在车载边缘计算（vehicular edge computing, VEC）环境中，车辆可以将计算密集型任务卸载给VEC服务器。但由于车辆的高速移动性，以及接入服务器车辆的不均匀分布，导致任务上传成功率低、不同VEC服务器之间工作负载不均衡，进而影响了服务质量。设计了一种基于交替方向乘子法的概率算法，用于解决车辆的任务上传问题，从而提高了任务上传的成功概率。此外，设计了以最小化卸载延迟为目标的边云协同负载均衡任务卸载方案，通过服务器之间的协作来均衡服务器之间的负载，从而提高服务质量。仿真结果表明，与基准方案相比，本文方法具有更低的卸载时延；在车辆到达率相同的情况下，相比于其他同类方案，本文方案在提高了任务上传成功率的同时，将卸载延迟降低了约20%。

In vehicular edge computing (VEC) environment, vehicles can offload computationally intensive tasks to VEC servers. However, the high-speed mobility of the vehicles and the uneven distribution of vehicles accessing the servers lead to a low success rate of the task upload and an unbalanced workload among the different VEC servers, which in turn affects the quality of service (QoS). A probabilistic algorithm based on alternating direction multiplier method (ADMM) was designed to solve the task upload problem of vehicles, which improved the success probability of task upload. In addition, an edge-cloud collaborative load balancing task offloading scheme was designed with the aim of minimizing the offloading delay to balance the load among servers through edge-cloud collaboration, thereby improving the QoS. Simulation results show that this paper has lower offloading latency compared to the benchmark scheme; and the scheme improves the task upload success rate compared to other similar schemes with the same vehicle arrival rate. It also reduces offloading latency by about 20%.