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이준웅(Joon-Woong Lee) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.8
This paper proposes an algorithm to detect the stop line and crosswalk on the road surface using edge information and blob coloring. The detection has been considered as an important area of autonomous vehicle technologies. The proposed algorithm is composed of three phases: 1) hypothesis generation of stop lines, 2) hypothesis generation of crosswalks, and 3) hypothesis verification of stop lines. The last two phases are not performed if the first phase does not provide a hypothesis of a stop line. The last one is carried out by the combination of both hypotheses of stop lines and crosswalks, and determines the stop lines among stop line hypotheses. The proposed algorithm is proven to be effective through experiments with various images captured on the roads.
이준웅(Joon-Woong Lee) 제어로봇시스템학회 2013 제어·로봇·시스템학회 논문지 Vol.19 No.6
Recently, the number of vehicles equipped with cameras that are generally used to recognize surroundings is increasing. For robust recognition, a huge amount of tests under various environments are performed. Furthermore, the installation position or orientation of the camera is also changed depending on the vehicle. This change also accompanies many tests. Correspondingly, a large cost and a great deal of manpower are required to perform these tests. This paper proposes a method to cut these costs while conducting enough tests through the construction of a database of videos and a geometric transformation of images.
이준웅(Joon Woong Lee),이운근(Un Kun Yi) 한국자동차공학회 2004 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
This paper presents a Lane Departure Warning System (LDWS) of a traveling vehicle on structured roads. <br/> This system is based on machine vision technologies. The fundamental goal of the system is to endow intelligence to a vehicle not to deviate its traveling lane without intention of a human driver. In order to realize the goal, lane-related information is needed, and furthermore, it is necessary to identify the Lane-Departure based on the information. We devised a Lane Boundary Pixel Extractor (LBPE) and departure ratios, and applied a Linear Regression (LR) in order to know the trend of vehicle's traveling direction. The departure ratios and results of the LR are integrated to identity whether or not a vehicle deviates its lane. As soon as a vehicle deviates its lane unintentionally the system warns to driver by sounds. We proved the efficiency of LDWS with a lot of experiments on real roads with normal driving conditions.