Supplementary data for the paper 'Blinded windows and empty driver seats: The effects of automated vehicle characteristics on cyclists’ decision-making'

Automated vehicles (AVs) may feature blinded (i.e., blacked-out) windows and external Human-Machine Interfaces (eHMIs), and the driver may be inattentive or absent, but how these features affect cyclists is unknown. In a crowdsourcing study, participants viewed images of approaching vehicles from a cyclist’s perspective and decided whether to brake. The images depicted different combinations of traditional versus automated vehicles, eHMI presence, vehicle approach direction, driver visibility/window-blinding, visual complexity of the surroundings, and distance to the cyclist (urgency). The results showed that the eHMI and urgency level had a strong impact on crossing decisions, whereas visual complexity had no significant influence. Blinded windows caused participants to brake for the traditional vehicle. A second crowdsourcing experiment aimed to clarify the findings of Experiment 1 by also requiring participants to detect the vehicle features. It was found that the eHMI ‘GO’ and blinded windows yielded high detection rates and that driver eye contact caused participants to continue pedalling. To conclude, blinded windows increase the probability that cyclists brake, and driver eye contact stimulates cyclists to continue cycling. Our findings, which were obtained in large international samples, may help elucidate how AVs (in which the driver may not be visible) affect cyclists’ behaviour.

自动驾驶车辆（Automated Vehicles, AVs）可能配备涂黑车窗与外部人机交互界面（external Human-Machine Interfaces, eHMIs），且驾驶员可能注意力分散或不在驾驶位，但此类特征对骑行者的影响尚不明确。在一项众包研究中，受试者以骑行者的视角观看迎面驶来的车辆图像，并判断是否需要刹车。图像涵盖了传统车辆与自动驾驶车辆、是否配备外部人机交互界面、车辆驶来方向、驾驶员可见性/车窗涂黑状态、周边环境视觉复杂度以及与骑行者的距离（即紧急程度）等多种组合变量。研究结果显示，外部人机交互界面与紧急程度对骑行者的过街决策具有显著影响，而周边环境视觉复杂度则无明显作用。车窗涂黑会使受试者在面对传统车辆时选择刹车。第二项众包实验旨在明确第一项实验的结论，实验中同时要求受试者识别车辆特征。结果发现，标注‘通行’的外部人机交互界面与车窗涂黑状态均能带来较高的特征识别率，而与驾驶员进行眼神交流则会促使受试者继续骑行。综上，车窗涂黑会提升骑行者刹车的概率，而与驾驶员的眼神交流会促使骑行者继续前行。本研究基于大规模国际样本得出的结论，有助于阐明驾驶员不可见的自动驾驶车辆对骑行者行为的影响机制。