1. 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