Vehicular Edge Computing (VEC) is a key research area in autonomous driving. As Intelligent Transportation Systems (ITSs) continue to expand, ground vehicles (GVs) face the challenge of handling huge amounts of sensor data to drive safely. Specifically, due to energy and capacity limitations, GVs will need to offload resource-hungry tasks to external (cloud) computing units for faster processing. In 6th generation (6G) wireless systems, the research community is exploring the concept of Non-Terrestrial Networks (NTNs), where satellites can serve as space edge computing nodes to aggregate, store, and process data from GVs. In this paper we propose new data offloading strategies between a cluster of GVs and satellites in the Low Earth Orbits (LEOs), to optimize the trade-off between coverage and end-to-end delay. For the accuracy of the simulations, we consider real data and orbits from the Starlink constellation, one of the most representative and popular examples of commercial satellite deployments for communication. Our results demonstrate that Starlink satellites can support real-time offloading under certain conditions that depend on the onboard computational capacity of the satellites, the frame rate of the sensors, and the number of GVs.

翻译：车载边缘计算（VEC）是自动驾驶领域的关键研究方向。随着智能交通系统（ITS）的持续扩展，地面车辆（GV）面临着处理海量传感器数据以实现安全驾驶的挑战。具体而言，由于能量与计算能力的限制，地面车辆需将资源密集型任务卸载至外部（云端）计算单元，以实现更快速的处理。在第六代（6G）无线通信系统中，研究界正在探索非地面网络（NTN）的概念，即卫星可作为空间边缘计算节点，对来自地面车辆的数据进行汇聚、存储与处理。本文针对低地球轨道（LEO）卫星与地面车辆集群之间的数据卸载，提出了新的策略，以优化覆盖范围与端到端时延之间的权衡。为确保仿真的准确性，我们采用了当前最具代表性且广泛商用的通信卫星部署实例——Starlink星座的真实数据与轨道参数。研究结果表明，在卫星机载计算能力、传感器帧率以及地面车辆数量等特定条件下，Starlink卫星能够支持实时数据卸载任务。