Integrating Anticipatory Competence into a Bayesian Driver Model

We present the probabilistic model architecture for combining a layered model of human driver expertise with beliefs about the vehicle state to describe the effect of anticipations on driver actions. It implements the sensory-motor system of human drivers in a psychological motivated mixture-of-behaviors (MoB) architecture with autonomous, goal-based attention allocation and anticipation processes. Our Bayesian Autonomous Driver Mixture-of-Behaviors (BAD-MoB) model offers sharing of behaviors in different driving maneuvers and is able to decompose complex skills into basic skills and to compose the expertise to drive complex maneuvers through combination of basic behaviors. Furthermore, the model combines reactive behavior with prospective behavior based on anticipated or imagined percepts. It has the ability to predict agent’s behavior, to abduct hazardous situations (what could have been the initial situation), to generate anticipatory plans and control the prevention of hazardous situations.