Decentralized strategies are of interest for local decision-making over multi-vehicle networks. This paper studies mixed traffic networks of human-driven and autonomous vehicles with partial sensor measurements. The idea is to enable the group of connected autonomous vehicles (CAVs) to track the state of a group of human-driven vehicles (HDVs) via distributed consensus-based observers/estimators. Particularly, we make no assumption that the group of HDVs is locally observable in the direct neighborhood of any CAV. Then, the main contribution is to design local residual-based fault detection and isolation (FDI) at every CAV to detect possible faults/attacks in the sensor measurements. This distributed detection strategy enables every CAV to locally find possible anomalies in its taken sensor measurement with no need for a central processing unit. Two FDI logics are proposed with and without considering the history of the residuals. These FDI techniques are based on probabilistic threshold design on the residuals (in contrast to the existing deterministic threshold FDI techniques) with no assumption that the noise is of bounded support. This is more realistic in real-world multi-vehicle transportation systems.

翻译：分散式策略对于多车辆网络中的局部决策具有重要意义。本文研究了部分传感器测量下人工驾驶与自动驾驶车辆混合交通网络。其核心思想是使联网自动驾驶车辆（CAV）群体通过基于分布式共识的观测器/估计器追踪人工驾驶车辆（HDV）群体的状态。特别地，我们未假设HDV群体在任意CAV的直接邻域内具有局部可观测性。主要贡献在于为每辆CAV设计基于局部残差的故障检测与隔离（FDI）方法，以检测传感器测量中可能存在的故障/攻击。这种分布式检测策略使每辆CAV能够在无需中央处理单元的情况下，自主发现其传感器测量值中的异常。本文提出两种考虑残差历史记录的FDI逻辑（以及不考虑残差历史记录的方案）。这些FDI技术基于残差的概率阈值设计（与现有确定性阈值FDI技术不同），且不假设噪声具有有界支撑。这一特性更符合真实多车辆交通系统的实际场景。