While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with safety-critical traffic scenarios. This difficulty primarily arises from the rarity of such scenarios in driving datasets and the complexities associated with predictive modeling of multiple vehicles. Effectively simulating safety-critical traffic situations is therefore a crucial challenge. In this paper, we introduce TrafficGamer, which facilitates game-theoretic traffic simulation by viewing common road driving as a multi-agent game. When we evaluate the empirical performance across various real-world datasets, TrafficGamer ensures both the fidelity, exploitability, and diversity of the simulated scenarios, guaranteeing that they not only statically align with real-world traffic distribution but also efficiently capture equilibria for representing safety-critical scenarios involving multiple agents compared with other methods. Additionally, the results demonstrate that TrafficGamer provides highly flexible simulations across various contexts. Specifically, we demonstrate that the generated scenarios can dynamically adapt to equilibria of varying tightness by configuring risk-sensitive constraints during optimization. We have provided a demo webpage at: https://anonymous.4open.science/api/repo/trafficgamer-demo-1EE0/file/index.html.

翻译：尽管现代自动驾驶系统能够在常规交通条件下制定可靠的驾驶策略，但在安全关键交通场景中却常常表现不佳。这一困难主要源于驾驶数据集中此类场景的稀缺性，以及多车辆预测建模的复杂性。因此，有效仿真安全关键交通情境成为一个至关重要的挑战。本文提出TrafficGamer，通过将常规道路驾驶视为多智能体博弈，实现了基于博弈论的交通仿真。在多个真实世界数据集上的实证评估表明，TrafficGamer确保了仿真场景的保真度、可探索性和多样性，不仅保证其静态分布与现实交通分布一致，而且相较于其他方法，能更有效地捕捉表征多智能体安全关键场景的均衡解。此外，结果显示TrafficGamer能够在不同情境下提供高度灵活的仿真。具体而言，我们证明了生成的场景可通过在优化过程中配置风险敏感约束，动态适应不同严格程度的均衡解。我们已提供演示网页：https://anonymous.4open.science/api/repo/trafficgamer-demo-1EE0/file/index.html。