Predicting human behavior using static and dynamic models

Abstract

Modeling human behavior is a challenging problem and it is necessary for the safe integration of autonomous systems into daily life. This thesis focuses on modeling human behavior through static and dynamic models. The first contribution of this thesis is a stochastic modeling framework, which is a synergistic combination of a static iterated reasoning approach and deep reinforcement learning. Using statistical goodness of fit tests, the proposed approach is shown to accurately predict human driver behavior in highway scenarios. Although human driver behavior are modeled successfully with the static model, the scope of interactions that can be modeled with this approach is limited to short duration interactions. For interactions that are long enough to induce adaptive behavior, we need models that incorporate learning. The second contribution of this thesis is a learning model for time extended human-human interactions. Through a hierarchical reasoning solution approach, equilibrium concepts are combined with Gaussian Processes to predict the learning behavior. As a result, a novel bounded rational learning model is proposed.