README.md

---
license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - Robotics
---
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<img src="docs/imgs/WE_title.png" width="800px">

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

[![License](https://img.shields.io/badge/License-CC--BY--NC--SA--4.0-blue.svg?style=for-the-badge)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[![ModelScope](https://img.shields.io/badge/ModelScope-Dataset-orange.svg?style=for-the-badge)](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-black.svg?style=for-the-badge&logo=github)](https://github.com/OpenDriveLab/WorldEngine)
[![Hugging Face](https://img.shields.io/badge/Hugging_Face-Dataset-ffc107.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/datasets/OpenDriveLab/WorldEngine)

</div>

<p align="center">
<img src="docs/imgs/README_overall.jpg" width="800px" >
</p>

> 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.

---

## Table of Contents

- [Highlights](#highlights)
- [News](#news)
- [Dataset Overview](#-dataset-overview)
- [Directory Structure](#-directory-structure)
- [Environment Setup](#️-environment-setup)
- [Usage](#-usage)
- [Citation](#-citation)
- [License](#-license)
- [Related Links](#-related-links)
- [Contact](#-contact)

## 📦 Dataset Overview

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

| Module | Function | Data Types |
|--------|----------|-----------|
| **Raw Data** | nuPlan & OpenScene base datasets | Sensor data, maps, annotations |
| **AlgEngine** | End-to-end model training & evaluation | Preprocessed annotations, ckpts, caches |
| **SimEngine** | Closed-loop simulation environments | Scene assets, config files |

```bash
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/`)

<details>
<summary><b>Click to expand full directory structure</b></summary>

After downloading the **nuPlan** and **OpenScene** raw datasets, set up the following structure via symlinks (`ln -s`):

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

</details>

### 2️⃣ AlgEngine Data (`data/alg_engine/`)

<details>
<summary><b>Click to expand full directory structure</b></summary>

Data for **end-to-end model training and evaluation**:

```bash
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
```

</details>

### 3️⃣ SimEngine Data (`data/sim_engine/`)

<details>
<summary><b>Click to expand full directory structure</b></summary>

Data for **closed-loop simulation**:

```bash
data/sim_engine/
├── assets/                        # Simulation scene assets (need extraction)
│   ├── navtest/                   # navtest scene assets (10 parts)
│   ├── navtrain/                  # navtrain scene assets (82 parts)
│   └── navtest_failures/          # navtest rare logs scene assets
│
└── 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/
```

**⚠️ Important: Scene Asset Extraction**

Scene assets in the `assets/` directory are stored as split archives and must be extracted before use:

```bash
cd data/sim_engine/assets

# Extract navtest scene assets (10 parts)
cd navtest
cat navtest.tar.gz.part* > navtest.tar.gz
tar -xzf navtest.tar.gz --strip-components=1  # Remove top-level directory from archive
rm navtest.tar.gz  # Optional: remove merged archive to save space

# Extract navtrain scene assets (82 parts)
cd ../navtrain
cat navtrain.tar.gz.part* > navtrain.tar.gz
tar -xzf navtrain.tar.gz --strip-components=1
rm navtrain.tar.gz

# Extract navtest_failures scene assets
cd ../navtest_failures
cat navtest_failures.tar.gz.part* > navtest_failures.tar.gz
tar -xzf navtest_failures.tar.gz --strip-components=1
rm navtest_failures.tar.gz

cd ../../..  # Return to WorldEngine root
```

💡 **Tips**:
- The `--strip-components=1` parameter ensures extraction to the current directory, avoiding nested structures like `navtest/navtest/`
- Extracted scene assets contain all files needed for 3D Gaussian Splatting (3DGS) rendering; each scene is approximately several hundred MB

</details>

---

## ⚙️ Environment Setup

Configure the following environment variables for proper data access:

### Quick Configuration

```bash
# 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

```bash
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:

| Step | Action | Description |
|:----:|--------|-------------|
| **1** | Download dataset | Use Hugging Face Hub or Git Clone |
| **2** | Extract scene assets | Extract split archives in `data/sim_engine/assets/` ([see instructions](#3️⃣-simengine-data-datasim_engine)) |
| **3** | Set environment variables | Configure `WORLDENGINE_ROOT` and related paths |
| **4** | Create symlinks | Link raw datasets (if needed) |
| **5** | Verify installation | Run the quick test script |

### Detailed Setup

<details>
<summary><b>2. Extract Scene Assets (Required)</b></summary>

SimEngine scene assets are stored as split archives and must be extracted before use:

```bash
cd data/sim_engine/assets

# Extract all scene assets
for dir in navtest navtrain navtest_failures; do
    echo "Processing ${dir}..."
    cd ${dir}
    cat ${dir}.tar.gz.part* > ${dir}.tar.gz
    tar -xzf ${dir}.tar.gz --strip-components=1  # Avoid nested directories
    rm ${dir}.tar.gz  # Optional: remove merged archive
    cd ..
done

cd ../../..
```

Or extract them manually one by one ([see detailed instructions in SimEngine Data section](#3️⃣-simengine-data-datasim_engine)).

</details>

<details>
<summary><b>4. Create Symlinks (Optional)</b></summary>

If you have already downloaded nuPlan and OpenScene data, use symlinks to avoid data duplication:

```bash
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
```

</details>

### Next Steps

After dataset setup, refer to the main project documentation:

- 📘 [Installation Guide](https://github.com/OpenDriveLab/WorldEngine/blob/main/docs/installation.md)
- 🚀 [Quick Start](https://github.com/OpenDriveLab/WorldEngine/blob/main/docs/quick_start.md)
- 🎮 [SimEngine Usage Guide](https://github.com/OpenDriveLab/WorldEngine/blob/main/docs/simengine_usage.md)
- 🧠 [AlgEngine Usage Guide](https://github.com/OpenDriveLab/WorldEngine/blob/main/docs/algengine_usage.md)

---

## 📝 Citation

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

```bibtex

```

If you use the Render Assets (MTGS), please also cite:
```bibtex
@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:
```bibtex
@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}
}
```
```bibtex
@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}
}
```

---

## 📄 License

This dataset is released under the **[CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/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

| Resource | Link |
|:--------:|:-----|
| 🏠 **Project Home** | [WorldEngine GitHub](https://github.com/OpenDriveLab/WorldEngine) |
| 🤗 **Hugging Face** | [Dataset Page](https://huggingface.co/datasets/OpenDriveLab/WorldEngine) |
| 📦 **ModelScope** | [Dataset Page](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine) |
| 💬 **Discussions** | [Hugging Face Discussions](https://huggingface.co/datasets/OpenDriveLab/WorldEngine/discussions) |
| 📖 **Full Documentation** | [Documentation](https://github.com/OpenDriveLab/WorldEngine/tree/main/docs) |
| 🎨 **Scene Reconstruction** | [MTGS Repository](https://github.com/OpenDriveLab/MTGS) |

---

## 📧 Contact

For questions or suggestions, feel free to reach out:

- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/OpenDriveLab/WorldEngine/issues)
- 💬 **Discussions**: [Hugging Face Discussions](https://huggingface.co/datasets/OpenDriveLab/WorldEngine/discussions)

---

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**⭐ If you find WorldEngine useful, please consider giving us a Star! ⭐**

**Thank you for your support of the WorldEngine project!**

</div>