• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.167-175
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• 2012

For establishing a standard of design element of the smart highway, this study investigated driver's anxiety EEG according to running speeds and geometric designs. Also, the experiment was implemented on 60 subjects. Based on running speed data and brainwave data, which were obtained from the experiment, this study analyzes anxiety EEG according to running speeds and geometric designs, and finally draws a forecasting model of anxiety EEG by selecting affecting factors of anxiety EEG. Forecasting model shows that left curve is the most influential on anxiety EEG figure. The reason is because when driver is driving on the first-lane, his or her visibility is impeded by a median strip. For this reason, anxiety EEG figure increases. And also steep downward slope and large radius of curve are heavily influential on driver's anxiety EEG figure. It is judged that anxiety EEG figure is increased by high speed on those section. Thus, the forecasting model of anxiety EEG suggested on this study will be utilized for design phase, and will decide the design speed on the superhighway. So, it will be used to make practical and safety road.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.127-135
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• 2009

Most traffic information that drivers receive while driving are stored in their short-term memory and disappear within a few seconds. Contemporary modeling approaches using a dummy variable can't fully explain this phenomenon. As such, this study proposes to use utility functions of real-time in-vehicle traffic safety information (IVTSI), analyzing its safety impacts based on empirical data from an on-site driving experiment at signalized intersection approach with a limited visibility. For this, a driving stability evaluation model is developed based on driver's driving speed choice, applying an ordered probit model. To estimate the specified utility functions, the model simultaneously accounts for various factors, such as traffic operation, geometry, road environment, and driver's characteristics. The results show three significant facts. First, a normal density function (exponential function) is appropriate to explain the utility of IVTSI proposed under study over time. Second, the IVTSI remains in driver's short-term memory for up to nearly 22 second after provision, decreasing over time. Three, IVTSI provision appears more important than the geometry factor but less than the traffic operation factor.

• Journal of Astronomy and Space Sciences
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• v.20 no.4
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• pp.359-364
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• 2003

In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle's position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satellite visibility environment exists. Hence, multi-sensor fusion including IMU (Inertial Measurement Unit), GPS(Global Positioning System), and DMI (Distance Measurement Indicator) is required to provide the vehicle's position to service provider and driver behind the wheel. The multi-sensor fusion is implemented via algorithm based on Kalman filtering technique. Navigation accuracy can be enhanced using this filtering approach. For the verification of fusion approach, land vehicle test was performed and the results were discussed. Results showed that the horizontal position errors were suppressed around 1 meter level accuracy under simulated non-GPS availability environment. Under normal GPS environment, the horizontal position errors were under 40㎝ in curve trajectory and 27㎝ in linear trajectory, which are definitely depending on vehicular dynamics.