Tire-Road Friction Coefficient Estimation based on Fusion of Model- and Data-Based Methods

Abstract

The tire-road interaction generates vehicle driving forces, which affect vehicle performance such as maximum acceleration and stability. Sequential extended Kalman filter (S-EKF) integrated with a slope method has been used for tire-road friction coefficient estimation with its own limitations, along with several “cause-based” and “effect-based” methods. This research proposes a new stochastic-based evaluation criterion using existing vehicle sensor signals with the help of data-driven Kriging model. The proposed estimation method is validated by both CarSimTM simulation and experimental studies, respectively, under different road conditions. The results shows that the proposed novel criterion has a strong correlation with the road friction coefficient and provides an improved tire-road friction coefficient estimation. A signal fusion estimation scheme based on both S-EKF and proposed evaluations is developed to improve estimation robustness.