Online Identification of Individual Driver Steering Behaviour and Experimental Results

Using switched systems, we model individual driver steering behaviour from a new point of view. This approach allows to incorporate the idea of human motion being built up by an individual and limited repertoire of learned patterns. The identification of the generating subsystem parameters of such individual motion primitives solely on measured output data requires a new identification method. We propose an algorithm using a multi-step model output error criterion and discuss different implementations in detail. We show that this method is capable of tracking real measurement data of driver steering motion trajectories with low model orders and number of switches respectively. The presented method is online-capable. Experimental driving results proof the concept.