Optimal Control and Inverse Optimal Control with Continuous Updating for Human Behavior Modeling

The theory of optimal control has received considerable attention to model motion behavior or decision making of humans. Most approaches are based on a fixed (or infinite) time horizon which implies that all information is available at the beginning of the time interval. Nevertheless, it is reasonable to believe that the human uses information defined by a continuously moving information horizon at each time instant and adapts accordingly. Therefore, in this paper, we propose an optimal feedback control approach based on the paradigm of continuous updating. The model parameters which define individual human behavior consist of the cost function parameters and the length of the information horizon, which can be identified via a corresponding inverse optimal control approach. We show the applicability of the approach with simulations of a potential application example of human behavior identification from the point of view of a driving assistance system