• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.130-136
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• 2015

In this paper, we propose a robust curved lane marking detection method. Several lane detection methods have been proposed, however most of them have considered only straight lanes. Compared to the number of straight lane detection researches, less number of curved-lane detection researches has been investigated. This paper proposes a new curved lane detection and tracking method which is robust to various illumination conditions. First, the proposed methods detect straight lanes using a robust road feature image. Using the geometric relation between a vehicle camera and the road plane, several circle models are generated, which are later projected as curved lane models on the camera images. On the top of the detected straight lanes, the curved lane models are superimposed to match with the road feature image. Then, each curve model is voted based on the distribution of road features. Finally, the curve model with highest votes is selected as the true curve model. The performance and efficiency of the proposed algorithm are shown in experimental results.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.34-36
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• 2009

This paper proposes an effective lane detection algorithm using hugh transform and lane model. The proposed lane detection algorithm includes two major components, i.e., lane marks segmentation and an exact lane extraction using a novel postprocessing technique. The first step is to segment lane marks from background images using HSV color model. Then, a novel postprocessing is used to detect an exact lane using Hugh transform and lane models(linear and curved lane models). The postprocessing consists of three parts, i.e, thinning process, Hugh Transform and filtering process. We divide input image into three regions of interests(ROIs). Based on lane curve function(LCF), we can detect an exact lane from various extracted lane lines. The lane models(linear and curved lane mode]) are used in order to judge whether each lane segment is fit or not in each ROIs. Experimental results show that the proposed scheme is very effective in lane detection.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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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.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.277-284
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• 2014

The detection method of curves for the lanes which is powerful for the variation by utilizing the lane variation vector and cardinal spline on the inverse perspective transformation screen images which do not required the camera parameters are suggested in this paper. This method detects the lane area by setting the expected lane area in the s frame and next s+1 frame where the inverse perspective transformation and entire process of the lane filter are adapted, and expects the points of lane location in the next frames with the lane variation vector calculation from the detected lane areas. The scan area is set from the nextly expected lane position and new lane positions are detected within these areas, and the lane variation vectors are renewed with the detected lane position and the lanes are detected with application of cardinal spline for the control points inside the lane areas. The suggested method is a powerful method for curved lane detection, but it was adopted to the linear lanes too. It showed an excellent lane detection speed of about 20ms in processing a frame.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.88-93
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• 2012

Lane detection methods using camera, which are part of the driver assistance system, have been developed due to the growth of the vehicle technologies. However, lane detection methods are often failed by varying road conditions such as rainy weather and degraded lanes. This paper proposes a method for lane detection which is robust in varying road condition. Lane candidates are extracted by intensity comparison and lane detection filter. Hough transform is applied to compute the lane pair using lane candidates which is straight line in image. Then, a curved lane is calculated by using B-Snake algorithm. Also, weighting value is computed using previous lane detection result to detect the lanes even in varying road conditions such as degraded/missed lanes. Experimental results proved that the proposed method can detect the lane even in challenging road conditions because of weighting process.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.81-90
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• 2016

As an autonomous driving vehicle (AV) need to cope with external road conditions by itself, its perception performance for road environment should be better than that of a human driver. A vision sensor, one of AV sensors, performs lane detection function to percept road environment for performing safe vehicle steering, which relates to define vehicle heading and lane departure prevention. Performance standards for a vision sensor in an ADAS(Advanced Driver Assistance System) focus on the function of 'driver assistance', not on the perception of 'independent situation'. So the performance requirements for a vision sensor in AV may different from those in an ADAS. In assuming that an AV keep previous steering due to lane detection failure, this study calculated lane departure distances between the AV location following curved road alignment and the other one driving to the straight in a curved section. We analysed lane departure distance and time with respect to the allowance of lane detection malfunction of an AV vision sensor. With the results, we found that an AV would encounter a critical lane departure situation if a vision sensor loses lane detection over 1 second. Therefore, it is concluded that the performance standards for an AV should contain more severe lane departure situations than those of an ADAS.

• Journal of the Semiconductor & Display Technology
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• v.15 no.1
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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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.97-106
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• 2010

In this paper, lane and curvature detection algorithm based on the curve template matching method is proposed. To eliminate the perspective effect of the original image, the input image is transformed to a top view image. From this top view image, its edge image is created. To increase the accuracy of detection, a novel edge detection method, which shows a strength in lane detection, is proposed. In the first step, straight lanes are detected from the edge image, and then the Curve Template Matching(CTM) method is applied to detect the curved lanes and to find their curvatures. Since the proposed CTM method uses only the simple equations, such as line and circle equations, to detect the curved lane, the algorithm is simple. Moreover, we used the detected lane information in the previous frames to detect the current frame's lanes, the detection results become more reliable. The proposed algorithm has been tested in various road conditions (highway, urban street, night time highway, etc.). Experimental results show that the proposed algorithm can process about 70 frames per second with the successful lane detection rate over 95% and curvature detection rate about 90%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.6
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• pp.322-332
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• 2014

This paper proposes a robust lane detection algorithm for non-flat roads by combining a piecewise linear model and dynamic programming. Compared with other lane models, the piecewise linear model can represent 3D shapes of roads from the scenes acquired by monocular camera since it can form a curved surface through a set of planar road. To represent the real road, the planar roads are created by various angles and positions at each section. And dynamic programming determines an optimal combination of planar roads based on lane properties. Experiment results demonstrate the robustness of proposed algorithm against non-flat road, curved road, and camera vibration.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.195-204
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• 2018

Lane keeping assist and departure prevention system are the key functions of ADAS. In this paper, we propose lane detection method which uses Gaussian function based RANSAC. The proposed method consists mainly of IPM (inverse perspective mapping), Canny edge detector, and Gaussian function based RANSAC (Random Sample Consensus). The RANSAC uses Gaussian function to extract the parameters of straight or curved lane. The proposed RANSAC is different from the conventional one, in the following two aspects. One is the selection of sample with different probability depending on the distance between sample and camera. Another is the inlier sample score that assigns higher weights to samples near to camera. Through simulations, we show that the proposed method can achieve good performance in various of environments.