Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference

Abstract

Virtual reality (VR) has recently been adopted for driving simulations to enhance their realism and thus improve the validity of the simulation results. However, given that perceived realism is a subjective factor that varies by individual, understanding and predicting perceived realism in VR driving simulations are prerequisites for enhancing their validity. Studies on VR have investigated how individual factors, such as psychophysiological metrics, are associated with perceived realism. However, how these psychophysiological metrics are associated with perceived realism in VR driving simulations has not yet been investigated. To address this problem, this study investigated the relationship between perceived realism and psychophysiological metrics, including individual characteristics (sex, age), personality traits (psychopathy, Machiavellianism, sensation seeking, impulsivity), heart rate changes during the event, and risky decisionmaking during the event, across three driving simulations. The results indicated that psychopathy, Machiavellianism, heart rate changes during the event, and risky decision-making during the event were significantly correlated with the perceived realism of VR driving simulations. In addition, we tested three types of machine learning models to find the appropriate ones for predicting perceived realism, showing that the tree-based algorithm had the highest prediction accuracy.