Purpose

With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.

Design/methodology/approach

In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.

Findings

The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.

Originality/value

The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

中文翻译：

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移动边缘计算下自动车-基础设施协同的资源优化管理与分配

目的

随着自动驾驶技术的不断发展和应用范围的扩大，车载设备的种类不断丰富，车载设备的计算能力不断增强，相应的应用也更加多样化。由于应用需要在车载设备上运行，对车载设备的计算能力要求越来越高。移动边缘计算是解决自动驾驶实际应用问题的有效方法之一。

设计/方法/方法

本研究根据实际需求，提出了一种移动边缘计算环境下自动车-基础设施协同的最优资源管理分配方法，并进行了实际应用实验。

发现

研究提出了边缘计算中路侧单元模块的设计及其对应的实时操作系统任务协调，以及边缘计算负载集成和异构计算的方法。然后，提出了高并发计算任务的实时调度、自适应计算任务迁移方法和边缘服务器协同资源分配方法。测试结果表明，本研究提出的方法可以大大降低任务计算延迟，功耗一般随着任务规模和任务复杂度的增加而增加。

原创性/价值

结果表明，该方法在保证用户服务质量的同时，可以实现更低的功耗和更低的计算开销，表明该方法具有很好的应用前景。