Augmented reality for supporting the interaction between pedestrians and automated vehicles: an experimental outdoor study.
Front Robot AI
; 11: 1324060, 2024.
Article
in En
| MEDLINE
| ID: mdl-38352957
ABSTRACT
Introduction:
Communication from automated vehicles (AVs) to pedestrians using augmented reality (AR) could positively contribute to traffic safety. However, previous AR research for pedestrians was mainly conducted through online questionnaires or experiments in virtual environments instead of real ones.Methods:
In this study, 28 participants conducted trials outdoors with an approaching AV and were supported by four different AR interfaces. The AR experience was created by having participants wear a Varjo XR-3 headset with see-through functionality, with the AV and AR elements virtually overlaid onto the real environment. The AR interfaces were vehicle-locked (Planes on vehicle), world-locked (Fixed pedestrian lights, Virtual fence), or head-locked (Pedestrian lights HUD). Participants had to hold down a button when they felt it was safe to cross, and their opinions were obtained through rating scales, interviews, and a questionnaire.Results:
The results showed that participants had a subjective preference for AR interfaces over no AR interface. Furthermore, the Pedestrian lights HUD was more effective than no AR interface in a statistically significant manner, as it led to participants more frequently keeping the button pressed. The Fixed pedestrian lights scored lower than the other interfaces, presumably due to low saliency and the fact that participants had to visually identify both this AR interface and the AV.Discussion:
In conclusion, while users favour AR in AV-pedestrian interactions over no AR, its effectiveness depends on design factors like location, visibility, and visual attention demands. In conclusion, this work provides important insights into the use of AR outdoors. The findings illustrate that, in these circumstances, a clear and easily interpretable AR interface is of key importance.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Prognostic_studies
Language:
En
Journal:
Front Robot AI
Year:
2024
Document type:
Article
Affiliation country:
Netherlands
Country of publication:
Switzerland