of ensuring the quality of task completion. First, the minimum signal to interference plus noise ratio needed to complete the task is obtained with the given maximum computing resources. Second, the optimal power allocation is carried out by using convex optimization theory, and the throughput gain is calculated, and the offloading decision is made by comparing the throughput gain. Finally, the channel allocation problem is solved by using the maximum matching algorithm of the bipartite graph, and the computation resource allocation is studied. The simulation results show that the performance of the proposed algorithm is better than that of the contrast algorithm.