TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908295310082048 |
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| author | Sun, Haowei Yan, Xintao Qiao, Zhijie Zhu, Haojie Sun, Yihao Wang, Jiawei Shen, Shengyin Hogue, Darian Ananta, Rajanikant Johnson, Derek Stevens, Greg McGuire, Greg Wei, Yifan Zheng, Wei Sun, Yong Fukai, Yasuo Liu, Henry X. |
| author_facet | Sun, Haowei Yan, Xintao Qiao, Zhijie Zhu, Haojie Sun, Yihao Wang, Jiawei Shen, Shengyin Hogue, Darian Ananta, Rajanikant Johnson, Derek Stevens, Greg McGuire, Greg Wei, Yifan Zheng, Wei Sun, Yong Fukai, Yasuo Liu, Henry X. |
| contents | Traffic simulation is essential for autonomous vehicle (AV) development, enabling comprehensive safety evaluation across diverse driving conditions. However, traditional rule-based simulators struggle to capture complex human interactions, while data-driven approaches often fail to maintain long-term behavioral realism or generate diverse safety-critical events. To address these challenges, we propose TeraSim, an open-source, high-fidelity traffic simulation platform designed to uncover unknown unsafe events and efficiently estimate AV statistical performance metrics, such as crash rates. TeraSim is designed for seamless integration with third-party physics simulators and standalone AV stacks, to construct a complete AV simulation system. Experimental results demonstrate its effectiveness in generating diverse safety-critical events involving both static and dynamic agents, identifying hidden deficiencies in AV systems, and enabling statistical performance evaluation. These findings highlight TeraSim's potential as a practical tool for AV safety assessment, benefiting researchers, developers, and policymakers. The code is available at https://github.com/mcity/TeraSim. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation Sun, Haowei Yan, Xintao Qiao, Zhijie Zhu, Haojie Sun, Yihao Wang, Jiawei Shen, Shengyin Hogue, Darian Ananta, Rajanikant Johnson, Derek Stevens, Greg McGuire, Greg Wei, Yifan Zheng, Wei Sun, Yong Fukai, Yasuo Liu, Henry X. Robotics Systems and Control Traffic simulation is essential for autonomous vehicle (AV) development, enabling comprehensive safety evaluation across diverse driving conditions. However, traditional rule-based simulators struggle to capture complex human interactions, while data-driven approaches often fail to maintain long-term behavioral realism or generate diverse safety-critical events. To address these challenges, we propose TeraSim, an open-source, high-fidelity traffic simulation platform designed to uncover unknown unsafe events and efficiently estimate AV statistical performance metrics, such as crash rates. TeraSim is designed for seamless integration with third-party physics simulators and standalone AV stacks, to construct a complete AV simulation system. Experimental results demonstrate its effectiveness in generating diverse safety-critical events involving both static and dynamic agents, identifying hidden deficiencies in AV systems, and enabling statistical performance evaluation. These findings highlight TeraSim's potential as a practical tool for AV safety assessment, benefiting researchers, developers, and policymakers. The code is available at https://github.com/mcity/TeraSim. |
| title | TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2503.03629 |