Due to the strong nonlinear characteristics of vehicle system, obtaining the current vehicle motion state accurately is the basis for improving vehicle control accuracy. Moreover, with the increase or decrease of passengers and goods, some parameters of the vehicle will change, and accurate values cannot be obtained at any time. In this paper, the Dual Unscenter Kalman filtering algorithm (DUKF) is compared with the Unscented Kalman filtering algorithm (UKF) by using the vehicle dynamics grey box model in Matlab. The results show that DUKF still has a certain estimation accuracy when estimating vehicle state and parameters.
The accurate estimation of the road adhesion coefficient can provide a judgment basis for the accurate decision of the vehicle active safety system. It can accurately reflect the interaction between tires and road surface. The decrease of the adhesion coefficient on wet road surface increases the probability of traffic accidents. The vehicle dynamics model and tire model are established in simulink, and the vehicle operating environment is established in CarSim. The proposed algorithm is verified by co-simulation.
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