  In this paper, we introduce the first learning-based planner to drive a car
in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our
planner, DriveIRL, generates a diverse set of trajectory proposals, filters
these trajectories with a lightweight and interpretable safety filter, and then
uses a learned model to score each remaining trajectory. The best trajectory is
then tracked by the low-level controller of our self-driving vehicle. We train
our trajectory scoring model on a 500+ hour real-world dataset of expert
driving demonstrations in Las Vegas within the maximum entropy IRL framework.
DriveIRL's benefits include: a simple design due to only learning the
trajectory scoring function, relatively interpretable features, and strong
real-world performance. We validated DriveIRL on the Las Vegas Strip and
demonstrated fully autonomous driving in heavy traffic, including scenarios
involving cut-ins, abrupt braking by the lead vehicle, and hotel pickup/dropoff
zones. Our dataset will be made public to help further research in this area.
