Abstract
This paper presents experimental results of using extended Kalman-Bucy filtering (EKBF) and Bayesian model selection to extract tire force characteristics and road friction coefficient from measured motion of ground vehicles on smooth surfaces. The filter estimates wheel slip and slip angles, along with longitudinal and per axle lateral tire forces. Force estimates are based on vehicle-mounted sensors and are derived without knowing road conditions and without a tire force model. Force estimates are compared statistically with those that result from a nominal tire model to select the most likely friction coefficient from hypothesized values. This paper verifies tire force estimation and road friction identification using off-line processing of field test data. Results confirm applicability of the EKBF and Bayesian selection approaches.
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