Abstract: The combination of the Internet of Vehicles(IoV) and connected autonomous vehicles(CAV) has promoted the rapid development of autonomous driving technology,but it has also created a huge demand for computing resources,which is challen-ging to resource-constrained vehicles.Vehicular edge computing(VEC) offers an entirely new solution.By offloading tasks to edge servers deployed in the roadside unit(RSU),we are able to service the IOV in a more efficient way.However,resource preemption will occur when multiple vehicles send offloading requests at the same time,which will increase the task processing delay.How to efficiently dispatch resources to maximize the quality of service is an urgent problem to be solved.To solve this pro-blem,we treat it as a multi-objective optimization pro-blem and propose a task offloading algorithm named NSGA2TO based on non-dominated sorting genetic algorithm-II.The algorithm can find the Pareto optimal solution of multi-objective optimization pro-blems,and extensive simulation results verify that NSGA2TO outperforms counterparts.In addition,we also explore the relationship between the delay and energy consumption involved in the Pareto optimal solution,which helps to better understand the complexity of the vehicle tasksoffloading problem.By properly balancing delay and energy consumption,we will be able to further improve the performance and efficiency of the connected autonomous system,providing users with a safer and more convenient travel experience.

Key words: Vehicular edge computing, Tasks offloading, Multi-objective optimization, NSGA-II, Pareto optimal solution