• International Journal of Highway Engineering
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• v.18 no.3
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• pp.115-125
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• 2016

PURPOSES : This study proposes a methodology to collect data necessary for microlevel emission estimation, such as second-by-second speeds and road grades, and to accordingly estimate emissions. METHODS : To ease data collection for microlevel emission estimation, a vehicle equipped with speed- and location-recording instruments as well as equipment for measuring road geometry was used. As a case study, this vehicle and the proposed methodology were used on a 10-km-long highway in Yongin City, Korea. Emissions from the vehicle during driving were estimated in various microscale driving conditions. RESULTS : Differences in the estimated emission under different microscale driving conditions cannot be ignored. Compared with the estimations obtained when second-by-second data were not considered, CO and NOx emissions were more than threefold higher when considering second-by-second speed; similarly, CO and NOx emission estimations were higher by approximately 10% and 3%, respectively, when considering second-by-second road grade. CONCLUSIONS : The proposed method can estimate vehicle emissions under real-world driving conditions in such applications as road design and traffic policy assessments.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.31-43
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• 2012

Domestic road type-and period-specific driving pattern measurement was required as Korea's participation in developing "Worldwide harmonized light-duty vehicle emission test procedure (WLTP/DHC)" studied by UN WP29. This study measured road driving data reflecting road and traffic conditions of Korea, and analyzed seven types of representative road type-and period-specific driving patterns with driving pattern standardization methodology proposed by WP29. PAMS (Portable Activity Monitoring Systems) equipment was used to collect enormous (35,410km) road driving data. There are significant difference among seven derived driving patterns.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.56-62
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• 2010

The purpose of this study was to compare the difference of driving pattern between male and female drivers for a straight driving and unexpected situation using driving simulator and motion data. The participants included total 60 university students; 30 males aged 24.3$\pm$1.4 years and 30 females aged 23.2$\pm$1.9 years with 1~3 years of driving experience. The driving task required participants to keep the constant distance (20m, 25m or 30m) with preceding vehicle running at 55~65km/hr speed using driving simulator which was programed unexpected situation for two minutes. Simulator and motion data were acquired. The acquired data was divided in straight driving block for 40 second and unexpected situation block for 2 second. The coefficient of variation (CV) of lane keeping and jerk-cost (JC) function were analyzed for straight driving and unexpected situation blocks. The results show that CV was smaller in males than females for both straight and unexpected situation blocks (p < .05). JC was smaller in females than males for both straight and unexpected situation blocks. As the distance of vehicles become longer, JC was smaller for both male and female (p < .05).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.152-166
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• 2022

This study analyzed the causal relationship among operating characteristics of commercial freight vehicles, dangerous driving behaviors, and traffic accident risk. The study applied the existing accident cause and prevention theory to arrive at this relationship. Data related to working characteristics of driver, driving experience, driving ability, driving psychology, vehicle characteristics (size), dangerous driving behavior, and traffic accidents were collected from 303 commercial freight vehicle drivers. Working characteristics and dangerous driving behavior data are based on the driver's digital driving record. The traffic accident data is based on the insurance accident data reflecting actual traffic accidents. First, a structural equation model was built and verified using the model fitness index. Then, the developed model was used to analyze the causal relationship between multiple independent and dependent variables simultaneously. Four dangerous driving behaviors (sudden deceleration, sudden acceleration, sudden passing, and sudden stop) were found to be highly related to traffic accidents. The results further indicate that it is necessary to establish a safety management policy and intensive management for small-sized freight vehicles, drivers with insufficient driving ability, and drivers with dangerous driving behaviors. Such policy and management are expected to reduce traffic accidents effectively.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.3-7
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• 2005

Recently, vehicle simulators are widely used to evaluate driver s responses and driver assistance systems. It needs much effort to construct the virtual driving environment for a vehicle simulator. In this study, it is described how to make effectively the roads and the driving environment for a vehicle simulator. GIS (Geographic Information System) is used to construct the roads and the environment effectively. Because the GIS is the integrated system of geographical data, it contains useful data to make virtual driving environment. First, boundaries and centerlines of roads are extracted from the GIS. From boundaries, the road width is calculated. Using centerlines, mesh models of roads are constructed. The final graphic model of roads is constructed by mapping road images to those mesh models considering the number of lanes and the kind of surface. Data of buildings from the GIS are extracted. Each shape and height of building is determined considering the kind of building to construct the final graphic model of buildings. Then, the graphic model of roadside trees is constructed to decide their locations. Finally, the driving environment for driving simulator is constructed by converting the three graphic models with the graphic format of Direct-X and by joining the three graphic models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.145-154
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• 1998

The effects of secondary task on driving performance and subjective workload were investigated during a simulated driving. The driving performance was determined by the appropriateness of break timing. The driving simulator was provided by the Korea Road Traffic Safety Association. The subjective workload was tested by using a multidimensional measure such as NASA-TLX. Road was categorized into two types: narrow alley and wide street. The secondary task included pushing the number on the cellular phone, pushing radio channel, and conversing with a passenger. Seventeen subjects volunteered in the study. The data were analyzed by using SAS. Results showed that using the cellular phone and pushing channel during driving caused 3∼22% decline of driving performance and 42∼59% increase of subjective workload respectively. These results indicated that the secondary task could be potentially dangerous although there was not a significant performance decrease due to the notable increase of mental workload. In the future, if we can use a more sensitive and realistic driving simulator, the effects of secondary task under a dynamic driving situation can be investigated.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.16-26
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• 2023

Recently, the importance of simulation in a virtual driving environment as well as real road-based tests for autonomous vehicle testing is increasing. Real road tests are being actively conducted at K-City, an autonomous driving test bed located at the Korea Automobile Safety Test & Research Institute of the Transportation Safety Authority. In addition, the need to advance the K-City virtual driving environment and build a virtual environment similar to the autonomous driving system test environment in real road tests is increasing. In this study, for K-City of Korea Automobile Safety Test & Research Institute, using detailed drawings and actual field data, K-City virtual driving environment was advanced, and similarity verification was verified through comparative analysis with actual K-City.

• ETRI Journal
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• v.34 no.5
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• pp.727-733
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• 2012

This paper demonstrates a new driving scheme that allows reducing the supply voltage of data drivers for low-power active matrix organic light-emitting diode (AMOLED) displays. The proposed technique drives down the data voltage range by 50%, which subsequently diminishes in the peak power consumption of data drivers at the full white pattern by 75%. Because the gate voltage of a driving thin film transistor covers the same range as a conventional driving scheme by means of a level-shifting scheme, the low-data supply scheme achieves the equivalent dynamic range of OLED currents. The average power consumption of data drivers is reduced by 60% over 24 test images, and power consumption is kept below 25%.

• Journal of Auto-vehicle Safety Association
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• v.3 no.2
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• pp.28-33
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• 2011

This paper presents driving path estimation algorithm for adaptive cruise control system and advanced emergency braking system using multi-sensor fusion. Through data collection, yaw rate filtering based road curvature and vision sensor road curvature characteristics are analyzed. Yaw rate filtering based road curvature and vision sensor road curvature are fused into the one curvature by weighting factor which are considering characteristics of each curvature data. The proposed driving path estimation algorithm has been investigated via simulation performed on a vehicle package Carsim and Matlab/Simulink. It has been shown via simulation that the proposed driving path estimation algorithm improves primary target detection rate.