Function offloading is a promising solution to address limitations concerning computational capacity and available energy of Connected Automated Vehicles~(CAVs) or other autonomous robots by distributing computational tasks between local and remote computing devices in form of distributed services. This paper presents a generic function offloading framework that can be used to offload an arbitrary set of computational tasks with a focus on autonomous driving. To provide flexibility, the function offloading framework is designed to incorporate different offloading decision making algorithms and quality of service~(QoS) requirements that can be adjusted to different scenarios or the objectives of the CAVs. With a focus on the applicability, we propose an efficient location-based approach, where the decision whether tasks are processed locally or remotely depends on the location of the CAV. We apply the proposed framework on the use case of service-oriented trajectory planning, where we offload the trajectory planning task of CAVs to a Multi-Access Edge Computing~(MEC) server. The evaluation is conducted in both simulation and real-world application. It demonstrates the potential of the function offloading framework to guarantee the QoS for trajectory planning while improving the computational efficiency of the CAVs. Moreover, the simulation results also show the adaptability of the framework to diverse scenarios involving simultaneous offloading requests from multiple CAVs.

翻译：功能卸载是一种通过以分布式服务形式在本地与远程计算设备间分配计算任务，有望解决联网自动驾驶车辆（CAV）或其他自主机器人在计算能力与可用能量方面局限性的方案。本文提出一种通用功能卸载框架，可用于卸载任意计算任务集合，并聚焦于自动驾驶应用。为提供灵活性，该框架被设计为可集成不同的卸载决策算法与服务质量（QoS）要求，并能根据不同场景或CAV的目标进行调整。着眼于适用性，我们提出一种高效的基于位置的方法，其中任务在本地或远程处理取决于CAV的位置。我们将所提框架应用于服务化轨迹规划用例，将CAV的轨迹规划任务卸载至多接入边缘计算（MEC）服务器。评估在仿真和实际应用中进行，结果表明该功能卸载框架能够在提升CAV计算效率的同时，保障轨迹规划的QoS。此外，仿真结果还展示了该框架对涉及多辆CAV同时发起卸载请求的多样化场景的适应能力。