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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    Titel :

    Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Stanton, Neville (Herausgeber:in) / Bazilinskyy, Pavlo (Autor:in) / Dodou, Dimitra (Autor:in) / De Winter, Joost C. F. (Autor:in)

    Kongress:

    International Conference on Applied Human Factors and Ergonomics ; 2021 July 25, 2021 - July 29, 2021



    Erscheinungsdatum :

    27.06.2021


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment

    Bazilinskyy, Pavlo / Dodou, Dimitra / Winter, Joost C. F. De | TIBKAT | 2021


    Counting pedestrians and bicycles in traffic scenes

    Somasundaram, Guruprasad / Morellas, Vassilios / Papanikolopoulos, Nikolaos | IEEE | 2009


    Understanding Pedestrians’ Car-Hailing Intention in Traffic Scenes

    Wang, Zhenghao / Lian, Jing / Li, Linhui et al. | Springer Verlag | 2022


    Pedestrians and traffic signals

    Bayley, J.M. | Engineering Index Backfile | 1966