• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.163-170
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• 2008

According to the rules, a crash cushion is supposed to set up products that is satisfied with the standard of a performance test after performing the car crash test by road safety facilities and management guide. For development of crash cushion, performance should be estimated through the car crash test eventually. However, there is no reasonable design method which considers passenger's safety and only depend on crash test without an alternative plan. Therefore it incurs a loss materially and takes a lot. Therefore, we are asked to create a systematic design of the crash cushion. This study shows that a scientific basis of applying single degree of freedom when it designs the crash cushion after analyzing vehicle crash test data of crash cushion and also represents design of crash cushion through single degree of freedom response spectrum using calculated by crash test data on crash cushion.

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

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.419-425
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• 2005

산악지역을 관통하는 도로의 경우 그 특성상 절토 후 도로를 시공하는 경우가 자주 있으며 때로는 매우 긴 구간에 일조가 적게 나타나 음영이 오래 지속되는 곳이 발생한다. 본 연구는 GIS를 이용하여 도로의 노선계획시 예상되는 일조영향을 평가하여 정확한 데이터를 제공하고자 한다. 실험대상지역을 선정하고 수치지형자료의 변환을 통하여 3D 지형 메쉬데이터를 작성하고 동일좌표체계의 도로선형자료를 반영하여 정확한 도로모델링을 생성하므로써 도로의 각 지점별 일조영향분석을 위한 기초자료를 구축하였다. 또한 도로노면상의 음영시간을 계산하고 일영이 도로 전체에 어느 정도 유지되는지를 가시적으로 모델링화하여 계절별 시간대별로 도식화하므로써 판단자의 시각적 분석을 가능케 하였다.

• Electronics and Telecommunications Trends
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• v.26 no.6
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• pp.77-85
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• 2011

2011년 7월에 도로명과 건물번호를 사용하는 새로운 주소 체계인 도로명주소가 고지되었다. 본 고에서는 100년 가까이 사용한 지번주소를 대체하기 위한 도로명주소와 관련 사업 등의 추진 동향을 살펴보고, 현재 추진상에서 쟁점이 되는 사항들과 도로명주소 등의 추진에 관련된 기술적인 이슈 및 동향 등에 대해서 살펴보기로 한다. 이를 위해서 기존 주소의 문제점 개선과 국제적인 표준 동향에 따르기 위한 도로명주소의 추진 상황과 국가의 기관별로 구역설정을 공통으로 활용하기 위한 구역번호의 추진 상황을 살펴본다. 도로명주소 추진 상황을 더욱 이해하는 차원에서 최근에 제기되고 있는 건물 단위의 행정구역과의 불일치 문제, 도로명의 표기 문제, 동과 공동주택 명칭 사용 제외 등에 대한 쟁점사항을 소개한다. 그리고 도로명주소와 관련된 기술적 이슈 및 동향들을 GIS 기술과의 결합, 데이터 정제 기술, 상세 주소의 정형화를 통한 데이터 호환, 정보시스템의 구현 및 연계 기술, 국제 표준화를 위한 대응, 우편물류 분야 측면에서 각각 살펴본다.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.185-202
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• 2005

In this research, to get over restrictions of a field expreiment, we modeled a planning road through the 3D Virtual Reality and achieved data about dynamic response related to sector fluctuation and about driver's visual behavior on testers' driving the Driving Simulator Car with Eye Camera. We made constant efforts to reduce the non-reality and side effect of Driving Simulator on maximizing the accord between motion reproduction and virtual reality based on data Driving Simulator's graphic module achieved by dynamic analysis module. Moreover, we achieved data of driver's natural visual behavior using Eye Camera(FaceLAB) that is able to make an expriment without such attaching equipments such as a helmet and lense. In this paper, to evaluate the level of road's safety, we grasp the meaning of the fluctuation of safety that drivers feel according to change of road geometric structure with methods of Driving Simulator and Eye Camera and investigate the relationship between road geometric structure and safety level. Through this process, we suggest the method to evaluate the road making drivers comfortable and pleasant from planning schemes.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.139-146
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• 2009

While air pollutants emission caused by the traffic is one of the major sources, few researches have done. This study investigated the extent to which traffic and road related characteristics such as traffic volumes, speeds and road weather data including wind speed, temperature and humidity, as well as the road geometry affect the air pollutant emission. We collected the real time air pollutant emission data from Seoul automatic stations and real time traffic volume counts as well as the road geometry. The regression air pollutant emission models were estimated. The results show followings. First, the more traffic volume increase, the more pollutant emission increase. The more vehicle speed increase, the more measurement quantity of pollutant decrease. Secondly, as the wind speed, temperature, and humidity increase, the amount of air pollutant is likely to decrease. Thirdly, the figure of intersections affects air pollutant emission. To verify the estimated models, we compared the estimates of the air pollutant emission with the real emission data. The result show the estimated results of Chunggae 4 station has the most reliable data compared with the others. This study is differentiated in the way the model used the real time air pollutant emission data and real time traffic data as well as the road geometry to explain the effects of the traffic and road characteristics on air quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.235-242
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• 2002

This study is focused on the analysis of road extracting accuracy from the high resolution IKONOS satellite image data. A geometric correction of the image is performed using the RFM and interpretation with the screen digitizing is also performed for extracting the roads information. For the evaluation of road extracting accuracy, the road locations and the road widths are compared with the national digital map. The comparison results shows that the road boundary and the size of road width are able to extract with the geometric accuracy of $\pm$3.4m and $\pm$1.1m.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.72-73
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• 2023

도로 사고의 30% 이상이 도로 환경 조건에 의해 발생되는 것으로 추정된다. 따라서, 도로 환경의 정보는 사고를 줄이기 위한 한 방편이 될 수 있다. 본 논문은 도로 환경을 모니터링하기 위해 설치된 센서들로부터 효율적으로 데이터를 수집하기 위한 멀티 채널 토폴로지 관리 기법을 제시한다. 센서들의 밀집도가 높아져도 제안하는 기법을 통해 데이터 충돌과 에너지 소모를 줄일 수 있다. 시뮬레이션 성능 분석에서 제안하는 방식을 통해 수집되는 데이터가 증가하는 것을 확인할 수 있다.