Citation

Bindschädel J, Krems I, Kiesel A. Transp. Res. F Traffic Psychol. Behav. 2021; 82: 316-332.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.trf.2021.08.018

PMID

unavailable

Abstract

External human machine interfaces (eHMI) might contribute to an enhanced traffic flow and road safety by providing relevant information to surrounding road users. To quantify the effect of eHMI on traffic flow, the majority of studies required participants to indicate their crossing decision in an explicit manner, such as pressing a button. While this approach proved to be efficient, the transfer to real-world behavior is unclear. Here, we propose a more realistic, motion-based approach allowing pedestrians to actually cross the road in front of a vehicle in a virtual reality environment. Participants (N = 51) encountered simulated automated vehicles (AVs) in two scenarios. We investigated the effect of different eHMIs on traffic flow and road safety. Pedestrians'́ body movements were obtained using a motion capturing system with six sensors. Our approach was validated using a two-step procedure. First, we assessed crossing behavior and subjective safety feeling while approaching AVs with and without eHMI. Second, we tested to which extent objective crossing behavior matched self-reported safety feeling. For this purpose, we evaluated if subjective safety feeling can be reliably predicted from actual crossing behavior using a functional data analysis. The proposed motion-based approach proved a valid investigation method for eHMI designs. The results indicated that eHMIs have a beneficial effect on traffic flow and road safety. Regarding traffic flow, participants crossed the road earlier and felt significantly safer when encountering an AV with an eHMI compared to no eHMI. In addition, in situations in which only some of the AVs were equipped with an eHMI, participants' crossing behavior and safety feeling became more conservative for encounters without eHMI, indicating higher road safety. Further, subjective safety feeling was significantly predicted from actual crossing behavior. These findings highlight that eHMIs are beneficial for pedestrians' crossing decision, both from an objective and subjective perspective.

Language: en

Keywords

Automated vehicles; Crossing behavior; Evaluation; External human-machine interface; Pedestrians