• 자동차안전학회지
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• 제12권4호
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• pp.39-47
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• 2020

In this study, crash situations of representative bus crash types were elicited by analyzing a total of 1,416 bus repair record which were collected in 2018~2019. K-means clustering was used as a methodology for this study. Bus repair record contain the information of repair term, type of bus operation, responsibility of accident, weather condition, road surface condition, type of accident, other party, type of road and type of location for each data. Also, by checking collision parts of each bus repair record, each record was classified by types of collision regions. From this, 760 record are classified to frontal type, 363 record are classified to middle-frontal type, 374 record are classified to middle-rear type and 331 record are classified to rear type. As mentioned, k-means clustering was performed on each type of collision parts. As a result, this study analyzed the severity of bus crash based on actual bus accident data which are based on bus repair record not the crash data from the TAAS. Also, this study presented crash situation of representative bus crash types. It is expected that this study can be expanded to analyzing hydrogen bus crash and defining indicators of hydrogen bus safety.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• 환경영향평가
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• 제33권2호
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• pp.74-83
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• 2024

본 연구에서는 충청남도를 중심으로 로드킬 발생을 예측하고 영향을 미치는 요인을 탐구하여 로드킬 예방 대책 수립에 이바지하고자 하였다. 날씨, 도로 및 환경 정보를 종합적으로 고려하여 기계학습을 기반으로 로드킬 발생을 예측하고 각 변수의 중요성을 분석하여 주요 영향 요인을 도출하였다. 가장 우수한 성능을 보인 Gradient Boosting Machine(GBM)은 정확도 92.0%, 재현율 84.6%, F1-score 89.2%, AUC 0.907을 기록했다. 로드킬에 영향을 미치는 주요 요인은 평균 지역 기압(hPa), 평균 지면 온도(℃), 월, 평균 이슬점 온도(℃), 중앙 분리대 존재 여부, 평균 풍속(m/s)이었다. 이러한 결과는 로드킬 예방 및 교통안전에 이바지할 것으로 기대되며, 생태계와 도로 개발 간의 균형 유지에 중요한 역할을 할 것으로 예상한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.341-363
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• 2016

Haze or fog is a common natural phenomenon. In foggy weather, the captured pictures are difficult to be applied to computer vision system, such as road traffic detection, target tracking, etc. Therefore, the image dehazing technique has become a hotspot in the field of image processing. This paper presents an overview of the existing achievements on the image dehazing technique. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on two main works, that is, image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior. After the overview and a comparative study, we propose an improved image dehazing method, which is based on two image dehazing schemes mentioned above. Our image dehazing method can obtain the fog-free images by proposing a more desirable atmospheric veil and estimating atmospheric light more accurately. In addition, we adjust the transmission of the sky regions and conduct tone mapping for the obtained images. Compared with other state of the art algorithms, experiment results show that images recovered by our algorithm are clearer and more natural, especially at distant scene and places where scene depth jumps abruptly.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 3부
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• pp.150-151
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• 2008

자동차의 폭발적인 보급과 발전은 이미 우리 생활에 없어서는 안될 아주 중요한 부분으로 자리잡았다. 이와 함께 최근의 갑작스러운 IT 기술 발전은 운전환경에서 운전자에게 보다 많은 정보를 제공함으로써 운전자는 실시간으로 들어오는 외부 자극에 능동적으로 대처하여야 한다. 운전자의 능력을 넘어선 정보제공은 때때로 운전자의 주의 분산을 야기 시켜 돌이킬 수 없는 사고를 초래하기도 한다. 따라서, 운전자에게 제공할 수 있는 다양한 정보를 수집/분석/저장하여, 효율적으로 차량단말을 통해 제공할 수 있는 기술이 필요하다. 이에 따라 한국전자통신연구원은 2007년 3월부터 운전자의 상태, 차량의 내/외부 정보를 수집/분석/저장하여 효율적으로 관리해줄 VDMS (Vehicle & Driver Management System)에 대한 기술을 개방해오고 있다. VDMS는 추후 물류차량이나 택시 또는 승용차에 적용이 가능하다. 또한 향후 상용화를 위해 본 과제에서는 실제 물류차량과 택시차량에 차량정보 수집장치를 장착하여 실 데이터를 수집하였다. 본 발표에서는 VDMS의 전반적인 구성과 진행 방향에 대해서 설명하고, 과제 내에서 수행 되어온 결과물에 대해서 소개할 예정이다.

