Fusion Estimation Method of Tire-road Friction Coefficient Based on Vehicle Vision and Dynamical Model

doi:10.3901/JME.2024.24.254

Abstract

Abstract: A fusion estimation method is proposed to rapidly estimate tire-road friction coefficient based on vehicle vision and dynamic response information. Firstly, the semantic segmentation data set of road type is marked, the semantic segmentation model of road type is trained using the Deeplabv3+ algorithm, the camera is calibrated according to the Zhang Zhengyou calibration method, the results of semantic segmentation of road type are mapped to the vehicle coordinate system, and the results of semantic segmentation of road type are fused using the time-series weighting method. And it is converted to the corresponding range of road adhesion coefficient. Secondly, the state-constrained square-root cubature Kalman filter(CSCKF) algorithm is derived and realized. Finally, the state and observation equation of the system are obtained based on the vehicle dynamics model and the Brush tire model. The road surface semantic segmentation results are used as the constrained boundary input of the CSCKF algorithm to estimate the adhesion coefficient of the road surface. The results show that the fusion estimation algorithm of tire-road friction coefficient can converge quickly and has high recognition accuracy when the road adhesion condition changes abruptly.

Key words: road-type semantic segmentation model, fusion estimation, state-constrained square-root cubature Kalman filter

CLC Number:

U461

YANG Xiantong, ZHENG Ling, JIN Yanlin, ZHANG Hanke, ZENG Di, JI Jie. Fusion Estimation Method of Tire-road Friction Coefficient Based on Vehicle Vision and Dynamical Model[J]. Journal of Mechanical Engineering, 2024, 60(24): 254-264.

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