Print Email Facebook Twitter In the driver's mind: cognitive modeling of human overtaking behavior when interacting with oncoming automated vehicles Title In the driver's mind: cognitive modeling of human overtaking behavior when interacting with oncoming automated vehicles Author Mohammad, Samir (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Zgonnikov, A. (mentor) Farah, H. (mentor) Abbink, D.A. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering Date 2023-10-30 Abstract Understanding human behavior in overtaking scenarios is crucial for enhancing road safety in mixed traffic with automated vehicles (AVs). Modeling plays a pivotal role in advancing our comprehension of human overtaking behavior in dynamically evolving scenarios. Currently, our understanding of overtaking behavior primarily revolves around straightforward interactions with human-driven vehicles (HDVs). To address this gap, we conducted a ``reverse" Wizard-of-Oz driving simulator experiment with 30 participants interacting with both oncoming AVs and HDVs, featuring time-varying dynamics. We hypothesized that the type of oncoming vehicle (AV or HDV) does not significantly influence gap acceptance during overtaking, while we anticipated an increase in gap acceptance when the oncoming vehicle briefly decelerates during interactions with the human ego-vehicle driver. Our findings reveal that participants did not significantly alter their overtaking behavior when interacting with oncoming AVs compared to HDVs. Surprisingly, brief decelerations in the oncoming vehicle's velocity did not significantly affect the decision-making processes of overtaking. Moreover, our results reinforced previous insights into the significance of the initial distance and time-to-arrival to the oncoming vehicle, and the ego-vehicle velocity on participants' overtaking behavior. We highlight the potential of simple drift-diffusion models (DDMs), a subset of cognitive models, in understanding human overtaking behavior in dynamically evolving scenarios involving oncoming AVs. Our proposed model accurately captures qualitative patterns in gap acceptance during these intricate overtaking scenarios, further advancing the ongoing development of safer interactions between human drivers and AVs during overtaking maneuvers. Subject OvertakingHuman-AV interactionCognitive modelingHuman factors driving simulator experiment To reference this document use: http://resolver.tudelft.nl/uuid:1f266bbb-b6c3-486e-9a4d-9f558d06ae1d Bibliographical note Dataset, code and supplementary information - https://osf.io/p2wme/ Part of collection Student theses Document type master thesis Rights © 2023 Samir Mohammad Files PDF MSc_Thesis_Samir.pdf 28.31 MB Close viewer /islandora/object/uuid:1f266bbb-b6c3-486e-9a4d-9f558d06ae1d/datastream/OBJ/view