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

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.1-13
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• 2018

Divers types of Personal Mobility(PM) are appeared on the market after the Segway is introduced. PMs have propagated very rapidly with their ease of use, and accidents related with PM show a sudden increase. Many studies on the PM are performed as its trend, but dring safety of passengers are excluded. In this study, criteria which can be adopted for PM's driving safety evaluation are reviewed. Also result of driving safety evaluation on 3 types of PM(wheel chair, kickboard, scooter(seating/standing) and walking using deducted criteria is given. COG(Center of the gravity) and SM(Stability Metric) are finally selected two criteria among many of them used in other fields. COG indicates how the center of mass deviates from the direction of the gravity. SM is a normalized value of generated force when PM moves as internal force, angular momentum, and ground reaction force. 0 means stop, and negative value means rollover, so it can be used for safety evaluation of PM. Average and standard deviation of measurement are standard of safety on the COG analysis. Wheel chair is the most safe and kickboard is the most unstable on the COG analysis. Wheel chair is also ranked as top safe on the SM analysis. Among two riding types(seating and standing) on the scooter, seating type is evaluated more safer than standing type. It is proposed that more various type of PMs are need to get safety evaluation for drivers and devices themselves together.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.135-142
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• 2014

As interest in social safety has recently increased at the national level, the various activities which can effectively prevent crimes are being carried out. Because the existing maps related to crimes provide the information about the present condition of crimes by administrative district for users, women and pedestrians who go by night could not actually grasp safe roads in advance. Therefore, this study developed the methodology that can easily extract dangerous areas due to crimes by the digital map 2.0. In the digital map 2.0, location and attribute information of center-lines of roads and building layers were used to find dangerous areas of crimes in these layers. Pavement materials and road width which are already built by the attribute information were used in the center-lines of roads. Crossing angles that roads and roads cross each other were additionally extracted and utilized. The attribute information about building types were input in the building layers of the digital map 2.0. The areas that are more the threshold values set by totaling up all the risk scores when considering pavement materials, road width, crossing angles of road, and building types in the center-lines of roads and road crossings were extracted as the dangerous areas that crimes can occur. Verification of the developed methodology was done by experiment. In the spatial apsect, the dangerous areas of crimes could be found by using the digital 2.0, roads, and building layers only through the experiment. In the administrative aspect to prevent crimes, additional installation of safety facilities such as street lights and security lights in the identified areas which are vulnerable for crimes is thought to be increasing safety of dangerous areas.