• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.849-856
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• 2020

Recently, as the number of high-height vehicles such as double-decker buses has increased, collision accidents have occurred in bridges and tunnels due to the deviation from the designated routes and driver's carelessness. In the case of the existing front collision warning system, it is limited to vehicles and pedestrians, so it is difficult to use it as a pass height warning system for the high height vehicles. In this paper, we propose a system that generates a warning by determining the correlation and time series characteristics of data for each segment using multiple lidar sensors and then determining the possibility of collision in the upper part of the vehicle. Also, the proposed system confirmed the proper operation through a real-time driving test and a system performance evaluation by the Korea Automobile Testing & Research Institute.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.437-444
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• 2008

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.497-498
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• 2023

많은 인구가 몰리면 군중 추돌 현상과 도미노 현상이 발생하여 압사 사고가 일어나 이에 대한 해결책이 요구된다. 본 논문에서는 위 문제를 개선하고자 보행자 상황 인식을 이용한 재난 경고 시스템을 구현하였다. 이 시스템은 재난관리기관에서 기존에 운영하고 있는 것과 같은 CCTV영상을 이용하여 딥러닝 영상인식 기술을 사용하여 보행자 안전 규정에 따른 보행자 상황을 인식해 재난 상황을 표시해주고, 경고를 한다. 보행자 상황 인식하기 위해 엣지컴퓨터에서 연결된 카메라 영상을 받아 상황인식을 하고, 인식된 상황과 영상을 서버로 전송하여 정보를 저장하고, 상황을 경고 한다. 상황인식을 위해 보행자 데이터는 직접 수집하여 학습시킨 weights 파일을 사용하였다. 보행자 인식은 YOLOv4-tiny를 사용하였고, 위험 단계는 총 4단계로 설정하였다. 이를 활용하여 기존의 CCTV영상을 활용하여 관리자를 보조하여 보행자 재난 상황시에 신속하게 재난을 인식하여 구호 조치를 할 수 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.349-356
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• 2020

This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.10a
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• pp.5-7
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• 2012

선교에는 항해사에게 정보를 전달하기 위하여 다양한 신호가 존재한다. 항해 및 통신 장비로부터 나오는 음향 신호 등 다양한 청각 신호들이 존재하지만 이러한 청각 신호, 청각 경고음에 대한 인간의 인지능력에 관한 연구는 미흡한 실정이다. 청각 경고음은 크게 음성(speech), 함축적 소리(abstract sound), 청각 아이콘(auditory icon)으로 구분 할 수 있다. 본 연구에서는 청각 경고음 중 청각아이콘을 활용하여 5가지의 경보상황(엔진, 화재, 조타, 전기, 충돌)에서 청각아이콘에 대한 감성평가를 통해 각 상황에 적합한 청각아이콘을 선별하였다. 본 연구 결과는 선교 내 청각표시장치와 통합선교알람관리시스템을 위한 기초자료로 활용될 수 있을 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.519-520
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• 2021

본 논문에서는 딥러닝 기반의 얼굴인식과 Harr Cascade 분류기를 이용한 눈인식, 스마트워치를 매개로 한 심박수 측정을 활용하여 운전자 졸음운전 경고 시스템을 제안하였다. 제안하는 시스템은 PERCLOS 방법을 적용하여 운전자의 눈 감은 시간을 누적시켜 졸음 상태 유무를 판단하고, 스마트워치의 HR센서를 활용한 운전자의 심박수 값 모니터링을 진행하여 졸음 발생 시 경고음을 발생시켜 졸음운전으로 인한 교통사고를 예방할 수 있다.

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.106-118
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• 2014

The objective of this study is to develop a methodology for evaluating the effectiveness of in-vehicle pedestrian warning systems. Driving Simulator-based experiments were conducted to collect data to represent driver's responsive behavior. The braking frequency, lane change duration, and collision speed were used as measure of effectiveness (MOE) to evaluate the effectiveness. Collision speed data obtained from the simulation experiments were further used to predict pedestrian injury severity. Results demonstrated the effectiveness of warning information systems by reducing the pedestrian injury severity. It is expected that the proposed evaluation methodology and outcomes will be useful in developing various vehicular technologies and relevant policies to enhance pedestrian safety.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.469-476
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• 2021

The purpose of this study is to explore the predictors of academic probation in college. Especially, this study focused on student engagement variables among the predictors of academic probation in college. Student engagement variables include hours of absence from class and numbers of log to LMS(Learning Management System) and in extracurricular program system during four weeks after the opening of a course and the numbers of faculty counseling. GPA(grade Point Average) is a dependent variable and GPA of prior semester is a control variable in this study. 17,261 student data were collected for the study. Linear regression model and logistic regression model analyses were conducted in the study. The finding showed that the hours of absence from class and numbers of log in extracurricular program system during four weeks after the opening of a course predicted academic achievement of college students. The result also indicated that hours of absence from class and numbers of log-ins to LMS(Learning Management System) and in extracurricular program system during four weeks after the opening of a course were the predictors of academic probation in college. This study will contribute to investigate indicators of students with low academic performance and to provide proper support for underachievers.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.59-70
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• 2009

This study presents a novel traffic information system which is capable of detecting unsafe traffic events leading to accident occurrence and providing warning information to drivers for safer driving. Unsafe traffic events are captured by a vehicle image processing-based detection system in real time. Surrogate safety measures (SSM) representing quantitative accident potentials were derived, and further utilized to develop a data processing algorithm and analysis techniques in the proposed system. This study also defined 'emergency warning area' and 'general warning area' for more effective provision of warning information. In addition, methodologies for determining thresholds to trigger warning information were presented. Technical issues and further studies to fully exploit the benefits of the proposed system were discussed. It is expected that the proposed system would be effective for better management of traffic flow to prevent traffic accidents on freeways.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.36-43
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• 2011

The forward collision of vehicles in high speed can cause a chain collisions and high fatality rate. Most of the forward collisions are caused by insufficient braking distance due to detection time of driver and safe distance. Also, accumulated detection time of driver is cause of chain collisions after the forward collision. The FVCWS prevents the forward collision by maintaining the safety distance inter-vehicle and reducing detection time of driver. However the FVCWS can cause chain collisions because the system that interacts only forward vehicle has accumulated detection time of driver. In this paper, we analyze forward and chain collisions of normal vehicles and FVCWS vehicles on static traveling scenario. And then, we analyze and compare V2V based FVCWS with them after explaining the system. The V2V FVCWS reduces detection time of driver alike FVCWS as well as remove accumulated detection time of driver by broadcasting emergence message to backward vehicles at the same time. Therefore, the system decrease possibility of forward and chain collisions. All backward normal vehicles and 3~4 backward FVCWS vehicles have possibility of forward and chain collisions in result of analysis. However V2V FVCWS vehicles almost do not chain collisions in the result.