VR Pedestrian Simulator Studies at Home: Comparing Google Cardboards to Simulators in the Lab and Reality - Maruhn, Philipp (Frontiers in Virtual Reality, 2021)
Virtual Reality is a commonly applied tool to analyze pedestrian behavior in a safe and controllable environment. Most studies use high-end hardware like Cave Automatic Virtual Environments (CAVEs), or recently more often consumer grade head-mounted displays, for the presentation of these virtual environments. The aim of this study is to evaluate the suitability of a low-cost alternative, Google cardboards, and in a next step to test subjects at home. A remote setting would ultimately allow to recruit more diverse subject samples or to facilitate experiments in different regions, for example, to investigate cultural differences. 60 subjects (30 female) were provided with a cardboard. One half of the sample performed the experiment in a laboratory at the university, the other half at home without an experimenter. The participants were instructed to install a mobile application on their own smartphone. This application guided participants through the experiment, included all questionnaires, and presented the virtual environment in combination with the cardboard. In the virtual environment, the participants stood at the edge of a straight road. On this road two vehicles approached with gaps of 1-5 seconds and a speed of 30 or 50 $km/h$. Participants were asked to signal by pressing a button whether they considered the gap large enough to cross safely. Gap acceptance and the time between passing the first vehicle and pressing the button were recorded and compared with data from other simulators and data from a real setting on a test track. A Bayesian approach was chosen to analyze the data. Overall, similar results were found as in the other simulators. The differences between the two cardboard conditions were marginal, but equivalence could not be demonstrated with the evaluation method used. It is worth mentioning, however, that in the setting without an experimenter at home, significantly more data points had to be treated or excluded from the analysis.