Graph-Based Framework for Complex Traffic Sign Arrangements in Vietnam Article Swipe
Chi Huy Kieu
,
Hong-Quan Nguyen
,
Thanh Tran
,
Chi Trung Nguyen
,
Kha Tu Huynh
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32508/stdj.v28i4.4577
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32508/stdj.v28i4.4577
As the demand for self-driving cars grows, the dependability of traffic sign recognition is critical to guaranteeing commuter safety. To overcome this issue, academics have investigated numerous techniques for traffic sign identification, including machine learning and deep learning. A variety of methods, such as machine learning and deep learning, have been studied by researchers to address this problem. However, the varied transportation settings in Vietnam provide particular difficulties. These range from complex urban intersections to highway situations where traffic regulations and lane restrictions are indicated by vertically stacked signs. While typical traffic signs in urban settings are well detected by traditional Object Detection techniques, the stacked sign groups that are frequently found on Vietnam’s highway network need further processing. In order to tackle this problem, by representing detected signs as nodes in a graph and modeling their relationships using edges, Graph Neural Networks (GNNs) can learn to identify patterns and groupings based on spatial and semantic connections. This approach ensures that the system not only detects each sign accurately but also comprehends the collective intent of grouped signs in both urban and highway contexts, thereby providing more reliable and contextually aware guidance to commuters navigating Vietnam’s diverse traffic sign system.
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Graph-Based Framework for Complex Traffic Sign Arrangements in VietnamWork title
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2025Year of publication
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Chi Huy Kieu, Hong-Quan Nguyen, Thanh Tran, Chi Trung Nguyen, Kha Tu HuynhList of authors in order
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