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Recent developments in vehicle stability control and active safety systems have led to an interest in reliable vehicle state estimation on various road conditions. This paper presents a novel method for tire force and velocity estimation at each corner to monitor tire capacities individually. This is entailed for more demanding advanced vehicle stability systems and especially in full autonomous driving in harsh maneuvers. By integrating the lumped LuGre tire model and the vehicle kinematics, it is shown that the proposed corner-based estimator does not require knowledge of the road friction and is robust to model uncertainties. The stability of the time-varying longitudinal and lateral velocity estimators is explored. The proposed method is experimentally validated in several maneuvers on different road surface frictions. The experimental results confirm the accuracy and robustness of the state estimators.

Abstract-This article seeks to develop a longitudinal vehicle velocity estimator robust to road conditions by employing a tire model at each corner. Combining the lumped LuGre tire model and the vehicle kinematics, the tires internal deflection state is used to gain an accurate estimation. Conventional kinematicbased velocity estimators use acceleration measurements, without correction with the tire forces. However, this results in inaccurate velocity estimation because of sensor uncertainties which should be handled with another measurement such as tire forces that depend on unknown road friction. The new Kalman-based observer in this paper addresses this issue by considering tire nonlinearities with a minimum number of required tire parameters and the road condition as uncertainty. Longitudinal forces obtained by the unscented Kalman filter on the wheel dynamics is employed as an observation for the Kalman-based velocity estimator at each corner. The stability of the proposed time-varying estimator is investigated and its performance is examined experimentally in several tests and on different road surface frictions. Road experiments and simulation results show the accuracy and robustness of the proposed approach in estimating longitudinal speed for ground vehicles.

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