• Proceedings of the KSME Conference
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• 2000.04a
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• pp.505-510
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• 2000

Vehicle performances such as fuel consumption and catalyst-out emissions are affected by a driving pattern, which is defined as a driving cycle with the grade in this study. We developed an algorithm to recognize a current driving pattern by using a neural network. And this algorithm can be used in adapting the driving control strategy to the recognized driving pattern. First, we classified the general driving patterns into 6 representative driving patterns, which are composed of 3 urban driving patterns, 2 suburban driving patterns and 1 expressway driving pattern. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, relative duration spent at stop, average acceleration and average grade are chosen to characterize the driving patterns. Second, we used a neural network (especially the Hamming network) to decide which representative driving pattern is closest to the current driving pattern by comparing the inner products between them. And before calculating inner product, each element of the current and representative driving patterns is transformed into 1 and -1 array as to 4 levels. In the end, we simulated the driving pattern recognition algorithm in a temporary pattern composed of 6 representative driving patterns and, verified the reliable recognition performance.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.107-122
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• 2015

PURPOSES : The use of virtual driving tests to determine actual road driving behavior is increasing. However, the results indicate a gap between real and virtual driving under same road conditions road based on ergonomic factors, such as anxiety and speed. In the future, the use of virtual driving tests is expected to increase. For this reason, the purpose of this study is to analyze the gap between real and virtual driving on same road conditions and to use a calibration formula to allow for higher reliability of virtual driving tests. METHODS : An intelligent driving recorder was used to capture real driving. A driving simulator was used to record virtual driving. Additionally, a virtual driving map was made with the UC-Win/Road software. We gathered data including geometric structure information, driving information, driver information, and road operation information for real driving and virtual driving on the same road conditions. In this study we investigated a range of gaps, driving speeds, and lateral positions, and introduced a calibration formula to the virtual record to achieve the same record as the real driving situation by applying the effects of the main causes of discrepancy between the two (driving speed and lateral position) using a linear regression model. RESULTS: In the virtual driving test, driving speed and lateral position were determined to be higher and bigger than in the real Driving test, respectively. Additionally, the virtual driving test reduces the concentration, anxiety, and reality when compared to the real driving test. The formula includes four variables to produce the calibration: tangent driving speed, curve driving speed, tangent lateral position, and curve lateral position. However, the tangent lateral position was excluded because it was not statistically significant. CONCLUSIONS: The results of analyzing the formula from MPB (mean prediction bias), MAD (mean absolute deviation) is after applying the formula to the virtual driving test, similar to the real driving test so that the formula works. Because this study was conducted on a national, two-way road, the road speed limit was 80 km/h, and the lane width was 3.0-3.5 m. It works in the same condition road restrictively.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.210-216
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• 2015

This paper proposes a method to determine vehicle driving state using the driving characteristics index. This index is a quantitative value to classify the driving state of a vehicle with its velocity and heading angle in that instant. It can classify driving state into straight driving, lane changing driving and curve driving in real time. In addition, the number of positional information is movably set up by designed region of interest. The proposed index is expressed on the stable driving states. Each driving state has characteristic tendency, and is compared with index distributional areas. The proposed method is verified by the actual driving experiment on the KATECH proving ground.

• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.105-112
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• 2013

In this paper, EV (Electric Vehicle) and ICE (Internal Combustion Engine) vehicle simulators are developed to compare maximum driving range of EV and ICE vehicle according to different driving patterns. And, simulations are performed for fourteen constant velocity cases (20, 30, 40, ${\ldots}$, 150 km/h) and four different driving cycles. From the simulation results of constant velocity, it is found that the decreasing rate of maximum driving range for EV is larger than the one for ICE as both the vehicle velocity and the driving power increase. It is because the battery efficiency of EV decreases as both the velocity and the driving power increase, whereas the engine and transmission efficiencies of ICE vehicle increase. From the results of four driving cycle simulation, the maximum driving range of EV is shown to decrease by 50% if the average driving power of driving cycle increases from 10 to 20kW. It is because the battery efficiency decreases as the driving power increases. In contrast, the maximum driving range of ICE vehicle also increases as the average driving power of driving cycle increases. It is because the engine and transmission efficiencies also increase as the driving power increases.

