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This paper examines the application of extended Kalman filtering (EKF) and Bayesian hypothesis selection to estimating vehicle motion, tire forces, and road coefficient of friction The EKF estimates vehicle state and tire forces from an incomplete, noise corrupted measurement set without requiring a-priori knowledge of road conditions or tire forces State and tire force estimates are used in the hypothesis selection procedure along with a nominal tire model to determine the most likely road coefficient of friction The paper presents results of applying these estimation techniques to field test data The methods presented have application to both off-line construction of tire models and development of vehicle control systems