• 시스템엔지니어링학술지
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• 제10권1호
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• pp.73-80
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• 2014

Designing test case flows for water crossing operation of a wheeled vehicle is a new attempt for which very limited experiences exist. In this paper, a Function Flow Block Diagram(FFBD) and a Classification Tree Method(CTM) were combined to see if this method is viable to generate the test case flows at the functional analysis stage. It was found that this method can be practically used for the very complicated test case generation.

• 한국정보통신학회논문지
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• 제10권5호
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• pp.877-889
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• 2006

교통제어나 차량에 연관된 범죄 등에서 자동차의 인식에 관한 연구의 중요성 때문에 이에 관련된 연구는 오래전부터 수행되어 왔다. 본 논문에서는 차량이 주행할 때의 정보와 영상을 획득하여 제조회사별 차종을 인식하는 방법을 제안하고자 한다. 본 논문의 차종인식은 차량의 압력을 이용한 차폭 검출방법, 그리고 보다 더 정확한 인식률을 얻기 위한 레이저 거리계를 이용한 차고 검출방법, $3\sim5$종의 구별을 위 한 차량의 번호판 인식 방법을 조합함으로써 차량 인식의 오류를 줄이는 시스템을 구현하였다. 구현된 차종인식 시스템은 2차원 CCD에 의한 차량의 영상획득과 이를 통한 다양한 영상처리 알고리즘에 의해서 국내의 전 차종에 적용할 수 있으며, 실제의 실험 결과는 높은 인식률을 나타내었다.

• 대한임베디드공학회논문지
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• 제11권5호
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• pp.313-323
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• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

• 정보처리학회논문지B
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• 제15B권1호
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• pp.1-8
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• 2008

일반적으로 감시영상에서 움직이는 물체들은 배경빼기 혹은 프레임 차를 이용하여 추출된다. 하지만 객체에 의해서 만들어지는 그림자는 심각한 탐지의 오류를 야기시킬 수 있다. 특히, 도로 상에 설치된 감시카메라로부터 획득된 영상으로부터 차량 정보를 분석할 때, 차량에 의해서 생성되는 그림자로 인하여 차량의 모양을 왜곡시켜 부정확한 결과를 만든다. 때문에 그림자의 제거는 감시 영상 내에서의 정확한 객체 추출을 위해서 반드시 필요하다. 본 논문은 도로감시영상 내에서 움직이는 차량의 차종판별 성능을 향상시키기 위한 움직이는 객체 내에 만들어지는 그림자를 제거한다. 제거된 객체의 영역은 소실점을 이용하여 3차원 객체로 피팅(Fitting)한 후 측정된 데이터를 감독 학습하여 원하는 차종 판별결과를 얻는데 사용한다. 실험은 3가지 기계학습 방법{IBL, C4.5, NN(Neural Network)}을 이용하여 그림자의 제거가 차종의 판별성능에 미치는 결과의 평가한다.

• 한국도로학회:학술대회논문집
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• 한국도로학회 2010년 추계학술대회 논문집
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• pp.457-460
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• 2010

• 한국도로학회논문집
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• 제17권6호
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• pp.11-18
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• 2015

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.

• 자동차안전학회지
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• 제13권4호
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Smart Structures and Systems
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• 제24권6호
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• 한국ITS학회 논문지
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• 제8권1호
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• pp.9-21
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• 2009

첨단교통체계(ITS: Intelligent Transportation Systems)는 효과적인 교통제어 및 안전증진을 추구하기 위하여 많은 연구와 사업이 진행 중이다. 이를 위하여 실시간으로 교통상황을 제어하는데 필요한 신뢰성 있는 교통정보를 얻기 위해 가장 많이 설치되어 있는 루프검지기를 통하여 기본적으로 교통량, 속도, 점유율 등의 자료를 수집할 수 있다. 본 연구에서는 국내에 가장 보편적으로 설치된 루프검지기에서 추출한 차량자기신호곡선(IVS: Inductive Vehicle Signature) 자료를 이용하여 차량길이 및 속도 등을 산출하고 이를 이용하여 차종을 구분하는 알고리즘을 개발하였다. 본 연구는 세가지 Case(소형-대형, 소형-중형-대형, 소형-중형-대형-특대형)로 구성되어 있으며, 각 Case에서 전체차량을 각 Class로 분류하는 기준이 되는 차량길이 기준값을 산출하였다. 일반적인 도로구간에서의 적용을 위해 시 공간적 이식성 Test를 하여 분석대상구간 외의 다른 구간에서 차량길이 기준값의 적용가능성을 검토하였다. 본 연구의 결과는 차종구분을 통해 세부적인 교통량 정보를 수집하고 도로를 통과하는 차량들의 차종구성을 파악하여 교통운영 및 전략수립 시에 활용자료로 이용 가능할 것으로 판단된다.