The recognition of road conditions is highly significant for improving the active safety of vehicles. Among them, the estimation of tire-road friction coefficient (TRFC) is particularly important. However, TRFC is difficult to measure directly by onboard sensors. In this paper, a novel scheme based on Cubature Kalman filter (CKF) and an adaptive backpropagation neural network (ABPNN) is proposed to estimate the TRFC. Firstly, a nonlinear 3-degree-of-freedom vehicle model and a Magic Formula tire model are established. Then, Cubature Kalman filter (CKF) algorithm is presented to estimate the vehicle driving state. Then, BP neural network is combined with adaptive learning rate to estimate TRFC. Finally, the estimation algorithm was validated using Carsim/Simulink. The co-simulation results show that the proposed algorithm has remarkable estimation accuracy and is suitable for different complex road conditions.
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Bei et al. (2024) studied this question.
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