Study on vehicle driving state and parameters estimation based on triple cubature Kalman filter

Abstract

For the problem of vehicle driving state and parameters estimation in the process of vehicle driving, the vehicle state and parameters estimation algorithms are studied based on the triple cubature Kalman filter. The nonlinear three degrees of freedom model with Dugoff tyre model is established. The vehicle driving state estimator, road adhesion coefficient estimator and vehicle parameters estimator are designed based on the theory of triple cubature Kalman filter. To estimate the driving state and parameters accurately, the three estimators connect to each other in the process of the estimation and form a closed-loop feedback in real time. The estimation algorithms are verified based on the driving simulator. The serpentine docking road conditions with changing speed are selected to verify the estimation algorithms based on the driving simulator in-loop simulation test. The experiments' results show that the vehicle driving state and parameters are estimated accurately by the estimation algorithms.