An identification method for individual driver steering behaviour modelled by switched affine systems

This paper addresses 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.