Performance Analysis of EKF and UKF for Estimating Tire Longitudinal and Side Angle Slip

Abstract

Abstract. Accurate tire slip estimation might be regarded as a small portion of the vehicle safety but is important criterion. Furthermore, as an autonomous vehicle system gets sophisticated, this type of technique will be more necessary. In this paper performance analysis for slip estimation in various situations is presented with several commonly known filter-extended Kalman filter (EKF) and unscented Kalman filter (UKF). Tire slip behaves differently depending on the surface type, the motion of the robot, and other environmental factors. Therefore, different kinds of situations and conditions are considered to estimate more accurate tire slip. Also as far as the tire slip is not based on actual data, it will be assumed to imitate the real tire slip behavior based on other study data. Finally, the performances of two filtering algorithms are compared to find more adequate algorithm with respect to the given condition for the future experimental results.

Modified Tire-Slip-Angle Model for Chaotic Vehicle Steering Motion

“…While suited for the tractor tires, the model does not apply to passenger cars. Aided by sensor data, researchers are employing various methods, e.g., Kalman filter, fuzzy logic, to estimate tire-slip-angle [6][7][8][9]. Obviously, these methods are unable to establish a tire-slip-angle model and not suitable for theoretical research on vehicle global dynamics.…”