• 한국융합학회논문지
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• 제9권10호
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• pp.249-255
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• 2018

본 논문은 자율주행 개인화를 위한 역 충돌시간 및 차두시간 융합 기반 인간중심 제어 알고리즘 개발에 관한 것이다. 운전자 및 탑승자의 자율주행에 대한 이질감 최소화를 위해 인간중심적 주행제어 기술이 필요하다. 운전자가 선행차량과 함께 주행하는 조건에서 운전자의 주행특성을 분석하고, 분석된 결과를 종방향 자율주행 제어에 반영하였다. 주행특성으로 가속도, 역 충돌시간, 차두시간 분포가 분석되었고, 운전자의 주행특성이 반영된 제어기 구성을 위해 역 충돌시간 및 차두시간을 이용한 종방향 제어기를 구성하였다. 본 연구에서 제안된 제어 알고리즘은 Matlab/Simulink 환경에서 구성되었으며 실 주행데이터 기반 성능평가가 수행되었다.

• 산업경영시스템학회지
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• 제23권58호
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• pp.71-79
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• 2000

In this study, through the roadside interview, drivers'feeling about fatigue has been evaluated synthetically and systematically when they drive a vehicle according to their sex, vehicle type, driving career, etc. Also, with the human sensibility evaluation technique, we grasped the human sensibility structure for the fatigue in a vehicle and as an objective evaluation index for comfort and fatigue in a vehicle, we developed a sensibility database. Through the survey and research, extracting and understanding the importance of factors which have influence on the fatigue in driving can be used as basic data that can suggest more comfort and pleasant driving environment to drivers. Also, human sensibility database regarding to the comfort and fatigue in a vehicle can be used as basic data in ergonomic vehicle design, evaluation for seat , comfort seat development, development of vibration reduction method and so on.

• 웰빙융합연구
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• 제7권1호
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• pp.27-33
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• 2024

Purpose: This study investigates human error accidents in the Korean railway sector, emphasizing the need for systematic management to prevent such incidents, which can have fatal consequences, especially in driving-related jobs. Research design, data and methodology: This paper analyzed data from the Aviation and Railway Accident Investigation Board and the Korea Transportation Safety Authority, examining 240 human error accidents that occurred over the last five years (2018-2022). The analysis focused on accidents in the driving, facility, electric, and control fields. Results: The findings indicate that the majority of human error accidents stem from negligence in confirmation checks, issues with work methods, and oversight in facility maintenance. In the driving field, errors such as signal check neglect and braking failures are prevalent, while in the facility and electric fields, the main issues are maintenance delays and neglect of safety measures. Conclusions: The paper concludes that human error accidents are complex and multifaceted, often resulting from a high workload on engineers and systemic issues within the railway system. Future research should delve into the causal relationships of these accidents and develop targeted prevention strategies through improved work processes, education, and training.

• 한국생활과학회지
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• 제24권3호
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• pp.341-358
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• 2015

This study aims to identify predictors of driving cessation among Korean elderly. Data from 2011 Elderly Survey conducted by Ministry of Health and Welfare and Korea Institute for Health and Social Affairs were used for the analysis. Based on Webber, Porter, Menec(2010)'s comprehensive theoretical framework for mobility, the model of this study tests five major determinants of driving cessation including financial, psychosocial, environmental, physical and cognitive factors. Results of logistic regression analysis showed that economic status, marital status, contacts with relatives and friends, residential location, taking medication, muscle strength, age, gender, and job were significant predictors of driving cessation of older drivers. Specifically, lower economic status, unmarried status, less contacts with relatives and friends, living in the city, taking medication, weaker muscle strength, older age, female, non-working status were significant risk factors for driving cessation. Practical implications in light of study findings were discussed.

• 자동차안전학회지
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• 제14권4호
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• pp.70-76
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• 2022

This paper investigates the effects of age, gender, and situational factors on take-over performance in automated driving. The existing automated driving systems still consider a driver as a fallback-ready user who is receptive to take-over requests. Thus, we need to understand the impact of situations and human factors on take-over performance. 34 drivers drove on a simulated track, consisting of one baseline and four event scenarios. The data, including the brake reaction time and the standard deviation of lane position, and physiological data, including the heart rate and skin conductance, were collected. The analysis was performed using repeated-measures ANOVA. The results showed that there were significant age, gender, and situational differences in the takeover performance and mental workload. Findings from this study indicated that older drivers may face risks due to their degraded driving performance, and female drivers may have a negative experience on automated driving.

