车辆间计算任务卸载算法与系统级仿真验证

Computation task offloading algorithm and system level simulation for vehicles

随着自动驾驶技术和车联网的发展,越来越多的车辆将具备强大的计算能力,并通过无线网络实现互联。这些计算资源不仅能够应用于自动驾驶中,也可以提供广泛的边缘计算服务。针对车辆间的计算卸载场景,以最小化平均卸载时延为目标,提出了基于在线学习的分布式计算任务卸载算法。进一步搭建了系统级仿真平台,分别在真实的高速公路和城市街区道路环境下,评估了车辆密度、任务卸载份数对平均卸载时延的影响,为不同交通环境下的服务资源分配部署提供了参考。

With the development of autonomous driving and vehicular network,more and more vehicles will have powerful computing capabilities and connection with each other via wireless network.These computing resources can not only be applied to automatic driving,but also provide a wide range of edge computing services.Aiming at the task offloading among vehicles,a distributed task offloading algorithm based on online learning was proposed to minimize the average offloading delay.Furthermore,a system-level simulation platform was built to evaluate the impact of vehicle density and number of tasks on the average offloading delay in both highway and urban scenarios.The results provide a reference for the resource allocation and deployment of task offloading in different traffic situations.