Human-like Planning of Swerve Maneuvers for Autonomous Vehicles Tianyu Gu, John M. Dolan, Jin-Woo Lee Carnegie Mellon University General Motors Research & Development Core: Developed a motion planner for on-road swerve maneuvers Developed a reinforcement learning (RL) formulation that learns human driving patterns in simulation playback Recorded human driving patterns and attempted to mimic human swerving styles Results： The learning routine demonstrates good mimicking results for independent training examples The limitation of planner parameterization, as well as the underlying uncertain human driving behavior, make the trained model hard to generalize Keywords: Autonomous driving, motion planning, path planning, reinforcement learning. Fig 1. Data collection on real car with RTK-GPS mounted on top. Fig 2. Train planner to mimic human maneuver trajectory