Systematic Literature Review to Explore use of VR in Transportation Research to Study Driver Behavior

This paper presents a survey on Virtual Reality (VR) immersion in the Intelligent Transportation System (ITS). The semantic meaning of training in virtual reality includes the analysis of functions and structure of a virtual system. Driver Assistance System (DAS) takes the three-dimensional input using radar and vision systems to assist drivers in driving activities. These systems are not only used to identify the key challenges of building an adaptive virtual environment but also to make the virtual driving training and urban planning possible and effective. There is a lack in understanding VR effectiveness, exposure of VR to the human psyche, and the efficient outcomes of using VR for training purposes. Currently, the focus of research is on the concept of presence but this paper gives an advanced review in the multifaceted field. It starts with the customary theory of reality and merges it with virtual reality. The relationship between driver training, urban development, and treatment of driving phobias result in an intelligent Driver Training System (DTS). DTS gives the missing key benefit for the full control of another dimension. Knowledge of these relevant factors can play a vital role in the development of new VR applications for DTS. In this paper, we will discuss three key characteristics which are an assessment, the involvement of users, and efficiency of results produced to evaluate factors affecting driver’s performance, transportation planning and treatment of phobic patients

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Publication Details

Journal
Journal of computing and artificial intelligence
Published
2024-12-28
DOI
https://doi.org/10.69591/jcai.2.2.5
Citations
1
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.64
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Systematic Literature Review to Explore use of VR in Transportation Research to Study Driver Behavior

Wasim Ahmad Khan, Maaz Khan, Mian Muhammad Mubasher, Shazia Saqib et al.
1 citations
Journal of computing and artificial intelligence
Human-Automation Interaction and Safety
0.64
article

Systematic Literature Review to Explore use of VR in Transportation Research to Study Driver Behavior

Wasim Ahmad Khan, Maaz Khan, Mian Muhammad Mubasher, Shazia Saqib, Ghulam Shabbir
article en
1 citations

Abstract

This paper presents a survey on Virtual Reality (VR) immersion in the Intelligent Transportation System (ITS). The semantic meaning of training in virtual reality includes the analysis of functions and structure of a virtual system. Driver Assistance System (DAS) takes the three-dimensional input using radar and vision systems to assist drivers in driving activities. These systems are not only used to identify the key challenges of building an adaptive virtual environment but also to make the virtual driving training and urban planning possible and effective. There is a lack in understanding VR effectiveness, exposure of VR to the human psyche, and the efficient outcomes of using VR for training purposes. Currently, the focus of research is on the concept of presence but this paper gives an advanced review in the multifaceted field. It starts with the customary theory of reality and merges it with virtual reality. The relationship between driver training, urban development, and treatment of driving phobias result in an intelligent Driver Training System (DTS). DTS gives the missing key benefit for the full control of another dimension. Knowledge of these relevant factors can play a vital role in the development of new VR applications for DTS. In this paper, we will discuss three key characteristics which are an assessment, the involvement of users, and efficiency of results produced to evaluate factors affecting driver’s performance, transportation planning and treatment of phobic patients

Journal of computing and artificial intelligenceVol. 2(2)
INTI International University (MY), University of the Punjab (PK), University of Washington (US), Institute of Art (PL)
Openalex Percentile: Top 23%
Human-Automation Interaction and Safety
0.64
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