• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.195-198
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• 2005

본 논문에서는 Hough 변환을 이용하여 도로 차선의 핵심 정보를 추출하고 차선을 인식하는 방법을 제안하고 실시간으로 차선 인식이 용이 하도록 차선 검출의 고속화 방법을 제안한다. 고속화를 위해 이미지를 작은 영역(Interest Zone)으로 분할하고 분할된 영역에 대해 Hough 변환을 수행하여 영역내의 차선을 검출한다. 검출된 차선의 패턴 정보를 이용하여 다음 Step의 Interest Zone을 결정하고 Hough 변환의 수행을 반하여 차선 검출을 시도 하였다. 또한 실험 영상을 대상으로 시뮬레이션 수행한 결과를 제시하고 제안 방법의 유효성을 검증하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.72-74
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• 2011

최근 IT 기술이 융합된 지능형 자동차 기술에 대한 관심이 높아짐에 따라 이에 대한 연구가 활발히 진행되고 있다. 차선 검출은 지능형 자동차의 주요 과제인 첨단 안전자동차 기술의 핵심적인 부분으로 국내외에서 다양한 방법들에 대한 연구가 진행되었다. 차량의 안전을 향상시키기 위해서는 충분한 제동거리 확보가 가능한 거리까지 정확하고 빠른 차선 검출이 이루어져야 한다. 기존의 경계선 검출기법들은 조명 변화에 따라 그 성능의 변화가 크게 발생하였다. 이는 차선과 도로의 사이의 값의 차이가 조명 조건에 따라 변하기 때문이다. 따라서 본 논문에서는 영상 분석을 통하여 경계선을 판단하는 값을 조절함으로써 환경에 적응적인 경계선 추출 방법을 제안한다. 차량 주행 영상에서 제안한 방법과 기존의 경계선 검출 기법을 적용하여 성능을 비교한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.103-107
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• 2013

In this paper, A real-time lane tracking algorithm is proposed for lane departure warning system. To eliminate perspective effect, input image is converted into Bird's View by inverse perspective mapping. Next, suitable features are extracted for lane detection. Lane feature that correspond to area of interest and RANSAC are used to detect lane candidates. And driving lane is decided by clustering of lane candidates. Finally, detected lane is tracked using the Kalman filter. Experimental results show that the proposed algorithm can be processed within 30ms and its detection rate is approximately 90% on the highway in a variety of environments such as day and night.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.165-168
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• 2009

차선 검출을 위한 영상처리연구는 Hough Transform을 이용하는 방법과 주파수 변환 방법, 히스토그램을 이용하는 방법, 템플릿을 이용하는 방법등이 사용되고 있다. 차선 검출에 가장 많이 사용되는 Hough Transform은 연산 과정이 복잡하여 차량의 속도가 증가하면 실제 상황과 오차가 생길 확률이 높다. 이러한 문제를 해결하기 위해 영상을 분할하여 최소한의 영역을 처리하여 처리량을 줄였으며, 차선 이외의 선이 추출될 경우 그 선의 각도와 위치를 고려하여 연산에 방해되는 선을 삭제한다. 또한 고속으로 진행하는 차량의 경우, 점선으로 이루어져 선이 보이지 않는 부분에서는 차선의 인식이 불가능하여 위험한 상황을 초래한다. 따라서 최소한의 차선을 이용하여 차선을 연장하고, 여러 직선으로 곡선을 표현하여 차량 진행 방향을 예측할 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12C
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• pp.727-737
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• 2011

This paper presents an implementation scheme of real-time lane and vehicle detection system with FPGA and DSP. In this type of implementation, defining the functionality of each device in efficient manner is of crucial importance. The FPGA is in charge of extracting features from input image sequences in reduced form, and the features are provided to the DSP so that tracking lanes and vehicles are performed based on them. In addition, a way of seamless interconnection between those devices is presented. The experimental results show that the system is able to process at least 15 frames per second for video image sequences with size of $640{\times}480$.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.129-136
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• 2018

In this paper, we propose a new real-time lane detection method that is efficient for road environment. Existing methods have a problem of low reliability under environmental changes. In order to overcome this problem, we emphasize the lane candidate area by using gray level division. And Extracts a straight line component near the lane by using the Hough transform, and generates an ROI for each straight line based on the extracted coordinates. And integrates the generated ROI images. Then, the lane is determined by dividing the object using the dual queue in the ROI image. The proposed method is able to detect lanes even in the environmental change unlike the conventional method. And It is possible to obtain an advantage that the area corresponding to the background such as sky, mountain, etc. is efficiently removed and high reliability is obtained.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.224-230
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• 2013

Lane boundary detection plays a key role in the driver assistance system. This study proposes a robust method for detecting lane boundary in severe environment. First, a horizontal line detects form the original image using improved Vertical Mean Distribution Method (iVMD) and the sub-region image which is under the horizontal line, is determined. Second, we extract the lane marking from the sub-region image using Canny edge detector. Finally, K-means clustering algorithm classifi left and right lane cluster under variant illumination, cracked road, complex lane marking and passing traffic. Experimental results show that the proposed method satisfie the real-time and efficient requirement of the intelligent transportation system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.186-201
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• 2021

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.284-285
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• 2008

논문에서는 비전센서로 전방도로영상을 획득하여 차선인식과 정을 거쳐 자율주행에 필요한 도로정보를 추출하고 사고를 방지할 수 있게 경보음을 발생하는 기법을 제시한다. 비전을 통해 입력되는 정보중 직선도로나 곡선도로의 외곽에 해당하는 백색 선만을 인식하는 알고리즘이 필요하다. 이러한 알고리즘을 수행하기 위해서는 많은 계산량이 필요로 하기 때문에 실시간의 자율주행 시스템에의 적용은 제약이 수반된다. 본 논문은 이와 같은 문제를 해결하기 위해 세선화 영역 및 차선이탈경보시스템(LDWS) 알고리즘을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2665-2674
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• 2012

This paper propose the method to recognize the lanes and distance between cars in real-time which detects dangerous situations and helps safe driving in the actual road environment. First of all, it extracts the area of interest corresponding to roads and cars from the road image photographed by using the forward-looking camera. Through the hough transform for the area of interest, this study detects linear components and also selects the lane and conducts filtering by calculating probability. And through the shadow threshold analysis of the cars in front within the area of interest, it extracts the objects of cars in front and calculates the distance from cars in front. According to the result of applying the suggested technology to recognize the lane and distance between cars to the road situation for testing, it showed over 95% recognition rate; thus, it has been proved that it can respond to safe driving.