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

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.299-300
• /
• 2022

4차 산업혁명으로 자율화·지능화됨에 따라 육·해상의 교통수단에 ICT 기술을 활용한 자율운행차 자율운항선박 등 신기술 및 서비스 개발이 점차 확대되고 있다. 본 연구에서는 해상분야의 자율운항선박 자율주행에 필요한 기술 도출을 위한 방법으로 육상분야의 자율주행차량의 핵심기술인 센서기술과 AI기술에 대해 알아보고 비교 분석하고자 한다. 자율주행차 주행을 위한 기술개발은 센서와 정밀도로정보(지도) 2트랙으로 개발이 진행되고 있으나, 최근 센서 오류 등으로 인한 사고발생이 잦아 정밀도로정보(지도) 중요성이 증가하고 있어 정밀도로 정보 구축을 서두르고 있다. 반면 자율운항선박의 경우 충돌회피 기술, 최적항로 개발, 정보보안 등 기술개발이 이루어지고 있으나 AI용 수로정보에 한 대비는 진행되고 있지 않고 있다. 따라서, 자율운항선박의 안전한 자율주행을 위한 방법으로 AI 알고리즘 연산과정에서 기계적으로 이용 가능하고 목적에 적합한 수로정보 구축이 필요하며 우리나라 선박의 안전운항을 위해 국가차원에서 수로정보 수집 및 생산을 담당해야 하므로 국가에서 주도하에 연구개발이 진행되어야 할 것이다.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.1
• /
• pp.39-51
• /
• 2021

Recently, there has been much interest in practical use of standardized HD map that can effectively define roads, lanes, junctions, road signs, and road facilities in autonomous driving. Various kinds of de jure or de facto standards such as ISO 22726-1, ISO 14296, HERE HD Live map, NDS open lane model, OpenDRIVE, and NGII HD map are currently being used. However, there are lots of differences in data modeling among these standards, it makes difficult to use them together in autonomous driving. Therefore, we propose a data model conversion method to enable an efficient use of various kinds of HD map standards in autonomous driving in this study. Specifically, we propose a conversion method between the NGII HD map model, which is easily accessible in the country, and the OpenDRIVE model, which is commonly used in the autonomous driving industry. The proposed method consists of simple conversion of NGII HD map layers into OpenDRIVE objects, new OpenDRIVE objects creation corresponding to NGII HD map layers, and linear transformation of NGII HD map layers for OpenDRIVE objects creation. Finally, we converted some test data of NGII HD map into OpenDRIVE objects, and checked the conversion results through Carla simulator. We expect that the proposed method will greatly contribute to improving the use of NGII HD map in autonomous driving.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.3
• /
• pp.99-106
• /
• 2019

Technology that cannot be excluded from 4th industry is self-driving sector. The self-driving sector can be seen as a key set of technologies in the fourth industry, especially in the DB sector is getting more and more popular as a business. The DB, which was previously produced and managed in two dimensions, is now evolving into three dimensions. Among the data obtained by Mobile Mapping System () to produce the HD MAP necessary for self-driving, Point Cloud, which is LiDAR data, is used as a DB because it contains accurate location information. However, at present, it is not widely used as a base data for 3D modeling in addition to HD MAP production. In this study, MMS Point Cloud was used to extract facilities around the road and to overlay the location to expand the usability of Point Cloud. Building utility poles and communication poles DB from Point Cloud and comparing road name address base and location, it is believed that the accuracy of the location of the facility DB extracted from Point Cloud is also higher than the basic road name address of the road, It is necessary to study the expansion of the facility field sufficiently.

