Abstract
This paper presents methods for estimating road coefficient of friction (/spl mu/) 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 /spl mu/ 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 /spl mu/ identification and EKF tasks are separate; therefore, EKF state estimates can be used for feedback control while /spl mu/ is identified, /spl mu/ identification results can be used for IVHS decision making and for determining controller setpoints. Simulation results show excellent convergence and accuracy of the /spl mu/ estimates. Computation and sensor requirements, and robustness of the /spl mu/ identification algorithm are considered.
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