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This paper adresses the issue of modelling and identification of individual driver steering behaviour from a new point of view, incorporating the idea of human motion being built up by an individual and limited repertoire of learned patterns. We introduce a switched affine model structure to explain a measurable motion alphabet in the driving context and show that this leads to a new identification problem that differs from general hybrid identification issues. To solve this problem, we derive a multi-step model output error criterion and propose an algorithm to simultaneously identify switching times and subsystem parameters out of measurable movement data. We show that this algorithm is capable of identifying the true parameters of known systems as well as fitting real movement trajectories even though no a priori information is given about the true system complexity.

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.Index Terms-individual driver steering model, motion primitives, switched systems, identification algorithm.

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Shared control is a promising approach for designing an Advanced Driver Assistance System, since it unifies the advantages of both manual control and full automation. However, for a true cooperative shared control ADAS the automation has to understand the human and thus a suitable model which describes the driver in the control loop is essential. Our gray-box approach bases on the biological concept that humans realize motion by combining a finite set of motion primitives (we call movemes). With the assumption that a driver switches between movemes based on perceived information, we propose a Hidden Markov Model which determines the probability of each moveme given a certain driving situation. Car turn maneuver experiments show a good approximation of steering trajectories recorded in a driving simulator. A comparison with a black-box model show that the moveme-based driver model performs significantly better. In addition, training algorithms are available and the probabilistic approach of the model allows further interpretation of the results.