SOURCE-LINKED INTELLIGENCE
Imitation Learning for Autonomous Driving in CARLA
Behavioral cloning trains a policy offline on expert demonstrations, but deployment is closed loop: each action affects the observations the policy receives next. We study how much closed-loop driving competence a compact multimodal policy can acquire from offline demonstrations in the CARLA simulator. The policy uses five-frame histories of RGB images, LiDAR, vehicle telemetry, and lane waypoints to predict throttle, brake, and steering at 20 Hz. Demonstrations were collected in three stages, ending with a systematic route-generation procedure that enumerates spawn points and feasible maneuve
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T19:08:03.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.