• Proceedings of the KSME Conference
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.654-659
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• 2001

In the virtual environment, reality can be enhanced by offering the motion based on a motion simulator in harmony with visual and auditory modalities. In this research, the Stewart platform based motion simulator has been developed. This motion simulator is driven by the electric motors, and offers the slightly wider workspace compared to the commercial available simulators. In order to compensate for the limited range of the motion platform, the washout filters with fixed coefficients have been usually adopted. In this paper the new approach is proposed to tune the filter coefficients based on the fuzzy logic on the real-time basis. It is shown that performance with the variable filter coefficients is better than that with the fixed ones. The driving simulator based on the bicycle dynamics was developed by integrating the motion simulator and graphic system.

• Journal of the Korea Institute of Military Science and Technology
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• 제20권6호
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• pp.803-812
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• 2017

This paper proposes a local path planning method for stable autonomous driving in rough terrain. There are various path planning techniques such as candidate paths, star algorithm, and Rapidly-exploring Random Tree algorithms. However, such existing path planning has limitations to reflecting the stability of unmanned ground vehicles. This paper suggest a path planning algorithm that considering the stability of unmanned ground vehicles. The algorithm is based on the genetic algorithm and assumes to have probability based obstacle map and elevation map. The simulation result show that the proposed algorithm can be used for real-time local path planning in rough terrain.

• Journal of Mechanical Science and Technology
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• 제20권1호
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• pp.29-41
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• 2006

Nowadays, with the advancement of computers, computer simulation linked with VR (Virtual Reality) technology has become a useful method for designing the automotive driving system. In this paper, the VR simulation system was developed to investigate the driving performances of the ASV (Advanced Safety Vehicle) equipped with an ACC (Adaptive Cruise Control) system. For this purpose, VR environment which generates visual and sound information of the vehicle, road, facilities, and terrain was organized for the realistic driving situation. Mathematical models of vehicle dynamic analysis, which includes the ACC algorithm, have been constructed for computer simulation. The ACC algorithm modulates the throttle and the brake functions of vehicles to regulate their speeds so that the vehicles can keep proper spacing. Also, the real-time simulation algorithm synchronizes vehicle dynamics simulation with VR rendering. With the developed VR simulation system, several scenarios are applied to evaluate the adaptive cruise controlled vehicle for various driving situations.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제66권5호
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• pp.792-799
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• 2017

The real time unmanned monitoring system of an equipment's internal parts and condition requires the monitoring device to be able to stop at a set location on the rail. However, due to the slip between the driving surface and the roller, an error occurs between the actual position and the command position. In this paper, a method to compensate the position error due to the roller slip is proposed. A proximity sensor located at both ends of the rail detects the starting point and the maximum position pulse, linearly compensating the error between the angular position of the motor and the mechanically fixed starting and maximum position pulse of the rail in forward and reverse direction. Moreover, unlike the existing servo position controller, the motor adopts the position detection method of Hall sensor in BLDC (Brushless DC) and applies an algorithm for low-speed driving so that a stable position control is possible. The proposed rail guided unmanned monitoring system with driving slip compensator was tested to verify the effectiveness.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.234-240
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• 2003

This study aims to investigate dynamic properties of express buses in the very low frequencies which affect motion sickness incidence. Since passengers often use express buses for long distance traveling, it is a critical point whether a give rise to motion sickness or not. In the study accelerations at the three points on the floor of the six test vehicles were measured during the driving at constant speeds. By applying frequency weighting curves suggested in ISO 2631-1 and ISO 2631-3, physical amount of accelerations were changed into perceptual amount which determines incidence of motion sickness. Motion sickness dose values were calculated from the frequency weighted time history of accelerations, and compared between the vehicles, driving conditions, and the seat positions in the bus. During the driving on public road and high ways for 50 minutes vomiting incidence ratios ranged 0.4 to 0.8%, which were equivalent to 2.4 to 4.8% for 5 hours' driving. The value of 4.8 % means two among 45 passengers may vomit after the traveling, which is very serious situation. Considering the very smooth driving condition at which the data were collected, motion sickness dose values will increase in real situations

• Journal of Convergence for Information Technology
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• 제8권3호
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• pp.193-198
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• 2018

Recently, the casualties of automobile traffic accidents are rapidly increasing, and serious accidents involving serious injury and death are increasing more than those of ordinary people. More than 70% of major accidents occur in drowsy driving. Therefore, in this paper, we studied the drowsiness prevention system to prevent large-scale disasters of traffic accidents. In this paper, we propose a real-time flicker recognition method for drowsy driving detection system and drowsy recognition according to the increase of carbon dioxide. The drowsy driving detection system applied the existing image detection and the deep running, and the carbon dioxide detection was developed based on the IoT. The drowsy prevention system using both of these techniques improved the accuracy compared to the existing products.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.142-148
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• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.83-88
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• 2010

Recently car navigation systems which have been widely spread and evolved. The systems use various information and techniques such as real time traffic information and augmented reality technique. In order to provide a shortest path with good flow, real-time traffic information provided by DMB is required. Augmented reality technique is also introduced to give a reality to driver by displaying real images captured by camera during driving. However, these operate well when the system receives GPS data normally. Exact information about the positions of vehicles is a base that supports the above function with realities. This paper proposes a model for acquiring exact position of vehicles. When the GPS does not operate normally, the proposed model uses various data which are generated by integrated vehicle networks.

• Journal of Korea Society of Digital Industry and Information Management
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• 제19권4호
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제7권3호
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• pp.15-28
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• 2008

According to the accident statistics published by the National Police Agency in 2006, it can be recognized that drivers' characteristics and driving behaviors are the most causational factors on the traffic accidents. At present, although many recording tools such as digital speedometer or black box are distributed in the market to meet social requests of decreasing traffic accidents and increasing safe driving behaviors, it is also true that it still lacks in obvious categories for dangerous driving types and then, the efficiency of the categories to be studied has been low. In this study, dangerous driving types are redefined. They are grouped into 7 classifications in the first level, and the seven classifications are regrouped into 16 in more detail. To verify the redefined dangerous driving types, a Data-logger is developed to receive and analyze the data that occur from the driving behaviors of the test vehicle. The developed Data-logger can be used to construct a real time warning system and safe driving management system with dangerous driving patterns based on acceleration, deceleration, Yaw rate, image data, etc.