• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.11
• /
• pp.967-976
• /
• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.593-596
• /
• 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.

• Korean Journal of Computational Design and Engineering
• /
• v.19 no.4
• /
• pp.340-346
• /
• 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
• /
• 1991.10a
• /
• pp.413-418
• /
• 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
• /
• v.37 no.5
• /
• pp.1032-1043
• /
• 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.

• Journal of Aerospace System Engineering
• /
• v.1 no.3
• /
• pp.37-42
• /
• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.2
• /
• pp.180-187
• /
• 2010

In this paper, a GNSS-based AGV system was designed, and steering tested on a golf cart using electric wires in order to confirm the control efficiency of the low speed vehicle which used only position information of GNSS. After analyzed the existing AGVs system, we developed controller and steering algorithm using GNSS based position information. To analyze the performance of the developed controller and steering algorithm, straight-type and circle-type trajectory test are executed. The results show that steering performance of GNSS-based AGV system is ${\pm}\;0.2m$ for a reference trajectory.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.30 no.1
• /
• pp.38-43
• /
• 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.

• Journal of KIISE
• /
• v.43 no.4
• /
• pp.404-410
• /
• 2016

Automated road map generation poses significant research challenges since GPS-based navigation systems prevail in most general vehicles. This paper proposes an automated detecting method for intersection points using GPS vehicle trajectory data without any background digital map information. The proposed method exploits the fact that the trajectories are generally split into several branches at an intersection point. One problem in previous work on this intersection detecting is that those approaches require stopping points and direction changes for every testing vehicle. However our approach does not require such complex auxiliary information for intersection detecting. Our method is based on partial trajectory matching among trajectories since a set of incoming trajectories split other trajectory cluster branches at the intersection point. We tested our method on a real GPS data set with 1266 vehicles in Gangnam District, Seoul. Our experiment showed that the proposed method works well at some bigger intersection points in Gangnam. Our system scored 75% sensitivity and 78% specificity according to the test data. We believe that more GPS trajectory data would make our system more reliable and applicable in a practice.

• Journal of information and communication convergence engineering
• /
• v.21 no.4
• /
• pp.346-350
• /
• 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.