Intelligent path prediction for vehicular travel

A method for predicting the motion of an observed vehicle by reasoning about the actions taken by the operator of the vehicle is presented. Reasoning about action is pursued by investigating decision-making strategies that might replicate the motion of the vehicle. Tracking a vehicle performing a transit mission, a mission that proceeds from a start location to a goal location guided by an intelligent planning strategy, is considered. The start location is assumed to be known, the goal location is assumed to be in a given set of candidate goal locations, and an intelligent planning criterion is assumed to be guiding the vehicle. Given the history of the observed vehicle´s path, the objectives are to select the cost criterion that best explains the observed motion, predict the goal location of the vehicle, and predict the future path leading to the goal location. In solving this problem, search pointer information provided by reverse graph searches is exploited