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README.md

@@ -1,7 +1,403 @@
 ---
 license: cc-by-nc-sa-4.0
-task_categories:
-- robotics
 language:
-- en
----
+  - en
+tags:
+  - Robotics
+---
+<div align="center">
+<img src="docs/imgs/WE_title.png" width="800px">
+
+# **WorldEngine: Towards the Era of Post-Training for Physical AI**
+
+[![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>
+
+> **WorldEngine** is the first post-training framework for Physical AI. This dataset contains preprocessed data, model checkpoints and reconstruction assets for algorithm training and closed-loop simulation.
+
+<p align="center">
+<img src="docs/imgs/README_overall.png" width="800px" >
+</p>
+
+> Joint effort by OpenDriveLab at The University of Hong Kong, Huawei Inc. and Shanghai Innovation Institute (SII).
+
+## Highlights
+
+- **First 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 code repository established. Data publication under preparation.
+- **[2026/04/09]** Official data release.
+
+---
+
+## Table of Contents
+
+- [Highlights](#highlights)
+- [News](#news)
+- [Dataset Overview](#-dataset-overview)
+- [Download](#-download)
+- [Directory Structure](#-directory-structure)
+- [Benchmark](#-benchmark)
+- [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
+```
+
+## 🚀 Download
+
+### Option 1: ModelScope SDK
+
+Recommended for quick download:
+```
+from modelscope import MsDataset
+
+# Load the dataset
+ds = MsDataset.load('OpenDriveLab/WorldEngine')
+```
+
+### Option 2: Git Clone
+
+For full version control:
+```
+# Install Git LFS (Large File Storage)
+git lfs install
+
+git clone https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine.git
+```
+
+### Option 3: ModelScope CLI
+```
+pip install modelscope
+modelscope download --dataset OpenDriveLab/WorldEngine
+```
+
+---
+
+## 📂 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
+│   ├── e2e_vadv2_50pct_rlft_synthetic_ep8.pth
+│   ├── e2e_vadv2_50pct_rlft_synthetic_ep8_merged.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 ModelScope SDK 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) |
+| 📦 **ModelScope** | [Dataset Page](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine) |
+| 💬 **Feedback** | [ModelScope Issues](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine/feedback) |
+| 📖 **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)
+- 📮 **Dataset Feedback**: [ModelScope Feedback](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine/feedback)
+
+---
+
+<div align="center">
+
+**⭐ If you find WorldEngine useful, please consider giving us a Star! ⭐**
+
+**Thank you for your support of the WorldEngine project!**
+
+</div>