Deep Reinforcement Learning-Based Resource Allocation with Enhanced Perception and Low-Latency for Autonomous Driving in ISAC-aided VEC

As autonomous driving technology advances, the intelligence levels of vehicles continue to increase. However, meeting the demands of autonomous driving in various scenarios requires improved wireless communication and vehicle perception capabilities. Integrated sensing and vehicular edge computing (VEC) technology can provide collaborative perception and computing resources for vehicles. Nevertheless, the high-speed mobility of vehicles leads to frequent changes in channel state information and distances between vehicles and roadside units (RSUs), which poses challenges for low-latency perception processing. Additionally, most research overlooks the impact of vehicle mobility on perception accuracy and lacks effective resource allocation strategies for multi-source perception data fusion tasks. Addressing existing research shortcomings, this paper proposes a deep reinforcement learning(DRL)-based resource allocation method. It first adopts Integrated Sensing and Communication (ISAC) technology in the same frequency band to improve spectrum efficiency and integration. Secondly, it constructs a data fusion model to enhance vehicle perception capabilities and describes the data fusion process between vehicle terminals and RSU terminals. Furthermore, this paper designs a resource allocation algorithm for multi-source perception data fusion tasks with the optimization goal of minimizing task completion delay and system average energy consumption. Considering the mobility of vehicles and the frequent changes in communication channel states, this paper transforms the constructed problem into a Markov decision process (MDP). It solves it using the Improved Dueling Twin Delayed Deep Deterministic policy gradient (ID-TD3) algorithm. Experiment results demonstrate that the proposed strategy can reasonably allocate system resources, effectively reducing task completion delay and system average energy consumption.