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

• 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 Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.472-472
• /
• 2021

우리나라는 하천법 제10조, 소하천정비법 제3조에 하천기본계획 수립 또는 하천의 지정 및 변경고시 시 하천구역을 결정하도록 정의되어 있다. 하천구역 설정 시 일반적으로 하천의 제방이 위치하는 부지 및 제방하심측 토지경계를 하천구역으로 지정하고 있으나, 제방계획이 없거나 무제부 구간으로 활용되고 있는 구간의 경우 하천법 제10조 3항에서 5항까지 3가지 항목을 기준으로 계획하폭에 해당하는 토지, 댐·하구둑·홍수조절지·저류지의 계획홍수위 아래에 해당하는 토지경계, 철도·도로 등 제방의 역할을 하는 선형공작물 하심측 토지경계로 구분하고 있다. 하천구역의 경계설정의 경우 불연속적인 특징을 갖는 하천의 횡단측점 자료의 특성상 정확한 평면상의 경계를 파악하기 어렵고, 철도·도로 등 선형공작물 경계를 하천구역으로 설정 시 편입용지의 보상이 상이하고 모호한 기준으로 인해 다량의 민원이 발생하는 실정이다. 본 연구에서는 부산시에 위치한 지방하천 대천천을 대상지로 설정하였으며, 계획홍수위를 기반으로 홍수범람예상도를 작성하여 정밀계획홍수위선을 산출하고 이를 지형자료와 중첩하여 계획홍수위 경계를 추출하였다. 또한 무제부 구간 내 드론촬영을 실시하여 대상지 드론영상 기반 3차원 정밀 지형자료를 구축하고 이를 앞서 산정한 계획홍수위 경계자료와 중첩하여 정밀 하천구역을 설정하였다. 대상지 정밀 하천구역 산정 결과를 기반으로 도심지내 하천과 도시외곽 하천으로 구분하고 다시 도심지내하천은 암거(복개)구간과 개거구간, 도시외곽하천은 유제부와 무제부 구간으로 구분하여 정밀 하천구역 결정기준을 수립하였다. 본 연구를 통해 대천천유역을 대상으로 실시한 무제부 구간 하천구역 결정과정을 기반으로 하천유형별 3차원 하천구역 산정기법을 정립하였다. 향후 해당기법을 실무에 적용하여 하천구역 산정 시 모호한 하천경계부분 또는 토지소유주와 담당부처 간 하천구역 논의 시 기반자료로 활용될 수 있을 것으로 사료되며, 기본계획 수립 시 해당 기법 적용을 통해 보다 정확한 하천구역 경계 수립이 될 수 있을 것으로 기대된다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.1
• /
• pp.81-89
• /
• 2011

The previous land gravity data in Korea showed locally biased irregular distribution. Especially, this problem was more serious in the mountainous area where the data density was significantly low. The same problem appeared in GPS/Levelling data thus the precision of the geoid could not be improved. From 2008, new gravity and GPS/Levelling data has been collected by the unified control point and survey on the benchmark project which were funded by the national geographic information institute. The newly obtained data has much better distribution and precision so that it could be used for update precision of geoid model. In this study, the new precision geoid has been calculated based old and new gravity data and this model showed 5.29cm of precision compared to 927 points of GPS/Levelling data. And the degree of fit and precision of hybrid geoid has been calculated 2.99cm and 3.67cm. The new gravimetric geoid has been updated about 27% over whole country. And it showed 42% of precision update due to collection of new gravity data on the Kangwon/Kyeongsang area which showed quite low distribution. In 2010, about 4,000 points of gravity and 300 points of GPS/Levelling data has been obtained by unified control and survey on benchmark project. We expect that new data will contribute to updating geoid precision and veri tying precision more objectively.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.2
• /
• pp.102-116
• /
• 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 the Korean Association of Geographic Information Studies
• /
• v.26 no.4
• /
• pp.116-129
• /
• 2023

Recently, interest in autonomous driving has drawn attention to autonomous cooperative driving, which considers the development of driving technology of autonomous vehicles and the development of infrastructure that constitutes a driving environment. According to the concept of autonomous cooperative driving, This study analyzes the new infrastructure for autonomous driving that can complement the information of existing precise road maps and adding HD map layer as the new infrastructure. The new infrastructure for autonomous driving presented two types of improved facilities and one type of sensor only facility. Analysis of HD maps shows that information such as junction points rarely changes, but it is expected that infrastructure for autonomous driving can be added to convey the meaning of paying attention to obstacles that may arise at the junction. In this way, the new infrastructure for autonomous driving needs to support the roles of guidance, instruction, and attention that existing road facilities.