This paper presents a fault detection and isolation approach for state estimation in autonomous vehicles, enhancing safety through the assurance of accurate measurement acquisition. Using a zonotopic method to guarantee robustness against unknown-but-bounded measurement noises, this study introduces a novel formulation of the Extended Kalman Filters, using the Linear Parameter-Varying modelling technique. Its performance is assessed alongside other state estimation methods. Experiments on a Renault Zoe (SAE level 3) enable a safety validation for the FDI process, finding the critical sensors for the state estimation algorithm. ; Peer Reviewed ; Postprint (author's final draft)
A zonotopic FDI approach with LPV-based EKF in autonomous vehicles
2024-01-01
doi:10.1016/j.ifacol.2024.07.258
Article (Journal)
Electronic Resource
English
DDC: | 000 |
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