Abstract
Advanced driver assistance systems in modern vehicles have gained interest in the past decades. Most of these systems rely decisively on knowledge of the vehicle's state and influential parameters. Due to changing system or environmental conditions the functionality of these systems may lead to decreased performance or even failure. Especially, the road condition, represented by the maximum friction coefficient, essentially influencing the interaction of tires and road, has major influence on the vehicle's behavior. Therefore, a vast improvement of the assistance systems' performance can be achieved by online maximum friction coefficient estimation. In this paper a simultaneous online estimation of the vehicle's state and maximum friction coefficient is presented using a joint Unscented Kalman Filter. The state and friction estimation results are validated by comparing to measurements taken on a Volkswagen Golf GTE Plug-In Hybrid and offline identified values, respectively.
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