---
license: apache-2.0
task_categories:
- robotics
language:
- en
tags:
- Autonomous-Driving
- Simulation
---
# **Learning to Drive via Real-World Simulation at Scale**
[](https://arxiv.org/abs/2511.23369)
[](https://opendrivelab.com/SimScale/)
[](https://github.com/OpenDriveLab/SimScale)
[](https://huggingface.co/datasets/OpenDriveLab/SimScale)
[](https://modelscope.cn/datasets/OpenDriveLab/SimScale)
[](https://github.com/OpenDriveLab/SimScale/blob/main/LICENSE)
> [Haochen Tian](https://github.com/hctian713),
> [Tianyu Li](https://github.com/sephyli),
> [Haochen Liu](https://georgeliu233.github.io/),
> [Jiazhi Yang](https://github.com/YTEP-ZHI),
> [Yihang Qiu](https://github.com/gihharwtw),
> [Guang Li](https://scholar.google.com/citations?user=McEfO8UAAAAJ&hl=en),
> [Junli Wang](https://openreview.net/profile?id=%7EJunli_Wang4),
> [Yinfeng Gao](https://scholar.google.com/citations?user=VTn0hqIAAAAJ&hl=en),
> [Zhang Zhang](https://scholar.google.com/citations?user=rnRNwEMAAAAJ&hl=en),
> [Liang Wang](https://scholar.google.com/citations?user=8kzzUboAAAAJ&hl=en),
> [Hangjun Ye](https://scholar.google.com/citations?user=68tXhe8AAAAJ&hl=en),
> [Tieniu Tan](https://scholar.google.com/citations?user=W-FGd_UAAAAJ&hl=en),
> [Long Chen](https://long.ooo/),
> [Hongyang Li](https://lihongyang.info/)
>
>
> - 📧 Primary Contact: Haochen Tian (tianhaochen2023@ia.ac.cn)
> - 📜 Materials: 🌐 [𝕏](https://x.com/OpenDriveLab/status/1999507869633527845) | 📰 [Media](https://mp.weixin.qq.com/s/OGV3Xlb0bHSSSloG11qFJA) | 🗂️ [Slides](https://docs.google.com/presentation/d/17qbsKZU9jdw7MfiPk7hZelaLb3leR2M76gPcMkuf1MI/edit?usp=sharing) | 🎬 [Talk (in Chinese)](https://www.bilibili.com/video/BV1tqrEBNECQ)
> - 🖊️ Joint effort by CASIA, OpenDriveLab at HKU, and Xiaomi EV.
---
## 🔥 Highlights
- 🏗️ A scalable simulation pipepline 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.