---
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).