• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.3
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• pp.341-347
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• 2015

This study aims to design a chief VDR(Voyage Data Recorder) system which is appropriate to small and medium sized vessels and also implement the data about marine communication devices, sensors, etc. to be stored or printed at the navigator when those data are connected to VDR through data communication between marine navigation and VDR which are based on serial communication or internet in order to prove efficiency of the marine navigator. Also, the design of VDR is intended to be small and light in order to expand to apply it to small and medium vessels, which enables to analyze causes of marine accidents precisely through its characteristic functions which are the same as those at "vehicle mounted black-box" (location of the car, image and voice storage) by which the same roles are played on land.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.69-83
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• 2009

According to the accident statistics of 2006, it can be recognized that drivers' characteristics and driving behaviors are the most causational factors on the traffic accidents. At present, many recording tools such as digital speedometer or black box are distributed in the market to meet social requests of decreasing traffic accidents and increasing safe driving behaviors. However, it is also true that the system preventing any possible vehicle accidents in advance has not been developed. In this study, we developed critical value for deciding dangerous driving behaviors. The developed critical value could be used to contribute to safety driving management systematization and safety driving behaviors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.105-115
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• 2009

According to the accident statistics published by the National Police Agency in 2007, the number of commercial vehicle(city, suburb and other buses) accidents consumes 3.5 percent of the total number of traffic accidents in this year. Since the commercial vehicles are responsible for not only the drivers but also the passengers, it leads more serious social and economic problems. There have been various forms of systems such as a digital speedometer or a black box to meet the social requirement for reducing traffic accidents and safe driving. however the system based on the data after accident control the driver by analyze dangerous drive behaviors, so there is a limit to control driver in real-time. Also speedometer currently managed provide the driver warning information in real-time, but using only the speed of vehicle and RPM information regardless of actual dangerous drive behaviors, disappear the effectiveness. In this study performed a simulation for drivers in general using a simulator programed with dangerous driving types we had developed in the previous study and judging the types. It'd be more effective system to provide the drivers warning information using weight valued in this study. However in this study is limited to apply weight as a result of simulation of drivers in general in actual situation should be made up the deficit based on information of driving type of actual commercial vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.