Nudging human drivers via implicit communication by automated vehicles

Empirical evidence and computational cognitive modeling

Bibliographic Data

ID21642486
AuthorsArkady Zgonnikov (0000-0002-6593-6948, Delft University of Technology, corresponding author), Niek Beckers (0000-0001-7077-9812, Delft University of Technology), Ashwin Mathew George (0000-0002-9668-9978, Delft University of Technology), Ashwin George, David Abbink, David A Abbink (0000-0001-7778-0090, Delft University of Technology), Catholijn M Jonker (0000-0003-4780-7461, Delft University of Technology), Catholijn Jonker
Year2024
Volume185
Pages103224
Publication date2024-05-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Human-Computer Studies (JOURNAL)
Journal identifiersISSN: 1071-5819 • E-ISSN: 1095-9300
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijhcs.2024.103224
OpenAlexW4391038019
LanguageEN
References cited40

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N=19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning

Cognition · Cognitive psychology · Empirical evidence · Human–computer interaction · Computer Science · Human-Automation Interaction and Safety · Neuroscience · Psychology · Traffic and Road Safety · Transportation and Mobility Innovations

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