• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.4D
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• pp.291-303
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• 2012

In order to lead safe driving, it is better to provide dynamic and detailed information on how the driver using the relevant road should behave as concerning movements of individual car rather than providing monotone and static information of reducing of speed to unspecified drivers. Assuming road and communication of highway where real-time collection and transfer of information on vehicles and road traffic status is possible, the purpose of this study was to provide real-time safe distance by considering road traffic condition such as road condition and driving condition, travel speed and distance between preceding/following vehicles. We intended to provide basic information about dangerous situation by defining different values of condition based column ($C_{condition}$) in accordance with the road surface condition, based on which Real-time Safety Distance Index(RSDI) is to be calculated comprehensively reflecting speed of preceding and following vehicles, distance between vehicles, vertical alignment and road surface condition on the scope of expression column ($C_n$). We intended to enable the driver to secure safety by providing the calculated Real-time Safety Distance Index (RSDI) so that the driver can intuitively sense and sufficiently cope with a dangerous situation where collision of vehicles may occur. The calculated RSDI value is comprised of 30 unit columns and will be provided to the driver being divided into risk evaluation grades of 3 predetermined steps, 'warning', 'dangerous' and 'normal'.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.1
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• pp.128-141
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• 1994

This roport explain the basic theory of desinging the accelerating durability test road and the role of each factors contributing to test road surface profile. Also this road is designed by considering the charactors of vehicle suspension system and condition of driving. In test road, the factors affecting to the vehicle Structural durability are correlation among surface shape of road profile, frequency of vehicle suspension system, distribution of axletwist angle and vibration profile height Road PSD magnitude and frequency delay is used to control these factors relation.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.599-604
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• 2008

Recently, many researches for autonomous mobile system have been proposed, which can recognize surrounded environment and navigate to destination without outside intervention. The basic sufficient condition for the autonomous mobile system is to navigate to destination safely without accident. In this paper, we propose a path planning method in local region for safe navigation of autonomous system through evaluation of the road surface distortion(damaged/deformed road, unpaved road, obstacle and etc.). We use laser distance sensor to get the information on the road surface distortion and apply image binalization method to evaluate safe region in the detected local region. We show the validity of the proposed method through the computer simulation based on the artificial local road map.

• International Journal of Highway Engineering
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• v.12 no.3
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• pp.37-47
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• 2010

This research is aimed at comprehensively evaluating the condition of a road surface of a highway in satisfaction of its users. This research conducted an overall evaluation of a road surface condition by adding qualitative data, or a driver's satisfaction to the existing quantitative elements, whereas the existing research put its focus on a correlation analysis with quantitative factors and qualitative factors through a statistical method. As for an evaluation method, this research conducted an overall evaluation by using Grey System Theory which makes possible an integrated evaluation. The analyzed results make it possible to diagnose the current conditions of each section of object roads and to predict the potentially changeable conditions for the time to come. In addition, these analyzed results could hopefully be applied to the maintenance of freeways through diverse methods. It is hoped that the evaluation of a road surface condition of a highway in satisfaction of its user could be helpful to keeping up the satisfaction of a driver and passenger on the highway by more than a certain level. In addition, the analyzed data on the influence of data value observed by comprehensively evaluating a variety of elements could be used as a secondary means of the decision-making process in relation to road maintenance. On top of that, it could be used as a means of improving road maintenance system and offering the improved driving environment of the highway.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1398-1400
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• 1996

In this paper, a method of the decision of the road direction for ALV(Autonomous Land Vehicle) road following by region-based segmentation is presented. The decision of the road direction requires extracting road regions from images in real-time to guide the navigation of ALV on the roadway. Two thresholds to discriminate between road and non-road region in the image are easily decided, using knowledge of problem region and polygonal approximation that searches multiple peaks and valleys in histogram of a road image. The most likely road region of the binary image is selected from original image by these steps. The location of a vanishing point to indicate the direction of the road can be obtained applying it to X-Y profile of the binary road region again. It can successfully steer a ALV along a road reliably, even in the presence of fluctuation of illumination condition, bad road surface condition such as hidden boundaries, shadows, road patches, dirt and water stains, and unusual road condition. Pyramid structure also saves time in processing road images and a real-time image processing for achieving navigation of ALV is implemented. The efficacy of this approach is demonstrated using several real-world road images.

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.284-293
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• 2015

The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.491-495
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• 2008

The effects of vehicles and pavement surface types on noise have been investigated at the Korea Expressway Corporation's Test Road along the southbound side of the Jungbu Inland Expressway, South Korea. The study was conducted in 2005 and 2006 through field measurements at nine surface sections of asphalt concrete and Portland cement concrete pavements using eleven vehicles. For the road noise analysis, the sound power levels (PWLs) of combined noise (e.g., tire/pavement interaction noise and power-train noise together) and tire/pavement interaction noise using various vehicles were calculated based on the novel close proximity (NCPX) and pass-by methods. Then, the characteristics of the PWLs were evaluated according to surface type, vehicle type, and vehicle speed. The results show that the PWLs of vehicles are diversely affected by vehicle speed and the condition of the road surface.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.101-107
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• 2020

Road surface measurement is an essential process for quantifying the degree and displacement of roughness in road surface management. For safer road surface management and quick maintenance, it is important to accurately measure the road surface while mounted on a vehicle. In this paper, we propose a sophisticated road surface measurement system that can be measured on a moving vehicle. The proposed road surface measurement system supports more accurate measurement of the road surface by using a high-performance line laser sensor. It is also possible to measure the transverse and longitudinal profile by matching the position information acquired from the RTK, and the velocity adaptive update algorithm allows a manager to monitor in a real-time manner. In order to evaluate the proposed system, the Gocator laser sensor, MRP module, and NVIDIA Xavier processor were mounted on a test mobile and tested on the road surface. Our evaluation results demonstrate that our system measures accurate profile base on the MSE. Our proposed system can be used not only for evaluating the condition of roads but also for evaluating the impact of adjacent excavation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.1
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• pp.48-55
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• 2009

In order to keep safe driving conditions under road networks, there are several formations such as road structure, road surface condition, traffic occupancy and supplement of an accurate information of traffic status ahead To support safe-driving on each road formation, each formation is supplied with various information to help the driver. However, in some cases like rapid status change at local-scale geography, traffic information systems often displays insufficient information because of the lack of information correlation. In order to accurately aware the driver, all road formation must be in sync. It is important to supply accurate information to the driver because this information directly impacts the drivers on the road. This paper discusses the amber information to keep the least safety driving over road formations including tunnels and bridges. This paper also will propose the informations for safe-driving conditions, information linkage on the road and rule-base safety information, as ITS technology, being displayed for all drivers under the worst weather conditions.