自动驾驶车辆在对抗性干扰下的弹性控制回路：基于频谱感知与网络层故障切换
Resilient Control Loops in Autonomous Vehicles Under Adversarial Jamming via Spectral Perception and Network-Layer Failover
- Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种结合物理层频谱感知与网络层路由优化的跨层框架，通过软件定义无线电和双接口热备用实现快速故障切换，在ROS2移动机器人上验证了141毫秒恢复时间并减少78.9%路径跟踪误差。
AI中文摘要：
自主移动机器人的运行完整性依赖于无线控制回路的持续可用性，这使其成为对抗性故意电磁干扰的高度吸引目标。本文提出了一种弹性的跨层框架，将物理层频谱感知与网络层路由优化相结合，以在故意电磁干扰期间保护中间件（如ROS2）的稳定性。利用软件定义无线电前端，系统提取动态频谱描述符，包括频谱熵和信道占用率，以通知随机森林分类器建立自适应环境基线。为确保数据流不中断，该架构在热备用配置中维护双预认证物理接口，通过自动网络路由表更新实现瞬时故障切换。在物理ROS2移动机器人测试平台上的实证验证表明，这种自适应硬件辅助架构将通信恢复平均优化至141毫秒。这种亚秒级恢复直接转化为与软件重新关联相比，池化均方根路径跟踪误差减少78.9%，成功保障了系统级任务完整性。
英文摘要：
The operational integrity of autonomous mobile robots relies on the continuous availability of wireless control loops, making them highly attractive targets for adversarial intentional electromagnetic interference. This paper introduces a resilient, cross-layer framework that combines physical-layer spectral perception with network-layer routing optimization to protect middleware stability, such as ROS2, during intentional electromagnetic interference. Utilizing a software-defined radio front-end, the system extracts dynamic spectral descriptors, including spectral entropy and channel occupancy, to inform a Random Forest classifier that establishes adaptive environmental baselines. To ensure uninterrupted data flow, the architecture maintains dual pre-authenticated physical interfaces in a hot-standby configuration, enabling instantaneous failover through automated network routing table updates. Empirical validation on a physical ROS2 mobile robot testbed demonstrates that this adaptive hardware-assisted architecture optimizes communication recovery to an average of 141ms. This sub-second restoration translates directly into a 78.9% reduction in pooled root-mean-square path tracking error compared to software re-association, successfully securing system-level mission integrity.