• 산업경영시스템학회지
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• 제23권57호
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• pp.51-62
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• 2000

In this study, the psycho-physiological response of drivers was investigated in terms of EEG(Electroencephalogram), especially with the fractal dimensions computed by Chaotic algorithm. The Chaotic algorithm Is well Known to sensitively analyze the non-linear information such as brain waves. An automobile with a fully equipped data acquisition system was used to collect the data. Ten healthy subjects participated in the experiment. EEG data were collected while subjects were driving the car between Won-ju and Shin-gal J.C. on Young-Dong highway The results were presented in terms of 3-Dimensional attractor to confirm the chaotic nature of the EEG data. The correlation dimension and fractal dimension were calculated to evaluate the complexity of the brain activity as the driving duration changes. In particular, the fractal dimension indicated a difference between the driving condition and non-driving condition while other spectral variables showed inconsistent results. Based upon the fractal dimension, drivers processed the most information at the beginning of the highway driving and the amount of brain activity gradually decreased and stabilized. No particular decrease of brain activity was observed even after 100 km driving. Considering the sensitivity and consistency of the analysis by Chaotic algorithm, the fractal dimension can be a useful parameter to evaluate the psycho-physiological responses of human brain at various driving conditions.

• 한국도로학회논문집
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• 제19권6호
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• pp.139-145
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• 2017

PURPOSES : This study analyzed the difference in a driver's workload between using a driving simulator and field driving in tunnel, highway. METHODS : Based on the literature review, it was found that a driver's workload could be quantified using biosignals. This study analyzed the biosignal data of 30 participants using data collected while they were using a driving simulator and during a field test involving tunnel driving. Relative energy parameter was used for biosignal analysis. RESULTS : The driver's workload was different between the driving simulator and field driving in tunnels, highway. Compared with the driving simulator test, the driver's workload exhibited high value in field driving. This result was significant at the 0.05 level. The same result was observed before the tunnel entrance section and 200 m after the entrance section. CONCLUSIONS : This study demonstrates the driving simulator effect that drivers feel safer and more comfortable using a driving simulator than during a field test. Future studies should be designed considering the result of this study, age, type of simulator, study site and so on.

• 대한기계학회논문집A
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• 제26권5호
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• pp.969-975
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• 2002

Visual effects are important cues for providing occupants with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant's posture, therefore, the total human body motion must be considered in a graphic simulator. A real-time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and lane-change for acquiring the accelerations of chassis of the vehicle model. A hybrid III 50%ile adult male dummy model was selected and modeled in an ellipsoid model. Multibody system analysis software, MADYMO, was used in the motion analysis of an occupant model in the seated position under the acceleration field of the vehicle model. Acceleration data of the head were collected as inputs to the viewpoint movement. Based on these data, a back-propagation neural network was composed to perform the real-time analysis of occupant motions under specified driving conditions and validated output of the composed neural network with MADYMO result in arbitrary driving scenario.

• 한국자동차공학회논문집
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• 제21권1호
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• pp.1-8
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• 2013

Recently, the automotive technology has developed with electronics and information technology as convergence technology while vehicles had been regarded as machines. Moreover, vehicles are becoming more intelligent and safer devices, assembly of advanced technologies by customers' demand. Even though all of installations of vehicle have attracted as diverting devices, it cause drivers' mistakes like delay of response on traffic condition. Here, we proposed the Field Operational Test (FOT) environment which could be used as driving and road conditions collector(Vehicle motion, Traffic condition, Driver input, Driver state, etc.) for researches about Driver Friendly Intelligent System(SCC, LDWS, etc.), Human Vehicle Interface(Driving Workload, etc.) and Economic Drive Model. Furthermore driving patten and fuel consumption patten of drivers were analyzed by measured data and direction of future research was suggested.

• 자동차안전학회지
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• 제14권3호
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.