• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.766-771
• /
• 2002

Automatic change detection based on the vector-to-raster comparison is hard especially in high-resolution image. This paper proposes a method to designate roads in high-resolution image in sequential manner using the information from vector map in which Hough transform is used for reliability. By its linearity, the road of urban areas in a vector map can be easily parameterized. Following some pre-processing to remove undesirable objects, we obtain the edge map of raster image. Then the edge map is transformed to a parameter space to find the selected road from vector map. The comparison is done in the parameter space to find the best matching. The set of parameters of a road from vector map is treated as the constraints to do matching. After designating the road, we may overlay it on the raster image for precise monitoring. The results can be used for detection of changes in road object in a semi-automatic fashion.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.1
• /
• pp.63-69
• /
• 2011

Recent growth of the geospatial information on the web made it possible to applicate spatial data. Also, the demand for rich and latest information shows a steady growth. The need for the new service using conflation of the existing spatial databases is on the increase. The information delivery of the services using the road vector and aerial image is reached intuitionally and accurately. However, the spatial inconsistencies in map services such as Daum map, Naver map and Google map is the problem. Our approach is processed to extract the road candidate image, match the template and filter the control points pair using vector median. Finally, CNS node and link are aligned to the real road with the aerial image. The experimental results show that our approach can align a set of CNS node and link with aerial imagery for daejon, such that the completeness and correctness of the aligned road have improved about 35% compare with the original roads.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.6_2
• /
• pp.1025-1032
• /
• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.363-369
• /
• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.234-237
• /
• 2007

Road networks are important geospatial databases for various GIS (Geographic Information System) applications. Road digital maps may contain geometric spatial errors due to human and scanning errors, but manually updating roads information is time consuming. In this paper, we developed a new road features updating methodology using from multispectral high-resolution satellite image and pre-existing vector map. The approach is based on initial seed point generation using line segment matching and a modified double snake algorithm. Firstly, we conducted line segment matching between the road vector data and the edges of image obtained by Canny operator. Then, the translated road data was used to initialize the seed points of the double snake model in order to refine the updating of road features. The double snake algorithm is composed of two open snake models which are evolving jointly to keep a parallel between them. In the proposed algorithm, a new energy term was added which behaved as a constraint. It forced the snake nodes not to be out of potential road pixels in multispectral image. The experiment was accomplished using a QuickBird pan-sharpened multispectral image and 1:5,000 digital road maps of Daejeon. We showed the feasibility of the approach by presenting results in this urban area.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.20 no.11
• /
• pp.1071-1077
• /
• 2010

The reduction of the vehicle interior noise has been the main interest of noise and vibration harshness(NVH) engineers. A passenger vehicle has various and complicated transmission paths of sound and vibration. In order to identify the mechanism of transfer path, estimation of excitation force and exact modeling of transfer path are required. This paper presents method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. And vector synthesis technique is employed to identify the characteristics of road noise and its transmission to vehicle compartment through noise and vibration analysis. Vibration reduction efficiency of each transfer path is evaluated by comparing individual vector components obtained virtual simulation.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.16 no.6
• /
• pp.1211-1216
• /
• 2002

To evaluate whole-body vibration(WBV) exposure and fatigue-decreased proficiency(FDP) boundary in passenger car driver, several roads in Busan were divided into 3 types by the condition of road surface; Road 1 was partially damaged, Road 2 was normal without damage, and Road 3 was better than Road 2. The results were following: The highest passenger driver's exposures to whole-body vibration acceleration and fatigue-decreased proficiency boundary at 40km/h were 0.108m/s² and about 2099 minutes in Road 2 for xh axis, 0.134m/s² and about 1585 minutes in Road 2 for yh axis, and 0.183m/s² and about 1053 minutes in Road 2 for zh axis, respectively. The highest passenger driver's exposures to whole-body vibration acceleration and fatigue-decreased proficiency boundary at 80km/h were 0.219m/s² and about 830 minutes in Road 3 xh axis, 0.203m/s² and about 918 minutes in Road 3 for yh axis, and 0.622m/s² and about 195 minutes in Road 1 for zh axis, respectively. The highest vector sums of whole-body vibration exposure at 40km/h and 804km/h were 0.328m/s² in Road 2 and 0.730m/s² in Road 1, respectively. The highest crest factors at 40km/h were 4.25 in Road 1 for xh, 4.51 in Road 3 for yh, and 5.81 in Road 2 for zh, respectively. The highest crest factors at 80km/h were 5.57 in Road 1 for xh, 5.60 in Road 2 for yh, and 6.46 in Road 3 for zh, respectively. The highest transmissibilities of whole-body vibration from floor to seat at 40km/h and 80km/h were 0.89 in Road 3 and 0.82 in Road 3 for xh axis, 0.83 in Road 3 and 0.87 in Road 1 and 2 for yh, and 0.80 in Road 2 and 0.92 in Road 1 tor zh axis, respectively. The highest fatigue-decreased proficiency boundaries for whole-body vibration exposure of passenger car driver in floor and seat were 457 minutes in Road 3 and 583 minutes in Road 3 at 40km/h and 159 minutes in Road 2 and 251 minutes in Road 2 at 80km/h, respectively.

• The Journal of Korea Robotics Society
• /
• v.12 no.2
• /
• pp.217-227
• /
• 2017

Crosswalk detection is an important part of the Pedestrian Protection System in autonomous vehicles. Different methods of crosswalk detection have been introduced so far using crosswalk edge features, the distance between crosswalk blocks, laser scanning, Hough Transformation, and Fourier Transformation. However, most of these methods failed to detect crosswalks accurately, when they are damaged, faded away or partly occluded. Furthermore, these methods face difficulties when applying on real road environment where there are lot of vehicles. In this paper, we solve this problem by first using a region based binarization technique and x-axis histogram to detect the candidate crosswalk areas. Then, we apply Support Vector Machine (SVM) based classification method to decide whether the candidate areas contain a crosswalk or not. Experiment results prove that our method can detect crosswalks in different environment conditions with higher recognition rate even they are faded away or partly occluded.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.6
• /
• pp.526-531
• /
• 2012

This paper, we describe the vector control of surface mounted PMSM (Permanent Magnet Synchronous Motor) using the fuzzy controller which is suggested algorithm. In these days, when vehicle is operated or not, whether the road is covered or not, the sensitivity of the steering column is not stable. To make up for it, the PI gain of a steering column controller is adjusted by experience. It becomes the price because it need a lot of sensor. Also it is difficult to implement robust control because we need a lot of parameters for variable road conditions which are the off road, the on road, a low battery voltage, a high battery voltage, a vehicle speed. In this paper, we propose fuzzy controller using the suggested algorithm which suitable for steering system. We test the fuzzy controller with the various condition. We get the good performance of fuzzy controller even if it is nonlinear system. We check a robust the fuzzy controller using the suggested algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.8 no.3
• /
• pp.593-600
• /
• 2007

In this paper, we composed several road network and evaluated the network for the performance of the network with protocols. The protocols we applied were the MANET routing protocols such as AODV(Ad-hoc On-Demand Distance Vector) and DSR(Dynamic Source Routing) protocols. Generally, the AODV performs better than the DSR. However, in my ad-hoc vehicular network, the performance of the DSR is the better than the AODV when there are more vehicles in the road environment than there are the less vehicles. For the simulation, we composed 4-lane road with vehicles and simulated in the OPNET.