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OpenDriveLab

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WorldEngine

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OpenDriveLab 101

The missing infrastructure for Physical AI post-training in AD. Open-source. Production-validated.

Joint effort by OpenDriveLab at The University of Hong Kong, Huawei Inc. and Shanghai Innovation Institute (SII).

Highlights

• A post-training framework for Physical AI: Systematically addresses the long-tail safety-critical data scarcity problem in autonomous driving.
• Data-driven long-tail discovery: Failure-prone scenarios are automatically identified from real-world driving logs by the pre-trained agent itself — no manual design, no synthetic perturbations.
• Photorealistic interactive simulation via 3D Gaussian Splatting (3DGS): Each discovered scenario is reconstructed into a fully controllable, real-time-renderable simulation environment.
• Behavior-driven scenario generation: Leverages Behavior World Model (BWM) to generalize and synthesize diverse traffic variations from long-tail scenarios, expanding sparse safety-critical events into a dense, learnable distribution.
• RL-based post-training on safety-critical rollouts substantially outperforms scaling pre-training data alone — competitive with a ~10x increase in pre-training data.
• Production-scale validation: Deployed on a mass-produced ADAS platform trained on 80,000+ hours of driving logs, reducing collision rate by up to 45.5% and achieving zero disengagements in a 200 km on-road test.

News

• [2026/04/09] Official data release.

📦 Dataset Overview

This dataset uses a modular data structure where each subsystem (AlgEngine, SimEngine) has its own data requirements while sharing common formats.

WorldEngine/
└── data/                          # Main data directory
   ├── raw/                       # Raw datasets (nuPlan, OpenScene)
   ├── alg_engine/                # AlgEngine-specific data
   └── sim_engine/                # SimEngine-specific data

📂 Directory Structure

1️⃣ Raw Data (data/raw/)

data/raw/
├── nuplan/                        # nuPlan raw dataset
│   └── dataset/
│      ├── maps/                  # HD maps (required for all modules)
│      │   ├── nuplan-maps-v1.0.json
│      │   ├── us-nv-las-vegas-strip/
│      │   ├── us-ma-boston/
│      │   ├── us-pa-pittsburgh-hazelwood/
│      │   └── sg-one-north/
│      └── nuplan-v1.1/
│          ├── sensor_blobs/      # Camera images and LiDAR
│          └── splits/            # Train/val/test splits
│   
│
└── openscene-v1.1/                # OpenScene dataset (nuPlan-based)
    ├── sensor_blobs/
    │   ├── trainval/              # Training sensor data
    │   └── test/                  # Test sensor data
    └── meta_datas/
        ├── trainval/              # Training metadata
        └── test/                  # Test metadata

2️⃣ AlgEngine Data (data/alg_engine/)

data/alg_engine/
├── openscene-synthetic/           # Synthetic data generated by SimEngine (need to generate)
│   ├── sensor_blobs/
│   ├── meta_datas/
│   └── pdms_pkl/
│
├── ckpts/                         # Pre-trained model checkpoints
│   ├── bevformerv2-r50-t1-base_epoch_48.pth
│   ├── e2e_vadv2_50pct_ep8.pth
│   ├── track_map_nuplan_r50_navtrain_100pct_bs1x8.pth
│   └── track_map_nuplan_r50_navtrain_50pct_bs1x8.pth
│
├── pdms_cache/                    # Pre-computed PDM metric caches
│   ├── pdm_8192_gt_cache_navtest.pkl
│   └── pdm_8192_gt_cache_navtrain.pkl
│
├── merged_infos_navformer/        # Preprocessed annotations
│   ├── nuplan_openscene_navtest.pkl
│   └── nuplan_openscene_navtrain.pkl
│
└── test_8192_kmeans.npy          # K-means clustering for PDM

3️⃣ SimEngine Data (data/sim_engine/)

data/sim_engine/
├── assets/                        # Scene assets for simulation
│   ├── navtest
│   │   ├── assets
│   │   └── configs
│   ├── navtrain/
│   └── navtest_failures/
│
└── scenarios/                     # Scenario configurations
    ├── original/                  # Original logged scenarios
    │   ├── navtest_failures/
    │   ├── navtrain_50pct_collision/
    │   ├── navtrain_ep_per1/
    │   ├── navtrain_failures_per1/
    │   └── navtrain_hydramdp_failures/
    │
    └── augmented/                 # Augmented scenarios (from BWM)
        ├── navtrain_50pct_collision/
        ├── navtrain_50pct_ep_1pct/
        └── navtrain_50pct_offroad/

