• Korean Journal of Occupational Therapy
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• v.26 no.4
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• pp.127-143
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• 2018

Objective : The purpose of this study was to compare with the effects of driving simulator and Dynavision training after stroke through the test of cognitive ability and driving performance. Methods : Twenty-one stroke patients were randomly classified to the driving simulator training group (N=11) and Dynavision training group (N=10), and were carried out respectively training for 15 times. The driving performances was measured by the driving simulator test, and cognitive-perceptive abilities was measured by the DriveABLE Cognitive Assessment Tool, Trail Making Test-A, Trail Making Test-B and Mini Mental State Examination-K. Results : The driving simulator training group showed significant changes in all cognitive tests and most of driving performances. The Dynavision training group also showed significant changes in all cognitive tests except for Trail Making Test-A and some driving performances. The significant differences on both groups were found regarding the estimated degree of results on the on-road evaluation, the number of off road accidents and collisions. In addition, the causal influence of the two training methods on these variables was analyzed to be more than 20%. Conclusion : The driving simulator and Dynavision training were found to be effective intervention in the driving rehabilitation after stroke. In particular, it was confirmed that the driving simulator is an effective training to improve overall driving ability of stroke patients. In addition, the difference in training effect between the two training methods was found to be more than 20%.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.115-123
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• 2018

Various types of robots running on power transmission lines have been developed for the purpose of line patrol monitoring. They usually have complex mechanism to run and avoid obstacles on the power line, but nevertheless did not show satisfactory performance for going over the obstacles. Moreover, they were so heavy that they could not be easily installed on the lines. To compensate these problems, flying robots have been developed and recently, multi-copter drones with flight stability have been used in the electric power industry. The drones could be remotely controlled by human operators to monitor power distribution lines. In the case of transmission line patrol, however, transmission towers are huge and their spans are very long, and thus, it is very difficult for the pilot to control the patrol drones with the naked eye from a long distance away. This means that the risk of a drone crash onto electric power facilities always resides. In addition, there exists another danger of electromagnetic interference with the drones on autopilot waypoint tracking under ultra-high voltage environments. This paper presents a patrol monitoring plan of autopilot drones for power transmission lines and its field tests. First, the magnetic field effect on an autopilot patrol drone is investigated. Then, how to build the flight path to avoid the magnetic interference is proposed and our autopilot drone system is introduced. Finally, the effectiveness of the proposed patrol plan is confirmed through its field test results in the 154 kV, 345 kV and 765 kV transmission lines in Chungcheongnam-do.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.18-34
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• 2021

Pedestrian deaths in Korea due to traffic accidents are 40 percent of the fatalities in traffic accidents, which is about twice the average of OECD member countries. To reduce severe pedestrian accidents, it is necessary to apply the accident reduction measures to high-risk drivers (novice, elderly, and commercial vehicle drivers) who are more likely to cause traffic accidents than general drivers. Therefore, this study analyzed the effect of safe driving education on high-risk drivers' behavior. Here, the safe driving education is chosen as the measure to reduce traffic accidents. As part of the study, sudden pedestrian crossing situations were implemented in the driving simulator, and the vehicle trajectory data were collected to compare the driving behavior before and after the education. Most surrogate safety measures showed no improvement in the driving behavior of novice and elderly drivers, and the effect of safe driving education was found to be significant only in the group of commercial vehicle drivers. The results implied that additional measures such as pedestrian safety infrastructure and driver assistance systems, apart from the safe driving education, may be needed for novice and elderly drivers to reduce pedestrian accidents caused by them. With the findings mentioned above, this study is expected to provide a foundation to establish a plan to reduce pedestrian accidents caused by high-risk drivers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.74-91
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• 2023

Several that analyzed the effectiveness of traffic safety facilities a method of comparing changes in the number of accidents, accident severity, speed through traffic accident data before and after installation or speed data collected from vehicle detection systems (VDS). , when traffic accident data is used, it takes a long time to collect because must be collected for at least one year before and after installation. , the road environment may change during this period, such as the addition of other traffic safety facilities in addition to the facilities to be analyzed. , the location of the VDSs for speed data is often different from the location where analysis is required, and there is a problem in that the investigators are exposed to the risk of traffic accident during on-site investigation. Therefore, this study a case study by establishing a methodology to determine effectiveness video images with a drone, extracting data using a program, and comparing vehicle driving speeds before and after speed reduction facilities. Vehicle speed surveys using drones are much safer than observational surveys conducted on highways and have the advantage of tracking speed changes along the vehicle, it is expected that they will be used for various traffic surveys in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.

• Fire Science and Engineering
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• v.27 no.6
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• pp.83-88
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• 2013

Catastrophic disasters are sprouting out recently, i.e., the radiation leaks and the hydrofluoric acid gas leaks, etc. The restoration work for these kinds of disasters is very harmful and dangerous for human beings to handle themselves, thus allowing manless robots to fly the reconnaissance planes over to the disaster stricken areas and do the necessary work instead. For this endeavor and purpose, we created and tested an intelligent robot that can inspect those areas, using Mbed (ARM processor) technology temperature sensors and gas sensors aided by CAM (Computer-Aided Manufacturing) cameras. Also, HTTP Server, PC, androids and their combined efforts allow their remote controlled operation from far away with timing control. These intelligent robots can be on duty for 24 hours, minimizing the accidents and crimes and what not, and can respond more quickly when these misfortunes actually happen. We can anticipate the economic effects as well, derived from the reduced needs for hiring human resources.

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.106-118
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• 2014

The objective of this study is to develop a methodology for evaluating the effectiveness of in-vehicle pedestrian warning systems. Driving Simulator-based experiments were conducted to collect data to represent driver's responsive behavior. The braking frequency, lane change duration, and collision speed were used as measure of effectiveness (MOE) to evaluate the effectiveness. Collision speed data obtained from the simulation experiments were further used to predict pedestrian injury severity. Results demonstrated the effectiveness of warning information systems by reducing the pedestrian injury severity. It is expected that the proposed evaluation methodology and outcomes will be useful in developing various vehicular technologies and relevant policies to enhance pedestrian safety.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.623-634
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• 2020

The purpose of this study is to suggest improvement measures focusing on traffic accidents in apartment complexes that occur to children among life safety accidents. Roads in most apartment complexes are not "roads" under the Road Traffic Act. In addition, there is no mandatory punishment or regulations, so the perception of the danger of traffic accidents in apartment complexes is very low. Recent, traffic in automobiles and motorcycles is increasing in the apartment complex, and traffic accidents are frequently occurring due to low safety awareness for both drivers and pedestrians. Accordingly, this study attempted to identify cases of traffic accidents in children's apartment complexes and to present problems and improvement measures for accidents. Problems of traffic accidents through child traffic accidents The current affairs are meaningful in that they are aware of dangers to children, carers, and drivers, and suggest traffic safety measures in apartment complexes.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.825-831
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• 2020

In modern society, automobiles have become an essential means of transportation. It is the only consumable that is worn by contacting the ground among automobile parts. If the tyres are severely worn, the tyres may be break, presenting a risk of a serious accident to the driver. To avoid this risk, drivers should check tyre pressure and tread condition before driving a car. Tyre inflation pressure can be easily measured by TPMS, but in the case of tyre tread conditions, it can be cumbersome when the driver measures it directly using a coin or vernier caliper. This hassle can expose the driver to traffic accidents due to tyre breakage by neglecting to measure the condition of the tyre tread. In this paper, we introduce the contents of research to design an IoT service-based system that can automatically measure automobile tyres, and we verified the possibility of realizing the system by actually implementing and testing some components of the system.