TY - GEN
T1 - Hybrid Dynamics-Data Estimation of Tire-Road Adhesion Coefficient under Water Film Effect
AU - Liu, Jiahui
AU - Wang, Liang
AU - Liu, Yang
AU - Qu, Xiaobo
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - multi-source information fusion
KW - state estimation
KW - Tire-road adhesion coefficient
UR - https://www.scopus.com/pages/publications/105036998158
U2 - 10.1109/ITSC60802.2025.11423033
DO - 10.1109/ITSC60802.2025.11423033
M3 - Conference contribution
AN - SCOPUS:105036998158
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1373
EP - 1378
BT - IEEE Intelligent Transportation Systems Conference, ITSC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th International Conference on Intelligent Transportation Systems, ITSC 2025
Y2 - 18 November 2025 through 21 November 2025
ER -