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

2026-05-04

AI Summary

This work addresses the challenges of limited local sensing, unobservable human-driven vehicles, and the absence of a central coordinator in mixed human–autonomous traffic networks by proposing a decentralized fault and attack detection method. Leveraging a distributed consensus observer, connected automated vehicles collaboratively estimate the states of human-driven vehicles. Local residuals are generated at each vehicle and evaluated using two probabilistic thresholding logics—one incorporating residual history and the other not—to enable real-time fault detection and isolation. The approach requires neither local observability of human-driven vehicles nor a central processing unit, and it achieves robust, real-time detection of sensor anomalies on individual vehicles even under unbounded noise conditions, thereby significantly enhancing the resilience of multi-vehicle systems.

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.

Problem

• fault detection
• multi-vehicle systems
• distributed observers
• sensor measurements
• mixed traffic

Innovation

• Distributed observer
• Fault detection and isolation
• Probabilistic threshold
• Multi-vehicle systems
• Consensus-based estimation