%0 Generic %A Bazilinskyy, Pavlo %A Dodou, Dimitra %A Eisma, Yke Bauke %A Vlakveld, W.P. (Willem) %A de Winter, Joost %D 2022 %T Supplementary data for the paper 'Blinded windows and empty driver seats: The effects of automated vehicle characteristics on cyclists’ decision-making' %U https://data.4tu.nl/articles/dataset/Supplementary_data_for_the_paper_Blinded_windows_and_empty_driver_seats_The_effects_of_automated_vehicle_characteristics_on_cyclists_decision-making_/20103188/2 %R 10.4121/20103188.v2 %K cycling %K automated driving %K external human-machine interface %X

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.

%I 4TU.ResearchData