Add model card and metadata for LEAD

Hi! I'm Niels from the Hugging Face community science team. I'm opening this PR to improve the model card for LEAD.

This PR adds:
- The `robotics` pipeline tag to improve discoverability.
- Metadata including the MIT license.
- Links to the paper, project page, and official GitHub repository.
- A summary of the TransFuser v6 (TFv6) features and results.
- The BibTeX citation for proper attribution.

Adding this information helps users understand the context of the model checkpoints and how to use them within the broader autonomous driving research ecosystem.

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).