  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.

Robotics;Artificial Intelligence;Human-Computer Interaction