LEAD: Minimizing Learner–Expert Asymmetry in End-to-End Driving

Project Page | Documentation | CARLA Model Zoo | NAVSIM Checkpoints | CARLA Dataset | Supplementary Material | Paper

qualitative_results.mp4

TransFuser v6: The latest iteration of the TransFuser linage in evaluation.

Overview

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.
• Optimized storage format, packs 72 hours of driving in ~260GB.
• Native support for NAVSIM and Waymo Vision-based E2E. Extending those benchmarks through closed-loop simulation and synthetic data for additional supervision during training.

Table of Contents

• Roadmap
• Updates
• Quick Start (Get Driving in 20 Minutes)
• Beyond CARLA: Cross-Benchmark Deployment
• Further Documentation
• Acknowledgements
• Citation
• License

Roadmap

• ✅ Checkpoints and inference code (stable)
• 🟨 Documentation, training pipeline and expert code (released, under test)
• 🟨 Full CARLA dataset release on HuggingFace (released, under test)
• 🚧 Datasets for cross-benchmark (coming soon)
• 🚧 Cross-benchmark training tools and documentation (coming soon)

Status: Active development.

Updates

• [2026/01/13] CARLA dataset and full CARLA training doc release

  We publicly release a CARLA dataset generated with the same pipeline as used in the paper. However, due to subsequent refactoring and cleanup of the expert driver, the released dataset is not bit-identical to the dataset used for the reported experiments. A verification of the dataset is running right now.

• [2026/01/05] Bug in RoutePlanner fixed

  An index error caused driving policy to to crash at end of routes in Town13. New Driving Score are updated.

• [2025/12/24] Arxiv paper and code release

Quick Start (Get Driving in 20 Minutes)

1. Environment initialization

Clone the repository and map the project root to your environment

git clone https://github.com/autonomousvision/lead.git
cd lead

# Set the project root directory and configure paths for CARLA, datasets, and dependencies.
{
  echo -e "export LEAD_PROJECT_ROOT=$(pwd)"  # Set project root variable
  echo "source $(pwd)/scripts/main.sh"       # Persist more environment variables
} >> ~/.bashrc  # Append to bash config to persist across sessions

source ~/.bashrc  # Reload config to apply changes immediately

Note

Please verify that ~/.bashrc reflects these paths correctly.

2. Setup experiment infrastructure

We utilize Miniconda, conda-lock and uv:

# Install conda-lock and create conda environment
pip install conda-lock && conda-lock install -n lead conda-lock.yml
# Activate conda environment
conda activate lead
# Install dependencies and setup git hooks
pip install uv && uv pip install -r requirements.txt && uv pip install -e .
# Install other tools needed for development
conda install conda-forge::ffmpeg conda-forge::parallel conda-forge::tree conda-forge::gcc
# Optional: Activate git hooks
pre-commit install

While waiting for dependencies installation, we recommend CARLA setup on parallel:

bash scripts/setup_carla.sh # Download and setup CARLA at 3rd_party/CARLA_0915

3. Model zoo

Pre-trained driving policies are hosted on HuggingFace for reproducibility. These checkpoints follow the TFv6 architecture, but differ in their sensor configurations, vision backbones or dataset composition.

Tab. 1 shows available checkpoints with their performance on three major CARLA benchmarks. As first step, we recommend tfv6_resnet34 as it provides a good balance between performance and resource usage.

Checkpoint	Description	Bench2Drive	Longest6 v2	Town13
tfv6_regnety032	TFv6	95.2	62	5.24
tfv6_resnet34	ResNet34 Backbone	94.7	57	5.01
4cameras_resnet34	Additional rear camera	95.1	53	-
noradar_resnet34	No radar sensor	94.7	52	-
visiononly_resnet34	Vision-only driving model	91.6	43	-
town13heldout_resnet34	Generalization evaluation	93.1	52	3.52

Table 1: Performance of pre-trained checkpoints. We report Driving Score, for which higher is better.

To download one checkpoint:

bash scripts/download_one_checkpoint.sh

Or download all checkpoints at once with git lfs

git clone https://huggingface.co/ln2697/tfv6 outputs/checkpoints
cd outputs/checkpoints
git lfs pull

4. Verify driving stack

To initiate closed-loop evaluation and verify the integration of the driving stack, execute the following:

# Start driving environment
bash scripts/start_carla.sh
# Start policy on one route
bash scripts/eval_bench2drive.sh

Driving logs will be saved to outputs/local_evaluation with the following structure:

outputs/local_evaluation/23687
├── 23687_debug.mp4
├── 23687_demo.mp4
├── checkpoint_endpoint.json
├── debug_images
├── demo_images
├── input_log
└── metric_info.json

Tip

1. Disable video recording in config_closed_loop by turning off produce_demo_video and produce_debug_video.
2. If memory is limited, modify the file prefixes to load only the first checkpoint seed. By default, the pipeline loads all three seeds as an ensemble.

5. Verify autopilot

Verify the expert policy and data acquisition pipeline by executing a test run on a sample route:

# Start CARLA if not done already
bash scripts/start_carla.sh
# Run expert on one route
bash scripts/run_expert.sh

Data collected will be stored at data/expert_debug and should have following structure:

data/expert_debug
├── data
│   └── BlockedIntersection
│       └── 999_Rep-1_Town06_13_route0_12_22_22_34_45
│           ├── bboxes
│           ├── depth
│           ├── depth_perturbated
│           ├── hdmap
│           ├── hdmap_perturbated
│           ├── lidar
│           ├── metas
│           ├── radar
│           ├── radar_perturbated
│           ├── results.json
│           ├── rgb
│           ├── rgb_perturbated
│           ├── semantics
│           └── semantics_perturbated
└── results
    └── Town06_13_result.json

Beyond CARLA: Cross-Benchmark Deployment

The LEAD pipeline and TFv6 models are deployed as reference implementations and benchmark entries across multiple autonomous driving simulators and evaluation suites:

• Waymo Vision-based End-to-End Driving Challenge (DiffusionLTF) Strong baseline entry for the inaugural end-to-end driving challenge hosted by Waymo, achieving 2nd place in the final leaderboard.

• NAVSIM v1 (LTFv6) Latent TransFuser v6 is an updated reference baseline for the navtest split, improving PDMS by +3 points over the Latent TransFuser baseline, used to evaluate navigation and control under diverse driving conditions.

• NAVSIM v2 (LTFv6) The same Latent TransFuser v6 improves EPMDS by +6 points over the Latent TransFuser baseline, targeting distribution shift and scenario complexity.

• NVIDIA AlpaSim Simulator (TransFuserModel) Adapting the NAVSIM's Latent TransFuser v6 checkpoints, AlpaSim also features an official TransFuser driver, serving as a baseline policy for closed-loop simulation.

Further Documentation

For more detailed instructions, see the full documentation. In particular:

• Training data collection
• Training
• Evaluation

Acknowledgements

Special thanks to carla_garage for the foundational codebase. We also thank the creators of the numerous open-source projects we use:

• PDM-Lite, leaderboard, scenario_runner, NAVSIM, Waymo Open Dataset

Other helpful repositories:

• SimLingo, PlanT2, Bench2Drive Leaderboard, Bench2Drive, CaRL

Long Nguyen led development of the project. Kashyap Chitta, Bernhard Jaeger, and Andreas Geiger contributed through technical discussion and advisory feedback. Daniel Dauner provided guidance with NAVSIM.

Citation

If you find this work useful, please consider giving this repository a star ⭐ and citing our work in your research:

@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. See LICENSE for details.