Experimental Validation Of Tire-road Friction Observer Based On Self-aligning Torque∗

Liang Shao, Jiawei Xiang, Arno Eichberger, Cornelia Lex

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Automated and cooperative driving is one of the most promising but challenging tasks of automotive industry. For guaranteeing safe operation of automated driving, observing vehicle and environment states is of necessity, in which the tire-road friction coefficient (μmax) is a crucial parameter. We give a solution to this problem by using the estimation framework proposed by the authors [1] for estimating the μmax by utilizing total aligning torque information and experimentally validate this observer. Firstly, we briefly introduce the proposed nonlinear adaptive observer. Then, a robust activation criteria is applied for reliable estimation. The estimation results from the proposed observer and Extended Kalman Filter (EKF) are subsequently compared under various μmax with experiments. The results show that 1) stability can be guaranteed with the proposed observer in various maneuvers while the EKF cannot. 2) The observer performs similar in terms of root mean square estimation error compared to EKF (when EKF is stable). Finally, detailed discussions are conducted to describe the potential limitations of the proposed method for μmax estimation.

Original languageEnglish
Title of host publication2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781665453745
Publication statusPublished - 2022
Event6th CAA International Conference on Vehicular Control and Intelligence: CVCI 2022 - Nanjing, China
Duration: 28 Oct 202230 Oct 2022


Conference6th CAA International Conference on Vehicular Control and Intelligence
Abbreviated titleCVCI 2022


  • automated driving
  • experimental validation
  • nonlinear adaptive observer
  • tire-road friction coefficient

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Automotive Engineering
  • Control and Optimization


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