Add model card and metadata for LEAD (#1)

- Add model card and metadata for LEAD (7e8ec5346a85dfd70104fb41a302169d4306ccbc)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,58 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: robotics
+tags:
+- autonomous-driving
+- imitation-learning
+- carla
+- transfuser
+---
+
+# 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 model weights for **LEAD** and **TransFuser v6 (TFv6)**, an expert-student policy pair for autonomous driving research in the CARLA simulator. 
+
+LEAD addresses the misalignment between privileged expert demonstrations and sensor-based student observations in imitation learning. By narrowing these asymmetries, the TFv6 student policy achieves state-of-the-art performance on major CARLA closed-loop benchmarks.
+
+## Main Features
+
+- **Lean pipeline**: Pure PyTorch implementation with minimal dependencies.
+- **Cross-dataset training**: Support for NAVSIM and Waymo datasets, with optional co-training on synthetic CARLA data.
+- **Data-centric infrastructure**: Enforced tensor typing with BearType/JaxTyping and extensive visualizations for debugging.
+- **State-of-the-Art Performance**: TFv6 reaches 95 DS on Bench2Drive and significantly outperforms prior models on Longest6 v2 and Town13.
+
+## Evaluation Results (Bench2Drive)
+
+| Method | Driving Score (DS) | Success Rate (SR) |
+|---|---|---|
+| TF++ (TFv5) | 84.21 | 67.27 |
+| **TFv6 (Ours)** | **95.28** | **86.80** |
+
+## Usage
+
+For setup instructions, data collection, and evaluation scripts, please refer to the [official GitHub repository](https://github.com/autonomousvision/lead) and the [full documentation](https://ln2697.github.io/lead/docs).
+
+Example evaluation command:
+```bash
+bash scripts/start_carla.sh # Start CARLA server
+bash scripts/eval_bench2drive.sh # Evaluate one Bench2Drive route
+```
+
+## 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).