• Knowledge Management Research
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• v.20 no.4
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• pp.57-74
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• 2019

Self-driving technology is changing the paradigm of automotive industry. Since it is imperative to converge this emerging technology with ICT and there is a time lag between securing this emerging technology and occurrence of firm performance, it is difficult to make decisions on technology development. This study investigates patent indicators to understand the impact of self-driving technology on performance of automotive parts companies. We analyze 611 of self-driving technology patents from 2000 to 2016 and the financial performance of 270 small and medium businesses that register the corresponding patents with consideration of up to four years of time lag. Our analysis results show that patents related to self-driving has a positive impact on asset growth of 2 years and sales growth of 3 years in terms of time lag. This study contributes to the literature by analyzing the impact of technology development on the firm performance in case of convergence of the automotive and ICT industries. Additionally, this study provides a role of patent in knowledge management planning through technology development as a practical implication.

• Convergence Security Journal
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• v.22 no.2
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• pp.77-90
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• 2022

Domestic and foreign automakers compete to lead the autonomous vehicle industry through continuously developing self-driving technologies. These self-driving technologies are evolving with dependencies on the connection between vehicles and other objects such as the environment of cars and roads. Therefore, cyber security vulnerabilities become more likely to occur in the self-driving environment, so it is necessary to prepare for them carefully. In this paper, we model the threats in autonomous vehicles and make the checklist to securely countermeasure them.

• Korean Journal of Occupational Therapy
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• v.26 no.4
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• pp.57-72
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• 2018

Objective : This study aims to identify the self-reported driving abilities of elderly drivers and their correlations to the demographic factors that influence them, and to verify the adequacy of the hypothetical model, constructed based on vision, auditory, cognition, motor, and psychological factors, in order to present a path model on the self-reported driving abilities of elderly drivers. Methods : The participants in this study were 122 elderly drivers aged 65 years or older residing in the community. This study evaluated the following factors of the participants: Vision and hearing, motor ability, cognitive ability, depression, self-reported driving abilities. Results : The results of this study are as follows. In the case of men, the self-reported driving ability score was higher than for women, and those driving 6-7 days per week had higher scores than those driving 3 days or less. The period of holding a driver's license and driving experience positively correlated with self-reported driving abilities. The final model of factors influencing the self-reported driving abilities of elderly drivers had a p value (.911) exceeding .05; TLI (1.202), NFI (.949), and CFI (1.000) of over .90; and RMSEA (.000) of lower than 0.1, indicating that the hypothesis model fit the data well. First, the directly influential factors on the self-reported driving abilities of elderly drivers were depression, decreased hearing, and grip strength. Second, age was found to have a direct influence on depression and grip strength; moreover, depression and grip strength as a mediator indirectly influenced their self-reported driving abilities. Third, depression was found to have a direct influence on their delayed cognitive processing and grip strength. Conclusion : The significance of this study is in the identification of direct and indirect factors influencing the self-reported driving abilities of elderly drivers in regional communities, and in the verification of multi-dimensional effects of diverse factors influencing such abilities.

• Journal of Platform Technology
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• v.9 no.2
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• pp.46-54
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• 2021

Recently, interest in the safety of Self-driving has been increasing. Self-driving have been studied and developed by many universities, research centers, car companies, and companies of other industries around the world since the middle 1980s. In this study, we propose the automatic extraction method of the threatening obstacle on the Road for the Self-driving. A threatening obstacle is defined in this study as a comparatively large object at center of the image. First of all, an input image and its decreased resolution images are segmented. Segmented areas are classified as the outer or the inner area. The outer area is adjacent to boundaries of the image and the other is not. Each area is merged with its neighbors when adjacent areas are included by a same area in the decreased resolution image. The Obstacle area and Non Obstacle area are selected from the inner area and outer area respectively. Obstacle areas are the representative areas for the obstacle and are selected by using the information about the area size and location. The Obstacle area and Non Obstacle area consist of the threatening obstacle on the road. Through experiments, we expect that the proposed method will be able to reduce accidents and casualties in Self-driving.

• Journal of Platform Technology
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• v.9 no.4
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• pp.71-78
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• 2021

Self-driving vehicles are increasing as new alternatives to solving problems such as human safety, environment and aging. And such technology development has a great ripple effect on other industries. However, various problems are occurring. The number of casualties caused by self-driving is increasing. Although the collision of fixed obstacles is somewhat decreasing, on the contrary, the technology by active obstacles is still insignificant. Therefore, in this study, in order to solve the core problem of self-driving vehicles, we propose a method of extracting active obstacles on the road. First, a center scene is extracted from a continuous image. In addition, it was proposed to extract activity obstacles using activity size and activity repeatability information from objects included in the center scene. The center scene is calculated using region segmentation and merging. Based on these results, the size of the frequency for each pixel in the region was calculated and the size of the activity of the obstacle was calculated using information that frequently appears in activity. Compared to the results extracted directly by humans, the extraction accuracy was somewhat lower, but satisfactory results were obtained. Therefore, it is believed that the proposed method will contribute to solving the problems of self-driving and reducing human accidents.

