In recent years, with the rapid development of Internet of Things (IoTs) and artificial intelligence, vehicular networks have transformed from simple interactive systems to smart integrated networks. The accompanying intelligent connected vehicles (ICVs) can communicate with each other and connect to the urban traffic information network, to support intelligent applications, i.e., autonomous driving, intelligent navigation, and in-vehicle entertainment services. These applications are usually delay-sensitive and compute-intensive, with the result that the computation resources of vehicles cannot meet the quality requirements of service for vehicles. To solve this problem, vehicular edge computing networks (VECNs) that utilize mobile edge computing offloading technology are seen as a promising paradigm. However, existing task offloading schemes lack consideration of the highly dynamic feature of vehicular networks, which makes them unable to give time-varying offloading decisions for dynamic changes in vehicular networks. Meanwhile, the current mobility model cannot truly reflect the actual road traffic situation. Toward this end, we study the task offloading problem in VECNs with the synchronized random walk model. Then, we propose a reinforcement learning-based scheme as our solution, and verify its superior performance in processing delay reduction and dynamic scene adaptability.

近年来，随着物联网和人工智能的快速发展，车载网络已经从简单的交互系统转变为智能集成网络。随附的智能互联汽车（ICV）可以相互通信并连接到城市交通信息网络，以支持智能应用，即自动驾驶，智能导航和车载娱乐服务。这些应用通常对时延敏感并且计算量大，结果是车辆的计算资源不能满足车辆服务的质量要求。为了解决这个问题，利用移动边缘计算卸载技术的车辆边缘计算网络（VECN）被视为一种有前途的范例。然而，现有的任务卸载方案缺乏对车载网络的高度动态特性的考虑，这使得它们无法针对车载网络的动态变化给出时变卸载决策。同时，目前的出行模型不能真正反映出实际的道路交通状况。为此，我们使用同步随机游走模型研究了VECN中的任务分流问题。然后，我们提出了一种基于强化学习的方案作为我们的解决方案，并验证了其在处理延迟减少和动态场景适应性方面的卓越性能。为此，我们使用同步随机游走模型研究了VECN中的任务分流问题。然后，我们提出了一种基于强化学习的方案作为我们的解决方案，并验证了其在减少处理延迟和动态场景适应性方面的卓越性能。为此，我们使用同步随机游走模型研究了VECN中的任务分流问题。然后，我们提出了一种基于强化学习的方案作为我们的解决方案，并验证了其在处理延迟减少和动态场景适应性方面的卓越性能。