• Journal of information and communication convergence engineering
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• 제20권4호
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• pp.273-279
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• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.

• 한국분무공학회지
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• 제21권4호
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• pp.207-213
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• 2016

To protect air pollution of urban area from motor vehicles, emission limits for diesel vehicles have been dramatically lowered in short period. But recent studies have shown that on-road NOx emissions of light-duty diesel vehicles are considerably higher than the values measured with laboratory test procedures used for emission certification. To tackle with this issue, Ministry of Environment have a plan to introduce EU RDE-LDV (Real-driving Emission-Light-duty Vehicle) regulation. In this study, 4 Euro 6 diesel vehicles have been tested with the new test procedures published by EU to estimate on-road NOx emissions using PEMS (Portable Emission Measurement System). The results have shown that the requirements of EU RDE-LDV could be met in driving condition of metropolitan area for constitution of test routes and validity of test results. In analysing with Moving Averaging Window method the completeness and normality of test data were validated with the requirement. On-road NOx emissions were quite deviated as test vehicles and higher than the new limit of on-road NOx emission enforced from Sept. 2017, which means that RDE-LDV can effectively reduce NOx emission of diesel vehicles in real driving conditions of Korea.

• 제어로봇시스템학회논문지
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• 제22권10호
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• pp.803-810
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• 2016

This paper presents a study of localization methods based on particle filter using 2D laser sensor measurements and road feature map information, for autonomous vehicles. In order to navigate in an urban environment, an autonomous vehicle should be able to estimate the location of the ego-vehicle with reasonable accuracy. In this study, road features such as curbs and road markings are detected to construct a grid-based feature map using 2D laser range finder measurements. Then, we describe a particle filter-based method for accurate positional estimation of the autonomous vehicle in real-time. Finally, the performance of the proposed method is verified through real road driving experiments, in comparison with accurate DGPS data as a reference.

• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.1025-1032
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• 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.

• 자동차안전학회지
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• 제13권1호
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• pp.38-44
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the real-world driving study, 52 drivers drove approximately 11.0 km of rural road (about 20 min), 7.9 km of urban road (about 25 min), and 20.8 km of highway (about 20 min). The results suggested that the appropriate number of blinks during the last 60 seconds was 4 times, and the head movement interval was 35 seconds. The results from drowsy driving data will be presented in another paper - part 2.

• 한국측량학회지
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• 제26권2호
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• pp.165-173
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• 2008

본 연구는 라이다데이터와 수치지도를 이용하여 도로의 3차원 기하모델을 자동으로 생성하는 것을 목표로 한다. 도로모델을 생성하는 주요 과정은 (1) 수치지도의 도로경계 레이어를 이용하여 도로영역을 나타내는 다각형을 생성하고, (2) 다각형을 이용하여 도로영역내의 라이다 점을 추출하고, (3) 점을 표면패치로 분할하고, 표면패치를 그룹화 하여 다시 표면패치집단으로 구성하고, (4) 도로표면패치집단을 식별하고 여기에 포함된 점을 추출하여, 추출된 점을 이용하여 표면모델을 구성하고, (5) 도로경계선을 수치지도를 이용하여 정제한다. 제안된 방법을 실측데이터에 적용하여 도로의 선형 및 표면정보를 성공적으로 추출할 수 있었다

• 한국항공운항학회지
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• 제20권4호
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• pp.39-44
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• 2012

This paper propose a transmission method for "Transportation system" that can decide precise position under wide area road traffic environment. For precise position detecting, central station collect multiple receiver station's satellite navigation data and generate correction information. In this process, we need efficient real time transmission method for satellite navigation message that has variable data size. We propose real time data transmission method. This real time transmission method offer efficient processing structure for multiple receiver station's satellite navigation message. This paper explains proposed real time transmission method and proofs this transmission method.

• 대한공간정보학회지
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• 제16권3호
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• pp.29-34
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• 2008

차량용 네비게이션 시스템은 자가 운전 차량의 증가, 여가 문화 확산 등으로 수요가 폭발적으로 증가하고 있으며, 실시간 교통 정보, 디지털 멀티미디어 방송 등의 기능이 융합되면서 텔레매틱스의 가장 중요한 분야로 급성장하고 있다. 이러한 네비게이션 시스템의 다양한 구성 요소 중 네트워크 데이터는 실세계의 도로망을 반영하며, 경로탐색의 기반이 되는 데이터로 가장 핵심 요소라 할 수 있다. 하지만, 현재의 네비게이션 시스템은 stand-alone 형태로 단말기 내의 네트워크 데이터는 자체가 과거의 데이터로 이를 보완하기 위하여 사용자는 주기적으로 업데이트를 수행해야 하는 단점을 가지고 있다. 따라서 본 연구에서는 무선 통신을 활용하여 사용자가 요구하는 경로를 검증하여 항상 최신의 네트워크 데이터를 활용할 수 있는 기법을 제안하고 시뮬레이션을 통하여 제안 기법의 타당성을 검증하였다.

• 한국ITS학회 논문지
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• 제17권6호
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• pp.173-184
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• 2018

프로브 차량을 이용한 이동식 교통정보수집체계가 확산되면서, 기존 소통정보 이외에 차량 내 센서를 이용한 포트홀, 낙하물, 노면결빙과 같은 도로위험정보 수집이 가능해지고 있다. 본 연구는 다수의 프로브 차량이 GPS 좌표 기반으로 도로위험정보와 같은 이벤트를 검지했을 때 시간 공간적으로 통합하여 실시간으로 처리하는 복합처리 알고리즘을 개발하였다. 알고리즘의 핵심기능은 특정 지점에 발생된 도로위험정보를 (1)다수의 프로브가 서로 다른 GPS 좌표로 검지한 결과로 부터 동일지점인지 여부를 판단하고, (2)그 지점을 국가표준노드링크 상에 특정하여 이벤트 데이터를 생성하며, (3)생성된 이벤트 데이터가 유효한지 지속적으로 판단하고, (4)도로위험상황이 종료되었을 때 이벤트를 종료시키는 것이다. 이를 위해 프로브 차량이 수집한 도로위험정보를 실시간으로 처리하여 조건부 확률을 지속적으로 갱신하는 과정을 통해 이벤트의 유효성을 판단하고 종료할 수 있도록 개발하였고, 시뮬레이션을 통해 알고리즘의 적용가능성을 검증하였다. 개발된 복합처리 알고리즘은 향후 C-ITS 및 자율주행자동차 등 프로브 기반의 교통정보 수집 및 이벤트 정보 처리에 적용 가능할 것으로 판단된다.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1836-1842
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• 2015

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.