Human-like planning of swerve maneuvers for autonomous vehicles

Abstract

In this paper, we develop a motion planner for on-road autonomous swerve maneuvers that is capable of learning passengers' individual driving styles. It uses a hybrid planning approach that combines sampling-based graph search and vehicle model-based evaluation to obtain a smooth trajectory plan. To automate the parameter tuning process, as well as to reflect individual driving styles, we further adapt inverse reinforcement learning techniques to distill human driving patterns from maneuver demonstrations collected from different individuals. We found that the proposed swerve planner and its learning routine can approximate a good variety of maneuver demonstrations. However, due to the underlying stochastic nature of human driving, more data are needed in order to obtain a more generative swerve model.