A Novel Fault-Tolerant Scheme for Multi-Model Ensemble Estimation of Tire Road Friction Coefficient With Missing Measurements

Yan Wang, Zhiguo Zhang, Henglai Wei, Guodong Yin, Hailong Huang, Boyuan Li, Chao Huang (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Accurate information on tire road friction coefficient (TRFC) is essential to autonomous driving systems. In this paper, a fault-tolerant estimation scheme is proposed to estimate TRFC in the case of missing measurements. First, a fault-tolerant unscented Kalman filter (FTUKF) is developed for estimating longitudinal and lateral tire forces in the condition of sensor signal loss. Then, longitudinal and lateral TRFCs are estimated separately with FTUKF based on tire forces information. Next, an event-driven multi-model fusion method based on the degree of data loss is designed to perform a weighted fusion of longitudinal and lateral TRFCs to further improve the estimation accuracy. Experiments with different working conditions are performed to demonstrate the validity of the fault-tolerant estimation framework. The results illustrate that the designed approach has higher estimation accuracy and strong adaptability under various roads.

Original languageEnglish
Pages (from-to)1066-1078
Number of pages13
JournalIEEE Transactions on Intelligent Vehicles
Volume9
Issue number1
DOIs
Publication statusPublished - Jan 2024

Keywords

  • Autonomous driving systems
  • event-driven fusion method
  • fault-tolerant unscented Kalman filter
  • tire road friction coefficient

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'A Novel Fault-Tolerant Scheme for Multi-Model Ensemble Estimation of Tire Road Friction Coefficient With Missing Measurements'. Together they form a unique fingerprint.

Cite this