Real time determination of road coefficient of friction for IVHS and advanced vehicle control

This paper presents methods for estimating road coefficient of friction (μ) in real time using an extended Kalman filter (EKF) and Bayesian decision making. The EKF estimates the motion and tire forces of an eight degree-of-freedom vehicle based on vehicle-mounted sensors. The filter requires no a priori knowledge of μ and does not require a tire force model. The resulting tire force, slip, and slip angle estimates are compared statistically with those that result from a nominal tire model to select the most likely coefficient of friction from a set of hypothesized values. The μ identification and EKF tasks are separate; therefore, EKF state estimates can be used for feedback control while μ is identified, μ identification results can be used for IVHS decision making and for determining controller setpoints. Simulation results show excellent convergence and accuracy of the μ estimates. Computation and sensor requirements, and robustness of the μ identification algorithm are considered