Deep Reinforcement Learning Based Radio Resource Selection Approach for C- V2X Mode 4 in Cooperative Perception Scenario

Abstract

In recent years, vehicles have been equipped with multiple sensors to enable assisted driving and even autonomous driving. However, due to the physical characteristics of the sensors, there are numerous shortcomings in the perception of the surrounding environment by a single vehicle. The development of vehicle-to-everything technology enables vehicles to extend their sensing range or enhance the reliability of perception by exchanging sensor data via vehicle-to-vehicle communication, which is called cooperative perception. In cellular vehicle-to-everything Mode 4, vehicles use the sensing-based semi-persistent scheduling scheme to select radio resource autonomously before transmission. But this scheme is hardly adaptable to cooperative perception scenario due to the time-sensitive of cooperative perception and the impact caused by the position of the per-ception information. In this paper, we modeled the cooperative perception scenario and the communication between vehicles, and then we formulated the optimization objective considering the characteristics of cooperative perception. Finally, we propose a multi-agent deep reinforcement learning based resource selection algorithm to tackle this problem and demonstrate its effectiveness through simulations.