Intelligent ϵ-optimal path prediction for vehicular travel

This paper addresses the problem of predicting the path of a vehicle performing a transit mission. Such a mission proceeds from a start location to a goal location guided by an intelligent planning strategy. Given the history of a path from a start location to a current location, the objectives are: 1) to estimate the cost criterion guiding the travel; 2) to predict the goal location; and 3) to predict the future path leading to the predicted goal location. First, a cost criterion explaining the decision-making strategy of the observed vehicle is estimated using a correlation measure comparing the observed path data to optimal path search information. This correlation is expressed in terms of the tolerance ε of an ε-optimal path. Next, a region of plausible goal locations is predicted assuming that the vehicle will proceed using either optimal decisions or ε-optimal decisions in the future. The predicted goal location of the vehicle is determined by selecting the point in the region of plausible goal locations that has the highest heuristic merit, as determined by a proposed ranking system. Finally, from auxiliary search information, the future path is predicted. This problem is generalized to predicting the future path of a point vehicle traveling in an arbitrary dimensional space