Real-Time Estimation of Tire-Road Friction Coefficient Based on Unscented Kalman Filtering

Abstract

Tire-road friction coefficient (TRFC) estimation is significant to ADAS and high-level autonomous driving. This paper presents a dynamics-based method of real-time TRFC estimation. 2D-LuGre model and unscented Kalman Filtering have been utilized to achieve real time TRFC estimation during both straight driving and steering condition. Observability of the established system based on LuGre model is proved. The observable condition is compatible with reality and simulation result, which can be considered as the theoretical effective boundary of all dynamics-based methods. The performance of our method has been verified by simulation experiment, and results show that our method can achieve high accuracy, convergence speed and robustness.