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Understanding adaptive human driving behavior, in particular how driversmanage uncertainty, is of key importance for developing simulated human drivermodels that can be used in the evaluation and development of autonomousvehicles. However, existing traffic psychology models of adaptive drivingbehavior either lack computational rigor or only address specific scenariosand/or behavioral phenomena. While models developed in the fields of machinelearning and robotics can effectively learn adaptive driving behavior fromdata, due to their black box nature, they offer little or no explanation of themechanisms underlying the adaptive behavior. Thus, a generalizable,interpretable, computational model of adaptive human driving behavior is stilllacking. This paper proposes such a model based on active inference, abehavioral modeling framework originating in computational neuroscience. Themodel offers a principled solution to how humans trade progress against cautionthrough policy selection based on the single mandate to minimize expected freeenergy. This casts goal-seeking and information-seeking (uncertainty-resolving)behavior under a single objective function, allowing the model to seamlesslyresolve uncertainty as a means to obtain its goals. We apply the model in twoapparently disparate driving scenarios that require managing uncertainty, (1)driving past an occluding object and (2) visual time sharing between drivingand a secondary task, and show how human-like adaptive driving behavior emergesfrom the single principle of expected free energy minimization.

Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference