---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- Robotics
---
> *The missing infrastructure for Physical AI post-training in AD. Open-source. Production-validated.*
>
[](https://arxiv.org/abs/2606.19836)
[](https://www.youtube.com/watch?v=P1zEyfqa1uY)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine)
[](https://github.com/OpenDriveLab/WorldEngine)
[](https://huggingface.co/datasets/OpenDriveLab/WorldEngine)
> 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
- **[2026/06/19]** Paper released on arXiv. See [World Engine: Towards the Era of Post-Training for Autonomous Driving](https://arxiv.org/abs/2606.19836).
- **[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/`)
Click to expand full directory structure
After downloading the **nuPlan** and **OpenScene** raw datasets, set up the following structure via symlinks (`ln -s`):
```bash
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/`)
Click to expand full directory structure
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
```
### 3️⃣ SimEngine Data (`data/sim_engine/`)
Click to expand full directory structure
Data for **closed-loop simulation**:
```bash
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
```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/` |
| **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
4. Create Symlinks (Optional)
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
```
### 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
@misc{li2026worldengineeraposttraining,
title={World Engine: Towards the Era of Post-Training for Autonomous Driving},
author={Tianyu Li and Li Chen and Caojun Wang and Haochen Liu and Kashyap Chitta and Zhenjie Yang and Yuhang Lu and Naisheng Ye and Yihang Qiu and Yufei Wang and Luoxi Zou and Jiaxin Peng and Jin Pan and Zhaoyu Su and Andrei Bursuc and Shengbo Eben Li and Andreas Geiger and Peng Su and Hongyang Li},
year={2026},
eprint={2606.19836},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2606.19836},
```
---
## 📄 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)
---
**⭐ If you find WorldEngine useful, please consider giving us a Star! ⭐**
**Thank you for your support of the WorldEngine project!**