• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.93-99
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• 2020

A majority of car crash is affected by careless driving that causes extensive economic and social costs, as well as injuries and fatalities. Thus, the research of precise crash detection systems is very significant issues in automotive safety. A lot of crash detection algorithms have been developed, but the coverage of these algorithms has been limited to few scenarios. Road scenes and situations need to be considered in order to expand the scope of a collision detection system to include a variety of collision modes. The proposed algorithm effectively handles the x, y, and z axes of the sensor, while considering time and suggests a method suitable for various real worlds. To reduce nuisance and false crash detection events, the algorithm discriminated between driving mode and parking mode. The performance of the suggested algorithm was evaluated under various scenarios, and it successfully discriminated between driving and parking modes, and it adjusted crash detection events depending on the real scenario. The proposed algorithm is expected to efficiently manage the space and lifespan of the storage device by allowing the vehicle's black box system to store only necessary crash event's videos.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.423-426
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• 2012

최근 자동차 급발진 문제가 사회적 이슈가 되고 있다. 기본 장착품으로 자리잡은 블랙박스에 찍힌 동영상이 유포되면서 급발진으로 의심되는 영상이 속속히 등장하고 있다. 지금까지 모든 급발진은 대부분 운전자의 실수로 판결났다. 미국에서도 자동차 결함을 인정하지 않으면서도 도의상 일부 보상해준 사례가 있으나 결론에는 차이가 없었다. 본 논문에서는 기존 블랙박스 영상만으로 차량 급발진 사고유무 분석이 어려웠던 부분을 OBD-II(Onboar Diagnostic-II)에 소형블랙박스를 장착하여 운전자가 패달을 밟았는지의 유무를 판단하고, 메모리카드가 장착이 됨으로써 OBD-II의 차량 정보 및 GPS를 이용한 현재 시간, 영상 정보를 저장하여 정확한 판단을 하기위해 OBD-II 내의 블랙박스를 적용한 시스템을 제안한다.

• Journal of Advanced Navigation Technology
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• v.18 no.3
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• pp.209-214
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• 2014

Current black boxes have a problem of storing unnecessary imagery data recordings without data classification. For this reason, users have to erase videos every time. This method is inadequate for black boxes with limited memory capacity. In this paper, we design and implement a system that recognizes traffic accident situations and saves these recordings by classifying them according to weighted values. The system was made to save video recorded at a 30-sec interval of every event to black box folders by changing names based on weighted value data under the external environment in a 1:10 scale model car. Based on this, when the tests were performed as a major car accident while driving, the videos were created in w2 folder, and when the tests were performed as a minor car accident while stopped, the videos were created in w1 folder.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.10
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• pp.80-85
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• 2013

Recently it is becoming more common to apply vehicle electronics(Vetronics) based on information technology to improve the operability of the combat vehicles. Although the vehicle electronics system has been gradually developed, it is hard not only to design the reliable system which is a built-in multi-function but also to analyze the fault failure in the state of on-vehicle when failures occur. Therefore, It is required the data logging system like a aircraft's black box for the combat vehicles to enhance the reliability. In this paper, we developed the multi data acquisition and analysis device which is acquiring real-time data such as communication data, video data and voice data available for checking operational status of system and managing history. The performance of device has been proved on the vehicle.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.149-160
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• 2007

Automobile black boxes store and provide accident and driving information. The accident and driving information can be utilized to build scientific traffic-event database and can be applied in various industries. The objective of this study is to develop a recognition system of dangerous driving through analyzing the driving characteristic patterns. In this paper, possible dangerous driving models are classified into four models on the basis of vehicle behaviors(acceleration, deceleration, rotation) and accident types from existing statistical data. Dangerous driving data have been acquired through vehicle tests using automobile black boxes. Characteristics of driving patterns have been analyzed in order to classify dangerous driving models. For the recognition of dangerous driving, this study selected critical value of each dangerous driving model and developed the recognition algorithm of dangerous driving. The study has been verified by the application of recognition algorithm of dangerous driving and vehicle tests using automobile black boxes. The presented recognition methods of dangerous driving can be used for on-line/off-line management of drivers and vehicles.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2532-2537
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• 2012

In recent, the researches on driver assistance systems have been actively performed with development of vehicle industry. AVM(Around View Monitoring) Systems, a part of these systems, have been researching and developing. Existing AVM systems have been developed in the forms of embedded systems or a SoC (System on Chip) to provide view around vehicle in real time. However, if Car PC is equipped with in vehicle, AVM can be developed using only software without additional cost. In this study, we implemented AVM system which provides location information by adding the informations such as latitude, longitude and speed to functions of "Car PC" based AVM system. If storing function is added to the AVM system implemented in this study which provides location information, the system with storing function can be used as AVM black box system.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.561-563
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• 1997

In this paper, we identified the Black-box system with serious nonlinerity and fast dynamics using Neural Network. This NN have new structure and learned by RLS. It identify system in real-time without priori data. We use this NN to 7-DOF vehicle identification.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.665-670
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• 2010

This paper suggests the vehicle remote control on the basis of Smart Phone. In general, most smart phone is mounted with G-sensor to control the motion. G-sensor is able to control several directions and movements of velocity along with X, Y, and Z axis. To access remote location and data system, we can also utilize Wi-Fi communication as well as bluetooth communication. In this study, we propose the scheme that is the car management application by remote control via real-time monitoring on mobile device for user convenience.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.2
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• pp.149-156
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• 2017

Various kinds of accessories(e.g., clock, radio, automatic door locks, alarm devices, etc.) or unit components (e.g., black box, navigation system, alarm, private audio, etc.) require dark current even when the vehicle power is turned off. However, accessories or unit components can be the causes of excessive dark current generation. It results in battery discharge and the vehicle's failure to start. Therefore, immediate detection of abnormal dark current and response are very important for a successful repair job. In this paper, we can increase the maintenance efficiency by presenting a standardized diagnostic process for the measurement of the dark current and the existing problem. As a result of the absence of a system to block the dark current in a vehicle, diagnosis and repair were performed immediately by using a standardized dark current diagnostic process.