| --- |
| 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 |
| @inproceedings{Nguyen2026CVPR, |
| author = {Long Nguyen and Micha Fauth and Bernhard Jaeger and Daniel Dauner and Maximilian Igl and Andreas Geiger and Kashyap Chitta}, |
| title = {LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving}, |
| booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year = {2026}, |
| } |
| ``` |
|
|
| ## License |
|
|
| This project is released under the [MIT License](LICENSE) |
