Vehicle active safety control was a key technology to avoid serious safety accidents, and accurate acquisition of vehicle states signals was a necessary prerequisite to achieve active vehicle safety control. Based on the purpose, a 3-DOF nonlinear vehicle dynamics model containing constant noise and a nonlinear tire model were established, and several vehicle key states were estimated by a strong tracking central different Kalman filter (CDKF). The conclusion showed that the proposed estimator had higher accuracy and less computation requirement than the CKF, CDKF, and UKF estimators. Numerical simulation and experiments indicated that the proposed vehicle state estimation method not only had higher estimation accuracy but also had higher real-time function.

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Vehicle path tracking problem has been a hot research topic in the field of automotive safety. Therefore, combined with a 3-DOF vehicle model is established. The basic idea behind the work was to identify the optimal steering angle input during a vehicle travels along a prescribed path. Based on Model Predictive Control (MPC), the steering angle input was determined as the control variable, tracking the desired path was determined as control object. By using the Model Predictive Control, the optimal control problem was converted into a secondary plan problem which was then solved by the active set method. The results show that the minimum error of lateral position for the generated path-tracking trajectory can be good indicators of successful solving of the path-tracking problem in vehicle handling inverse dynamics for MPC. The study can help drivers identify safe lane-keeping trajectories and area easily as well as evaluating performance of emergency collision-avoidance for a vehicle.