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CC BY-NC V4.0, Open Access
Journal article
ViCubeLab-An Integrated Platform Using VR to Visualise and Analyse Road Traffic Conditions
Journal of Advanced Research in Applied Sciences and Engineering Technology, Vol.49(2), pp.176-186
2024
Abstract
The main contribution of this paper is to introduce a framework for integrating Machine Learning (ML), Human, and Virtual Reality (VR) into one platform to promote a collaborative visualisation environment that can assist in better analysis and improve the human-machine teaming capability. This platform was demonstrated using a case study in ana-lysing road traffic conditions. The ‘Ab-normal Machine Learning Road Traffic Detection in VR (AbnMLRTD-VR)’ prototype system was developed to assist the human analyst. The proposed system has two main integrative components: a data-driven ML model and a 3D real-time visualisation in a VR environment. An unsupervised ML model was built using real traffic data. The AbnMLRTD-VR system highlights the outliers in the road sections in actual road contexts of a road traffic network. This gives the human analyst a 3D real-time immersive visualisation in a VR environment to evaluate road conditions. The AbnMLRTD-VR system demonstrated that it could help minimise the need for human pre-labelling of the data. It enables the visualisation of the road traffic conditions more meaningfully and to understand the context of the road traffic conditions of road sections at any given time.
Details
- Title
- ViCubeLab-An Integrated Platform Using VR to Visualise and Analyse Road Traffic Conditions
- Authors/Creators
- Bruno OliveiraMohd Fairuz Shiratuddin - Murdoch University, School of Information TechnologyWai Wong Kok - Murdoch University, Centre for Water, Energy and WasteShri Rai
- Publication Details
- Journal of Advanced Research in Applied Sciences and Engineering Technology, Vol.49(2), pp.176-186
- Publisher
- Semarak Ilmu SDN BHD
- Identifiers
- 991005698268907891
- Murdoch Affiliation
- School of Information Technology; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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