Modeling Gap Acceptance in Overtaking

Abstract

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 interactions between humans and automated vehicles (AVs) in the context of high-speed scenarios like overtaking. In this study, we address this issue by employing a cognitive process modeling approach. We investigate a variety of drift-diffusion models to describe the dynamic decision-making process of the driver during overtaking maneuvers. Our findings reveal that a drift-diffusion model incorporating an initial decision-making bias dependent on the initial velocity 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.