In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

翻译：本文聚焦于通过车辆到基础设施（V2I）链路，将蜂窝车辆（CV）的任务卸载至多接入边缘计算（MEC）服务器，以提升自主驾驶安全性。考虑到V2I链路所用频率可复用于车辆到车辆（V2V）通信以提高频谱利用率，每条V2I链路的接收端可能遭受严重干扰，导致任务卸载过程出现中断。为解决该问题，我们提出部署可重构智能计算表面（RICS），不仅实现V2I反射链路，还可利用其超材料的计算能力对V2V链路进行干扰消除。我们联合优化CV与MEC服务器之间的任务卸载比例、V2V与V2I通信的频谱共享策略，以及RICS的反射与折射矩阵，目标为最大化基于安全性的自主驾驶任务收益。鉴于问题的非凸性及其自由变量间的耦合关系，我们将其转化为更易处理的等价形式，进而分解为三个子问题，并通过交替近似方法求解。仿真结果表明，所提出的RICS优化方案在提升自主驾驶网络安全性方面具有显著有效性。