OpenDriveLab/SimScale

Haochen Tian, Tianyu Li, Haochen Liu, Jiazhi Yang, Yihang Qiu, Guang Li, Junli Wang, Yinfeng Gao, Zhang Zhang, Liang Wang, Hangjun Ye, Tieniu Tan, Long Chen, Hongyang Li

• 📧 Primary Contact: Haochen Tian (tianhaochen2023@ia.ac.cn)
• 📜 Materials: 🌐 𝕏 | 📰 Media | 🗂️ Slides | 🎬 Talk (in Chinese)
• 🖊️ Joint effort by CASIA, OpenDriveLab at HKU, and Xiaomi EV.

🔥 Highlights

• 🏗️ A scalable simulation pipeline that synthesizes diverse and high-fidelity reactive driving scenarios with pseudo-expert demonstrations.
• 🚀 An effective sim-real co-training strategy that improves robustness and generalization synergistically across various end-to-end planners.
• 🔬 A comprehensive recipe that reveals crucial insights into the underlying scaling properties of sim-real learning systems for end-to-end autonomy.

📦 Data Preparation

Our released simulation data is based on nuPlan and NAVSIM. We recommend first preparing the real-world data by following the instructions in Download NAVSIM. If you plan to use GTRS, please directly refer Download NAVSIM.

1. Download Dataset

We provide 🤗 Script (Hugging Face) and 👾 Script (ModelScope) (users in China) for downloading the simulation data.

Our simulation data format follows that of OpenScene, with each clip/log has a fixed temporal horizon of 6 seconds at 2 Hz (2 s history + 4 s future), which are stored separately in sensor_blobs_hist and sensor_blobs_fut, respectively. For policy training, sensor_blobs_hist alone is sufficient.

📊 Overview Table of Simulated Synthetic Data

Split / Sim. Round	# Tokens	Logs	Sensors_Hist	Sensors_Fut	Link
Planner-based Pseudo-Expert
reaction_pdm_v1.0-0	65K	9.9GB	569GB	1.2T	HF+ HF_Fut / MS
reaction_pdm_v1.0-1	55K	8.5GB	448GB	964GB	HF+ HF_Fut / MS
reaction_pdm_v1.0-2	46K	6.9GB	402GB	801GB	HF+ HF_Fut / MS
reaction_pdm_v1.0-3	38K	5.6GB	333GB	663GB	HF+ HF_Fut / MS
reaction_pdm_v1.0-4	32K	4.7GB	279GB	554GB	HF+ HF_Fut / MS
Recovery-based Pseudo-Expert
reaction_recovery_v1.0-0	45K	6.8GB	395GB	789GB	HF+ HF_Fut / MS
reaction_recovery_v1.0-1	36K	5.5GB	316GB	631GB	HF+ HF_Fut / MS
reaction_recovery_v1.0-2	28K	4.3GB	244GB	488GB	HF+ HF_Fut / MS
reaction_recovery_v1.0-3	22K	3.3GB	189GB	378GB	HF+ HF_Fut / MS
reaction_recovery_v1.0-4	17K	2.7GB	148GB	296GB	HF+ HF_Fut / MS

Before downloading, we recommend checking the table above to select the appropriate split and sensor_blobs.

🏭 Simulation Data Pipeline

🧩 Examples of Simulated Synthetic Data

5c9694f15f9c5537	367cfa28901257ee	d37c49db3dcd59fa
Sim. 1 Sim. 2 Sim. 3	Sim. 1 Sim. 2 Sim. 3	Sim. 1 Sim. 2 Sim. 3

2. Set Up Configuration

We provide a Script for moving the download simulation data to create the following structure.

navsim_workspace/
├── simscale/
├── exp/
└── dataset/
    ├── maps/
    ├── navsim_logs/
    │   ├── test/
    │   ├── trainval/
    │   ├── synthetic_reaction_pdm_v1.0-*/
    │   │   ├── [log]-00*.pkl
    │   │   └── ...
    │   └── synthetic_reaction_recovery_v1.0-*/
    ├── sensor_blobs/
    │   ├── test/
    │   ├── trainval/
    │   ├── synthetic_reaction_pdm_v1.0-*/
    │   │   └── [token]-00*/
    │   │       ├── CAM_B0/
    │   │       └── ...
    │   └── synthetic_reaction_recovery_v1.0-*/
    └── navhard_two_stage/

⭐ License and Citation

All content in this repository is under the Apache-2.0 license. The released data is based on nuPlan and is under the CC-BY-NC-SA 4.0 license.

If any parts of our paper and code help your research, please consider citing us and giving a star to our repository.

@article{tian2025simscale,
  title={SimScale: Learning to Drive via Real-World Simulation at Scale},
  author={Haochen Tian and Tianyu Li and Haochen Liu and Jiazhi Yang and Yihang Qiu and Guang Li and Junli Wang and Yinfeng Gao and Zhang Zhang and Liang Wang and Hangjun Ye and Tieniu Tan and Long Chen and Hongyang Li},
  journal={arXiv preprint arXiv:2511.23369},
  year={2025}
}