  Understanding human behavior in overtaking scenarios is crucial for enhancing
road safety in mixed traffic with automated vehicles (AVs). Computational
models of behavior play a pivotal role in advancing this understanding, as they
can provide insight into human behavior generalizing beyond empirical studies.
However, existing studies and models of human overtaking behavior have mostly
focused on scenarios with simplistic, constant-speed dynamics of oncoming
vehicles, disregarding the potential of AVs to proactively influence the
decision-making process of the human drivers via implicit communication.
Furthermore, so far it remained unknown whether overtaking decisions of human
drivers are affected by whether they are interacting with an AV or a
human-driven vehicle (HDV). To address these gaps, we conducted a "reverse
Wizard-of-Oz" driving simulator experiment with 30 participants who repeatedly
interacted with oncoming AVs and HDVs, measuring the drivers' gap acceptance
decisions and response times. The oncoming vehicles featured time-varying
dynamics designed to influence the overtaking decisions of the participants by
briefly decelerating and then recovering to their initial speed. We found that
participants did not alter their overtaking behavior when interacting with
oncoming AVs compared to HDVs. Furthermore, we did not find any evidence of
brief decelerations of the oncoming vehicle affecting the decisions or response
times of the participants. Cognitive modeling of the obtained data revealed
that a generalized drift-diffusion model with dynamic drift rate and
velocity-dependent decision bias best explained the gap acceptance outcomes and
response times observed in the experiment. Overall, our findings highlight the
potential of cognitive models for further advancing the ongoing development of
safer interactions between human drivers and AVs during overtaking maneuvers.
