• Korean Journal of Culture and Social Issue
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• v.22 no.1
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• pp.19-39
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• 2016

Though research has shown that public transportation drivers experience greater burnout than other drivers, the sources of their burnout and possible mediators remain largely unknown. In response, in this study we investigate the relationships among sensation seeking, Type A driving behavior, and meaning of work to elucidate the burnout experienced by bus drivers in Gyeonggi-do, South Korea. To collect data regarding these relationships, 188 bus drivers answered a questionnaire involving the sensation seeking scale, burnout scale, and meaning of work scale. Results showed that Type A driving behavior mediated the relationship between sensation seeking and burnout, while meaning of work moderated the mediated model. These findings demonstrate that sensation-seeking bus drivers tend to experience greater burnout given their tendency to exhibit Type A driving behavior, and this relationship depends on perceived meaning of work. This study therefore contributes meaningful information and outlines significant implications in understanding drivers' burnout.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.243-247
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• 2016

Driving Event Data such as the rapid acceleration, the rapid deceleration, the sudden braking, and the sudden departure, and over speeding provide important information to predict or analyze the driving habits and accident risk of a driver. Most of the data that represent the driver's driving habits generally fit to the parametric distribution, whereas extreme parts of the data to estimate the accident risk of a driver may not. This paper presents an empirical distribution that is divided into two regions, one is from the normal distribution, and the other is from the general pareto distribution for the driving habits of a driver.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.1-9
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• 2024

In this paper, we propose a new approach by analyzing driver behavior and gaze changes within the vehicle in real-time to assess and predict the risk of traffic accidents. Utilizing data analysis and machine learning algorithms, this research precisely measures drivers' abnormal behaviors and gaze movement patterns in real-time, and aggregates these into an overall Risk Score to evaluate the potential for traffic accidents. This research underscores the significance of internal factors, previously unexplored, providing a novel perspective in the field of traffic safety research. Such an innovative approach suggests the feasibility of developing real-time predictive models for traffic accident prevention and safety enhancement, expected to offer critical foundational data for future traffic accident prevention strategies and policy formulation.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.70-82
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• 2022

At the current stage of a semi-autonomous vehicle, there are situations in which the vehicle has to request take-over control to the driver quickly. However, current self-driving cars use only simple messages and warning sounds to notify drivers when handing over control, so they do not adequately convey considerations of individual characteristics or explanations of various emergent situations. This study investigated how visual and auditory information and the efficacy of drivers in self-driving cars can improve efficient take-over requests between the car and the driver. We found that there were significant differences in driver's cognitive load, reliability, safety, usability, and usefulness according to the combination of three visual and auditory information provided in the experiment of the take-over request situation. The results of this study are expected to help design self-driving vehicles that can communicate more safely and efficiently with drivers in urgent control transition situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.124-140
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• 2017

There exists a limitation to provide proper advance information for safe maneuvering through guidance and caution signs in freeway mobile work zones, unlike fixed work zones. Although a work-protection vehicle is currently deployed at the rear of the work vehicle, more active countermeasures to prevent crashes are required. The purpose of this study was to propose a method to evaluate the safety in mobile work zones and to present effective countermeasures. Driving simulation experiments were conducted to analyze characteristics of driver's behavior in mobile and fixed work zones. Safety distance index (SDI) based on the comparison of stopping distances of a work-protection vehicle and a following subject vehicle was used to evaluate traffic safety. More dangerous driving behavior was observed in the mobile work zone. Especially, it was identified that the lane-change of vehicles following the work- protection vehicle was late. Therefore, it is necessary to actively introduce methods to provide warning information so that the driver can recognize the work-protection vehicle in advance and carry out appropriate evasive maneuvers.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.9-20
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• 2009

