用生成式模拟发现自动驾驶未知危险场景，提升安全评估效率。
TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation
- 基于生成式方法构建高保真交通仿真平台，捕捉复杂人车交互。
- 可生成多样高危事件，有效识别自动驾驶系统隐藏缺陷。
- 支持与主流物理引擎和自动驾驶系统集成，适合研究与评测。
交通仿真对自动驾驶（AV）开发至关重要，可全面评估不同驾驶条件下的安全性。然而，传统规则驱动的模拟器难以捕捉复杂的交通行为，而数据驱动方法常无法保持长期行为真实感或生成多样化安全关键事件。为此，我们提出 TeraSim，一个开源、高保真的交通仿真平台，旨在发现未知的不安全事件，并高效估算自动驾驶系统的统计性能指标（如碰撞率）。TeraSim 可无缝集成第三方物理仿真器和独立的自动驾驶系统栈，构建完整的自动驾驶仿真系统。实验表明，该平台能生成包含静态与动态代理的多样化安全关键事件，揭示自动驾驶系统的潜在缺陷，并支持统计性能评估。这些结果凸显了 TeraSim 在自动驾驶安全评估中的实用潜力，惠及研究人员、开发者与政策制定者。代码已开源：https://github.com/mcity/TeraSim。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。