With autonomous vehicles (AV) set to integrate further into regular human traffic, there is an increasing consensus of treating AV motion planning as a multi-agent problem. However, the traditional game theoretic assumption of complete rationality is too strong for the purpose of human driving, and there is a need for understanding human driving as a bounded rational activity through a behavioral game theoretic lens. To that end, we adapt three metamodels of bounded rational behavior; two based on Quantal level-k and one based on Nash equilibrium with quantal errors. We formalize the different solution concepts that can be applied in the context of hierarchical games, a framework used in multi-agent motion planning, for the purpose of creating game theoretic models of driving behavior. Furthermore, based on a contributed dataset of human driving at a busy urban intersection with a total of ~4k agents and ~44k decision points, we evaluate the behavior models on the basis of model fit to naturalistic data, as well as their predictive capacity. Our results suggest that among the behavior models evaluated, modeling driving behavior as pure strategy NE with quantal errors at the level of maneuvers with bounds sampling of actions at the level of trajectories provides the best fit to naturalistic driving behavior.

中文翻译：

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应用于自动驾驶的分层游戏中的解决方案概念

随着自动驾驶汽车 (AV) 进一步融入常规人流，人们越来越多地将 AV 运动规划视为多智能体问题。然而，传统博弈论完全理性的假设对于人类驾驶的目的来说太强了，需要通过行为博弈论的视角将人类驾驶理解为一种有限的理性活动。为此，我们采用了三种有限理性行为的元模型；两种基于量子水平k，一种基于具有量子误差的纳什均衡。我们形式化了可在分层游戏环境中应用的不同解决方案概念，分层游戏是多智能体运动规划中使用的框架，目的是创建驾驶行为的博弈论模型。此外，基于在繁忙的城市交叉路口提供的人类驾驶数据集，共有约 4k 个代理和约 44k 个决策点，我们根据模型对自然数据的拟合程度以及它们的预测能力来评估行为模型。我们的结果表明，在评估的行为模型中，将驾驶行为建模为纯策略 NE，在操纵级别具有量子误差，在轨迹级别对动作进行边界采样，最适合自然驾驶行为。