• Journal of Advanced Navigation Technology
• /
• v.14 no.6
• /
• pp.800-807
• /
• 2010

Jack knifing accident of semi trailer vehicle is one of the most dangerous accident type because the vehicle cross over its lane by the accident. Jack knifing accident can be predicted and detected by GNSS precise relative positioning. But integer ambiguity resolution procedure is inevitable in GNSS precise relative positioning. In this paper, success rate improving method of integer ambiguity resolution is proposed for jack knifing accident prediction and detection of semi trailer vehicle, and proposed method is tested by simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.4
• /
• pp.55-63
• /
• 2019

Proximity sensors and rearview cameras for automobile safety are common, but many accidents are still occurring. Using the All Around View and object recognition algorithm to show the front, back, left, right and bottom of the vehicle, the sensor detects the presence of a living body when the vehicle starts or parks, and displays the outside of the vehicle on the screen. In addition, the object recognition algorithm is used to visualize the object by expressing the position of the object. In this way, we propose a strong safety system that can prevent human accidents caused by the vehicle by sensing, screen, and expression.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.603-604
• /
• 2021

It is a enforcement system to prevent collisions caused by unmanageable parking that may occur in parking lots. There are handicapped people who can get up in parking lots, general vehicles parked in electric vehicle parking areas, and vehicles parked in two lanes. The vehicle above is detected and notified through the deep learning object recognition function. By using a picture or video of an unmanageable parking situation as learning data, the learning data is produced so that the situation can be recognized, and the situation is recognized to determine the presence or absence of unmanageable parking. The purpose is to reduce collisions between parking lot users by making the environment of the parking lot more comfortable.

• Annual Conference of KIPS
• /
• 2021.11a
• /
• pp.1269-1272
• /
• 2021

급격한 차량 수요의 증가로 인한 주차공간의 부족으로 인력기반 주차관리의 한계점을 이용한 여러 위법 행위가 발생함에 따라 관리자의 정기적인 순찰 및 관리의 필요성이 증가하고 있다. 기존 인력기반 주차관리 시스템은 관리자에게 전적으로 의존하고 있어 관리의 정확도 및 효율성이 비교적 낮다. 본 논문에서는 엣지 컴퓨팅 기반의 클라우드 주차관리 시스템을 제안하며, 이는 효율적인 주차관리를 통한 시간과 노동력 절감에 기여한다. 본 시스템이 제공하는 주요 기능은 다음과 같다. 1) 주차구역 및 주차 차량 정보를 분석하여 입·출차를 관리하고 관리자에게 실시간 주차 현황 정보를 제공한다. 2) 추출된 차량 정보를 바탕으로 부정주차 여부를 감지하여 지정주차 구역 관리를 자동화한다. 3) 관리자에게 시간대별 주차구역 점유율 정보를 제공하여 거주자의 가용 주차공간을 확보한다. 4) 거주자의 선호 주차구역 및 시간대를 파악하여 거주자의 주차 편의성을 제공한다. 위 기능을 통해 기존 주차관리의 비효율성을 개선하고자 한다.

• Transactions of the KSME C: Technology and Education
• /
• v.3 no.3
• /
• pp.175-181
• /
• 2015

Because internal space of combat vehicle reachs about $80^{\circ}C$ at high temperature period, Proximity Sensor exposed high temperature and humidity, which has function to sense the distance and transfer signal for control unit, have enlarged sensing distance and finally locked on. Malfunction of sensing itself occur frequently, therefore we carried out cause analysis and improvement. We accomplish improvement activity secondly. Through-out many trial and error, we find out that malfunction of sensor occur at high temperature circumstance. To improve, the another Emitter Coil is added to increase voltage difference and improve sensing accuracy about 5~10 times. And we accomplish design improvement to dull temperature and humity change after increasing molding surface to add vibration and shock resistance. We prove that the improved product do not fail after enduring 136hr at $85^{\circ}C$ temperature and 85% relative humidity circumstance chamber.

• International Journal of Highway Engineering
• /
• v.10 no.3
• /
• pp.19-31
• /
• 2008

Video image processing systems(VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on wide-area detection, i.e., multi-lane surveillance algorithm provide traffic parameters with single camera such as flow and velocity, as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. The objective of this research was to relate traffic safety to VIPS tracking and this paper has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, volumes, speed, and occupancy rate as well as traffic information via tripwire image detectors. Also the developed system has been verified by comparing with commercial VIP detectors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.483-486
• /
• 2011

Recently, large and small vehicle accidents due to human life and property due to loss of function similar to that used on the plane with a black box mounted on the vehicle by the driver of the vehicle in order to analyze the cause of the accident vehicle you are using a black box. The black box used in the existing operating system, unlike the Android OS portability is good compared to other OS support an open platform for the development of additional costs or proven, which includes many libraries need to use any external libraries there are no advantages. In addition, the existing black box on the incident can not be sent automatically to report an accident notification has a problem. In this paper, another advantage of the OS used in a black box with an Android-based acceleration sensor on the test board GPS module and smart phones using the information, and incident detection capability to send a message to the specified number of black boxes with was implemented.

• Journal of the Korea Convergence Society
• /
• v.10 no.11
• /
• pp.327-333
• /
• 2019

The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.23 no.1
• /
• pp.123-133
• /
• 2024

Existing autonomous driving technology has been developed based on sensors attached to the vehicles to detect the environment and formulate driving plans. On the other hand, it has limitations, such as performance degradation in specific situations like adverse weather conditions, backlighting, and obstruction-induced occlusion. To address these issues, cooperative autonomous driving technology, which extends the perception range of autonomous vehicles through the support of road infrastructure, has attracted attention. Nevertheless, the real-time analysis of the 3D centroids of objects, as required by international standards, is challenging using single-lens cameras. This paper proposes an approach to detect objects and estimate the centroid of vehicles using the fixed field of view of road infrastructure and pre-measured geometric information in real-time. The proposed method has been confirmed to effectively estimate the center point of objects using GPS positioning equipment, and it is expected to contribute to the proliferation and adoption of cooperative autonomous driving infrastructure technology, applicable to both vehicles and road infrastructure.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.1
• /
• pp.197-205
• /
• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).