Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment

P. Bazilinskyy; D. Dodou; J.C.F. de Winter

doi:10.1007/978-3-030-80012-3_18

Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment

P. Bazilinskyy, D. Dodou, J.C.F. de Winter

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

50 Downloads (Pure)

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.

Original language	English
Title of host publication	Advances in Human Aspects of Transportation
Subtitle of host publication	Proceedings of the AHFE 2021 Virtual Conference on Human Aspects of Transportation, July 25-29, 2021, USA
Editors	Neville Stanton
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	147-154
ISBN (Electronic)	978-3-030-80012-3
ISBN (Print)	978-3-030-80011-6
DOIs	https://doi.org/10.1007/978-3-030-80012-3_18
Publication status	Published - 2021
Event	AHFE 2021: International Conference on Applied Human Factors and Ergonomics (Virtual) - Duration: 25 Jul 2021 → 29 Jul 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	270
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	AHFE 2021: International Conference on Applied Human Factors and Ergonomics (Virtual)
Period	25/07/21 → 29/07/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Eye gazes
Pedestrians
Automated driving
Crowdsourcing

Access to Document

10.1007/978-3-030-80012-3_18

Bazilinskyy2021_Chapter_VisualAttentionOfPedestriansInFinal published version, 1.67 MB

Cite this

Bazilinskyy, P., Dodou, D., & de Winter, J. C. F. (2021). Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment. In N. Stanton (Ed.), Advances in Human Aspects of Transportation: Proceedings of the AHFE 2021 Virtual Conference on Human Aspects of Transportation, July 25-29, 2021, USA (pp. 147-154). (Lecture Notes in Networks and Systems; Vol. 270). Springer. https://doi.org/10.1007/978-3-030-80012-3_18

Bazilinskyy, P. ; Dodou, D. ; de Winter, J.C.F. / Visual Attention of Pedestrians in Traffic Scenes : A Crowdsourcing Experiment. Advances in Human Aspects of Transportation: Proceedings of the AHFE 2021 Virtual Conference on Human Aspects of Transportation, July 25-29, 2021, USA. editor / Neville Stanton. Cham, Switzerland : Springer, 2021. pp. 147-154 (Lecture Notes in Networks and Systems).

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title = "Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment",

abstract = "In a crowdsourced experiment, the effects of distance and type of the approaching vehicle, traffic density, and visual clutter on pedestrians{\textquoteright} attention distribution were explored. 966 participants viewed 107 images of diverse traffic scenes for durations between 100 and 4000 ms. Participants{\textquoteright} 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.",

keywords = "Eye gazes, Pedestrians, Automated driving, Crowdsourcing",

author = "P. Bazilinskyy and D. Dodou and {de Winter}, J.C.F.",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; AHFE 2021: International Conference on Applied Human Factors and Ergonomics (Virtual) ; Conference date: 25-07-2021 Through 29-07-2021",

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doi = "10.1007/978-3-030-80012-3_18",

language = "English",

isbn = "978-3-030-80011-6",

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pages = "147--154",

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Bazilinskyy, P, Dodou, D & de Winter, JCF 2021, Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment. in N Stanton (ed.), Advances in Human Aspects of Transportation: Proceedings of the AHFE 2021 Virtual Conference on Human Aspects of Transportation, July 25-29, 2021, USA. Lecture Notes in Networks and Systems, vol. 270, Springer, Cham, Switzerland, pp. 147-154, AHFE 2021: International Conference on Applied Human Factors and Ergonomics (Virtual), 25/07/21. https://doi.org/10.1007/978-3-030-80012-3_18

Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment. / Bazilinskyy, P.; Dodou, D.; de Winter, J.C.F.
Advances in Human Aspects of Transportation: Proceedings of the AHFE 2021 Virtual Conference on Human Aspects of Transportation, July 25-29, 2021, USA. ed. / Neville Stanton. Cham, Switzerland: Springer, 2021. p. 147-154 (Lecture Notes in Networks and Systems; Vol. 270).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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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Bazilinskyy P, Dodou D , de Winter JCF. Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment. In Stanton N, editor, Advances in Human Aspects of Transportation: Proceedings of the AHFE 2021 Virtual Conference on Human Aspects of Transportation, July 25-29, 2021, USA. Cham, Switzerland: Springer. 2021. p. 147-154. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-80012-3_18

Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment

Abstract

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Keywords

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