• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.375-382
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• 2020

As the 4th Industrial Revolution era in worldwide, interest in autonomous vehicles is increasing. but due to recent safety issues such as pedestrian accidents and car accidents, as a technical model for this, the demand for 3D HD maps (High Definition maps) is increasing in including lanes, road markings, road information, traffic lights and traffic signs etc. However, since some complementary points have been continuously raised according to demand, It is necessary to collect the opinions of institutions and companies utilizing HD maps and to improve HD maps. This study was conducted by utilizing the results of the contest for usability verification of HD Maps hosted by the National Geographic Information Institute and organized by the Spatial Information Industry Promotion Institute. For this study, we researched HD maps' layers and codes for HD maps object usability to improve HD maps, constructed HD maps object usability items accordingly, and contested usability verification of HD maps according to the items The contestants conducted verification and analyzed the results. As a result, the most frequently used code for each layer was the flat intersection, and the code showing the highest usage rate was a safety sign. In addition, the use rate of the sub-section and height obstacles was 16.67% and 8.88%, respectively, showing a low ratio. In order to utilize HD maps in the future, this study is expected to require research to continuously collect opinions from customers and improve data objects and data models that are actually needed by customers.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1163-1175
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• 2021

In autonomous driving, road markings are an essential element for object tracking, path planning and they are able to provide important information for localization. This paper presents an approach to update and measure road surface markers with HD maps as well as matching using inverse perspective mapping. The IPM removes perspective effects from the vehicle's front camera image and remaps them to the 2D domain to create a bird-view region to fit with HD map regions. In addition, letters and arrows such as stop lines, crosswalks, dotted lines, and straight lines are recognized and compared to objects on the HD map to determine whether they are updated. The localization of a newly installed object can be obtained by referring to the measurement value of the surrounding object on the HD map. Therefore, we are able to obtain high accuracy update results with very low computational costs and low-cost cameras and GNSS/INS sensors alone.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.5-10
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• 2003

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.575-580
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• 2003

KOMPSAT-1 위성의 EOC영상은 위성에서 지구를 촬영하는 동안 발생하는 영상 왜곡을 포함하고 있다. 본 연구는 EOC영상의 영상왜곡을 보정하기 위하여 수치지도를 이용하는 정밀기하보정에 대하여 연구한다. 정밀기하보정 과정은 수치지도와 EOC영상의 좌표계를 통합하는 과정을 거쳐 오버레이를 만들어 수치지도의 삼각점을 기준으로 위성영상에서 GCP를 선택하고, 이 GCP를 이용하여 위성 영상을 딜로니 삼각형들의 Mesh형태로 변환하여 모든 딜로니 삼각형을 리샘플링하는 과정을 거쳐 보정된 EOC영상을 얻는다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.391-399
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• 2020

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. In korea, the NGII (National Geographic Information Institute) produces and supplies high definition map for autonomous vehicles. Accordingly, in this study, errors occurring in the process of e data editing and dtructured esditing of high definition map are systematically typed providing by the National Geographic Information Institute. In addition, by presenting the error search process and solution for each situation, we conducted a study to quickly correct errors in high definition map, and largely classify the error items for shape integrity, spatial relationship, and reference relationship, and examine them in detail. The method was derived.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.186-201
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• 2021

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.493-502
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• 2013

For various geospatial information such as planimetric and topographic features, the required accuracy may be defined depending on the purpose of GIS applications. Also, the accuracy of the geospatial information have a major impact on the quality of the raw surveying data. In order to be usefully applied the precise geospatial information, the accuracy standards must be appropriately set so that the digital map as base map can be accurately made. Before computer mapping and GIS technology existed, paper maps were drawn by hand. So, the map scale was a significant contributor to the map accuracy. As such the past, the accuracy of maps is determined the scale at which the map would be drawn, but recent trends are to treat accuracy as a one of quality elements, rather than a specification for producing the map. Therefore, the purpose of this paper is to set the new korean map accuracy standards appropriate for the construction and application of the precise geospatial information on behalf of the current representation of korean digital maps.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.102-116
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• 2021

For the safe operation of autonomous vehicles, the representative technology of the 4^th industrial revolution era, a combination of various technologies such as sensor technology, software technology and car technology is required. An autonomous vehicle is a vehicle that recognizes current location and situation by using the various sensors, and makes its own decisions without depending on the driver. Perfect recognition technology is required for fully autonomous driving. Since the precise road maps provide various road information including lanes, stop lines, traffic lights and crosswalks, it is possible to minimize the cognitive errors that occur in autonomous vehicles by using the precise road maps with location information of the road facilities. In this study, the definition, necessity and technical trends of the precise road map have been analyzed, and the HD(High Definition) map prototype based on the web GIS has been built in the autonomous driving-specialized areas of Daegu Metropolitan City(Suseong Medical District, about 24km), the Happy City of Sejong Special Self-Governing City(about 33km), and the FMTC(Future Mobility Technical Center) PG(Proving Ground) of Seoul National University Siheung Campus using the MMS(Mobile Mapping System) surveying results given by the National Geographic Information Institute. In future research, the built-in precise road map service will be installed in the autonomous vehicles and control systems to verify the real-time locations and its location correction algorithm.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1617-1623
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• 2018

It is necessary to develop a next generation location referencing method with higher accuracy as advanced technologies such as autonomous vehicles require higher accuracy of location data. Thus, we proposed a framework for a lane-level location referencing method (L-LRM) based on high-precision digital road network map, and developed a tool which is capable of analyzing and evaluating the proposed method. Firstly, the necessity and definition of location referencing method was presented, followed by the proposal of an L-LRM framework with a fundamental structure of high-precision digital road network map for the method. Secondly, an architecture of the analysis and evaluation tool was described and then the Windows application program was developed using C/C++ programming language. Finally, we demonstrated the performance of the proposed framework and the application program using two different high precision digital maps with randomly generated road event data.