Autonomous Driving

Autonomous driving studies how vehicles can sense their surroundings, anticipate other road users, and choose safe, effective actions in traffic. Systems range from modular stacks that connect camera, LiDAR, and radar perception to prediction and model-predictive control, to end-to-end policies trained with imitation learning, reinforcement learning, or vision-language and world models. Researchers evaluate these methods on datasets such as nuScenes, Waymo, and Argoverse, in simulators such as CARLA and nuPlan, and through physical vehicle tests. A central challenge is turning strong benchmark results into reliable closed-loop behavior across rare events, changing conditions, and real-world deployment constraints.