• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.3
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• pp.328-330
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• 2016

A lane departure warning system based on single lane mark detection is presented in this paper. The proposed system is focused on the robust detection of lane departure, even when one of the left or the right lane mark cannot be detected because of poor illumination or abrasion. Experimental results show that the proposed system warns lane departure, even if only a single lane is detected.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.815-818
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• 2016

Forward collision warning systems(FCWS) and lane departure warning systems(LDWS) need regions of interest for detecting lanes and objects as road regions. In general, the lane departure warning system using a vehicle front camera is tracking a lane curve using RANSAC or the like in the form of a straight line obtained image are compared with the center of the vehicle. This algorithm has weaknesses that requires a wide range of the lane being vulnerable to the curve. This paper presents an algorithm that checks whether the current lane departure by car from the Top-view space. The algorithm also can check whether the vehicle in the lane departure of the narrow range, and shows the result that is almost not affected by noise.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.110.3-110
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• 2002

$\textbullet$ Lane detection by TFALDA $\textbullet$ SVM for large scale data and multiclass classification problem $\textbullet$ TLC Classification $\textbullet$ Lateral offset estimation by IPT $\textbullet$ Lane departure warning by a fuzzy system $\textbullet$ Experimental results by HiLS $\textbullet$ Conclusion

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.428-437
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• 2005

In this paper, we developed lane departure warning system for lane departure situation of vehicle, accidentally. Color level of yellow lane of road side is changed to black and white level. And black and white level value of relevant pixel of original and yellow lane through added process of this are emphasized. In lane departure detection system, color image data processing method could improve the recognition of the yellow lane(central lane). We could get a system Performance of the high-speed image data Processing. Therefore, lane departure warning system will be utilized at the device for the safety going of the vehicle.

• 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 Korea Multimedia Society
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• v.24 no.7
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• pp.860-867
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• 2021

Lane detection is important technology for implementing ADAS or autonomous driving. Although edge detection has been typically used for the lane detection however, false detections occur frequently. To improve this problem, a deep learning based lane detection algorithm is proposed in this paper. This algorithm is mounted on an ARM-based embedded system to implement a LDW(lane departure warning). Since the embedded environment lacks computing power, the VGG-11, a lightweight model based on VGG-13, has been proposed. In order to evaluate the performance of the LDW, the test was conducted according to the test scenario of NHTSA.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.791-799
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• 2010

In this paper, integrated navigation algorithm is designed for land transport sector which is needed high accuracy and monitoring program is developed for lane departure warning. High accuracy position information which is possible lane separation is needed for lane departure warning, so position detection algorithm based GPS/DR which combine GPS with dead reckoning is proposed. For the verification of the designed integrated navigation algorithm, we drived to acquire data and showed post-processing experiment results with monitoring program. Vehicle driving movie and aerial photograph in monitoring program is designed to show lane keeping and lane separation.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.3
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• pp.143-154
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• 2001

An algorithm for estimating the lane departure of a vehicle is derived and implemented based on an EDF(edge distribution function) obtained from gray-level images taken by a CCD camera mounted on a vehicle. As the function of edge direction, the EDF is aimed to show the distribution of edge direction and to estimate the possibility of lane departure with respect to its symmetric axis and local mamma. The EDF plays important roles: 1) It reduces noisy effects caused by dynamic road scene. 2) It makes possible lane identification without camera modeling. 3) It also leads LDW(lane departure warning) problem to a mathematical approach. When the situations of lane departure such that the vehicle approaches to lane marks or runs in the vicinity of the lane marks are occurred, the orientation of lane marks in images is changed, and then the situations are immediately reflected to the EDF. Accordingly, the lane departure is estimated by studying the shape of the EDF. The proposed EDF-based algorithm enhanced the adaptability to cope with the random and dynamic road environments, and eventually led to the reliable LDW system.

• Journal of Auto-vehicle Safety Association
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• v.8 no.1
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• pp.13-18
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• 2016

More than 80 percent of traffic accidents related with lane departure believed to be the result of crossing the lane due to either negligence or drowsiness of the driver. Lane-departure related accident in the highway usually involve high fatality. Even though LDWS is believed to prevent accident 25% and reduce fatalities by 15% respectively, its effectiveness in performance is yet to be confirmed in many aspects. In this study, the vehicle lateral locations relative to warning zone envelop (earliest and latest warning zone) defined in ISO standard, ECE and NHTSA regulations are compared with respect to various factors including delays, vehicle speed and vehicle heading angle with respect to the lane. Since LDWS is designed to be activated at the speed over 60 km/h, vehicle speed range for the study is set to be from 60 to 100 km/h. The vehicle heading angle (yaw angle) is set to be up to 5 degree away from the lane (abrupt lane change) considering standard for lane change test using double lane-change test specification. The TLC is calculated using factors like vehicle speed, yaw angle and reaction time. In addition, the effect of vehicle type and reaction time have been considered to assess LDWS safety.