• 제어로봇시스템학회논문지
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• 제22권11호
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• pp.967-976
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• 2016

This paper presents a 3D carrier-smoothed GNSS/DR (Global Navigation Satellite System/Dead Reckoning) integrated system for precise ground-vehicle trajectory estimation. For precise DR navigation on sloping roads, the AHRS (Attitude Heading Reference System) methodology is employed. By combining the integrated carrier phase of GNSS and DR sensor measurements, a vehicle trajectory with an accuracy of less than 20cm is obtained even when cycle slip or change of visibility occur. In order to supplement the weak GNSS environment with DR successfully, the DR sensor is precisely compensated for using GNSS Doppler measurements when GNSS visibility is good. By integrating a multi-GNSS receiver with low-cost IMU, a precise 3D navigation system for land vehicles is proposed in this paper. For real-time implementation, a decoupled Kalman filter is employed in the integrated system. Through field experiments, the performance of the proposed system is verified in various road environments, including sloping roads, good-visibility areas, high multi-path areas, and under-ground parking areas.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.593-596
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• 2018

The speed of vehicles has remained a significant factor that influences the severity of accidents and traffic accident rate in many parts of the world including South Korea. This behavior where drivers drive at speeds which exceed a posted safe threshold is known as 'speeding'. Over the past twenty years, the Korean National Police Agency (NPA) has become aware of an increased frequency of drivers who are speeding. Therefore, fixed-type ASE systems [1] have been installed on hazardous road sections of many highways. These system monitor vehicle speeds using a camera. However, the use of ASE systems has changed the behavior of the drivers. Specifically, drivers reduce speed or avoid the route where the cameras are mounted. It is not practical to install cameras at every possible location. Therefore, it is challenging to thoroughly explore the location where speeding occurs. In view of these problems, the author of this paper designed and implemented a prototype visualization system in which point and color are used to show vehicle location and associated over-speed information. All of this information was used to create a comprehensive visualization application to show information about vehicle driving. In this paper, we present an approach detecting vehicles moving at speeds which exceed a threshold and visualizing the points those violations occur on a map. This was done using vehicle trajectory data collected in Daegu city. We propose steps for exploring the data collected from those sensors. The resulting mapping has two layers. The first layer contains the dynamic vehicle trajectory data. The second underlying layer contains the static road networks. This allows comparing the speed of vehicles on roads with the known maximum safe speed of those roads, and presents the results with a visualization tool. We also compared data about people who drive over threshold safe speeds on each road on days and weekends based on vehicle trajectories. Finally, our study suggests improved times and locations where law enforcement should use monitoring with speed cameras, and where they should be stricter with traffic law enforcement. We learned that people will drive over the speed limit at midnight more than 1.9 times as often when compared with rush hour traffic at 8 o'clock in the morning, and 4.5 times as often when compared with traffic at 7 o'clock in the evening. Our study can benefit the government by helping them select better locations for installation of speed cameras. This would ultimately reduce police labor in traffic speed enforcement, and also has the potential to improve traffic safety in Daegu city.

• 한국CDE학회논문집
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• 제19권4호
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• pp.340-346
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• 2014

There are many factors that contribute to hit probability of the gun shot of ground combat vehicles. Aiming accuracy is mainly affected by the dynamic state of the vehicle. The stabilization error of the turret under system vibration is one of the major factors that affect the aiming accuracy. The vibration of the vehicle is affected by both the state of the road and the speed of the vehicle. This paper analyzes the aiming accuracy of the gun equipped on the GCV when the vehicle drives on the different roads and at different speed. The vertical displacement and the pitch angle of the gun are calculated and the impact points of the target are calculated. Distribution of the impact points on the target is greatly influenced by the pitch rotation rather than vertical displacement. And this aiming errors result in the errors of point of impacts on the target after the bullet flies through the air under trajectory equations. The GCV is modeled using a half-car model with 6 D.O.F. and the specifications of the M2 machine gun are used in trajectory calculation simulation and the target is located in 1000 m away from the gun.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.413-418
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• 1991

Two methods are applied to the problem of trajectory optimization for launch vehicles which burn solid propellant. One is 'Optimal Control' theory, the other is 'NonLinear Programming' method. Trajectory optimization for solid rocket motors has a special problem. The special problem is that the payload of launch vehicle is not the function of control variable. This paper deals with this special problem.

• ETRI Journal
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• 제37권5호
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• pp.1032-1043
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• 2015

ETRI's Co-Pilot project is aimed at the development of an automated vehicle that cooperates with a driver and interacts with other vehicles on the road while obeying traffic rules without collisions. This paper presents a core block within the Co-Pilot system; the block is named "Co-Pilot agent" and consists of several main modules, such as road map generation, decision-making, and trajectory generation. The road map generation builds road map data to provide enhanced and detailed map data. The decision-making, designed to serve situation assessment and behavior planning, evaluates a collision risk of traffic situations and determines maneuvers to follow a global path as well as to avoid collisions. The trajectory generation generates a trajectory to achieve the given maneuver by the decision-making module. The system is implemented in an open-source robot operating system to provide a reusable, hardware-independent software platform; it is then tested on a closed road with other vehicles in several scenarios similar to real road environments to verify that it works properly for cooperative driving with a driver and automated driving.

