Augmented reality interfaces for pedestrian-vehicle interactions: An online study

Using ChatGPT for human–computer interaction research: a primer

“…The scatter plot in figure 3 shows the mean sentiment score from the ChatGPT outputs and the composite score based on the rating scales.

Figure 3Scatter plot showing the composite score based on rating scales in the online questionnaire study (extracted from [53]) against the mean sentiment score calculated from the ChatGPT outputs, for nine augmented reality interfaces. The composite score has a mean of 0 and a standard deviation of 1 in the population of respondents.

…”

“…In the original study, the text-based responses were analysed through a manual review process where representative answers were selected. From the average 46 text comments selected per interface, four representative comments (two with positive sentiments and two with negative sentiments) were chosen for inclusion in the publication by Tabone et al [53]. A limitation of this thematic analysis was the potential influence of the researcher's subjectivity on the analysis.…”

“…After all the batches were submitted, the mean sentiment score for each interface was calculated by averaging the sentiment scores of its 10 batches. The correlation coefficient was computed between the mean sentiment score of the interfaces and the composite score of the interfaces as previously reported by Tabone et al [53].…”

“…In Tabone et al [54], the effect of nine AR interfaces, as well as a control condition without AR interface, on pedestrian crossing behaviour was investigated in a virtual simulator environment. This was a follow- royalsocietypublishing.org/journal/rsos R. Soc.…”

Augmented reality for supporting the interaction between pedestrians and automated vehicles: an experimental outdoor study

“…One of the problems is that AR is currently challenging to implement, with only the most recent headsets capable of creating a compelling and user-friendly experience (e.g., Microsoft Hololens 2, Varjo XR 3, Magic Leap 2). While exceptions exist (e.g., Kang et al, 2023 ; Zhang et al, 2023 ), much previous AR research for pedestrians has limitations: some studies focus solely on the orienting phase without any form of human-subject evaluation ( Tabone et al, 2021b ; Tong et al, 2021 ), others use questionnaires with photos or videos ( Hesenius et al, 2018 ; Tabone et al, 2023a ; Wilbrink et al, 2023 ), and still others test AR concepts in immersive virtual reality (VR) environments ( Pratticò et al, 2021 ; Tran et al, 2022 ; Tabone et al, 2023b ; Malik et al, 2023 ). A critical note that should be made in this last point is that, although valid results can be obtained in VR, this is strictly speaking not AR.…”