• Korean Journal of Culture and Social Issue
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• v.20 no.4
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• pp.281-305
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• 2014

This study investigated the relationship between older driver's safe driving behavior, driving mobility(amount of driving, assessment for driving-related physical ability, confidence for adaptation in driving situation, amount of social activity) and subjective well-being(life satisfaction, positive/negative affect). The data of these variables were collected by questionnaire method based on face-to-face interview. The results can be summarized as followings. First, the older driver who reported higher scores in the self-report safe driving behavior questionnaire tended to show higher level of subjective well-being and driving-based mobility. Second, all the sub-factors in the driving-based mobility questionnaire were positively related to life satisfaction, positive affect but negatively related to negative affect except amount of driving. Finally, it was found that both confidence for adaptation in driving situation and amount of social activity positively mediated the relationship between self-report safe driving behavior and life satisfaction and positive affect, but confidence for adaptation in driving situation negatively mediated the relationship between self-report safe driving behavior and negative affect, Implication and suggestion were discussed.

• Korean Journal of Culture and Social Issue
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• v.17 no.4
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• pp.381-393
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• 2011

Eco driving is a strategy to reduce energy consumption and greenhouse gas emissions from motor vehicle. However, it has not received much attention until recently. Psychological studies on this issue have been limited and the majority of existing studies have primarily been based on engineering and educational approaches. This study examined the effects of a self-management technique on two driving behaviors: speeding and putting the gears in neutral while waiting at the signal. The self-management technique consisted of three behavior interventions: goal-setting, self-monitoring, and reward. Three drivers participated in this study. An AB multiple baseline design across participants was adopted. Results showed that the self-management technique was effective in increasing both driving behaviors. Implications of the present findings and suggestions for future research were discussed.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.18-31
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• 2021

The purpose of the present study was to present evidence for driving interventions for the elderly by conducting programs that can improve visual perception and cognitive function in a driving simulator for elderly drivers and analyse their effects. Three elderly subjects who were 65 years or older, did not have physical and cognitive impairments, and were able to drive themselves participated in the present study. A total of 12 intervention sessions, of which subjects participated in 10 sessions of nine different visual perception and cognitive function programs available in a driving simulator, were conducted and pre- and post-program assessments were conducted (two assessments in total). The assessments included the evaluation of visual perception, frontal lobe function, concentration, safe driving behaviour, and self-efficacy. The results showed positive effects of the driving simulator program on the visual perception, frontal lobe function, concentration, safe driving behaviour, and self-efficacy of all subjects. Changes in the simulator results showed remarkable improvement in the response evaluation, judgment evaluation, and predictive power evaluation, but showed difficulties in interference tasks and depth perception in common. The results showed positive effects of driving simulator training on the driving ability of the elderly, and consistent provision of such training is expected to improve the quality of life of the elderly by securing the safety of driving and actively supporting social participation.

• Safety and Health at Work
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• v.13 no.2
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• pp.187-191
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• 2022

Background: Traffic injuries have become a significant public health problem in low- and middle-income countries. Several studies have examined the role of personality and attitude toward traffic safety in predicting driving behaviors in diverse types of drivers. Few studies have investigated risky behavior among public passenger van drivers. This study aims to identify the predictors of self-reported risky driving behavior among public van drivers. Method: A total of 410 public van drivers were interviewed at terminal stations in Bangkok. Hierarchical regression models were applied to determine the effects of demographics, personality traits, and attitude on self-reported risky driving behaviors. Results: The results indicated that drivers with a high education level, more working days, and high scores for normlessness and anger were more likely to report risky driving behaviors (p < 0.05). Conclusion: The personality traits and attitude toward speeding account for aberrant self-reported risky driving behavior in passenger van drivers. This could be another empirical basis for evidence-based road safety interventions in the context of public transport.

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.151-200
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• 2018

At the Davos Forum in 2016, the Fourth Industrial Revolution, a reference to cloud Schwab, is dramatically changing our lives, and at its height, self-driving cars are emerging as the talk of the day. But there are still many hurdles to overcome before the nation can successfully introduce and establish self-driving cars. In particular, it is necessary to change the paradigm of the legal system centered on human beings to one that includes artificial intelligence. The stable operation of the self-driving car era requires drastic changes to the people-centric legislation system. That is, it is necessary to collect information on the total number of drivers of self-driving cars (what is available), general vehicles on general roads, civil and criminal liability issues in the event of traffic accidents, and collection of insurance problems concerning autonomous driving vehicles. In this study, a separate bill was proposed to address the various legal issues arising from the operation of self-driving cars from a legislative perspective by considering the domestic laws related to road transport, the current state of legislation on foreign soil and legal issues related to self-driving cars.