• 항공우주시스템공학회지
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• 제1권3호
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• pp.37-42
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• 2007

The flight safety system for the satellite launch vehicles is required in order to minimize the risk due to launch vehicle failure. For prompt and reliable decision of flight termination, the flight safety system usually uses multiple sensors to estimate launch vehicle's flight trajectory. In that case, multiple types of observed tracking data makes it difficult to identify the flight termination condition. Therefore, a fusion tracking filter handling the multiple tracking data is necessary for the flight safety system. This research developed a simulation software for generating multiple types of launch vehicle tracking data, and then processed the data with fusion filters.

• 한국항공우주학회지
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• 제38권2호
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• pp.180-187
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• 2010

본 논문에서는 위성항법 기반의 위치 정보만을 이용하여 저속으로 운행하는 이동체의 제어 성능을 확인하기 위해서 골프장에 무인으로 운행하는 AGV(Autonomous Guided Vehicle)를 위성항법 기반의 AGV로 구성하여 조향 시험을 수행하였다. 이를 위해 기존 AGV 시스템의 구성에 대한 분석을 수행한 후 위성항법 기반의 위치 정보를 이용하여 조향 제어가 가능하도록 제어기 및 조향 제어 알고리즘을 개발하였다. AGV의 조향 성능을 알기 위해서 직선과 원형으로 이루어진 기준궤적을 생성하여 시험을 수행하였으며 시험 결과 기준궤적에서 ${\pm}0.2m$ 범위 안으로 조향 제어가 가능함을 확인하였다.

• 한국항공운항학회지
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• 제30권1호
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• pp.38-43
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• 2022

As the use of unmanned aerial vehicles increases, in order to expand the operability of the unmanned aerial vehicle, it is essential to develop an unmanned aerial vehicle traffic management system, and to establish the system, it is necessary to analyze the integrated navigation performance of the unmanned aerial vehicle to be operated. Integrated navigation performance is affected by various factors such as the type of unmanned aerial vehicle, flight environment, and guidance law algorithm. In addition, since a large amount of flight data is required to obtain high-reliability analysis results, efficient and consistent flight scenarios are required. In this paper, a flight scenario that satisfies the requirements for integrated navigation performance analysis of rotary and fixed-wing unmanned aerial vehicles was designed and verified through flight experiments.

• 정보과학회 논문지
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• 제43권4호
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• pp.404-410
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• 2016

대부분의 차량에서 GPS 기반의 내비게이션을 사용함에 따라, 도로 지도를 자동적으로 생성하는 것은 중요한 연구 문제이다. 본 논문에서는 지도 정보 없이 GPS 궤적을 이용한 교차로 검출 기법을 제안한다. 이 기법은 궤적이 교차로에서 여러 갈래로 나누어지는 것을 이용한다. 이전의 교차로 검출 연구에서는 정차 빈도나 회전방향을 이용하였다. 그러나 제안하는 교차로 검출 기법은 이러한 복잡한 정보를 이용하지 않는다. 이 기법은 주어진 궤적에 대한 부분 궤적 매칭 결과를 이용하여 교차로에 진입한 궤적들이 서로 다른 도로로 나뉘어 이동하는 것을 이용한다. 강남구에서 수집된 실제 차량 궤적 1266개를 대상으로 실험하였다. 실험 결과 제안한 기법은 일반적인 십자 모양의 교차로에서 좋은 성능을 보였다. 제안 시스템은 선정한 교차로에 대해 재현율 75%, 민감도 78%의 성능을 보였다. 더 많은 궤적을 이용하면 더 신뢰할 수 있는 검출 결과를 낼 수 있을 것으로 예상된다.

• Journal of information and communication convergence engineering
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• 제21권4호
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• pp.346-350
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• 2023

Trajectory prediction is an essential element for driving autonomous vehicles, and various trajectory prediction models have emerged with the development of deep learning technology. Convolutional neural network (CNN) is the most commonly used neural network architecture for extracting the features of visual images, and the latest models exhibit high performances. This study was conducted to identify an efficient CNN backbone model among the components of deep learning models for trajectory prediction. We changed the existing CNN backbone network of multiple-trajectory prediction models used as feature extractors to various state-of-the-art CNN models. The experiment was conducted using nuScenes, which is a dataset used for the development of autonomous vehicles. The results of each model were compared using frequently used evaluation metrics for trajectory prediction. Analyzing the impact of the backbone can improve the performance of the trajectory prediction task. Investigating the influence of the backbone on multiple deep learning models can be a future challenge.