Abstract Background Gaze is the primary way for pedestrians to obtain clues from traffic scenes before making decisions. Therefore, understanding pedestrian gaze pattern is vital for traffic safety in general and for the design of autonomous vehicles. Methods In this study, participants made road-crossing decisions in a naturalistic traffic scene, with an eye-tracker recording their gaze behaviors. We manually encoded the recorded videos with 14,898 fixations, and then analyzed the gaze pattern at three levels from general to specific: gaze towards overall scenes, gaze towards vehicles and gaze towards components of vehicles. Findings At the first level, our findings indicate that frequent fixations began to appear at the distance of 100 m and peaked around 5–30 m away from pedestrians. Transversely pedestrians mainly gazed at the two lanes adjacent to themselves. Pedestrians allocated 53% gaze duration to motor vehicles. For a specific vehicle, which is the second level, the gaze duration varied with vehicles' attributes such as distances, sizes, and types. Finally, at the third level, we discovered that pedestrians’ gaze duration on different vehicle components varied with the longitudinal distance. As vehicles approach, the main area of fixation expanded from the near side headlight to the whole front and near side, and finally shift to the near side of a vehicle. Implications The distribution of fixations in space and vehicle components before pedestrian crossing can provide fundamental information for understanding and modeling of pedestrian's road-crossing behaviors. In practice, our findings can guide the timing and position of information displays on autonomous vehicles to facilitate friendly interaction with pedestrians.


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    Title :

    Pedestrian gaze pattern before crossing road in a naturalistic traffic setting


    Contributors:
    Qingxian Zhao (author) / Xiangling Zhuang (author) / Tong Zhang (author) / Yunqi He (author) / Guojie Ma (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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