SimScale: Learning to Drive via Real-World Simulation at Scale
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913020359213056 |
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| author | Tian, Haochen Li, Tianyu Liu, Haochen Yang, Jiazhi Qiu, Yihang Li, Guang Wang, Junli Gao, Yinfeng Zhang, Zhang Wang, Liang Ye, Hangjun Tan, Tieniu Chen, Long Li, Hongyang |
| author_facet | Tian, Haochen Li, Tianyu Liu, Haochen Yang, Jiazhi Qiu, Yihang Li, Guang Wang, Junli Gao, Yinfeng Zhang, Zhang Wang, Liang Ye, Hangjun Tan, Tieniu Chen, Long Li, Hongyang |
| contents | Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs. Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations controlled by the perturbed ego trajectory. Furthermore, we develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision. Upon the synthesized data, we find that a simple co-training strategy on both real-world and simulated samples can lead to significant improvements in both robustness and generalization for various planning methods on challenging real-world benchmarks, up to +8.6 EPDMS on navhard and +2.9 on navtest. More importantly, such policy improvement scales smoothly by increasing simulation data only, even without extra real-world data streaming in. We further reveal several crucial findings of such a sim-real learning system, which we term SimScale, including the design of pseudo-experts and the scaling properties for different policy architectures. Simulation data and code have been released at https://github.com/OpenDriveLab/SimScale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_23369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimScale: Learning to Drive via Real-World Simulation at Scale Tian, Haochen Li, Tianyu Liu, Haochen Yang, Jiazhi Qiu, Yihang Li, Guang Wang, Junli Gao, Yinfeng Zhang, Zhang Wang, Liang Ye, Hangjun Tan, Tieniu Chen, Long Li, Hongyang Computer Vision and Pattern Recognition Robotics Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs. Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations controlled by the perturbed ego trajectory. Furthermore, we develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision. Upon the synthesized data, we find that a simple co-training strategy on both real-world and simulated samples can lead to significant improvements in both robustness and generalization for various planning methods on challenging real-world benchmarks, up to +8.6 EPDMS on navhard and +2.9 on navtest. More importantly, such policy improvement scales smoothly by increasing simulation data only, even without extra real-world data streaming in. We further reveal several crucial findings of such a sim-real learning system, which we term SimScale, including the design of pseudo-experts and the scaling properties for different policy architectures. Simulation data and code have been released at https://github.com/OpenDriveLab/SimScale. |
| title | SimScale: Learning to Drive via Real-World Simulation at Scale |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2511.23369 |