Verified Maneuvers, Unmeasured Reliability: Lessons from CARLA Imitation Learning
TL;DR for operators An engineering team can train a driving controller to reproduce recorded steering, throttle, and braking with very little prediction error. That does not establish how reliably the controller will drive when its own decisions determine the next situation it encounters. Jordy Kieto’s Imitation Learning for Autonomous Driving in CARLA1 investigates this gap through a compact driving policy trained on systematically collected demonstrations. Its data-generation procedure verifies completed maneuvers, yet 89.47% of the resulting training windows represent straight driving. The released model achieves strong offline prediction accuracy, while the author also reports extended autonomous driving, operation in an unfamiliar town, and recovery from substantial deviations. ...