Distributed Fault Detection of Autonomous Vehicle Networks Using Local Relative Measurements
Yan Li, Yang Chen, Qijun Chen · IEEE Transactions on Instrumentation and Measurement · 2025
This study addresses the distributed fault detection (FD) problem in networks of autonomous vehicles where each vehicle relies solely on local relative measurements from onboard sensors. A novel aggregated observer-based fault detector is proposed to simultaneously detect faults originating from the vehicle itself and its neighbors, thereby significantly reducing the computational burden associated with the FD scheme. The$H_{\infty } $optimization approach is employed in conjunction with the ingenious design of the detector structure and coefficient matrices to ensure optimal detection performance. Additionally, utilizing the information entropy method, a new fault isolation algorithm is developed to efficiently distinguish faults among different neighbors. Numerical and physical experimental results demonstrate that the proposed FD scheme achieves optimal detection performance while significantly reducing computational requirements, highlighting its effectiveness and improvements.