Abstract: On the basis of vehicular edge computing,vehicleedgecloud collaboration can further en-able coordination between vehicles and the cloud,providing vehicles with additional computing and storage resources to achieve a smarter,safer,and more reliable driving experience.In traditional research,the computational tasks of vehicle users are assumed to be independent and indivisible,with no dependencies between tasks.However,in real-world applications,with the advancement of artificial intelligence,many applications consist of multiple interdependent components,making the consideration of such dependency-based computational demands essential.Therefore,this paper focuses on a multi-vehicle,multi-task edge computing scenario under vehicleedgecloud collaboration,constructing a model that accounts for vehicleedgecloud coordination,task dependencies,and task priorities to address task offloading decisions,task scheduling decisions,and resource allocation.With the goal of minimizing system energy consumption,a joint optimization algorithm JPDDO based on a priority algorithm and a double deep Q-network (DDQN) is proposed:Firstly,prioritizing multiple sets of dependent tasks;Secondly,solving the offloading decisions,scheduling decisions,computing frequency,and transmission power for the resulting task queue using the DDQN algorithm.Simulation results validate the effectiveness of the proposed method,demonstrating consistently low energy consumption under different network environments and parameter settings.

Key words: vehicle edge computing, vehicleedgecloud, task dependency, task priority, task offloading and scheduling, resource allocation