Real-time estimation of vehicle state and tire-road friction forces


An approach to estimate vehicle state and tire road friction forces using an extended Kalman filter (EKF) is presented. A numerically stable algorithm is used to implement the EKF. This approach does not require knowledge of the tire model and road friction coefficient. This is an advantage, because although many tire models have been developed so far, there is still a significant difference between these models and the real behavior of the tire-road interface. The main advantages of the proposed method are numerical stability, computational efficiency and to use vehicle mounted sensors. The effectiveness of the presented method is confirmed by simulation of a lane-change and an ABS braking maneuver for a full vehicle. In these simulations, a seven DOF vehicle model, a Pacejka tire model and a nonlinear model for a hydraulic brake system are used. The results show that the EKF has good performance in presence of significant sensor noise in both scenarios.

American Control Conference (ACC)