The overall goal of this project is to integrate planning and learning to enable a robot vehicle to drive autonomously among many pedestrians and vehicles.
Y.F. Luo, P.P. Cai, A. Bera, D. Hsu, W.S. Lee, and D. Manocha. PORCA: Modeling and planning for autonomous driving among many pedestrians. In IEEE Robotics & Automation Letters. 2018.
This projects investigates a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is a motion prediction model for pedestrians and vehicles. It accounts for both a pedestrian’s global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrians’ intentions a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in real time. Experiments show that it enables a robot scooter to drive safely, efficiently, and smoothly in a crowd with a density of nearly one person per square meter.