• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10a
• /
• pp.102-104
• /
• 1999

인터넷의 발전으로 WWW상에서 지도 정보를 서비스하는 사이트들이 늘어나고 있다. 본 논문은 이러한 WWW상의 지리 정보 서비스를 확장한 도로 주행 안내 시스템의 구현에 관하여 기술한다. 도로 주행 안내 시스템은 도로, 건물 등의 지리 정보를 표시해주며 사용자의 최단 경로질의를 받고 빠른 시간내에 최단경로를 탐색할 수 있어야 한다. 본 연구에서는 이러한 요구를 수용할 수 있도록 OODBMS, CORBA, Java를 사용하여 WWW상의 도로 주행 안내 시스템을 설계 및 구현하였다. 이를 위해 이 논문에서는 도로 데이터 캐싱과 그래프 모델링, HEPV (Hierarchical Encoded Path View) 알고리즘 구현 등의 사항을 기술하였다.

• Proceedings of the KIEE Conference
• /
• 2005.07c
• /
• pp.2351-2353
• /
• 2005

본 논문은 포장도로의 표면 상태를 고성능의 레이저 변위 센서를 사용하여 정밀하게 측정하고, GPS(Global Positioning System)를 사용하여 측정 위치 데이터를 획득하는 도로 표면 측정 장비 개발에 관한 논문이다. 본 연구에서는 전체 시스템을 설계하고, 차량 주행을 모사한 실험 모형을 제작하여 실내 실험을 실시하였으며, GPS 단말기로부터 실시간으로 위치 신호를 수신하여 도로면 데이터와 연동할 수 있도록 하였다. 그리고 평가 차량의 전면에 레일(rail)을 장착하여 레이저 변위 센서가 좌우로 왕복운동이 가능하도록 하였으며, 레일을 작동시킨 상태에서 도로면을 측정해 보았다. 실험 모형의 측정 곁과는 차량이 80km/h로 주행할 때 도로 표지 타이닝(tinning)의 폭 오차 3.24%, 깊이 오차 5%였다. 차량이 정지된 상태에서 레일을 작동시켜 요철을 측정하였을 경우 레일 방향에 대한 폭 오차는 0.07%였다.

• Journal of Korean Society for Geospatial Information Science
• /
• v.24 no.4
• /
• pp.115-123
• /
• 2016

The road name address system has been in effect in Korea since 2012. However, the existing address system is still being used in many fields because of the difference between the spatial awareness of people and the road name address system. For the spatial awareness based on the road name address system, various spatial datasets in daily life should be referenced by the road names. The goal of this paper is to link the road name address system with the wayfinding process, which is closely related to the spatial awareness. To achieve our goal, we designed and implemented a geometric matching method for spatial data sets. This method generates network neighborhoods from road objects in the 'road name address map' and the 'pedestrian network data'. Then it computes the geometric similarities between the neighborhoods to identify corresponding road name for each object in the network data. The performance by F0.5 was assessed at 0.936 and it was improved to 0.978 by the manual check for 10% of the test data selected by the similarity. By help of our method, the road name address system can be utilized in the wayfinding services, and further in the spatial awareness of people.

• Journal of Convergence for Information Technology
• /
• v.11 no.7
• /
• pp.14-20
• /
• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

• The Journal of the Korea Contents Association
• /
• v.20 no.12
• /
• pp.301-310
• /
• 2020

The purpose of paper is to find index for building digital database of bike infrastructure statistics by analyzing the existing design standards and manuals. The design standards and guides of both central and local governments which designates the design elements of bikeway were investigated to find the primary index, and then the attribute data of bikeway were grouped into four categories including transportation, geometry, pavement, safety and utility facility, and were analyzed to find the secondary index. A FGI(Focus Group Interview) was conducted with experts in the arena of transportation to collect the opinions on the selected index. It is found that the final twenty four design elements were selected and additional six elements were added. The proposed twenty four index of bikeway design elements are considered to be included when building the digital bikeway register database.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.758-759
• /
• 2019

4차산업혁명 시대의 도래로 빅데이터, AI 기술 등을 이용하여 데이터 수집, 분석, 가공, 활용에 관심이 높아졌다. 정부도 공공부문 정보시스템의 보유데이터를 개방하여 산업 전반에 데이터 연계·활용을제고하고 있다. 본 연구는 도로건설사업의 업무처리를 지원하는 건설사업정보시스템(CALS)에 보유중인 건설데이터를 개방하기 위한 방안을 마련하였다. 이를 위해 건설데이터 중 개방 가능한 데이터를 선정하고 키워드를 통한 데이터 및 파일의 정보검색 환경과 데이터 공개서비스 구축방법을 제시하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.5
• /
• pp.162-177
• /
• 2020

The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.01a
• /
• pp.79-81
• /
• 2020

최근 블랙 아이스에 의한 사고 사례가 많다. 블랙 아이스는 사람의 눈으로 식별하기 힘들고, 보인다 하더라도 도로가 조금 젖은 것으로 판단할 가능성이 높아 차량 사고를 유발할 확률이 높다. 본 논문에서는 블랙 아이스로 인한 사고를 조금이라도 줄이기 위해 센서를 통하여 블랙 아이스를 판별하고 사전 예방할 수 있는 방법과 해결책에 대해 고찰해보고자 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.549-554
• /
• 2021

Precise 3D road geospatial information is the basic infrastructure for autonomous driving and is essential data for safe autonomous driving. MMS (Mobile Mapping System) is being used as equipment for road spatial information construction, and related research is being conducted. However, there are insufficient studies to analyze the effect of the baseline reference point distance, which is an important factor in the accuracy of the MMS outcome, on the accuracy of the outcome. Therefore, in this study, the accuracy of the data acquired using MMS by reference point distance was analyzed. Point cloud data was constructed using MMS for the road in the study site. For data processing, 4 data were constructed considering the distance from the reference point for MMS data, and the accuracy was analyzed by comparing the results of 12 checkpoints for accuracy evaluation. The accuracy of the MMS data showed a difference of -0.09 m to 0.11 m in the horizontal direction and 0.04 m to 0.19 m in the height direction. The error in the vertical direction was larger than that in the horizontal direction, and it was found that the accuracy decreased as the distance from the reference point increased. In addition, as the length of the road increases, the distance from the reference point may vary, so additional research is needed. If the accuracy evaluation of the method using multiple reference points is made in the future, it will be possible to present an effective method of using reference points for the construction of precise road spatial information.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.4
• /
• pp.55-66
• /
• 2020

This study developed four algorithms and their associated indices that can quantify and qualify road hazards along roadways. Initially, relevant raw data can be collected from commercial vehicles by camera and DTG. Well-processed data, such as potholes, road freezing, and fog, can be generated from the Integrated management system. Road hazard algorithms combine these data with road inventory data in the Data Sharing Platform. Depending on well-processed data, four different road hazard algorithms and their associated indices were developed. To test the algorithms, an experimental plan based on passive DTG attached in probe vehicles was performed at two different test locations. Selection of the test routes was based on historical data. Although there were limitations using random data for commercial vehicles, hazardous roadways sections, such as fog, road freezing, and potholes, were generated based on actual historical data. As a result, no algorithm error was found in the entire test. Because this study provides road hazard information according to a section, not a point, it can be practically helpful to road users as well as road agencies.