• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.161-164
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• 1999

An effective approach to lane detection in driver assistance system (DAS) is proposed, based on the decomposition of lane markings. The properties of the decomposed lane markings are discussed, and analyses on lane curvature are given. The current lane on road is detected quickly, the neighboring lane regions are also extracted for lane planning of the vehicle, and the parameters of lane structure are accurately estimated.

• 한국통신학회논문지
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• 제41권3호
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• pp.328-330
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• 2016

본 논문에서는 단일 차선표시선 검출 기반 차선 이탈 경보 시스템을 제안한다. 제안하는 방법은 어두운 조명이나 마모로 인하여 좌 우 차선표시선의 동시 검출이 불가능한 경우에도 단일 차선표시선의 검출 및 추적을 통해 강건한 차선이탈 감지를 목표로 한다. 실험을 통해 제안 방법이 한쪽 차선표시선만 검출된 경우에도 차선이탈을 감지하는 것을 확인하였다.

• 제어로봇시스템학회논문지
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• 제15권5호
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• pp.480-487
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• 2009

Autonomous Lane detection with vision is a difficult problem because of various road conditions, such as shadowy road surface, various light conditions, and the signs on the road. In this paper we propose a robust lane detection algorithm to overcome shadowy road problem using a statistical method. The algorithm is applied to the vision-based mobile robot system and the robot followed the lane with the lane following controller. In parallel with the lane following controller, the global position of the robot is estimated by the developed localization method to specify the locations where the lane is discontinued. The results of experiments, done in the region where the GPS measurement is unreliable, show good performance to detect and to follow the lane in complex conditions with shades, water marks, and so on.

• 방송공학회논문지
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• 제17권6호
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• pp.1069-1079
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• 2012

영상을 통한 차선검출은 지능형 주행보조장치의 향상을 위해 필수적인 작업이다. 이 논문에서는 차선의 에지를 Canny 방법을 사용하여 생성한다. Canny 방법은 환경 상태에 따라 결과가 달라진다. 노면 상태가 분명함의 여부에 따라 잘못된 차선 검출을 할 수 있다. 그래서 안전한 에지 검출을 위해 에지 검출시 파라미터를 자동 조절하여 환경 변화에 강인한 알고리즘을 제안한다. 획득한 에지 검출을 기반으로 Catmull Rom spline 을 사용하여 스플라인으로 차선을 생성한다.

• 한국ITS학회 논문지
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• 제10권3호
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• pp.64-73
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• 2011

본 논문에서는 모서리 검출을 이용하여 추출된 모서리를 추적함으로써 차선을 검출하고 추적하는 알고리즘을 제안한다. 제안하는 알고리즘은 양 차선이 연속되지 않고 끊긴 형태로 존재하거나 교차되는 등의 다양한 차선의 형태에도 높은 추출률을 보이는 장점을 가지고 있다. 이는 이러한 형태의 차선의 비율이 높은 시내도로 와 국도에서의 차선 추출에 보다 유리하다. 이러한 점을 증명하기 위해 테스트는 주로 불연속적이고 교차되는 형태의 차선이 많은 도로에서 실시하였고 평균 87% 이상의 추출률을 보여주었다.

• 한국컴퓨터정보학회논문지
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• 제22권3호
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• pp.27-34
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• 2017

We propose a cascade lane detector that uses biased discriminant analysis (BDA) to work effectively even when there are various external factors on the road. The proposed cascade detector was designed with an existing lane detector and the detection module using BDA. By placing the BDA module in the latter stage of the cascade detector, the erroneously detected results by the existing detector due to sunlight or road fraction were filtered out at the final lane detection results. Experimental results on road images taken under various environmental conditions showed that the proposed method gave more robust lane detection results than conventional methods alone.

• 센서학회지
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• 제27권2호
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• pp.137-141
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• 2018

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.

• 대한임베디드공학회논문지
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• 제11권3호
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• pp.163-171
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• 2016

Lane detection is a widely researched topic. Although simple road detection is easily achieved by previous methods, lane detection becomes very difficult in several complex cases involving noisy edges. To address this, we use a Convolution neural network (CNN) for image enhancement. CNN is a deep learning method that has been very successfully applied in object detection and recognition. In this paper, we introduce a robust lane detection method based on a CNN combined with random sample consensus (RANSAC) algorithm. Initially, we calculate edges in an image using a hat shaped kernel, then we detect lanes using the CNN combined with the RANSAC. In the training process of the CNN, input data consists of edge images and target data is images that have real white color lanes on an otherwise black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) of 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged to form a deep structure. Our proposed lane detection algorithm successfully eliminates noise lines and was found to perform better than other formal line detection algorithms such as RANSAC

• 제어로봇시스템학회논문지
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• 제22권1호
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• pp.1-7
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• 2016

ADAS (Advanced Driver Assistance Systems) requires not only real-time robust lane detection, both straight and curved, but also predicting upcoming steering direction by detecting the curvature of lanes. In this paper, a curvature lane detection algorithm is proposed to enhance the accuracy and detection rate based on using inverse perspective images and Gaussian Mixture Model (GMM) to segment the lanes from the background under various illumination condition. To increase the speed and accuracy of the lane detection, this paper used template matching, RANSAC and proposed post processing method. Through experiments, it is validated that the proposed algorithm can detect both straight and curved lanes as well as predicting the upcoming direction with 92.95% of detection accuracy and 50fps speed.

• 센서학회지
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• 제28권3호
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• pp.164-170
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• 2019

In this paper, a lane detection and tracking algorithm based on vision sensors and employing a robust filter for inner edge detection is proposed for developing a lane departure warning system (LDWS). The lateral offset value was precisely calculated by applying the proposed filter for inner edge detection in the region of interest. The proposed algorithm was subsequently compared with an existing algorithm having lateral offset-based warning alarm occurrence time, and an average error of approximately 15ms was observed. Tests were also conducted to verify whether a warning alarm is generated when a driver departs from a lane, and an average accuracy of approximately 94% was observed. Additionally, the proposed LDWS was implemented as an embedded system, mounted on a test vehicle, and was made to travel for approximately 100km for obtaining experimental results. Obtained results indicate that the average lane detection rates at day time and night time are approximately 97% and 96%, respectively. Furthermore, the processing time of the embedded system is found to be approximately 12fps.