Road friction coefficient estimation under low longitudinal, lateral and combined excitation

On the way to autonomous driving more and more advanced driver assistance systems (ADASs) are devel-oped to increase safety and comfort. These functions require additional quantified information about the environment, for example information about the current road condition, which a human driver would perceive intuitively. In this paper, a road condition estimation, described by a road friction coefficient, is developed using a second order divided differences filter in combination with estimates of the time varying parameters, namely the dynamic wheel radius and the tire stiffness. Using real measurement data of commercial vehicles, the estimation algorithm is compared to a static and less complex longitudinal friction coefficient estimator. The comparison shows good results for both estimators with differences at low excitation and during cornering maneuvers.