• International Journal of Highway Engineering
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• v.10 no.3
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• pp.19-31
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• 2008

Video image processing systems(VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on wide-area detection, i.e., multi-lane surveillance algorithm provide traffic parameters with single camera such as flow and velocity, as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. The objective of this research was to relate traffic safety to VIPS tracking and this paper has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, volumes, speed, and occupancy rate as well as traffic information via tripwire image detectors. Also the developed system has been verified by comparing with commercial VIP detectors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.49-60
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• 2008

The current VIPS are not effective in safety point of view, because they are originally developed for mimicking loop detectors. Therefore, it is important to identify vehicle trajectories in real time, because recognizing vehicle movements over a detection zone enables to identify which situations are hazardous, and what causes them to be hazardous. In order to improve limited safety functions of the current VIPS, this research has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, incident detection and conflict as well as traffic information via tracking image detectors. This system is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of various traffic situations. Experiments were conducted for measuring the cases of incident detection and abnormal vehicle trajectory with rapid lane change.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.186-190
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• 2015

Intelligent vehicle plans motion and navigate itself based on the surrounding environment perception. Hence, the precise environment recognition is an essential part of self-driving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environment, the vehicle must percept all the dynamic obstacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. First, exceptional radar measurement model for vehicle, concentrated on the corner, is described by mixture density network (MDN), and applied to particle filter weighting. Also, to conquer the curse of dimensionality of particle filter and estimate the time-varying number of multi-target states, reversible jump markov chain monte carlo (RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstrated through several computer simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1804-1809
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• 2015

The study in railway system to apply a fuel cell system with high efficiency and mobility than other renewable energy is being actively conducted. It is needed a analysis on load characteristics and control method of rolling stock in order to apply to rolling stock. This paper presents study on control small-scale prototype power converter electric railway vehicle using fuel cell generation system. Experiment is conducted through real fuel cell generation system and reference speed applying the driving curve of the actual electric railway vehicle was applied. Also, output voltage of boost converter is controlled considering characteristic of fuel cell. And it was confirmed characteristic according to powering and regeneration of inverter.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.863-870
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• 2008

In this paper, a new filtering scheme for vehicle tracking with distorted measurement is presented. This filtering scheme is essential for the implementation of the adaptive cruise control (ACC) system. The proposed method combines the IMM and the probabilistic fuzzy model and is named as the Fuzzy IMM (FIMM). The IMM is employed to recognize the intention of the preceding vehicle. The probabilistic furry model is introduced to compensate the distortion of the range sensor. Finally, a computer simulation is performed to illustrate the validity of the suggested algorithms.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.461-468
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• 2005

This paper proposes a wheel-assembly automation system, which assembles a wheel into a hub of a vehicle hung to a moving hanger in a car manufacturing line. A macro-micro manipulator control strategy is introduced to increase the system bandwidth and tracking accuracy to ensure insertion tolerance. A camera is equipped at the newly designed wheel gripper, which is attached at the center of the end-effector of the macro-micro manipulator and is used to measure position error of the hub of the vehicle in real time. The redundancy problem in the macro-micro manipulator is solved without complicated calculation by assigning proper functions to each part so that the macro part tracks the velocity error while the micro part regulates the fine position error. Experimental results indicate that tracking error satisfies the insertion tolerance of assembly $({\pm}1mm)$, and thus it is verified that the proposed system can be applied to the wheel assembly task on a moving hanger in the manufacturing line.

• The Journal of Korea Robotics Society
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• v.11 no.3
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• pp.172-182
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• 2016

Lane-level vehicle positioning is an important task for enhancing the accuracy of in-vehicle navigation systems and the safety of autonomous vehicles. GPS (Global Positioning System) and DGPS (Differential GPS) are generally used in navigation service systems, which however only provide an accuracy level up to 2~3 m. In this paper, we propose a 3D vision based lane-level positioning technique which can provides accurate vehicle position. The proposed method determines the current driving lane of a vehicle by tracking the 3D position of traffic signs which stand at the side of the road. Using a stereo camera, the 3D tracking paths of traffic signs are computed and their projections to the 2D road plane are used to determine the distance from the vehicle to the signs. Several experiments are performed to analyze the feasibility of the proposed method in many real roads. According to the experimental results, the proposed method can achieve 90.9% accuracy in lane-level positioning.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.227-232
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• 2018

In this paper, we propose a faster object detection and tracking method using Deep Learning, UAV(unmanned aerial vehicle), Kalman filter and YOLO(You Only Look Once)v3 algorithms. The performance of the object tracking system is decided by the performance and the accuracy of object detecting and tracking algorithms. So we applied to the YOLOv3 algorithm which is the best detection algorithm now at our proposed detecting system and also used the Kalman Filter algorithm that uses a variable detection area as the tracking system. In the experiment result, we could find the proposed system is an excellent result more than a fixed area detection system.

• Bulletin of the Korean Space Science Society
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• 2004.10b
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• pp.332-336
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• 2004

This paper describes the remote tracking algorithm using measurements (azimuth, elevation, and slant range) of the radar ground station. Kalman filter model for noise reduction of the measured information is first derived by linearizing with respect to angle, angular rate, range, and range rate. And then a tracking algorithm is introduced to calculate the position of the vehicle during in-flight. The simulation results show that the algorithm is practical and effective enough tracking position of the vehicle in considerably less error.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.29-32
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• 1999

Korea Multi-Purpose SATellite(KOMPSAT) is scheduled to be launched by TAURUS launch vehicle in November, 1999. Tracking, Telemetry and Command(TT&C) operation and the flight dynamics support should be performed for the successful Launch and Early Orbit Phase(LEOP) operation. After the first contact of the KOMPSAT spacecraft, initial orbit determination using ground based tracking data should be performed for the acquisition of the orbit. Although the KOMPSAT is planned to be directly inserted into the Sun- synchronous orbit of 685 km altitude, the orbit maneuvers are required fur the correction of the launch vehicle dispersion. Flight dynamics support such as orbit determination and maneuver planning will be performed by using KOMPSAT Mission Analysis and Planning Subsystem(MAPS) in KOMPSAT Mission Control Element(MCE). The KOMPSAT MAPS have been jointly developed by Electronics and Telecommunications Research Institute(ETRI) and Hyundai Space & Aircraft Company(HYSA). The KOMPSAT MCE was installed in Korea Aerospace Research Institute(KARI) site for the KOMPSAT operation. In this paper, the orbit determination and maneuver planning are introduced and simulated for the KOMPSAT spacecraft in LEOP operation. Initial orbit determination using short arc tracking data and definitive orbit determination using multiple passes tracking data are performed. Orbit maneuvers for the altitude correction and inclination correction are planned for achieving the final mission orbit of the KOMPSAT.