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.

本项众包实验（crowdsourced experiment）探究了来车距离、来车类型、交通密度与视觉杂乱程度对行人注意力分布的影响。实验共招募966名被试，令其观看107张涵盖多样交通场景的图片，单张图片的呈现时长介于100毫秒至4000毫秒之间。研究人员采用TurkEyes方法（TurkEyes）采集所有被试的眼注视数据，该方法的操作流程为：每张图片呈现结束后，短暂展示编码图表，并要求被试输入其最后看到的编码。实验结果显示，自动车辆相较于手动驾驶车辆，获得的行人注视次数更多。无需眼动仪（eye tracker）即可采集眼注视数据的方案颇具应用前景。