Combined Estimation of Vehicle Slip Angle and Lateral Tire Forces with an Unscented Kalman Filter with Outlier Detection

Knowledge of a vehicle's dynamic state is crucial for active safety systems like Electronic Stability Control (ESC) or active yaw. Furthermore it is an unavoidable component of fully automated vehicles like autonomous cars. The most important properties for vehicle stability are the wheel slips, the slip angles of the wheels and the tire forces. This contribution presents an approach for tracking the vehicle's lateral dynamics. Therefore, the lateral tire forces per axle as well as the slip angle of the vehicle are estimated using the unscented Kalman filter (UKF). With the estimated quantities, the slip angle can be transformed to the middle of the front and rear axle. Thus, the wheel slip angles of the corresponding single track model can be obtained. The method only needs sensor information which is available in standard production vehicles. A model for the tire-ground contact is not required. The update step of the algorithm is implemented in a numerically robust sequential formulation, which additionally allows for detection of measurement outliers. The results are demonstrated in simulations with the software CarMaker.