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.

A longitudinal force estimation based on wheel dynamics and unscented Kalman filter is proposed in this report to address the difficulties in the conventional tire-based approaches. Although it seems that implementation of a tire model in the estimation procedure should result in more accurate results, especially for non-linear regions, complexities in identifying the tire parameters due to the variation of the road and tire conditions leads to inaccurate results for harsh maneuvers on slippery roads. Moreover, the estimation process requires reliable measurements and this necessitates utilizing dynamic models with feasible measurements. Consequently, wheel dynamics is employed to extend the fidelity of the algorithm. For such a model, wheel speeds as reliable and feasible measurements are available. In this strategy, the complex tire-road interaction can be discarded since the wheel speeds are being observed by wheel sensors and the values of the effective torques are provided by motor drives then the longitudinal forces at each individual corner of the vehicle can be estimated independently. Experimental and simulation results confirm the validity of the algorithm in slippery road conditions as well as normal conditions. The newly developed structure has a strong potential to be integrated with other state estimation, such as longitudinal/lateral velocities and lateral forces.

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This paper presents a comparative analysis of different analytical methods for identification of vehicle inertial parameters. The effectiveness of four different identification methods namely Recursive Least Squares (RLS), Recursive Kalman Filter (RKF), Gradient, and Extended Kalman Filter (EKF) for estimation of mass, moment of inertia and location of center of gravity of a vehicle is investigated. Requirements, capabilities and drawbacks of each method for real time applications are highlighted based on a comprehensive simulation analysis using CarSim. The Extended Kalman Filter method is shown to be the most reliable method for online identification of vehicle inertial parameters for active vehicle control, vehicle stability, and driver assistant systems.