• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1411-1412
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• 2015

Detection of road lanes is an important technology, which is being used in autonomous vehicles from last few years. This method is very helpful and supportive for the drivers to provide them safety and to avoid road accidents. Alot of methods are being used to detect road lane markings. We can categorize them into three major categories: sensor-based, feature-based, and model-based methods. And in this study we give the comprehensive survey on lane marking techniques.

• 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.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.137-144
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• 2009

Understanding lane markings in a live video captured from a moving vehicle is essential to build services for intelligent vehicles such as LDWS(Lane Departure Warning Systems), unmanned vehicles, video-based car navigation systems. In this paper, we present a novel approach to recognize the color of lane markings and the lane number that he/she is driving on. More specifically, we present a background-color removal approach to understand the color of lane markings for various illumination conditions, such as backlight, sunset, and so on. In addition, we present a probabilistic network approach to decide the lane number. According to our experimental results, the proposed idea shows promising results to detect lane number in a various illumination conditions and road environments.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.173-184
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• 2005

Pavement markings delineate driver's sight and attentions at day and night. Retroreflectivities of the pavement markings are known to be affected by some factors such as geometrical characteristics of road sections, marking materials, traffic volumes, weather, and so on. Therefore, pavement markings would have different retroreflectivities place by place. The objective of this paper is to contend that there are rises and falls of retroreflectivity of specific geometrical characteristics of road section based on observed field data. For this purpose, retroreflectivities of yellow continued lines and white skip lines were measured on the freeway sections from Hanam to Nami interchanges. And then, the sections are categorized based on road geometric or facility types. From this study, it may be concluded that there exist considerably lower retroreflectivity trends of pavement markings at sharply curved sections, climbing lane sections, ramp sections, and tunnel sections than averaged retroreflectivity trends on freeway sections. This may leads to the necessity of differential pavement marking managements.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.824-833
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• 2006

In this paper, the image modelling of road's lane markings is established using view frustum(VF) model. From this model, a measurement system of lane markings and obstacles is proposed. The system also involve the real time processing of the 3D position coordinate and the distance data from the camera to the points on the 3D world coordinate by virtue of the camera calibration. In order to reduce their measurement error, an useful algorithm for which analyze the geometric variations due to traveling vehicle's fluctuation using VF model is proposed. In experiments, without correction, for instance, the $0.4^{\circ}$ of pitching rotation gives the error of $0.4{\sim}0.6m$ at the distance of 10m, but the more far distance cause exponentially the more error. We con finned that this algorithm can be reduced less than 0.1m of error at the same condition.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.181-199
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• 2022

This study presented a methodology for evaluating the performance of the lane departure warning system was derived by calculating the relationship between the behavior information of the car and the location of the high-precision map using a high-precision map. The evaluation criteria of the mood and lane departure warning system for the installation of road markings in Korea were analyzed, a high-precision map was constructed to meet the evaluation criteria, and an evaluation system was constructed to verify the proposed methodology. Evaluation of lane departure warning systems using high-precision maps can be quantified and applied to various road environments through accurate location-based comparative analysis and reduced manual post-processing work time to confirm evaluation results.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.108-115
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• 2006

In this paper, a recognition algorithm of the straight line components of lane markings and an obstacle in the travelling lane region is proposed. This algorithm also involve the pitching error correction algorithm due to traveling vehicle's fluctuation. In order to reduce their error a practical road image modelling algorithm using V.F. model and camera calibration procedure are suggested to adapt the geometric variations. It is obtained the 3D world coordinate data by the 2D road images. In experimental test, we showed that this algorithm is available to recognize lane markings and an obstacle in the traveling lane.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.6
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• pp.15-24
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• 2011

As increase awareness of intelligent vehicle systems, many kinds of lane detection algorithm have been proposed. General boundary extraction method can bring good result in detection of lane on the road. But a shadow on the road, or other boundaries, such as horizontal lines can be detected. The method using morphological operations was used to extract information about Lane. By applying HSV color model for color information of lane, the candidate of the lane can be extracted. In this paper, the lane detection region was set by Hough transformation using the candidate of the lane. By extracting lane markings on the lane detection region, lane detection method can bring good result.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.163-171
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• 2005

Current roads were designed and constructed based on the design criteria and thus those were overly simplified drivers' needs. The road criteria do not suggest the desirable range of the design values but suggest the minimum requirements for the road design. Therefore, a completed road design based on the design criteria does not always guarantee the best design in terms of safety and it sometimes violates drivers' expectation. Therefore, the ROSSAV(ROad Safety Survey and Analysis Vehicle) is being developed by the KICT to evaluate road safety and increase driving safety. In this paper, the image capture system was described in detail. The image capture system is consisted of two front view cameras, two side down-looking cameras and a synchronization device. Two front view cameras were used to take a picture of road and road facilities at the driver's viewpoint. Also, two side down-looking cameras were used to capture road surface image to extract lane markings. A synchronization device were used to generate image capturing signal at the fixed distance spacing huck as every 10m. The front view images could be used to calculate and measure highway geometry such as shoulder width because every image is saved with it's locational information. And also the side down looking images could be used to extract median lane mark which representing road alignement efficiently.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.190-200
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• 2005

In this paper, the image modelling of road's lane markings is established using view frustum(VF) modeling. This algorithm also involve the real time processing of the 3D position coordinate and the distance data from the camera to the points on the 3D world coordinate by the camera calibration. In order to reduce their measurement error, an useful algorithm for which analyze the geometric variations clue to traveling vehicle's fluctuation using VF model is proposed. In experiments, without correction, for instance, the $0.4^{\circ}$ of pitching rotation gives the error of $0.4^{\sim}0.6m$ at the distance of 10m, but the more far distance cause exponentially the more error. We confirmed that this algorithm can be reduced less than 0.1m of error at the same condition.