SimScale: Learning to Drive via Real-World Simulation at Scale

• URL: http://arxiv.org/abs/2511.23369v1
• Date: Fri, 28 Nov 2025 17:17:38 GMT
• Title: SimScale: Learning to Drive via Real-World Simulation at Scale
• Authors: 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,
• Abstract summary: We introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs.<n>Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations.<n>We develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision.
• Score: 45.08991279559151
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 +6.8 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. Our simulation data and code would be released.

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