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---
license: mit
pipeline_tag: robotics
tags:
- autonomous-driving
- imitation-learning
- carla
- transfuser
pretty_name: LEAD Carla Leaderboard 2.0
size_categories:
- 1M<n<10M
---
# 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 CARLA dataset 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).
## Format
Each route is stored as a sequence of synchronized frames. All sensor modalities are ego-centric and time-aligned.
In addition to the nominal sensor suite, we provide a second, perturbated sensor stack corresponding to a counterfactual ego state used for recovery supervision.
```html
├── bboxes/ # Per-frame 3D bounding boxes for all actors
├── depth/ # Compressed depth maps (should be used for auxiliary supervision only)
├── depth_perturbated # Depth from a perturbated ego state
├── hdmap/ # Ego-centric rasterized HD map
├── hdmap_perturbated # HD map aligned to perturbated ego pose
├── lidar/ # LiDAR point clouds
├── metas/ # Per-frame metadata and ego state
├── radar/ # Radar detections
├── radar_perturbated # Radar detections from perturbated ego state
├── rgb/ # Front-facing RGB images
├── rgb_perturbated # RGB images from perturbated ego state
├── semantics/ # Semantic segmentation maps
├── semantics_perturbated # Semantics from perturbated ego state
└── results.json # Route-level summary and evaluation metadata
```
## Download
You can either download a **single route** (useful for quick inspection / debugging) or **clone the full dataset** via Git LFS and unzip all routes.
**Note:** Download the dataset after setting up the [lead repository](https://github.com/autonomousvision/lead).
### Option 1: Download a single route
```bash
bash scripts/download_one_route.sh
```
### Option 2: Download all routes (Git LFS)
Clone the dataset repository directly into the expected directory:
```bash
git lfs install
git clone https://huggingface.co/datasets/ln2697/lead_carla data/carla_leaderboard2/zip
```
### Unzip routes
Run
```bash
bash scripts/unzip_routes.sh
```
## 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) |