Abstract:

The friction dependence between tire and road is highly nonlinear and varies heavily between different surfaces. Knowledge of the tire friction is important for real-time vehicle control, but difficult to estimate with automotive-grade sensors. Based on recent advances in particle filtering and Markov chain Monte-Carlo methods, we propose a batch method for identifying the tire friction as a function of the wheel slip. The unknown function mapping the wheel slip to tire friction is modeled as a Gaussian process (GP) that is included in a dynamic vehicle model relating the GP to the vehicle state. The method is able to efficiently learn the tire friction using only wheel-speed, steering-wheel angle, and inertial automotivegrade sensors. We illustrate the efficacy of the method using several experimental data sets obtained on a snow-covered road.