Tire-Stiffness and Vehicle-State Estimation Based on Noise-Adaptive Particle Filtering

We present a novel approach to learning online the tire stiffness and vehicle state using only wheel-speed and inertial sensors. The deviations from nominal stiffness values are treated as a Gaussian disturbance acting on the vehicle. We formulate a Bayesian approach, in which we leverage particle filtering and the marginalization concept to estimate in a computationally efficient way the tire-stiffness parameters and the vehicle state. In the estimation model, the process and measurement noises are dependent on each other, and we present an efficient approach to account for the dependence. Our algorithm outperforms some previously reported approaches, both in terms of accuracy and robustness, and the results indicate significantly improved performance compared with a standard particle filter. Monte-Carlo trials on several experimental data sets verify that the estimator identifies the tire stiffness on both snow and dry asphalt within 1% on average, with a settling time of a few seconds. On snow, the largest steady-state error in any Monte-Carlo trial is less than 4%.