Title: A review of road surface recognition and tyre-road friction coefficient estimation methods

Authors: Linhui Wang; Xiaobin Fan; Xueliang Yu; Zipeng Huang; Kaikai Zhao

Addresses: School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China

Abstract: The tyre-road friction coefficient (TRFC) characterises the maximum interaction force that can be generated between the road surface and the tyre, which directly affects the driving, braking, and handling stability of vehicles. Obtaining accurate estimates of TRFC can optimise the vehicle's active safety control and improve decision-making and planning performance in autonomous driving. However, the existing TRFC identification methods are not very accurate and real-time when dealing with sudden changes in road conditions under extreme working conditions. Therefore, this paper discusses the current domestic and international road recognition methods, provides a review based on recognition principles, and elaborates on the two main categories of existing identification methods. It introduces the adhesion rate estimation and road type recognition methods commonly involved in TRFC identification, analyses the new methods brought by neural networks and rubber friction theory to the adhesion coefficient estimation issue, and provides an outlook on future development directions.

Keywords: TRFC; tyre-road friction coefficient estimation; road recognition; Kalman filtering; neural networks; rubber friction.

DOI: 10.1504/IJHVS.2025.147049

International Journal of Heavy Vehicle Systems, 2025 Vol.32 No.3, pp.327 - 367

Received: 05 Apr 2024
Accepted: 14 May 2024
Published online: 10 Jul 2025 *

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