• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.127-130
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• 2001

In this paper, we describes a method of estimating traveling direction of a autonomous vehicle. For the development of autonomous vehicle, it is important to detect road lane and to reckon traveling direction. The object of a propose algorithm is to perform lane detection in real-time for standalone vision system. And we calculate efficent traveling direction to find steering angie for lateral control system. Therefore autonomous vehicle go forward the center of lane by adjusting the current steering angle using traveling direction.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.521-525
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• 2011

Lane marking detection is one of important issues in the field of autonomous mobile robot. Especially, in urban environment, like pavement roads of downtown or tour tracks of Science Park, which have continuous patterns on the surface of the road, the lane marking detection becomes more important ability. Although there were many researches about lane detection and lane tracing, many of them used vision sensors mainly to detect lane marking. In this paper, we obtain 2 dimensional library data of 'Intensity' and 'Distance' using one laser rangefinder only. We design a simple classifier and filtering algorithm for the lane detection which uses only one LRF (Laser Range Finder). Allowing extended usage of LRF, this research provides more functionality not only in range finding but also in lane detecting to mobile robots. This work will be technically helpful for robot developers to design more simple and efficient autonomous driving system using LRF.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.103-107
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• 2013

In this paper, A real-time lane tracking algorithm is proposed for lane departure warning system. To eliminate perspective effect, input image is converted into Bird's View by inverse perspective mapping. Next, suitable features are extracted for lane detection. Lane feature that correspond to area of interest and RANSAC are used to detect lane candidates. And driving lane is decided by clustering of lane candidates. Finally, detected lane is tracked using the Kalman filter. Experimental results show that the proposed algorithm can be processed within 30ms and its detection rate is approximately 90% on the highway in a variety of environments such as day and night.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.329-336
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• 2006

This paper describes a lane change assistance system for the help of safe lane change, which detects vehicles approaching from the rear side by using a computer vision algorithm and notifies the possibility of safe lane change to a driver. In case a driver tries to lane change, the proposed system can detect vehicles and keep track of them. After detecting side lane lines, region of interest for vehicle detection is decided. For detection a vehicle, optical flow technique is applied. The experimental result of the proposed algorithm and system showed that the vehicle detection rate was 91% and the embedded system would have application to a lane change assistance system being commercialized in the near future.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.93-103
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• 1999

To detect obstacles and lane-markers for driving vehicles, a new moving window scheme where moving windows are assigned to an image frame captured by a camera is addressed. For the detection of obstacles, it is important to estimate lane-markers precisely and rapidly. For this purpose, selecting some partes of an image frame at the expected lane locations, i.e., selecting window are generally adopted for extracting lane-markers efficiently. In this paper, a new scheme that extracts lane-markers precisely by assigning variable size windows at the expected locations of lane-markers considering the road curvature and finally detects obstacles within a driving lane is proposed. The accuracy improvement using this moving window scheme is showed by comparing to the conventional fixed window method and to using radar to laser sensors.

• 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.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.201-212
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• 2011

In this paper, we suggest a system of detecting a vehicle with lane violation, which can detect the vehicle with lane violation, by using the feature point tracking. The whole algorism in the suggested system of detecting a vehicle with lane violation is composed of three stages such as feature extraction, register and tracking in feature for the tracking-targeted vehicle, and detecting a vehicle with lane violation. The feature is extracted from the morphological gradient image, which results in constructing robust detection system against shadows, weather conditions, head lights and illumination conditions without distinction day and night. The system shows excellent performance for the data captured at day time, night time, and rainy night time as much as 99.49% for positive recognition ratio and 0.51% for error ratio. Also the system is so fast as much as 91.34 frames per second in average that it may be possible for real-time processing.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.35-46
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• 2009

Lane extraction and lane departure warning algorithms using the image sensor attached in the vehicle are addressed. With the research about intelligent automobile, there have been many algorithms about lane recognition and lane departure warning system. However, since these algorithms require to detect 2 lanes, the high time complexity and the low recognition rate under various driving circumstances are critical problems. In this paper, we present a lane departure warning algorithm using single lane extraction and center point analysis that achieves the fast processing time and high detection rate. From the geometry between camera and objects, the region of interest (ROI) is determined and splitted into two parts. Hough transform detects the part of the lane. After the detected lane is restored to have a pre-determined size, lane departure is estimated by calculating the distance from the center point. On real driving environments, the presented algorithm is compared with previous algorithms. Experiment results support that the presented algorithm is fast and accurate.

• 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%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.2942-2950
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• 2010

Car black box helps to investigate the cause of accident by recording time, position and videos as well as shock information. In addition, the car black box need a function to support safe driving for preventing accident. The representative driving support function is a lane departure warning. In order to implement the function, it is necessary to carry out the image processing to detect the lane first. The image processing algorithm requires computational burden to handle so much data and complicated structure of algorithm. This paper describes the efficient image processing algorithm with relatively low amount of computation for car black box embedded platform to detect lanes from the real-time lane image.