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Electronics
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Recent Progress of Artificial Intelligence in Virtual Reality
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Recent Progress of Artificial Intelligence in Virtual Reality

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 1309

Special Issue Editors

Prof. Dr. Wernhuar Tarng

E-Mail Website
Guest Editor
Institute of Learning Sciences and Technologies, National Tsing Hua University, Hsinchu 300044, Taiwan
Interests: virtual reality; augmented reality; artificial intelligence; STEM education
Dr. Kuo Liang Ou

E-Mail Website
Guest Editor
Institute of Learning Sciences and Technologies, National Tsing Hua University, Hsinchu 300044, Taiwan
Interests: machine learning; data mining; virtual reality; augmented reality

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and Virtual Reality (VR) have emerged as two transformative technologies that are revolutionizing various aspects of our lives. The integration of AI and VR has led to groundbreaking advancements, opening up new avenues for research and development across multiple domains. To explore the recent progress and potential synergies between these two fields, we are pleased to announce a Special Issue on the "Recent Progress of Artificial Intelligence in Virtual Reality". Authors are encouraged to submit original research papers, review articles, case studies, and perspective pieces that provide valuable insights and contribute to advancing the state of the art in AI-enabled VR technologies. This Special Issue aims to bring together researchers, practitioners, and industry experts to present their latest research findings, innovative solutions, and practical applications at the intersection of AI and VR. We invite submissions on topics including but not limited to the following:

  • AI-driven VR content creation and generation;
  • Intelligent virtual agents and avatars in VR environments;
  • Machine learning and deep learning techniques for enhancing VR experiences;
  • AI-powered interaction and user interface design in VR;
  • Adaptive and personalized VR systems using AI algorithms;
  • Applications of AI in VR-based education, training, and simulation;
  • Ethical considerations and challenges in AI-driven VR systems;
  • Case studies and real-world implementations of AI in VR applications.

Prof. Dr. Wernhuar Tarng
Dr. Kuo Liang Ou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • virtual reality
  • augmented reality
  • mixed reality

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Published Papers (1 paper)

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Research

33 pages, 18667 KiB  
Article
Multimodal Dataset Construction and Validation for Driving-Related Anger: A Wearable Physiological Conduction and Vehicle Driving Data Approach
by Lichen Sun, Hongze Yang and Bo Li
Electronics 2024, 13(19), 3904; https://doi.org/10.3390/electronics13193904 - 2 Oct 2024
Viewed by 671 | Correction
Abstract
Anger impairs a driver’s control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers’ self-reported anger during simulated [...] Read more.
Anger impairs a driver’s control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers’ self-reported anger during simulated driving tasks. In Experiment 1, responses from 624 participants to anger-inducing videos and driving scenarios were collected via questionnaires to select appropriate materials. In Experiments 2 and 3, multimodal dynamic data and self-reported SAM emotion ratings were collected during simulated and real-vehicle tasks, capturing physiological and vehicle responses in neutral and anger states. Spearman’s correlation coefficient analysis validated the DPV-MFD’s effectiveness and explored the relationships between multimodal data and emotional dimensions. The CNN-LSTM deep learning network was used to assess the emotion recognition performance of the DPV-MFD across different time windows, and its applicability in real-world driving scenarios was validated. Compared to using EEG data alone, integrating multimodal data significantly improved anger recognition accuracy, with accuracy and F1 scores rising by 4.49% and 9.14%, respectively. Additionally, real-vehicle data closely matched simulated data, confirming the dataset’s effectiveness for real-world applications. This research is pivotal for advancing emotion-aware human–machine- interaction and intelligent transportation systems. Full article
(This article belongs to the Special Issue Recent Progress of Artificial Intelligence in Virtual Reality)
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