• 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.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.29 no.5
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• pp.229-237
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• 2019

For maintaining high visibility of lane, the high refractive index glass beads with excellent retro-reflectivity are usually applied on the road marking paint. The retro-reflectivity standard of general road marking is different in each country; it is defined as $240mcd/m^2{\cdot}lux$ based on white light in Korea and $250{\sim}300mcd/m^2{\cdot}lux$ in developed countries. In this paper, the recent trends on the manufacturing technology of glass beads suitable for autonomous driving age as well as the road marking regulations has been reviewed. Also, in preparation for the commercialization of autonomous vehicles in the future, the necessity of raising the standard value and improving the glass bead properties were proposed.

• 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.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.87-96
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• 2016

Recently, there is a need for automatic recognition of a variety of symbols on roads because of activation of information services using digital maps on the Web or mobile devices. This paper proposes a method which automatically recognizes 11 kinds of symbolic road markings on the road surface with HOG-SP(Histogram of oriented Gradients-Split Projection) descriptor and shows improvement of lane position detection with recognized symbolic road markings. With the proposed method, recognition rate of 81.99% has been proven on NAVER road view images and the experiments proves the superiority of proposed method by comparisons with other existing methods. Moreover, this paper shows 7.64% higher lane position detection rate by recognizing road surface marking beforehand than only detecting lanes' positions.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.979-987
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• 2020

This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.

• 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.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.131-140
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• 2012

Lane markings such as edgelines, centerlines, and lines that delineate lanes generally provide drivers with the various information for safe driving. Drivers can easily recognize the lane markings through the color differences between the markings and road surfaces during the daytime. However, it is a bit difficult for drivers to perceive them during the nighttime due to the lack of artificial lights. Although the glass beads with the 1.5-refractive index have been used to improve the visibility of the lane markings during the nighttime, it is still difficult for drivers to recognize the lane markings properly, especially during the rainy nighttime, which may often lead to traffic accidents. To improve the retroreflectivity and visibility of the lane markings during the rainy nighttime, the high refractive beads with the 2.4-refractive index are essentially required, but they do not work appropriately during the dry nighttime. Thus, the mixed materials with the 1.5, 1.9, and 2.4-refractive beads should be considered for the satisfactory implementation of the lane markings. This study reveals the best mixing rates of the beads by conducting benefit-cost analysis under various weather conditions in Korea. The analysis results show that the lane markings with the 100% of the 2.4-refractive beads provide the highest visibility of lane markings regardless of the roadway conditions, but the benefit-cost (B/C) ratio of the bead mixture is merely 0.46. The best mixing rate of the beads, from the highest B/C ratio viewpoint, was identified as the mixture with a 80% of 1.5-refractive beads and a 20% of 2.4-refractive beads. Some limitations and future research agenda have also been discussed.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.22-26
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• 2022

This paper proposes a new method for the recognition of road surface marks and numbers. The proposed method designates a region of interest on the road surface without first detecting a lane. The road surface markings are extracted by location and size using a connection component analysis. Distortion due to the perspective effect is minimized by normalizing the size of the road markings. The road surface marking of the connected component is recognized by matching it with the stored road marking templates. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the recognition of road surface marks and numbers.

• International Journal of Highway Engineering
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• v.14 no.1
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• pp.73-83
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• 2012

Effective use of traffic marking can lead to driver's safer driving by speed management and visibility enhancement. One of the critical factors in designing traffic marking is how to incorporate human factor issues into the design process. This study presented a methodology for evaluating the effectiveness of Hi-pass marking on Korean freeways based on kansei engineering. Hi-pass marking scenarios made by a virtual reality tool were used to evaluate the user's perceptions. Various combination of chevron angles and peripheral transverse(PT) bars were used for the evaluation. The results show that the most effective scenario is a combination of 60 degree chevron angle and an askew parallelogramshaped PT bar shaped. It is expected that the proposed method and outcome will be useful for making safer driving maneuvers.