Abstract: The paper discusses to use low-cost sensors and Kalman filter to achieve high-precision vehicle motion state estimation. Firstly, considering the lateral, yaw motion and roll motion of the vehicle, a nonlinear 3-DOF dynamic vehicle model is established and further linearized to realize extended Kalman filter design. To solve the problem of truncation error caused by linearization, the least square expression of largest posterior state estimation is established by Bayesian estimation, and the iterative extended Kalman filter（IEKF） algorithm is finally completed by further solving. Through simulations under different driving conditions, the IEKF algorithm is verified on filtering noise and tracking the actual value. The simulation results show that under complex driving conditions the IEKF algorithm can effectively filter noise and track the actual state of the vehicle slip angle and yaw rate.