• International Journal of Automotive Technology
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• v.8 no.4
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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.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.445-453
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• 2000

In this paper we propose the method which extracts lane using the grouped block snake algorithm. In the proposed algorithm, input image is divided into $8\times{8}$ blocks and then noise-included blocks are removed by a probability-based method. And also, we use hough transform to separate lane from the background image and suggest a grouped block snake method to detect road lane blocks. The proposed method reduces computational complexity and removes the noise in a more effective way compared to the pixel-based snake method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.126-137
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• 1997

This paper describes the algorithm which can estimate road following direction and deetect obstacle using a monocular vision system. This algorithm can estimate the course of vehicle using the vanishing point properties and detect obstacle by statistical method. The proposed algorithm is composed of four steps, which are lane prediction, lane extraction, road following parameter estimation and obstacle detection. It is designed for high processing speed and high accuracy. The former is achieved by a small area named sub-windown in lane existence area, the later is realized by using connected edge points of lane. We would like to present that the new mehod can detect obstacle using the simple statistical method. The paracticalities of the processing speed, the accuracy of the algorithm and proposing obstacle detection method, have been justified through the experiment applied VTR image of the real road to the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.98.3-98
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• 2002

Vision-based systems for finding road lanes have to operate robustly under a wide variety of environ-mental conditions including large amount of scene clutters. This paper presents a method for finding the lane boundaries by combining a local line extraction method and dynamic programming as a search tool. The line extractor obtains an initial position estimation of road lane boundaries from the noisy edge fragments. Dynamic programming then improves the initial approximation to an accurate configuration of lane boundaries. Input image frame is divided into a few sub-regions along the vertical direction. The local line extractor then performs to extract candidate lines of road lanes in the...

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.1-11
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• 2021

This paper presents a procedure to extract lane line models using a set of proposed methods. Firstly, an image warping method based on homography is proposed to transform a target image into an image which is efficient to find lane pixels within a certain region in the image. Secondly, a method to use the combination of the results of edge detection and HSL (Hue, Saturation, and Lightness) transform is proposed to detect lane candidate pixels with reliability. Thirdly, erroneous candidate lane pixels are eliminated using a selection area method. Fourthly, a method to fit lane pixels to quadratic polynomials is proposed. In order to test the validity of the proposed procedure, a set of black-box images captured under varying illumination and noise conditions were used. The experimental results show that the proposed procedure could overcome the problems of color-only and edge-only based methods and extract lane pixels and model the lane line geometry effectively within less than 0.6 seconds per frame under a low-cost computing environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.158-164
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• 2015

Lane detection is one of the key parts among autonomous vehicle technologies because lane keeping and path planning are based on lane detection. Camera is used for lane detection but there are severe limitations such as narrow field of view and effect of illumination. On the other hands, Lidar sensor has the merits of having large field of view and being little influenced by illumination because it uses intensity information. Existing researches that use methods such as Hough transform, histogram hardly handle multiple lanes in the co-occuring situation of lanes and road marking. In this paper, we propose a method based on RANSAC and regularization which provides a stable and precise detection result in the co-occuring situation of lanes and road marking in highway scenarios. This is performed by precise lane point extraction using circular model RANSAC and regularization aided least square fitting. Through quantitative evaluation, we verify that the proposed algorithm is capable of multi lane detection with high accuracy in real-time on our own acquired road data.

• Korean Journal of Geomatics
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• v.3 no.1
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• pp.67-72
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• 2003

We have seen rapid changes in road systems and networks in urban areas due to fast urbanization and increased traffic demands. As a result, many researchers have put greater importance on extraction, correction and updating of information about road systems. Also, by using the various data on road systems and its condition, we can manage our road more efficiently and economically. Furthermore, such information can be used as input for digital map and GIS analysis. In this research, we used a high resolution aerial photo of the roads in Seongnam area. First, we applied the top-hat filter to the area of interest so that the road markings could be extracted in an efficient manner. The lane separation lines were selected, considering the shape similarity between the selected lane separation line and reference data. Next, we extracted the roads in the urban area using the aforementioned road marking. Using this technique, we could easily extract roads in urban area in semi-automatic way.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.183-190
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• 2002

This study is about the extraction of highway's superelevation using real time kinematic surveying among of GPS surveying methods which is economic method to construct data base in the side of highway maintain management. Using the developed vehicle, center line and shoulder of highway are measured and enough precision is obtained after analyzing the result. The result is show that 1.3 cm to 2.0 cm error in the clothed and about 0.8 cm to 1.2 cm error in the circular curve. Those errors are proved error to lane making during construction. This study is expected to become efficient method for extraction of highway alignment elements in the Mobile Mapping System.