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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.18631 |
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| _version_ | 1866918033753112576 |
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| author | Wang, Li Yang, Guangqi Yang, Lei Song, Ziying Zhang, Xinyu Chen, Ying Liu, Lin Gao, Junjie Li, Zhiwei Yang, Qingshan Li, Jun Wang, Liangliang Yu, Wenhao Xu, Bin Wang, Weida Liu, Huaping |
| author_facet | Wang, Li Yang, Guangqi Yang, Lei Song, Ziying Zhang, Xinyu Chen, Ying Liu, Lin Gao, Junjie Li, Zhiwei Yang, Qingshan Li, Jun Wang, Liangliang Yu, Wenhao Xu, Bin Wang, Weida Liu, Huaping |
| contents | Safety is a long-standing and the final pursuit in the development of autonomous driving systems, with a significant portion of safety challenge arising from perception. How to effectively evaluate the safety as well as the reliability of perception algorithms is becoming an emerging issue. Despite its critical importance, existing perception methods exhibit a limitation in their robustness, primarily due to the use of benchmarks are entierly simulated, which fail to align predicted results with actual outcomes, particularly under extreme weather conditions and sensor anomalies that are prevalent in real-world scenarios. To fill this gap, in this study, we propose a Sim-to-Real Evaluation Benchmark for Autonomous Driving (S2R-Bench). We collect diverse sensor anomaly data under various road conditions to evaluate the robustness of autonomous driving perception methods in a comprehensive and realistic manner. This is the first corruption robustness benchmark based on real-world scenarios, encompassing various road conditions, weather conditions, lighting intensities, and time periods. By comparing real-world data with simulated data, we demonstrate the reliability and practical significance of the collected data for real-world applications. We hope that this dataset will advance future research and contribute to the development of more robust perception models for autonomous driving. This dataset is released on https://github.com/adept-thu/S2R-Bench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | S2R-Bench: A Sim-to-Real Evaluation Benchmark for Autonomous Driving Wang, Li Yang, Guangqi Yang, Lei Song, Ziying Zhang, Xinyu Chen, Ying Liu, Lin Gao, Junjie Li, Zhiwei Yang, Qingshan Li, Jun Wang, Liangliang Yu, Wenhao Xu, Bin Wang, Weida Liu, Huaping Robotics Safety is a long-standing and the final pursuit in the development of autonomous driving systems, with a significant portion of safety challenge arising from perception. How to effectively evaluate the safety as well as the reliability of perception algorithms is becoming an emerging issue. Despite its critical importance, existing perception methods exhibit a limitation in their robustness, primarily due to the use of benchmarks are entierly simulated, which fail to align predicted results with actual outcomes, particularly under extreme weather conditions and sensor anomalies that are prevalent in real-world scenarios. To fill this gap, in this study, we propose a Sim-to-Real Evaluation Benchmark for Autonomous Driving (S2R-Bench). We collect diverse sensor anomaly data under various road conditions to evaluate the robustness of autonomous driving perception methods in a comprehensive and realistic manner. This is the first corruption robustness benchmark based on real-world scenarios, encompassing various road conditions, weather conditions, lighting intensities, and time periods. By comparing real-world data with simulated data, we demonstrate the reliability and practical significance of the collected data for real-world applications. We hope that this dataset will advance future research and contribute to the development of more robust perception models for autonomous driving. This dataset is released on https://github.com/adept-thu/S2R-Bench. |
| title | S2R-Bench: A Sim-to-Real Evaluation Benchmark for Autonomous Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.18631 |