A Human-like Trajectory Planning Method by Learning from Naturalistic Driving Data

Abstract

Trajectory planning has generally been framed as finding the lowest cost one from a set of trajectory candidates, where the cost function has been hand-crafted with carefully tuned parameters by experts. Such methods have technological feasibility of achieving vehicle autonomy, while the resultant behaviors could be much different with those of human drivers. This research proposes a humanlike trajectory planning method by learning from naturalistic driving data. A cost function is formulated by incorporating not only the components on comfort, efficiency and safety, but also lane incentive by referring to a human driver’s lane change decisions. Coefficients of the cost components are learnt by correlating the probability of a trajectory being selected with its distance (i.e. similarity) to the human driven one at the same driving situation. A data set is developed by using the naturalistic data of human drivers on the motorways in Beijing, containing samples of lane changes to the left and right lanes, and car followings. Experiments are conducted on three aspects: 1) lane change trajectory planning to a given target lane; 2) lane change trajectory planning with simultaneous decision of a target lane; and 3) trajectory planning with simultaneous decision of maneuver. Promising results are presented.