Road-Wheel Interaction in Vehicles - A Mechatronic View of Friction

In this paper the road surface condition is detected based on multi-sensor data fusion for an improved preconditioning of automotive control systems. It involves the use of different measuring systems in three levels: the environment description, the slip based statistical slippery recognition and the reactive friction detection and adaptation. The signals of optical and acoustic sensors build the inputs of a pre-processing block, where a specific frequency - and statistical analysis is implemented. To estimate the road state a decision block based on a fuzzy expert system has been defined and tested. A further topic of this contribution is the use of a mobile friction measuring platform for investigations of the texture road impact on the grip. The friction coefficient between road and a small rubber wheel is measured at a high driving slip rate. Simultaneously the road profile is captured with a laser and roughness descriptors are computed. The regression between descriptors and grip is obtained by an artificial neural network, which can be used for prognostication after learning