⚙️ Environment Setup

Configure the following environment variables for proper data access:

Quick Configuration

# Add to ~/.bashrc or ~/.zshrc
export WORLDENGINE_ROOT="/path/to/WorldEngine"
export NUPLAN_MAPS_ROOT="${WORLDENGINE_ROOT}/data/raw/nuplan/maps"
export PYTHONPATH=$WORLDENGINE_ROOT:$PYTHONPATH

Apply Changes

source ~/.bashrc  # or source ~/.zshrc

💡 Tip: After adding the above to your shell config file, these environment variables will be automatically loaded every time you open a new terminal.

📖 Usage

Quick Start

Follow these steps to set up the dataset:

Detailed Setup

cd WorldEngine/data/raw
ln -s /path/to/nuplan nuplan
ln -s /path/to/openscene-v1.1 openscene-v1.1
cd openscene-v1.1
ln -s ../nuplan/maps maps

Next Steps

After dataset setup, refer to the main project documentation:

• 📘 Installation Guide
• 🚀 Quick Start
• 🎮 SimEngine Usage Guide
• 🧠 AlgEngine Usage Guide

📝 Citation

If this project is helpful to your research, please consider citing:

If you use the Render Assets (MTGS), please also cite:

@article{li2025mtgs,
  title={MTGS: Multi-Traversal Gaussian Splatting},
  author={Li, Tianyu and Qiu, Yihang and Wu, Zhenhua and Lindstr{\"o}m, Carl and Su, Peng and Nie{\ss}ner, Matthias and Li, Hongyang},
  journal={arXiv preprint arXiv:2503.12552},
  year={2025}
}

If you use the scenario data generated by Behavior World Model (BWM), please also cite:

@inproceedings{zhou2025nexus,
  title={Decoupled Diffusion Sparks Adaptive Scene Generation},
  author={Zhou, Yunsong and Ye, Naisheng and Ljungbergh, William and Li, Tianyu and Yang, Jiazhi and Yang, Zetong and Zhu, Hongzi and Petersson, Christoffer and Li, Hongyang},
  booktitle={ICCV},
  year={2025}
}

@article{li2025optimization,
  title={Optimization-Guided Diffusion for Interactive Scene Generation},
  author={Li, Shihao and Ye, Naisheng and Li, Tianyu and Chitta, Kashyap and An, Tuo and Su, Peng and Wang, Boyang and Liu, Haiou and Lv, Chen and Li, Hongyang},
  journal={arXiv preprint arXiv:2512.07661},
  year={2025}
}

If you find AlgEngine well, please cite as well:

@ARTICLE{11353028,
  author={Liu, Haochen and Li, Tianyu and Yang, Haohan and Chen, Li and Wang, Caojun and Guo, Ke and Tian, Haochen and Li, Hongchen and Li, Hongyang and Lv, Chen},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Reinforced Refinement With Self-Aware Expansion for End-to-End Autonomous Driving}, 
  year={2026},
  volume={48},
  number={5},
  pages={5774-5792},
  keywords={Adaptation models;Self-aware;Autonomous vehicles;Pipelines;Planning;Training;Reinforcement learning;Uncertainty;Data models;Safety;End-to-end autonomous driving;reinforced finetuning;imitation learning;motion planning},
  doi={10.1109/TPAMI.2026.3653866}}

📄 License

This dataset is released under the CC-BY-NC-SA-4.0 license.

Terms of Use

• ✅ Allowed: Modification, distribution, private use
• 📝 Required: Attribution, share alike
• ⚠️ Restricted: No commercial use; copyright and license notices must be retained

🔗 Related Links

📧 Contact

For questions or suggestions, feel free to reach out:

• 🐛 Bug Reports: GitHub Issues
• 💬 Discussions: Hugging Face Discussions