• 제어로봇시스템학회논문지
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• 제22권2호
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(1)
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• pp.53-56
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• 2001

This paper has been developed a millimeter-wave radar to prevent car collision. This system needs to progress the problem as follows; (1) Increase of traffic accidents causing damage and injuries due to the increased number of motor vehicles and long distance driving, (2) Need for a device to help drivers who are in trouble due to bad weather conditions. (3) Need for a millimeter-wave radar as obstacles which need to be detected are small. This system is composited with some major technologies, Narrow beams to recognize obstacles or other objects, One-side circuit technology to prevent interference between electric waves, and Parts designed for radar products which are able to transmit millimeter - waves. The system has a various a application Field, Car distance auto-control system, prevent bump collision due to unexpected stoppage of the front car or careless driving, obstacle warning system, Car following system, and industrial and military purposes system. We have a looking forward to propose to develop field tests under various road conditions and hybrid car sensor by combining with other sensors

• 한국도로학회논문집
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• 제19권2호
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• pp.1-6
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• 2017

PURPOSES : Pavement textures can be categorized into four according to wavelength: microtexture, macrotexture, megatexture (roads), and roughness. Pavement surface texture influences a number of aspects of tire-pavement interaction such as wet-weather friction, tire-pavement noise, splash, spray, tire-wear, and rolling resistance. In particular, macrotexture is the pavement surface characteristic that considerably impacts tire-pavement noise. In general, it can be demonstrated that tire-pavement noise increases with the increase of texture depth and wavelength. Recently, mean profile depth (MPD) and wavelength have been used to evaluate tire-pavement noise. This study aimed to identify the relationship between mean profile depth and average wavelength for asphalt pavement based on the information obtained on a number of asphalt pavement sections. METHODS : Profile data were collected from a number of expressway sections in Korea. In addition, mean profile depth and average wavelength were calculated by using this profile data. Statistical analysis was performed to determine the correlationship between mean profile depth and average wavelength. RESULTS:This study demonstrates a linear relationship between mean profile depth and average wavelength for asphalt concrete pavement. CONCLUSIONS :The strong relationship between mean profile depth and average wavelength of asphalt pavement was determined in this study.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.37-37
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• 2003

Rice is one of the most important crop in the world and is a major export of Thailand. Optical sensors are not useful for rice monitoring, because most cultivated areas are often obscured by cloud during the growing period, especially in South East Asia. Spaceborne Synthetic Aperture Radar (SAR) such as RADARSAT, can see through regardless of weather condition which make it possible to monitor rice growth and to retrieve rice acreage, using the unique temporal signature of rice fields. This paper presents the result of a study of examining the backscatter behavior of rice using multi-temporal RADARSAT dataset. Ground measurements of paddy parameters and water and soil condition were collected. The ground truth information was also used to identify mature rice crops, orchard, road, residence, and aquaculture ponds. Land use class distributions from the RADARSAT image were analyzed. Comparison of the mean DB of each land use class indicated significant differences. Schematic representation of temporal backscatter of rice crop were plotted. Based on the study carried out in Pathum Thani Province test site, the results showed variation of sigma naught from first tillering vegatative phase until ripenning phase. It is suggested that at least, three radar data acquisitions taken at 3 stages of rice growth circle namely; those are at the beginning of rice growth when the field is still covered with water, in the ear differentiation period, and at the beginning of the harvest season, are required for rice monitoring. This pilot project was an experimental one aiming at future operational rice monitoring and potential yield predicttion.

• 한국인터넷방송통신학회논문지
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• 제22권6호
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• pp.13-20
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• 2022

겨울철 도로에서 발생하는 안개 및 결빙구간 교통사고 사망률의 경우는 도로조건 및 기상조건이 매우 중요한 요소 이다. 본 논문에서는 교통사고 예측 데이터를 가정하고 교통사고 위험율을 에측 하는 모의실험을 수행하였다. 그뿐만 아니라, 본 논문에서는 교통사고를 줄이고 교통사고를 예방하기 위해서, 교통공단에서 제공하는 교통사고 사망자 데이터를 WEKA 데이터 마이닝 기법 및 TENSOR FLOW 공개 소스를 이용해서 요인 분석 및 교통사고 치사율 사망을 예측하였다. 추가적인 기능으로는 지도 표시 기능을 이용해서, 운전자가 WEB 기반에서, 안개 및 결빙구간 정보를 운전자에게 제공하는 모의실험 및 교통사고 사진을 실시간으로 전송하는 모의실험 결과를 설명하였다.