即时解决不确定性：将自适应驾驶行为建模为主动推理
Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference
AI总结：
本文提出基于主动推理的自适应驾驶模型，通过最小化期望自由能统一目标与信息寻求，在遮挡和分心场景中涌现类人行为。
摘要：
理解自适应的人类驾驶行为，特别是驾驶员如何管理不确定性，对于开发可用于自动驾驶车辆评估和开发的模拟人类驾驶员模型至关重要。然而，现有的自适应驾驶行为的交通心理学模型要么缺乏计算严谨性，要么仅针对特定场景和/或行为现象。虽然机器学习和机器人学领域开发的模型能够有效地从数据中学习自适应驾驶行为，但由于其黑箱性质，它们对自适应行为背后的机制提供很少或根本不提供解释。因此，一个可推广、可解释的自适应人类驾驶行为的计算模型仍然缺乏。本文提出了这样一个基于主动推理的模型，主动推理是一种起源于计算神经科学的行为建模框架。该模型通过基于最小化期望自由能的单一指令进行策略选择，为人类如何在谨慎与进展之间权衡提供了原则性解决方案。这将目标寻求和信息寻求（不确定性解决）行为置于单一目标函数之下，使模型能够无缝地解决不确定性作为实现其目标的手段。我们将该模型应用于两个明显不同但需要管理不确定性的驾驶场景：（1）驶过遮挡物和（2）驾驶与次要任务之间的视觉时间共享，并展示了类人的自适应驾驶行为如何从期望自由能最小化的单一原则中涌现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
