Distributed Observer-based Fault Detection over Intelligent Networked Multi-Vehicle Systems

TL;DR

This paper develops distributed fault detection methods for multi-vehicle systems, enabling autonomous vehicles to identify sensor faults or attacks locally without central coordination, using probabilistic thresholds.

Contribution

It introduces novel residual-based fault detection techniques that do not assume bounded noise, suitable for realistic multi-vehicle transportation scenarios.

Findings

Two FDI logics with and without residual history are proposed.

Probabilistic threshold design improves detection accuracy over deterministic methods.

Distributed detection allows local anomaly identification without central processing.

Abstract

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.