Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, a generalizable, interpretable, computational model of adaptive human driving behavior is still lacking. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

翻译：理解人类自适应驾驶行为，尤其是驾驶员如何管理不确定性，对于开发可用于评估与优化自动驾驶系统的仿真驾驶员模型至关重要。然而，现有的交通心理学自适应驾驶行为模型要么缺乏计算严谨性，要么仅能处理特定场景和/或特定行为现象。尽管机器学习和机器人领域开发的模型能够从数据中有效学习自适应驾驶行为，但由于其黑箱特性，这类模型几乎无法解释自适应行为背后的内在机制。因此，当前仍缺乏一种兼具通用性、可解释性的自适应人类驾驶行为计算模型。本文基于主动推断（源自计算神经科学的行为建模框架）提出此类模型。该模型通过基于单一原则——最小化预期自由能——的策略选择机制，为人类如何在谨慎与效率之间进行权衡提供了规范性解决方案。该机制将目标导向行为与信息寻求（不确定性消解）行为统一于同一目标函数之下，使模型能够通过消解不确定性作为实现目标的手段。我们将该模型应用于两个需要管理不确定性的明显不同场景：（1）绕过遮挡物行驶，及（2）驾驶与次要任务之间的视觉注意力分配，并展示人类自适应驾驶行为如何从预期自由能最小化这一单一原则中涌现。