• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.771-782
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• 2021

Regular or irregular situations such as traffic accidents, damage to road facilities, maintenance or repair work, and vehicle breakdowns occur frequently on highways. It is required to provide traffic services to drivers by promptly recognizing these regular or irregular situations, various techniques have been developed for rapidly collecting data and detecting abnormal traffic conditions to solve the problem. We propose a method that can be used for verification and demonstration of unexpected situation algorithms by establishing a system and developing algorithms for detecting unexpected situations on highways. For the detection of emergencies on expressways, a system was established by defining the expressway contingency and algorithm development, and a test bed was operated to suggest a method that can be used for verification and demonstration of contingency algorithms. In this study, a system was established by defining the unexpected situation and developing an algorithm to detect the unexpected situation on the highway, and a method that can be used verifying and demonstrating unexpected situations. It is expected to secure golden time for the injured by reducing the effectiveness of secondary accidents. Also predictable accidents can be reduced in case of unexpected situations and the detection time of unpredictable accidents.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.127-137
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• 2006

This Paper Presents a novel incident detection method for using the speed-density difference between detectors. When a incident is happened the downstream traffic condition is mostly lower speed and higher density and the upstream is higher speed and lower density In respect of such characteristics, we can suggest a method for detecting an incident based on the speed-density information provided from detectors. The incident detection method is tested by using the accident data collected from the Cheonan-Nonsan Highway. The results show that suck an incident detection method can analyze the position of accident by comparing the speed-density difference between detectors. The work described in this paper is only at on early stage, in the sense that there are several areas to be further investigated in application of this method. such as setting a critical value for judging the incident. a level of accident wave, a varying Pattern between the incident locations and detectors, etc.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.753-762
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• 2024

Tunnels have limited evacuation areas, and It is difficult for cars coming from behind to recognize the accident situation in front. Since an accident is very likely to lead to a serious secondary accident, a IoT-based smart tunnel accident warning system was studied to prepare for traffic accidents that occur in tunnels. If the measured values from the flame detection sensor, gas detection sensor, and shock detection sensor in the tunnel exceed the standard, it is judged to be an emergency situation and an alert system is designed to operate. The accident information message was designed to be displayed on the LCD and transmitted to drivers inside and outside the tunnel through a Wi-Fi communication network. A performance test system was established and performance evaluation was performed for several accident scenarios. As a result of the test, it was confirmed that the accident alert system can accurately detect accidents based on given reference values, perform alert procedures, and transmit alert messages to smart phones through Wi-Fi wireless communication. And through this, its effectiveness could be confirmed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.137-140
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• 2010

교통사고의 발생은 교통 혼잡의 주요 원인으로 작용되어 교통사고에 의한 직 간접적 손해비용까지 지출되고 있다. 따라서 교통사고를 사전에 예방하거나 사고가 발생한 후 신속하게 처리할 수 있는 실시간 교통사고 대처 시스템이 요구되고 있다. 즉, 교통사고 자동검지 시스템의 필요성은 가 피해자의 구분에 활용하는 것 이외에 신속한 인명구조와 사고처리 등의 교차로 유고관리가 가능하며, 교통사고로 발생할 수 있는 교통 혼잡을 최소화 할 수 있다. 본 논문에서는 다양한 형태의 충돌 및 추돌 사고를 검지하는 시스템의 성능을 개선하기 위한 것으로 영상 또는 소리라는 매체에 기반을 둔 시스템에서 자동 검지의 한계성을 도출하고 개선하고자 하였다. 테스트 베드를 기반으로 자동검지 실패의 원인을 분석하고 그 원인에 따른 오인식의 문제점을 개선하여 운전자 단독사고로 인하여 차량 추적이 불가능한 경우, 소리 없이 발생한 사고, 야간에 발생한 사고 등의 문제점들을 극복함과 동시에 성능을 개선하는데 그 목적이 있다.

• The KIPS Transactions:PartC
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• v.11C no.2
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• pp.157-162
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• 2004

Recently, the growth of information infrastructure is getting fatter and faster. At the same time, the security accidents are increasing together. We have problem that do not handle traffic because we have the Intrusion Detection Systems in low speed environment. In order to overcome this, we need effective security analysis techniques that ran Processed data of high-capacity because high speed network environment. In this paper we proposed the Gigabit Intrusion Detection System for coordinated security function such as intrusion detection, response on the high speed network. We suggested the detection mechanism in high speed network environment that have pattern matching function based packet header and based packet data that is proceeded in system kernel area, we are shown that this mechanism was excellent until maximum 20 times than existing system in traffic processing performance.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.27-35
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• 2002

A knowledge-based image retrieval technique is image searching methods using some features from the queried image. The materials in this study are automobile head lamps. The input data is composed of characters and images which have various pattern. The numbers, special symbols, and general letters are under the category of the character. The image informations are made up of the distribution of pixel data, statistical analysis, and state of pattern which are useful for the knowledge data. In this paper, we implemented a retrieval system for the scientific crime detection at traffic accident using the proposed multi knowledge-based image retrieval technique. The values for the multi knowledge-based image features were extracted from color and gray scale each. With this 22 features, we improved the retrieval efficiency about the color information and pattern information. Visual basic, crystal report and MS access DB were used for this application. We anticipate the efficient scientific detection for the traffic accident and the tracking of suspicious vehicle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.416-421
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• 2021

In case of the road slips due to heavy snow and the temperature drops below 0 degrees, black ice which mainly occurs on the road, bridges for vehicles, and tunnel entrances, is not recognized by the driver's view because the image of the asphalt is transmitted through it. So cars' slip situation occurs, which leads to a big traffic accident and a large amount of loss of life and property. This study proposes a method to check the road condition using an infrared camera and to identify black ice through deep learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.827-833
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• 2022

This study aims to prevent secondary traffic accidents with high severity by overcoming the limitations of existing traffic information collection systems through analysis of traffic information collection detectors and various algorithms used to detect unexpected situations. In other words, this study is meaningful present that analyzing the 'unexpected situation that causes secondary traffic accidents' and 'Existing traffic information collection system' accordingly presenting a solution that can preemptively prevent secondary traffic accidents, intelligent traffic information collection system that enables accurate information collection on all sections of the road. As a result of the experiment, the reliability of data transmission reached 97% based on 95%, the data transmission speed averaged 209ms based on 1000ms, and the network failover time achieved targets of 50sec based on 120sec.