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--- |
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license: mit |
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pipeline_tag: robotics |
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tags: |
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- autonomous-driving |
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- imitation-learning |
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- carla |
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- transfuser |
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pretty_name: LEAD Carla Leaderboard 2.0 |
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size_categories: |
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- 1M<n<10M |
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--- |
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# LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving |
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[**Project Page**](https://ln2697.github.io/lead) | [**Paper**](https://huggingface.co/papers/2512.20563) | [**Code**](https://github.com/autonomousvision/lead) |
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Official CARLA dataset accompanies our paper LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving. |
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> 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: |
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> |
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> - Extensive visualization suite and runtime type validation for easier debugging. |
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> - Optimized storage format, packs 72 hours of driving in ~200GB. |
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> - 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. |
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Find more information on [https://github.com/autonomousvision/lead](https://github.com/autonomousvision/lead). |
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## Format |
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Each route is stored as a sequence of synchronized frames. All sensor modalities are ego-centric and time-aligned. |
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In addition to the nominal sensor suite, we provide a second, perturbated sensor stack corresponding to a counterfactual ego state used for recovery supervision. |
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```html |
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├── bboxes/ # Per-frame 3D bounding boxes for all actors |
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├── depth/ # Compressed depth maps (should be used for auxiliary supervision only) |
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├── depth_perturbated # Depth from a perturbated ego state |
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├── hdmap/ # Ego-centric rasterized HD map |
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├── hdmap_perturbated # HD map aligned to perturbated ego pose |
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├── lidar/ # LiDAR point clouds |
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├── metas/ # Per-frame metadata and ego state |
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├── radar/ # Radar detections |
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├── radar_perturbated # Radar detections from perturbated ego state |
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├── rgb/ # Front-facing RGB images |
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├── rgb_perturbated # RGB images from perturbated ego state |
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├── semantics/ # Semantic segmentation maps |
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├── semantics_perturbated # Semantics from perturbated ego state |
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└── results.json # Route-level summary and evaluation metadata |
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``` |
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## Download |
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You can either download a **single route** (useful for quick inspection / debugging) or **clone the full dataset** via Git LFS and unzip all routes. |
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**Note:** Download the dataset after setting up the [lead repository](https://github.com/autonomousvision/lead). |
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### Option 1: Download a single route |
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```bash |
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bash scripts/download_one_route.sh |
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``` |
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### Option 2: Download all routes (Git LFS) |
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Clone the dataset repository directly into the expected directory: |
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```bash |
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git lfs install |
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git clone https://huggingface.co/datasets/ln2697/lead_carla data/carla_leaderboard2/zip |
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``` |
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### Unzip routes |
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Run |
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```bash |
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bash scripts/unzip_routes.sh |
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``` |
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## Citation |
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If you find this work useful, please cite: |
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```bibtex |
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@article{Nguyen2025ARXIV, |
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title={LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving}, |
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author={Nguyen, Long and Fauth, Micha and Jaeger, Bernhard and Dauner, Daniel and Igl, Maximilian and Geiger, Andreas and Chitta, Kashyap}, |
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journal={arXiv preprint arXiv:2512.20563}, |
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year={2025} |
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} |
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``` |
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## License |
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This project is released under the [MIT License](LICENSE) |
