【摘要】 车联网（IoV）与联网自动驾驶汽车（CAV）的结合推动了自动驾驶技术的飞速发展，但也带来了对计算资源的巨大需求，给资源受限的车辆带来了挑战。车辆边缘计算（VEC）的出现，提供了一种全新的解决方案，通过将任务卸载到路侧单元中的边缘服务器上，能够以更高效的方式为车联网提供服务。然而，多个车辆同时发出卸载请求时会产生资源抢占，增大任务处理延时，如何高效调度资源以最大化服务质量是一个亟待解决的问题。为此，文中旨在从多目标优化的角度，详细分析VEC计算卸载的延迟和能耗，使延迟和成本最小化，并提出了名为NSGA2TO的基于改进非支配排序遗传算法的任务卸载算法。该算法能够寻找出多目标优化问题的Pareto最优解，大量仿真结果验证了NSGA2TO的优越性能。此外，还探究了Pareto最优解所涉及的延迟与能耗之间的关系，有助于更好地理解车辆任务卸载问题的复杂性。通过合理平衡延迟和能耗，将能够进一步提升车联网系统的性能和效率，为用户提供更安全、更便捷的出行体验。更多还原

【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 challenging 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 problem,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.更多还原