• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.453-464
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• 2012

This is a research of an adaptive longitudinal control system for situated cognition in wide range, traffic accidents reduction and safety driving environment by integrated system which graft a road infrastructure's information based on IT onto the intelligent vehicle combined automobile and IT technology. The road infrastructure installed by laser scanner in intersection, speed limited area and sharp curve area where is many risk of traffic accident. The road infra conducts objects recognition, segmentation, and tracking for determining dangerous situation and communicates real-time information by Ethernet with vehicle. Also, the data which transmitted from infrastructure supports safety driving by integrated with laser scanner's data on vehicle bumper.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.236-238
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• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

• The Journal of Bigdata
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• v.7 no.1
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• pp.15-27
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• 2022

In the past five years, the fatality rate of single-vehicle accidents has been 4.7 times higher than that of all accidents, so it is necessary to establish a system that can detect and respond to single-vehicle accidents immediately. The IoT(Internet of Thing)-based real-time traffic accident recognition system proposed in this study is as following. By attaching an IoT sensor which detects the impact and vehicle ingress to the guardrail, when an impact occurs to the guardrail, the image of the accident site is analyzed through artificial intelligence technology and transmitted to a rescue organization to perform quick rescue operations to damage minimization. An IoT sensor module that recognizes vehicles entering the monitoring area and detects the impact of a guardrail and an AI-based object detection module based on vehicle image data learning were implemented. In addition, a monitoring and operation module that imanages sensor information and image data in integrate was also implemented. For the validation of the system, it was confirmed that the target values were all met by measuring the shock detection transmission speed, the object detection accuracy of vehicles and people, and the sensor failure detection accuracy. In the future, we plan to apply it to actual roads to verify the validity using real data and to commercialize it. This system will contribute to improving road safety.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.128-141
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• 2018

In this paper, a methodology to analyze the potential safe benefit of six cooperative driver assistance systems via V2V (vehicle-to-vehicle) communications is proposed. Although it is quite necessary to assess social impact with respect to new safety technologies for cooperative vehicles with V2V communications, there are few studies in Korea to predict the quantitative safety benefit analysis. In this study, traffic accident scenarios are classified based on traffic fatality between passenger cars. The sequential collision type is classified for a multiple pile-up with respect to collision direction such as forward, side, head-on collisions. Then movement of surrounding vehicle is considered for the scenario classification. Next, the cooperative driver assistance systems such as forward collision warning, blind spot detection, and intersection movement assistance are related with the corresponding accident scenarios. Finally, it is summarized how much traffic fatality may be reduced potentially due to the V2V communication based safety services.

• 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.

• 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.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1500-1505
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• 2019

Currently, as part of the ICT Smart City, the company is building C-ITS(Cooperative-Intelligent Transport Systems) for solving urban traffic problems. In order to realize autonomous driving service with C-ITS, the role of advanced road infrastructure is important. In addition to the study of mid- to long-term C-ITS and autonomous driving services, it is necessary to present more realistic solutions for road traffic safety in the short term. Therefore, in this paper, we propose a highway accident detection alarm system that can detect and analyze traffic flow and risk information, which are essential information of C-ITS, based on IoT guard rail and provide immediate alarm and remote control. Intelligent IoT guard rail is expected to be used as an intelligent advanced road infrastructure that provides data at actual road sites that are required by C-ITS and self-driving services in the long term.

• 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.