• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.132-151
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• 2022

Vehicle localization is one of the core technologies for autonomous driving. Image-based localization provides location information efficiently, and various related studies have been conducted. However, the image-based localization methods using feature points or lane information has a limitation that positioning accuracy may be greatly affected by road and driving environments. In this study, we propose a line segment matching framework for accurate vehicle localization. The proposed framework consists of four steps: line segment extraction, merging, overlap area detection, and MSLD-based segment matching. The proposed framework stably performed line segment matching at a sufficient level for vehicle positioning regardless of vehicle speed, driving method, and surrounding environment.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.249-255
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• 2018

This paper presents a human-centered control algorithm for personalized autonomous driving based on the integration of inverse time-to-collision and time headway. In order to minimize the sense of difference between driver and autonomous driving, the human-centered control technology is required. Driving characteristics in case that vehicle drives with the preceding vehicle have been analyzed and reflected to the longitudinal control algorithm. The driving characteristics such as acceleration, inverse time-to-collision, time headway have been analyzed for longitudinal control. The control algorithm proposed in this study has been constructed on Matlab/Simulink environment and the performance evaluation has been conducted by using actual driving data.

• International Journal of Automotive Technology
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• v.4 no.2
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1125-1134
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• 2004

This paper presents a mathematical model which is about the dynamics of not only a two wheel steering vehicle but a four wheel steering vehicle. A sliding mode ABS control strategy and PID rear wheel control logic are developed to improve the brake and cornering performances, and enhance the stability during emergency maneuvers. The performances of the controllers are evaluated under the various driving road conditions and driving situations. The numerical study shows that the proposed full car model is sufficient to accurately predict the vehicle response. The proposed ABS controller reduces the stopping distance and increases the vehicle stability. The results also prove that the ABS controller can be employed to a four wheel steering vehicle and improves its performance. The four wheel steering vehicle with PID rear wheel controller shows increase of stability when a vehicle speed is high and sharp cornering maneuver when a vehicle speed is low compared to that of a two wheel steer vehicle.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.1055-1062
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• 1996

A method for automatically driving paddy vehicles, such as rice transplanters, etc., straight ahead in a paddy field was investigated . The direction of such vehicles must be precisely controlled to do the operations as straight. However, the alignment of the from wheels becomes distorted due to the unevenness of the ground, preventing the vehicle form going straight. If the proper alignment of the front wheels is maintained , the vehicle can be driven straight ahead greater precision. To investigate the influence of the ground uneveness, the behavior of a paddy vehicle running over an obstacle was quantified. The left wheel ran over an obstacle on a flat concrete road surfaced. When the steering wheel was free, the front wheels were forced toward the left when vehicle went up the obstacle and toward the right when the vehicle went down it. The torsion of the wheel when the vehicle went down the obstacle was larger than that when it went up ,so it turned right 5 degrees. Sinc hydraulic control steering decreased the steering angle , it turned right 3 degrees. These results suggest that a vehicle can be driven straight ahead with high precision when the steering angle is changed in response to the direction and inclination of the vehicle . Such results were obtained in a paddy field tests.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.6
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• pp.694-701
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• 2016

According to revolutionary developments in automobile technologies, eco-friendly advanced vehicles (hybrid vehicle, hydrogen fuel-cell vehicle, electric vehicle, etc.) are rapidly increasing. The electromagnetic compatibility is getting more important for development of a vehicle because those advanced vehicles are driven by electric energy and equipped with more electric systems. In general, electromagnetic compatibility tests consist of an electromagnetic interference(EMI) test and an electromagnetic susceptibility(EMS) test. EMI test of the electric vehicles are needed not only in driving mode but also in charging mode because they must be recharged by much electric energy for driving. Depending on vehicle manufacturers, the charging device type and the location of charging device inlet in electric vehicles are various. In this paper, in order to investigate EMI of electric vehicles in charging mode in consideration of the direction of measuring antenna and the location of charging device inlet, a series of electromagnetic emission tests are conducted using three electric vehicles (neighborhood electric vehicle, electric vehicle and electric vehicle-bus). The test results show that electromagnetic emission measurements in charging mode are dependent on the direction of measuring antenna and the location of charging device inlet.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.1
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• pp.148-156
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• 2014

The objective of this study is to compare NOx emissions from light duty diesel vehicles measured from on-road tests that conducted under various driving routes and seasonal conditions. We measured real-driving NOx emissions using PEMS, portable emissions measurement system, under the urban, rural and motorway road traffic conditions. On-road tests were repeated at summer, fall and winter season. The accumulated driving distance is more than 1,200 km per each vehicle. Route average NOx emission factors were compared among nine route-season combinations. The emission characteristics of each combinations were investigated using time series mass emission rates and vehicle operation-based emission rates and activities, which is based on U.S. EPA's MOVES model. Most concerned route-season combination is "urban road condition at summer", which shows two to eleven times higher NOx emissions than other combinations. The emission rates and activities under low speed operating conditions should be managed in order to reduce urban-summer NOx. From a NOx control strategy perspective, the exhaust gas recirculation, EGR, is observed to be properly operated under wide range of vehicle driving conditions in Euro-5 vehicles, even if the air conditioner turns on. In high power demanding conditions, the effect of overspeeding could be more critical than that of air conditioner activation.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.734-744
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• 2013

In this study, the telematics device was installed on the car (OBD-II) to collect the information on the operation conditions from each sample vehicle. Based on the information the domestic driving pattern was analysed and the ratio of cold start length was estimated. As a result of analysis for driving pattern, we found a difference in the frequency of driving on the hourly or seasonal basis. Then, the driving pattern of the rush hours, weekdays, and weekends could be derived. Also, from the study, an average of 2.22 times per day occurred in a single trip and average driving distance for the trip was 15.72 km. In addition, the proportion of cold start length was analyzed to be 16.11%. The seasonal cold start length has big difference from season to season (Winter 26.63%, Summer 8.22%, Intermediate 12.65%). There was an inverse relationship between the outside temperature and ratio of cold start length. In order to improve the accuracy of the cold emission estimation, it is necessary to apply domestic ratio of cold start length that driving pattern and temperature in Korea is reflected.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.411-423
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• 2018

This study was conducted to identify the correlation between driving under the influence (DUI) and bicycle riding under the influence and the impact factors of DUI among Korean adults. The participants were 3,385 Korean adults. Data were collected Seventh Korea National Health and Nutrition Examination Survey. The pervasiveness of DUI was 7.8% of the adult population in Korea. The relevance of DUI and bicycle riding under the influence was a statistically significant difference. The factors affecting of DUI were bicycle riding under the influence, aerobic physical activity, one-time drinking, frequency of binge drinking, experience aboard a driving under the influence vehicle. To prevent DUI among Korean Adults, we would need to encourage aerobic physical activity in daily and workplace, recognize that bicycle drunk riding can lead to drunk vehicle driving, and that management of alcohol use disorders and be planning to the vehicle driving before drinking.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.