Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference
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 drivin...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10403821/ |
| _version_ | 1797346309852626944 |
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| author | Uijong Ju Sanghyeon Kim |
| author_facet | Uijong Ju Sanghyeon Kim |
| author_sort | Uijong Ju |
| collection | DOAJ |
| description | 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 decision-making 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. |
| first_indexed | 2024-03-08T11:31:11Z |
| format | Article |
| id | doaj.art-908734ba9c384221b56ed729b305a313 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:31:11Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-908734ba9c384221b56ed729b305a3132024-01-26T00:01:17ZengIEEEIEEE Access2169-35362024-01-0112121381214810.1109/ACCESS.2024.335543910403821Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk PreferenceUijong Ju0https://orcid.org/0000-0002-9391-3938Sanghyeon Kim1Department of Information Display, Kyung Hee University, Dongdaemun-gu, Seoul, South KoreaDepartment of Information Display, Kyung Hee University, Dongdaemun-gu, Seoul, South KoreaVirtual 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 decision-making 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.https://ieeexplore.ieee.org/document/10403821/Virtual realitydriving simulationperceived realismpersonality traitsheart rateprediction |
| spellingShingle | Uijong Ju Sanghyeon Kim Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference IEEE Access Virtual reality driving simulation perceived realism personality traits heart rate prediction |
| title | Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference |
| title_full | Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference |
| title_fullStr | Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference |
| title_full_unstemmed | Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference |
| title_short | Predicting Perceived Realism in Virtual Reality Driving Simulations Using Participants’ Personality Traits, Heart Rate Changes, and Risk Preference |
| title_sort | predicting perceived realism in virtual reality driving simulations using participants x2019 personality traits heart rate changes and risk preference |
| topic | Virtual reality driving simulation perceived realism personality traits heart rate prediction |
| url | https://ieeexplore.ieee.org/document/10403821/ |
| work_keys_str_mv | AT uijongju predictingperceivedrealisminvirtualrealitydrivingsimulationsusingparticipantsx2019personalitytraitsheartratechangesandriskpreference AT sanghyeonkim predictingperceivedrealisminvirtualrealitydrivingsimulationsusingparticipantsx2019personalitytraitsheartratechangesandriskpreference |