• 자동차안전학회지
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• 제15권3호
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• pp.43-52
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• 2023

When a children's school bus stops on the road, the operator enables an amber flashing light (indicating stopping or slowing) or a red flashing light (indicating that children are getting on and off). Drivers of vehicles passing by the stopped school bus, as well as vehicles in adjacent lanes to the school bus must stop temporarily. However, many drivers are not aware of the laws and do not comply with them, so children are exposed to an increased risk of being hit, especially at night as the color recognition of the vehicle is significantly lower than during the day. In our experiments, messages and shapes using light were projected to the front and rear of a parked school bus, in addition to its red lights flashing.

• Smart Structures and Systems
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• 제24권6호
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• 대한교통학회지
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• 제26권2호
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• pp.25-33
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• 2008

야간에 발생하는 교통사고를 줄이기 위해서는 일반적인 교통사고 방지대책과 구분하여 야간의 특수성을 고려한 실효성 있는 교통사고 방지대책이 수립되어야 한다. 횡단보도에서 보행자 교통사고 잦은 지점의 개선을 위하여 설치한 도로조명 개선 시행효과분석을 실시하였다. 집중조명 방식의 가로등에 대한 설치효과 분석 결과, 평균 22.4%의 야간 교통사고가 감소한 것으로 나타났고, 시행효과분석에서 공용기간 내 순현재가치는 25,648백만원, 편익비용비는 12.85로서 매우 효과가 있는 것으로 분석되었다. 또한 집중조명시설 설치 전 후의 교통사고 비교 유의성 검정 결과 유의수준 0.05에서 집중조명시설 설치 후 교통사고 감소 효과가 있는 것으로 분석되었다. 본 연구는 조명시설의 설치시기가 서로 다르며 그 표본의 수가 적어 전국적인 대표성을 가지고 있다고 볼 수는 없으나, 체계적인 분석을 통한 개선사업의 효과를 구체화 할 수 있는 대안은 제시할 수 있다고 판단된다.

• 조명전기설비학회논문지
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• 제25권10호
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• pp.87-93
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• 2011

Nonlinear loads including lighting fixtures generate harmonic currents and create distortions on the sinusoidal voltage of the power system. Harmonic field measurements have shown that the harmonic contents of a waveform varies with time. A cumulative probabilistic approach is the most commonly used method to solve time varying harmonics. In this paper the time varying harmonics of lighting loads are evaluated by international harmonic standards(IEC 61000-3-6).

• 대한임베디드공학회논문지
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• 제6권1호
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• pp.8-15
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• 2011

This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, in order to effectively extract spotlight of interest, a pre-signal-processing process based on camera lens filter and labeling method is applied on road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process use light tracking method and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with visible light mono-camera and tested it in urban and rural roads. Through the test, classification performances are above 89% of precision rate and 94% of recall rate evaluated on real-time environment.

• International Journal of Automotive Technology
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• 제6권1호
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• 제어로봇시스템학회논문지
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• 제8권6호
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• 한국도로학회논문집
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• 제17권5호
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• pp.57-66
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• 2015

PURPOSES: Nighttime driving is very different from daytime driving because drivers must obtain nighttime sight-distances based on road lights and headlights. Unfortunately, nighttime driving conditions in Korea are far from ideal due to poor lighting and an insufficient number of road lights and inadequate operation and maintenance of delineators. This study is conducted to develop new standards for nighttime road visibility based on experiments of driver perception for nighttime visibility conditions. METHODS : In the study, perception level and satisfaction of nighttime visibility were investigated. A total of 60 drivers participated, including 34 older drivers and 31 young drivers. To evaluate driver perceptions of nighttime road visibility, fuzzy set theory was used because the conventional analysis methods for driver perception are limited in effectiveness for considering the characteristics of perception which are subjective and vague, and are generally expressed in terms of linguistic terminologies rather than numerical parameters. RESULTS : This study found that levels of nighttime visibility, as perceived by drivers, are remarkably similar to their satisfactions in different nighttime driving conditions with a log-function relationship. Older drivers evaluated unambiguously degree of nighttime visibility but evaluations by young drivers regarding it were unclear. CONCLUSIONS : A minimum value of brightness on roads was established as YUX 30, based on final analyzed results. In other words, road lights should be installed and operated to obtain more than YUX 30 brightness for the safety and comfort of nighttime driving.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2002년도 학술대회논문집
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• pp.217-222
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• 2002

노면 반사특성에 관한 연구와 함께 국내에서 도로조명 설계시 적용하는 조도 휘도 환산계수에 관한 연구는, 휘도가 기준이 되는 도로 조명을 설계하는 데에 있어서 매우 중요하다. 현재 조도 휘도 환산계수를 이용하여 설계한 도로조명이 적정하게 유지되고 있는지 알아내기 위하여 실제 도로에서의 조도 및 휘도 분포를 측정하고 조도휘도 환산계수를 계산하였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 추계학술대회 논문집
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• pp.173-176
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• 2009

조명기구의 종류에 따른 도로조명 실태를 조사하기 위하여 최근 조명기구가 새롭게 교체된 도심지의 도로를 대상으로 조도와 휘도를 측정하였다. 도로조명기준에 제시된 측정법에 맞도록 장비를 구성하여 측정되었으며, 측정결과를 비교 분석하였다.