---
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 **TransFuser v6 (TFv6)**, a set of CARLA driving policy checkpoints accompanies our paper LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving.

> 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:
> 
> - 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 and extending those 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).

<p align="center">
  <img src="https://ln2697.github.io/lead/static/images/tfv6.png" alt="TFv6 Architecture" width="80%" >
</p>

## 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).

## 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)