TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation

Haowei Sun, Xintao Yan, Zhijie Qiao, Haojie Zhu, Yihao Sun, Jiawei Wang, Shengyin Shen, Darian Hogue, Rajanikant Patnaik Ananta, Derek Johnson, Greg Stevens, Greg McGuire, Yifan Wei, Wei Zheng, Yong Sun, Yasuo Fukai, Henry X. Liu

Keywords: Traffic Agent Simulation, Multi-Agent Interaction, Safety-Critical Event Generation, Corner Case Discovery, Statistical Safety Evaluation, Closed-Loop AV Testing

TL;DR: Generative multi-agent traffic simulation that discovers safety-critical corner cases and enables statistical safety evaluation of autonomous vehicles through closed-loop testing.

Abstract: 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.

Submission Number: 16