Understanding cyclists' perception of driverless vehicles through eye-tracking and interviews
Siri Hegna Berge, Joost de Winter, Dimitra Dodou, Amir Pooyan Afghari,, Eleonora Papadimitriou, Nagarjun Reddy, Yongqi Dong, Narayana Raju, Haneen, Farah

TL;DR
This study explores how cyclists perceive driverless vehicles using eye-tracking and interviews, revealing that cyclists can detect the absence of a driver and that their attention varies with instructions and vehicle proximity.
Contribution
It provides novel insights into cyclists' spontaneous detection and attention patterns towards driverless vehicles through combined eye-tracking and interview methods.
Findings
30% of cyclists spontaneously noted no driver present
Cyclists detected driver presence/absence with 93% accuracy when instructed
Eye-tracking showed increased attention to vehicles when instructed
Abstract
As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants' gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when…
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Taxonomy
TopicsHuman-Automation Interaction and Safety · Traffic and Road Safety · Safety Warnings and Signage
