• 한국도로학회:학술대회논문집
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• 2004.10a
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• pp.83-86
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• 2004

• 한국도로학회:학술대회논문집
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• 2001.10a
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• pp.51-55
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• 2001

• 도로교통
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• s.76
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• pp.50-62
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• 1999

• 도로교통
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• s.79
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• pp.46-61
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• 2000

• 도로교통
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• s.89
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• pp.49-54
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• 2002

• 도로교통
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• s.93
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• pp.113-125
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• 2003

• 한국도로학회:학술대회논문집
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• 2008.10a
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• pp.83-86
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• 2008

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.153-166
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• 2024

The digitalization of tasks for smart construction requires the smooth exchange of digital data among stakeholders to be effective, but there is a lack of digital data standardization and utilization methods. This paper proposes a digital construction information structure to transform information from road construction sites into digital formats. The study targets include significant tasks, such as work planning, scheduling, safety management, and quality control. The key to the construction information structure is separating construction information into objects and activities, defining unit works by combining these two types of information to ensure flexibility in representing and modifying construction information. The objects and activities have their respective hierarchical structures, which are defined flexibly to match the actual content. This structure achieves both efficiency and detail. The pilot structure was applied to highway construction projects and implemented digitally using general formats. This study enables the digitalization of road construction processes that closely resemble reality, accelerating the digital transformation of the civil engineering industry by developing a digital twin of the entire road construction lifecycle.

• Journal of Korean Society of Transportation
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• v.30 no.5
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• pp.61-69
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• 2012

It is important for highway maintenance and safety assessment to get the accurate highway geometric information. However, it is difficult to acquire good highway geometric information due to missing blueprints or deteriorated highway sections. This research, to get highway geometric information rapidly, has developed a highway geometric analysis algorithm that uses data from vehicles with GPS-IMU integrated system. In conclusion, the result shows that 3.38% of error-ratio for the horizontal alignment and 0.083 absolute value difference for vertical grade comparing with highway drawings. Therefore, the result suggest that the developed method can be applied to the road safety inspection or road safety audit.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1437-1444
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• 2020

With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.