  Driving automation holds significant potential for enhancing traffic safety.
However, effectively handling interactions with human drivers in mixed traffic
remains a challenging task. Several models exist that attempt to capture human
behavior in traffic interactions, often focusing on gap acceptance. However, it
is not clear how models of an individual driver's gap acceptance can be
translated to dynamic human-AV interactions in the context of high-speed
scenarios like overtaking. In this study, we address this issue by employing a
cognitive process approach to describe the dynamic interactions by the oncoming
vehicle during overtaking maneuvers. Our findings reveal that by incorporating
an initial decision-making bias dependent on the initial velocity into existing
drift-diffusion models, we can accurately describe the qualitative patterns of
overtaking gap acceptance observed previously. Our results demonstrate the
potential of the cognitive process approach in modeling human overtaking
behavior when the oncoming vehicle is an AV. To this end, this study
contributes to the development of effective strategies for ensuring safe and
efficient overtaking interactions between human drivers and AVs.
