  This paper describes a novel method for allowing an autonomous ground vehicle
to predict the intent of other agents in an urban environment. This method,
termed the cognitive driving framework, models both the intent and the
potentially false beliefs of an obstacle vehicle. By modeling the relationships
between these variables as a dynamic Bayesian network, filtering can be
performed to calculate the intent of the obstacle vehicle as well as its belief
about the environment. This joint knowledge can be exploited to plan safer and
more efficient trajectories when navigating in an urban environment. Simulation
results are presented that demonstrate the ability of the proposed method to
calculate the intent of obstacle vehicles as an autonomous vehicle navigates a
road intersection such that preventative maneuvers can be taken to avoid
imminent collisions.
