Supplementary data for the paper 'Predicting perceived risk of traffic scenes using computer vision'

Perceived risk, or subjective risk, is an important concept in the field of traffic psychology and automated driving. In this paper, we investigate whether perceived risk in images of traffic scenes can be predicted from computer vision features that may also be used by automated vehicles (AVs). We conducted an international crowdsourcing study with 1378 participants, who rated the perceived risk of 100 randomly selected dashcam images on German roads. The population-level perceived risk was found to be statistically reliable, with a split-half reliability of 0.98. We used linear regression analysis to predict (<em>r</em> = 0.62) perceived risk from two features obtained with the YOLOv4 computer vision algorithm: the number of people in the scene and the mean size of the bounding boxes surrounding other road users. When the ego-vehicle’s speed was added as a predictor variable, the prediction strength increased to <em>r</em> = 0.75. Interestingly, the sign of the speed prediction was negative, indicating that a higher vehicle speed was associated with a lower perceived risk. This finding aligns with the principle of self-explaining roads. Our results suggest that computer-vision features and vehicle speed contribute to an accurate prediction of population subjective risk, outperforming the ratings provided by individual participants (mean <em>r</em> = 0.41). These findings may have implications for AV development and the modeling of psychological constructs in traffic psychology.

感知风险（Perceived risk），又称主观风险（subjective risk），是交通心理学与自动驾驶领域的重要研究概念。本文旨在探究能否通过可被自动驾驶车辆（Automated Vehicles, AVs）使用的计算机视觉特征，预测交通场景图像中的感知风险。我们开展了一项国际众包研究，招募1378名参与者，对100张随机选取的德国道路行车记录仪图像的感知风险进行评分。研究发现，群体层面的感知风险具有统计可靠性，其分半信度达0.98。我们采用线性回归分析，基于YOLOv4计算机视觉算法提取的两项特征预测感知风险：场景内的人员数量，以及其他道路参与者的边界框平均尺寸，此时预测相关系数r=0.62。当加入自车（ego-vehicle）速度作为预测变量后，预测性能提升至r=0.75。有趣的是，车速的预测系数符号为负，表明车辆速度越高，感知风险越低，这一发现契合自我解释道路（self-explaining roads）的原则。研究结果表明，计算机视觉特征与车辆速度可实现群体主观风险的精准预测，其预测效果优于单个参与者的评分（平均相关系数r=0.41）。上述研究结果可为自动驾驶车辆开发及交通心理学中心理构念的建模提供参考。