Create README.md

README.md

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+---
+license: mit
+pipeline_tag: robotics
+tags:
+- autonomous-driving
+- imitation-learning
+- carla
+- transfuser
+pretty_name: LEAD Carla Leaderboard 2.0
+size_categories:
+- 1M<n<10M
+---
+
+# LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving
+
+[**Project Page**](https://ln2697.github.io/lead) | [**Paper**](https://huggingface.co/papers/2512.20563) | [**Code**](https://github.com/autonomousvision/lead)
+
+Official CARLA dataset accompanies our paper LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving.
+
+> We release the complete pipeline (covering scenario descriptions, expert driver, data preprocessing scripts, training code, and evaluation infrastructure) 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, with LEAD extending these benchmarks through closed-loop simulation and synthetic data for additional supervision during training
+
+Find more information on [https://github.com/autonomousvision/lead](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.
+
+```html
+├── bboxes/                  # Per-frame 3D bounding boxes for all actors
+├── depth/                   # Compressed depth maps (should be used for auxiliary supervision only)
+├── depth_perturbated        # Depth from a perturbated ego state
+├── hdmap/                   # Ego-centric rasterized HD map
+├── hdmap_perturbated        # HD map aligned to perturbated ego pose
+├── lidar/                   # LiDAR point clouds
+├── metas/                   # Per-frame metadata and ego state
+├── radar/                   # Radar detections
+├── radar_perturbated        # Radar detections from perturbated ego state
+├── rgb/                     # Front-facing RGB images
+├── rgb_perturbated          # RGB images from perturbated ego state
+├── semantics/               # Semantic segmentation maps
+├── semantics_perturbated    # Semantics from perturbated ego state
+└── results.json             # Route-level summary and evaluation metadata
+```
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{Nguyen2025ARXIV,
+  title={LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving},
+  author={Nguyen, Long and Fauth, Micha and Jaeger, Bernhard and Dauner, Daniel and Igl, Maximilian and Geiger, Andreas and Chitta, Kashyap},
+  journal={arXiv preprint arXiv:2512.20563},
+  year={2025}
+}
+```
+
+## License
+
+This project is released under the [MIT License](LICENSE)