Abstract
Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real-time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this paper proposes an allocation and collaboration scheme of vehicle edge computing resources based on the Lyapunov function and Twin Delayed Deep Deterministic Policy Gradient (TD3). In this solution, this paper uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real-time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi-objective dynamic optimization problem. It solves the problem by applying the TD3 algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing.