• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.349-356
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• 2020

This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

• Journal of the Korean Society for Railway
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• v.14 no.6
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• pp.575-583
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• 2011

In order to minimize the maintenance cost at local lines, Information & Communication Technology based onboard train control system is being developed. Unlike the central traffic control based fixed block system, this system use a moving block method and railway driver direct control switch and railway crossing. The purpose of this paper is to analyze the concerned main operational issues are as follows: the preparation of train operation, drivability, the role of driver and controller, block system and cost. We defined the role of driver and driver's input data for train service, and we designed the business process of driver using UML tool. We considered the aspect of drivability, DMI is needed to support the braking moment for the driver and driver training simulator. We designed the driver business process for control of switch and railway crossing. We analyzed the fixed block system and moving block system to confirm the difference with the existing operational method. The cost analysis structure is also needed for the operation cost comparison.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.739-742
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• 2010

The demand of MOST interface module is increasing with car-multimedia network system. MOST devices consist of INIC part which controls MOST network and EHC part which is used by user. The efficient data communication between EHC and INIC demands implementation of a proper device driver. This paper presents a design method for I2C communication driver which is used for transmitting control messages between nodes of MOST network. For effetive I2C communication, we design driver with NetService API. For testing the experiment, we use the MOST audio interface deivce for porting driver sources and will develop various driver on MOST device based OS.

• Korean Journal of Applied Biomechanics
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• v.15 no.3
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• pp.79-94
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• 2005

Because the golf swing is very complex movement, it is varied in different gender, skill level, and club. This study measured kinematic variables in golf swing regarding gender, skill level, and club types using FasTrak electromagnetic tracking system. Golf swing kinematics including time variables, linear and angular displacement variables, angular velocity variables were analyzed and compared through three-way ANOVA The results were as follows: 1. In time variables, Female and driver showed longer backswing time than male and iron. Downswing time was longer in female and nonexperts than male and experts. Backswing time over downswing time was longer in experts than nonexperts. Uncocking time was longer in male and experts than female and nonexperts. The differences were statistically significant (p<.05). 2. In displacement variables, Female and nonexperts showed greater backswing head lift than male and experts. Impact head lift was greater in female, nonexperts, and iron than male, experts, and driver. The differences were statistically significant (p<.05). Experts and driver showed greater top hip rotation angle than nonexperts and iron. Top shoulder rotation angle was greater in male, experts and driver than female, nonexperts, and iron. X-factor was greater in male, experts, and driver than female, nonexperts, and iron. Male and experts showed greater backswing hip sway than female and nonexperts. Impact hip sway was greater in male and iron than female and driver. The differences were statistically significant (p<.05). 3. In velocity variables, Experts displayed higher impact hip rotation velocity than nonexperts. Impact shoulder rotation velocity was greater in male and iron than female and driver (p<.05).

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.173-182
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• 2021

In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver's behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver's distraction behavior.

• Database Research
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• v.34 no.3
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• pp.45-57
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• 2018

The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver's condition, but there is a limit to the generalized driver's condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver's condition. This paper confirms the effect of driver's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver's condition into three categories showed a classification accuracy of 96%.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.229-242
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• 2020

Purpose - To study the factors that are related with compliance for the voluntary surrender program of drive's license for senior drives and to estimate the rate of voluntary surrender. Design/methodology/approach - We online surveyed 147 individuals in order to analyze the intention to comply the program. The surveyees were driver's license holders aged 54 to 65 and drawn to answer their willingness to comply in a 5-point Likert scale. We developed an ordered probit model to analyze the data. Findings - According to the main results of the empirical analysis, the higher the average number of driving per week, regardless of gender and age, the more negative was the driver toward voluntarily surrender of driver's license. Various policy measures need to be prepared to activate the voluntary surrender system using the willingness to voluntarily surrender the driver's license and the characteristics of the voluntary returners, and the implementation of customized safe driving training for elderly drivers may be one such method. Research implications or originality - Population aging is known to cause various social problems, and in the transportation field, the number of elderly drivers and traffic accidents by elderly drivers are also increasing. The government is implementing a program for elderly drivers to voluntarily surrender of their driver's licenses in order to reduce traffic accidents caused by elderly drivers. If only elderly drivers who rarely drive surrender their driver's licenses then traffic accidents may not reduce as much as the program targets, however, and further policy instruments may be needed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.219-227
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• 2022

In Korea, as the aging phenomenon accelerates, the problem of traffic accidents related to the elderly is continuously emerging. Efforts to improve this are being implemented, but unfortunately the results of these effects are not clear. Therefore, in this study, the effect of traffic characteristics and incentive policies on the return of driver's licenses of elderly drivers was reviewed. As a result of the analysis, it was confirmed that the intention to return the driver's license was low in the case of men, older people, those with low dependence on public transportation, those who undertook long driving hours, and those who took frequent trips. On the other hand, financial incentives were found to play a positive role with regard to the intention to return the driver's license. However, the effect is expected to be insignificant for those with a low intention to return the driver's license. As a result, under the current policy, it is predicted that there is a limit to improving the social problems caused by elderly drivers, meaning that it is necessary to review approaches that induce the return of their driver's licenses.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.361-370
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• 2017

In this paper, correlation analysis was performed between questionnaire and machine learning based aggressive tendency measurements. this study is part of a aggressive driver detection using machine learning and questionnaire. To collect two types tendency from questionnaire and measurements system, we constructed experiments environments and acquired the data from 30 drivers. In experiment, the machine learning based aggressive tendency measurements system was designed using a driver behavior detection model. And the model was constructed using accelerate and brake position data and hidden markov model method through supervised learning. We performed a correlation analysis between two types tendency using Pearson method. The result was represented to high correlation. The results will be utilize for fusing questionnaire and machine learning. Furthermore, It is verified that the machine learning based aggressive tendency is unique to each driver. The aggressive tendency of driver will be utilized as measurements for advanced driver assistance system such as attention assist, driver identification and anti-theft system.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.191-207
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• 2001

■ The 1/sup st/ Driver: Electronic Documents ■ EDI via VAN ■ Limited use of electronic processing ■ The 2/sup nd/ Driver: Internet Infrastructure ■ Web/EDI, HTTP, FTP, MIME ■ Open network ■ The 3/sup rd/ Driver: XML ■ Enables the definition of platform-independent protocols for the exchange of data ■ Business Processes and Documents in XML format ■ XML/EDI ■ XML message exchange: SOAP(omitted)