LEAD: Minimizing Learner–Expert Asymmetry in End to End Driving Project Page Paper Code Official CARLA dataset accompanies our paper LEAD: Minimizing Learner–Expert Asymmetry in End to End Driving. We release the complete pipeline required to achieve state of the art closed loop performance on the Bench2Drive benchmark. Built around the CARLA simulator, the stack features a data centric design with: Extensive visualization suite and runtime type validation for easier debugging. Optimized storage format, packs 72 hours of driving in ~200GB. Native support for NAVSIM and Waymo Vision based E2E and extending those benchmarks through closed loop simulation and synthetic data for additional supervision during training. Find more information on https://github.com/autonomousvision/lead. Format Each route is stored as a sequence of synchronized frames. All sensor modalities are ego centric and time aligned. In addition to the nominal sensor suite, we provide a second, perturbated sensor stack corresponding to a counterfactual ego state used for recovery supervision. Download You can either download a single route (useful for quick inspection / debugging) or clone the full dataset via Git L…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy