用进化博弈建模人类驾驶的有限理性，提升自动驾驶仿真真实性与安全性。
EvoQRE: Modeling Bounded Rationality in Safety-Critical Traffic Simulation via Evolutionary Quantal Response Equilibrium
- 基于量化响应均衡和演化动态，模拟人类驾驶的非完全理性行为。
- 在Waymo和nuPlan数据集上实现最优真实度与安全指标，收敛速度为O(log k / k^{1/3})。
- 可调节理性参数生成多样高危场景，适合自动驾驶安全验证研究者。
现有自动驾驶交通仿真框架多依赖模仿学习或博弈论方法，求解纳什或粗相关均衡，隐含假设参与者为完全理性。然而人类驾驶员受认知与感知限制，仅做出近似最优决策。本文提出EvoQRE，一种基于量化响应均衡（QRE）与演化博弈动态的普适框架，用于建模安全关键交通交互。该框架整合预训练生成世界模型与熵正则化复制者动态，既捕捉人类行为的随机性，又保持均衡结构。理论证明：在弱单调性假设下，双时标随机逼近下该动态以速率O(log k / k^{1/3})收敛至对数-量反应均衡。进一步通过混合与能量基策略表示，将QRE拓展至连续动作空间。在Waymo Open Motion Dataset与nuPlan基准测试中，EvoQRE实现最先进的仿真真实度、更优的安全性指标，并可通过可解释的理性参数可控生成多样化高危场景。
原文摘要 · Abstract (English)
Existing traffic simulation frameworks for autonomous vehicles typically rely on imitation learning or game-theoretic approaches that solve for Nash or coarse correlated equilibria, implicitly assuming perfectly rational agents. However, human drivers exhibit bounded rationality, making approximately optimal decisions under cognitive and perceptual constraints. We propose EvoQRE, a principled framework for modeling safety-critical traffic interactions as general-sum Markov games solved via Quantal Response Equilibrium (QRE) and evolutionary game dynamics. EvoQRE integrates a pre-trained generative world model with entropy-regularized replicator dynamics, capturing stochastic human behavior while maintaining equilibrium structure. We provide rigorous theoretical results, proving that the proposed dynamics converge to Logit-QRE under a two-timescale stochastic approximation with an explicit convergence rate of O(log k / k^{1/3}) under weak monotonicity assumptions. We further extend QRE to continuous action spaces using mixture-based and energy-based policy representations. Experiments on the Waymo Open Motion Dataset and nuPlan benchmark demonstrate that EvoQRE achieves state-of-the-art realism, improved safety metrics, and controllable generation of diverse safety-critical scenarios through interpretable rationality parameters.
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