Visual Attention of Pedestrians in Traffic Scenes

A Crowdsourcing Experiment

Conference paper (2021)

Authors

P. Bazilinskyy Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
D. Dodou Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering
J.C.F. de Winter Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
Research Group
Human-Robot Interaction (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 P. Bazilinskyy, D. Dodou, J.C.F. de Winter
DOI: https://doi.org/10.1007/978-3-030-80012-3_18
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Human-Robot Interaction

Abstract

In a crowdsourced experiment, the effects of distance and type of the approaching vehicle, traffic density, and visual clutter on pedestrians’ attention distribution were explored. 966 participants viewed 107 images of diverse traffic scenes for durations between 100 and 4000 ms. Participants’ eye-gaze data were collected using the TurkEyes method. The method involved briefly showing codecharts after each image and asking the participants to type the code they saw last. The results indicate that automated vehicles were more often glanced at than manual vehicles. Measuring eye gaze without an eye tracker is promising.

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