• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.62-77
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• 2022

A cooperative driving automation system is imperative to overcome the limitation of the stand-alone automated driving technology. By definition, a cooperative driving automation system refers to a technology in which an automated vehicle cooperates with other vehicles or infrastructure to increase driving efficiency and safety. Specifically, in this study, the technical elements necessary for the cooperative driving automation technology and the technological research trends were investigated. Subsequently, implications for future cooperative driving automation technology development were drawn through the research trends. Finally, the importance of cooperative driving automation technology and infra-guidance service for automated vehicles were discussed.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.56-63
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• 2003

This paper introduces a vision based autonomous mini mobile robot that is an approach to produce real autonomous vehicle. Previous autonomous vehicles are dependent on PC, because of complexity of designing hardware, difficulty of installation and abundant calculations. In this paper, we present an autonomous motile robot system that has abilities of accurate steering, quick movement in high speed and intelligent recognition as a stand-alone system using a mono camera. The proposed system has been implemented on mini track of which width is 25~30cm, and length is about 200cm. Test robot can run at average 32.9km/h speed on straight lane and average 22.3km/h speed on curved lane with 30~40m radius. This system provides a model of autonomous mobile robot adapted a lane recognition algorithm in odor to make real autonomous vehicle easily.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.77-91
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• 2023

Recently, there has been a worldwide increase in research and development on automated vehicles for commercialization. It is expected that the use of level 3 autonomous vehicles on continuous-flow roads will be introduced and will increase. Consequently, various studies have been conducted to investigate the impact of mixed traffic flow with automated vehicles based on the market penetration rate (MPR). However, these studies have been conducted independently, and the results have shown different trends. Therefore, this study attempted a quantitative analysis of the impact of automated vehicles on mixed traffic flow on uninterrupted roads through a meta-analysis. The results showed that the effect size estimated from an MPR of 75% or higher was statistically significant.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.967-978
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• 2023

This study proposed and validated a combined segmentation network (CSN) designed to effectively train on multiple drone image datasets and enhance the accuracy of semantic segmentation. CSN shares the entire encoding domain to accommodate the diversity of three drone datasets, while the decoding domains are trained independently. During training, the segmentation accuracy of CSN was lower compared to U-Net and the pyramid scene parsing network (PSPNet) on single datasets because it considers loss values for all dataset simultaneously. However, when applied to domestic autonomous drone images, CSN demonstrated the ability to classify pixels into appropriate classes without requiring additional training, outperforming PSPNet. This research suggests that CSN can serve as a valuable tool for effectively training on diverse drone image datasets and improving object recognition accuracy in new regions.

• Knowledge Management Research
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• v.21 no.3
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• pp.1-25
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• 2020

This study analyzed key success factors of Korean car-sharing enterprises, Socar and Greencar, and the responsive strategies of Korean car-manufacturing company, Hyundai Motor Group, in the face of emerging sharing economy under the specific economic and regulatory system in Korea. The outcomes of the analysis are as follows. 'Timely market entry' in early startup phase and 'use of external resources' in early growth phase were key success factors common to both Socar and Greencar. However, the differences in the eventual business directions of the two companies also resulted in different key success factors in the expansion phase of their business. For Socar which focused on maintaining its independence and the external growth of B2C business, customer relation marketing and sufficient capital raising were key success factors. For Greencar which became a part of a business group and focused on improving the efficiency of business operations, timely market entry (B2B market) was key success factor. The use of external resources and cooperation with large corporations emerged as key success factors common to both companies in the rapid growth phase. The responsive strategies of the Hyundai Motor Group were collaboration, investment and direct management of DeliveryCar. The short-term goal of the responsive strategy was the operation of test-bed in collaboration with car-sharing company while the mid/long term goal was planning new mobility services by utilizing collected data. Securing opportunities for early market dominance for autonomous car industry was also found to be an important goal.