• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.542-545
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• 2004

The vibration reduction process for the driver comfort of a forklift truck is studied in this study since the related driver comfort is a primary design target in the vehicle design recently. However, the underlying study for a vibration analysis regarding to the driver comfort is still an element stage. Thus, a preceding large work has to be needed to apply the CAE technology for the detail vehicle design, and it prevents the vehicle optimal design. To reduce the proceeding large works, the evaluated process and required data are comply with the accumulated trouble shooting experiences in this study. Since the driver comfort is a human related problem, the human vibration index associated with analysis vibration result is additionally introduced as a driver comfort judgement value.

• 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.

• 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.

• Journal of Korean Society of Transportation
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• v.20 no.2
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• pp.17-26
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• 2002

Driver's psychophysiological load is one of the key measures for evaluating the safety of the highway. The purpose of this study is to propose and to test the methodology of evaluating the length limit of tangent section using driver's psychophysiological load. Driver's psychophysiological data is represented by the data acquire by frontal and occipital lobe. In order to compare the differences between tangent segments and the orders, real road driving experiments were performed. We collected psychophysiological data during the operation of vehicles. The experimental data were analyzed using FFT (Fast Fourier Transform) and relative power spectrum tools. These routine produces the beta value which is a major factor in consideration of driver's condition. The results in this study are summarized as follows: (1) A new methodology of evaluating the length limit in tangent section of highway using driver's psychophysiological load was proposed. (2) It was observed that driver's work load at tangent section was three times lower than that at the other section types. The beta value at tangent section is 2.219, while that at general section is 0.821. (3) It was observed that the driver's work load was significantly dropped to 0.428 after the continuous driving of 4.2km tangent section. (4) Based on the experimental subjects(from 27 Years to 31). we suggest that 30 times of design speed(3.0 km) could be acceptable as the length limit of tangent section in highway rather than the Previous limit which is 20 times of design speed(2.0km).

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.573-574
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• 2008

In this paper, we propose a mixed driving circuit for the source driver of QVGA-scale TFT-LCD driver IC to reduce the area of the source driver. In the mixed driving circuit, graphic data pass or go through the mixed channel driver whether RGB data are the same or not. The mixed driving circuit has been designed in transistor level using the 0.35um CMOS technology and has been verified using Hspice.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.37-44
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• 2015

In this paper, we propose the assistance method to enable safe driving through analysis of dangerous driving behavior using real-time alarm by vehicle speed, azimuth data and smartphone. For this method, smartphone is receiving driving data from digital tachograph using communication. Safe driving habit is a very important issue to commercial vehicle because that driver's long time driving than other vehicle type driver. Existing methods are very inefficient to improve immediately dangerous driving habits during driving because proceed driving behavior analysis after the vehicle operation. We propose the new safe driving assistance method that can prevent traffic accidents by real-time and improve the driver's wrong driving habits through real-time dangerous driving behavior analysis and notification the result to the driver. We have confirmed that the method in this paper will help to improve driving habits and can be applied through the proposed method implementation and simulation experiment.

• 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.

• The Journal of Industrial Distribution & Business
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• v.8 no.6
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• pp.25-31
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• 2017

Purpose - This study examines car accidents that occurred in South Korea territory, and analyzes criminal liability of the offender and certain issues of driver's insurance, but a civil liability to the injured is excluded as civil liability belongs to auto insurance. Research design, data, and methodology - With carrying out this research, case study of driver's liability and literature review were adopted throughout. For this, car accidents that occurred in South Korean territory were examined and then criminal liability of the offender and certain issues of driver's insurance were analyzed. Results - From this case study on driver's liability it was found that the offender cannot receive insurance money from the insurer irrespective of the valid drive insurance, if there is no 'bill of agreement of criminal consensus'. This study suggests some ideas, offers suggestions of convenience and assistance of qualified claim staff to overcome a hurdle of drive insurance. Conclusions - As long as the accident is not a fraud and scam by the parties concerned, advance payment of agreement of criminal consensus is required to the insured, the policy holder within the limit of liability of driver insurance, on condition that the drive insurance is valid.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.1-6
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• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.