Navigation system providing route-guidance and traffic information is one of the most widely used driver-support system these days. Most of the navigation system is based on the 2D map paradigm so the information is ed and encoded from the real world. As a result it imposes a cognitive burden to the driver to interpret and translate the ed information to real world information. As a new concept of navigation system, augmented-reality navigation system (AR navigation) is suggested recently. It provides navigational guidance by imposing graphical information on real image captured by camera mounted on a vehicle in real-time. The ultimate goal of navigation system is to assist the driving task with least driving workload whether it is based on the abstracted graphic paradigm or realistic image paradigm. In this paper, we describe the comparative studies on how map navigation and AR navigation affect for driving tasks by experimental research. From the result of this research we obtained a basic knowledge about the two paradigms of navigation systems. On the basis of this knowledge, we are going to find the optimal design of navigation system supporting driving task most effectively, by analyzing characteristics of driving tasks and navigational information from the human-vehicle interface point of view.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.4D
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• pp.313-322
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• 2012

This study analyzes taxi driver's risk-taking propensity with respect to risk-taking behaviour and traffic locus of control. In order to explore the traffic risk-taking, we present a predictive model by structural analysis of driver's risk-taking propensity. By applying this model to survey data from taxi drivers, we can observe that driver's risk-taking propensity has a significant impact on the traffic violation intention, and the higher perception of law and the lower lack of law-abiding drivers have, the more they tend to violate. Second, we test using multivariate analysis if the level of risk-taking propensity differs by the locus of control( external or internal). Drivers of external control shows higher risk-taking level compared to those of internal control so that the risk-taking propensity shows difference according to the locus of control for the responsibility of traffic accidents. The structural equation model of our study yielded ${\chi}^2$ = 279.7, ${\chi}^2$/df = 1.55, RMSEA = 0.44, GFI = 0.911, TLI = 0.916, CFI = 0.929.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06b
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• pp.455-459
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• 2010

본 논문에서는 운전자의 운전 패턴에 따른 연료 소비량을 감소하는 ECO_Driving에서 효율적으로 방법을 제안하였다. 이 ECO-Driving 시스템은 차량의 정확한 상태 정보를 바탕으로 운전 상태를 운전자에게 제공하여 경제운전 행동으로 배기가스 감축, 에너지절약을 유도하였다. 차량의 상태정보를 추출하기 위해서 차량 네트워크인 CAN 버스를 사용하였으며, 이를 하드웨어로 구현하였다. 효율적인 ECO_Driving을 위한 차량 데이터들을 추출하여 분석하였으며, 이들 데이터를 바탕으로 운전자에게 에코여부를 제공하는 프로그램을 구현하였다. 제한적인 실험이었지만 연비를 감소하는 것을 확인할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.813-814
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• 2023

매년 졸음 및 부주의 운전으로 인한 교통사고로 인명 및 재산 피해가 끊이지 않고 있다. 즉각적인 졸음 감지만을 위한 기존 시스템의 단점과 한계를 보완하고자, 본 논문에서는 위험 행동을 감지한 후 당시 사진과 데이터를 저장하고 이를 점수로 환산하여 장기적인 운전 습관 개선을 목표로 하는 운전자 관리 감독 시스템을 구현하였다. 이 시스템은 화물차 운전자와 같이 장시간 운전을 하는 대상에게 안전 주행을 장려하고 올바른 운전문화를 확립하게 하여 교통안전에 긍정적 역할을 담당할 수 있다.

• Journal of IKEEE
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• v.22 no.3
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• pp.866-869
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• 2018

This paper presents the analysis of the human driving behavior based on the expression as a GMDH technique focusing on the driver's collision avoidance maneuver. The driving data are collected by using the three dimensional driving simulator based on CAVE, which provides stereoscopic immersive vision. A GMDH is also introduced and applied to the measured data in order to build a mathematical model of driving behavior. From the obtained model, it is found that the longitudinal distance between cars($x_1$), the longitudinal relative velocity($x_2$) and the lateral displacement between cars($x_4$) play important roles in the collision avoidance maneuver under the 3D environments.