Researchers explored imitation learning for autonomous driving in a CARLA simulator, training a compact multimodal policy to predict throttle, brake, and steering based on RGB images, LiDAR, vehicle telemetry, and lane waypoints. The policy was trained on 236,882 windows of 3.3 hours of driving data and demonstrated autonomous driving without collisions on both training and held-out routes.