• Korean Journal of Culture and Social Issue
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• v.12 no.3
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• pp.23-47
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• 2006

The purpose of this research is to study the relation between the change of driver's driving confidence level in the age categories and driving behavior. To survey the driving confidence level, we used the 'Driving Confidence Scale' questionnaire and surveyed the drive career, mileage, driving days, violation of traffic regulation (drunk driving, overspeed), traffic accident experience (assaulter, sufferer) together. The subjects of investigation were from 19-year-old to 80-year-old and 1,055 persons were participated in the research totally. To examinethe structure of driving confidence level, we executed the factor analysis. We compared the driving confidence level in the age categories (under 29-year-old, 30~39, 40~49, 50~64, over 65-year-old) and studied the relation between driving confidence level and driving behavior. Driving confidence level was composed of 4 factors such as 'insensibility to situation', 'unsafe driving', 'careless concentration' and 'self-efficacy of driving', and there was decreasing tendency for driving confidence level and overall driving behavior according to increasing age. Driving confidence level had the interrelation with age range, assaulting accident, suffered accident, driving period, drunk driving, overspeed, driving career and so on. We examined the difference of driving confidence level and driving behavior by dividing the participated drivers' groups into the traffic accident experienced group, drunk driving group and overspeed driving group, and there was a significant difference on driving confidence level and driving behavior between the group who had not experienced the violation of traffic regulation or traffic accident and another group who had experienced the violation of traffic regulation or traffic accident.

• Korean Journal of Materials Research
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• v.32 no.7
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• pp.320-325
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• 2022

Single OLED and tandem OLED was manufactured to analyze the electroluminescence characteristics of DC driving, AC driving, and full-wave rectification driving. The threshold voltage of OLED was the highest in DC driving, and the lowest in full-wave rectification driving due to an improvement of current injection characteristics. The luminance at a driving voltage lower than 10.5 V (8,534 cd/m²) of single OLED and 20 V (7,377 cd/m²) of a tandem OLED showed that the full-wave rectification drive is higher than that of DC drive. The luminous efficiency of OLED is higher in full-wave rectification driving than in DC driving at low voltage, but decrease at high voltage. The full-wave rectification power source may obtain higher current density, higher luminance, and higher current efficiency than the AC power source. In addition, it was confirmed that the characteristics of AC driving and full-wave rectification driving can be predicted from DC driving characteristics by comparing the measured values and calculated values of AC driving and full-wave rectification driving emission characteristics. From the above results, it can be seen that OLED lighting with improved electroluminescence characteristics compared to DC driving is possible using full-wave rectification driving and tandem OLED.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.75-82
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• 2014

The purpose of this study is to identify the effects of sensation seeking and driving confidence level on reckless driving behavior in motor sports club's members. Data was collected from 265participants of the Trackday, driving school. Subjects answered the questionnaire using convenient sampling method. Data which is obtained through self-administration was analyzed by using the frequency analysis and multiple regression. Results are as follows. First sensation seeking has influenced on reckless driving behavior such as, drunken driving and errors. Second, driving confidence level has influenced on reckless driving behavior such as, drunken driving, overspeed driving and errors.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.3
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• pp.129-133
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• 2012

As people are getting concerned to Environment recently, researches on the environmentally-friendly and effective railway system have been conducted in every aspects. Especially as it became known that the pattern of train driving causes the difference in energy consumption, the researches on the train driving to minimize the energy consumption are gaining a lot of interest. The main study showed the optimal driving to minimize energy consumption while driving after analyzing real driving data measured by EMU of Bundang-line real driving, determining the impact on energy consumption due to train driving pattern changes, executing a variety of simulation on real driving patterns by Matlab Simulink and finally driving between stations by given driving times.

• Korean Journal of Culture and Social Issue
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• v.13 no.4
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• pp.101-112
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• 2007

We investigated the influences of 'Circumstance Insensibility', 'Unsafe Driving', 'Incautious Driving', and 'Self-efficacy of Driving', consisting of driving confidence level, on traffic accidents. 1055 drivers conducted Driving Confidence Level Questionnaire, items about their dangerous driving experience and traffic accidents. Among them, after checking the missing items, we analyzed data of 998 drivers. As a result, we found the relation between driving confidence levels and traffic accidents. Specially, 'Circumstance Insensibility' and 'Unsafe Driving' influenced traffic accidents. However 'Circumstance Insensibility' had negative effects, the other side 'Unsafe Driving' had positive effects on traffic accidents. This result means each factor of driving confidence levels have different relation with traffic accidents. 'Incautious Driving' and 'Self-efficacy of Driving' didn't have any effects on traffic accidents in this research. In future, it should be investigated 'Incautious Driving' and 'Self-efficacy of Driving'.