Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle's cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). We design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, we propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. We formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, we develop a surrogate problem. Then, we use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that our approach significantly minimizes the age of processing in data offloading with 90.34 % improvement over similar method.

翻译：当前，车辆通过智能传感器收集道路环境数据。这些数据往往需借助昂贵车载硬件在车内进行处理，导致车辆成本攀升、电池电量迅速消耗及计算资源枯竭。因此，将任务卸载至云端成为必要。然而，安全可靠的车辆驾驶决策对低延迟的高要求使得数据卸载面临挑战。此外，现有关于自动驾驶车辆低延迟卸载的研究未考虑处理年龄问题。本文提出一种基于处理年龄的卸载方法，该方法融合无监督机器学习、多无线接入技术（multi-RATs）及开放无线接入网（O-RAN）中的边缘计算。我们设计了一种边缘云协作空间，可在自动驾驶车辆近端处理数据。为降低卸载延迟波动，我们提出一种新型通信规划方案，使车辆能在本地资源不足时，最优预选可用的RAT（如Wi-Fi、LTE或5G），将任务卸载至边缘云。我们构建了一个基于年龄的卸载优化问题，旨在最小化从任务生成到接收计算输出的时间间隔。针对这一非凸问题，我们引入替代问题进行转化，随后利用拉格朗日法将替代问题转化为无约束优化问题，并应用对偶分解法进行求解。仿真结果表明，该方法显著降低了数据卸载中的处理年龄，相比同类方法性能提升达90.34%。