Supplementary data for the paper 'crowdsourced gazes'

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.

本众包实验（crowdsourced experiment）探究了趋近车辆的距离、车辆类型、交通密度以及视觉杂乱程度对行人注意力分布的影响。实验共有966名受试者参与，他们观看了107张涵盖多样交通场景的图像，单张图像的观看时长介于100至4000毫秒之间。研究采用TurkEyes方法采集受试者的眼动数据，该方法的具体流程为：在每张图像展示完毕后，短暂呈现编码图表，并要求受试者输入其最后看到的代码。实验结果表明，相较于手动驾驶车辆（manual vehicles），自动驾驶车辆（automated vehicles）更易受到行人的目光注视。无需眼动仪的眼动测量方案具备可观的应用前景。