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 \emph{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, and there is a significant impact of situational factors on the performance of behavior models.
翻译:自动驾驶器(AV)被进一步整合到正常的人类交通中,人们越来越一致地认为AV运动规划是一个多剂问题。然而,传统的游戏理论假设完全理性对于人类驾驶目的而言过于强烈,需要通过行为游戏理论透镜来理解人类驾驶是一种累进理性的活动。为此,我们调整了三种受约束的合理行为模式:两个基于量子平准,一个基于纳什平衡,带有孔子差。我们正式确定了在等级游戏中可以应用的不同解决方案概念,多剂运动规划中所使用的框架,目的是创造游戏理论行为模式。此外,根据在繁忙的城市交汇点与总共~4k介质和~44k决定点之间提供的成套人类驾驶数据,我们根据适合自然数据模型的基础评价了行为模式及其预测能力。我们的结果表明,在所评价的行为模型中,将驱动行为模拟作为纯战略运动规划中所使用的一个框架框架,目的是创建游戏理论行为模式模式,从而形成驱动行为行为的模型,从而能够使大气行为发生重大影响。