• 한국정보통신학회논문지
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• 제16권12호
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• pp.2755-2761
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• 2012

본 논문에서는 Eccentricity를 이용한 차선 검출 알고리듬을 제안한다. 차선 검출 알고리듬은 자동차 운전자의 안정성을 증가시키는 차선 이탈 경보 시스템 등에 활용될 수 있다. 차선 검출율을 개선하기 위하여 그래프 이론에서 소개되는 Eccentricity를 정의하고, 이를 차선 검출 알고리듬에 이용하여 Eccentricity를 계산하였다. 직선도로인 경우 Eccentricity는 1이고 1차 함수로 구현이 가능하다. 그래서 시간 복잡도와 공간 복잡도를 개선하였고, 아울러 기존의 방법들보다 차선 검출율이 향상됨을 확인하였다.

• 방송공학회논문지
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• 제14권2호
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• pp.253-264
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• 2009

본 논문에서는 실용화를 목적으로 비전 시스템을 기반으로 한 차선검출의 성능개선과 처리과정의 고속화 알고리즘을 제안한다. 차선검출의 고속화를 위해 전처리 과정으로 수평소실선의 추정과 관심영역(ROI-LB)의 최적 선정으로 획기적인 검출영역의 감소가 가능하다. 블록단위의 ROI-LB 내에서 영상의 특징정보를 추출하고 이를 기반으로 한 Hough 변환의 적용에 의한 nonparametric 모델 매칭 기법으로 차선을 검출한다. Laplacian 필터를 사용해서 잡음제거와 동시에 에지 보강 과정을 처리함으로서 다양한 차선 패턴에 대한 특징정보 추출의 신뢰성을 향상시킨다. 또한 ROI-LB 내 블록별 에지의 방향성 정보의 클러스터링으로 차선으로 오인식되는 에지들의 제거가 가능해 차선검출의 성능을 개선할 수 있다. 제안 방법의 유효성을 검증하기 위해 다양한 실제 차선 패턴을 대상으로 한 실험결과를 제시한다.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.1029-1035
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• 2013

This paper proposes a real time lane detection algorithm using LRF (Laser Range Finder) for autonomous navigation of a mobile robot. There are many technologies for safety of the vehicles such as airbags, ABS, EPS etc. The real time lane detection is a fundamental requirement for an automobile system that utilizes outside information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. By the vision-based system, recognition of environment for three dimensional space becomes excellent only in good conditions for capturing images. However there are so many unexpected barriers such as bad illumination, occlusions, and vibrations that the vision cannot be used for satisfying the fundamental requirement. In this paper, we introduce a three dimensional lane detection algorithm using LRF, which is very robust against the illumination. For the three dimensional lane detections, the laser reflection difference between the asphalt and lane according to the color and distance has been utilized with the extraction of feature points. Also a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been verified through the real experiments.

• 대한전자공학회논문지SP
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• 제42권4호
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• pp.21-30
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• 2005

본 논문에서는 B-snake 차선 모델을 이용한 차선 검출 및 추적에 관한 알고리즘을 제안한다. 제안된 이론의 특성은 첫째, 다른 알고리즘에 비해 직선, 굴곡이 있는 도로와 같은 보다 넓은 범위의 차선 구조의 표현이 가능하며, 또한 평면 도로의 평행 특성을 이용하여 그림자, 잡음 등에 강하고, 둘째, 잡음에 강한 CHEVP(Canny/Hough Estimation Vanish Point) 알고리즘을 사용하여 차선 위치의 초기값을 제공한다. 셋째, GYP(Gradient Vector Flow)와 최소 평균 제곱 에러를 이용하여 B-Snake 차선 모델에서 발생하는 외부의 힘을 줄여 차선 검출의 에러를 줄이고 차선 추적을 효과적으로 수행한다. 측정 실험 결과 도로영상을 날씨 별로 맑은 날, 흐린 날 그리고 비오는 날로 구분하여 본 알고리즘을 수행하였으며 95$\%$ 이상의 차선 검출률을 보였다.

• 자동차안전학회지
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• 제9권3호
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• pp.24-30
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• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• 반도체디스플레이기술학회지
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• 제15권1호
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• pp.27-32
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• 2016

A new lane detection algorithm is proposed for the analysis of DNA fingerprints from a polymerase chain reaction (PCR) gel electrophoresis image. Although several research results have been previously reported, it is still challenging to extract lanes precisely from images having abrupt background brightness difference and bent lanes. We propose an edge based algorithm for calculating the average lane width and lane cycle. Our method adopts sub-pixel algorithm for extracting rising-edges and falling edges precisely and estimates the lane width and cycle by using k-means clustering algorithm. To handle the curved lanes, we partition the gel image into small portions, and track the lane centers in each partitioned image. 32 gel images including 534 lanes are used to evaluate the performance of our method. Experimental results show that our method is robust to images having background difference and bent lanes without any preprocessing.

• International Journal of Automotive Technology
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• 제8권4호
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• pp.513-520
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• 2007

In this paper, we propose an approach to identify the driving environment for intelligent highway vehicles by means of image processing and computer vision techniques. The proposed approach mainly consists of two consecutive computational steps. The first step is the lane marking detection, which is used to identify the location of the host vehicle and road geometry. In this step, related standard image processing techniques are adapted for lane-related information. In the second step, by using the output from the first step, a four-stage algorithm for vehicle detection is proposed to provide information on the relative position and speed between the host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenarios. Herein, experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.97-100
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• 1996

In this paper, we describe an algorithm which estimate road following direction using the vanishing point property and obstacle detection. This method of detecting the lane markers in a set of continuous lane highway images using linear approximation is presented. This algorithm is designed for accurate and robust extraction of this data as well as high processing speed. Also, this algorithm reckon distance and chase about an obstacle. It include four algorithms which are lane prediction, lane extraction, road following parameter estimation and obstacle detection algorithm. High accuracy was proven by quantitative evaluation using simulated images. Both robustness and the practicality of real time video rate processing were then confirmed through experiment using VTR real road images.

• 한국자동차공학회논문집
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• 제7권5호
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• pp.178-185
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• 1999

Recently, Collision Warning System is developed to improve vehicle safety. This system chiefly uses radar. But the detected vehicle from radar must be decide whether it is the vehicle in the same lane of my vehicle or not. Therefore, Vision System is needed to detect traffic lane. As a preparative step, this study presents the development of algorithm to recognize traffic lane curve direction. That is, the Neural Network that can recognize traffic lane curve direction is constructed by using the information of short distance, middle distance, and decline of traffic lane. For this procedure, the relation between used information and traffic lane curve direction must be analyzed. As the result of application to sampled 2,000 frames, the rate of success is over 90%.t text here.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.621-626
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• 2015

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.