Liu, Jiahui (Tsinghua University), Wang, Liang (Tsinghua University), Liu, Yang (Tsinghua University), Qu, Xiaobo (Tsinghua University)

Hybrid Dynamics-Data Estimation of Tire-Road Adhesion Coefficient under Water Film Effect

Scheduled for presentation during the Regular Session "S28a-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (TH-LM-T28), Thursday, November 20, 2025, 11:50−12:10, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

The tire-road adhesion coefficient (TRAC) is crucial for vehicle decision-making and active safety control. Existing TRAC estimation methods face issues like inaccurate dynamic models and unreliable road-based classification. In this study, a hybrid dynamics-data estimation (HDDE) method is proposed. Firstly, an Adaptive Cubature Kalman Filter (ACKF) is designed to estimate the coefficient and vehicle states considering vehicle and tire dynamics. Secondly, a Support Vector Regression (SVR) model accounting for water film height is developed, as the water film effect significantly impacts tire-road interactions. Thirdly, a fusion strategy based on the confidence levels of the ACKF and SVR results is presented to optimize the TRAC estimated values. Finally, co-simulation tests on the CarSim and MATLAB platform in typical lateral maneuver scenarios show that the HDDE method improves the estimation accuracy and vehicle stability, outperforming traditional methods.