• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.34-40
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• 2018

This paper presents a new method to estimate the pose of a moving object accurately using a monocular camera and a low-end GPS+IMU sensor system. For this goal, we adopted a deep neural network for the semantic segmentation of input images and compared the results with a semantic map of a neighborhood. In this map matching, we use weight tables to deal with label inconsistency effectively. Signals from a low-end GPS+IMU sensor system are used to limit search spaces and minimize the proposed function. For the evaluation, we added noise to the signals from a high-end GPS-IMU system. The results show that the pose can be recovered from the noisy signals. We also show that the proposed method is effective in handling non-open-sky situations.

• Journal of the Korean Geophysical Society
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• v.10 no.1
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• pp.37-44
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• 2007

Nowadays, Global Navigation Satellite System(GNSS) which is the new concept of positioning system has been developed because of satisfaction human's intelligent desire and rapid science development. GNSS which is represented by GPS provides 3-Dimension positioning information not expensively in whenever, wherever. The industry of positioning information has extending civil market widely as well as military market. So GNSS is running the role of society infra structure including car and airborne navigation, civil engineering, GIS resource, telematics and LBS, and so on. As USA removes the SA(Selective Availability), GPS has monopolizing the market and other countries have been depended on GPS, absolutely. In this paper, the author developed the software for analysis of influence using next generation, Galileo system. The local analysis was performed according to positioning mode. And GPS/Galileo combined system can implement positioning in the worst mask environment like urban cannon.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.5
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• pp.45-55
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• 2002

In this paper, we describe an outdoor augmented reality system using GPS tracking for position and attitude information. The system consist of a remote mobile operation unit and a ground operation unit. The remote mobile operation unit includes a real-time image acquiring device, a GPS tracking device, and a wireless data transceiver; the ground operation unit includes a wireless transceiver, a virtual image generating device, and an image superimposing device. The GPS tracking device for measurement of position and attitude of the remote mobile operation unit was designed by TANS Vector and RT-20 for DGPS. The wireless data transceiver was for data transmission between the remote mobile operation unit and the ground operation unit. After the remote mobile operation unit was installed on a vehicle and a helicopter, the system was evaluated to verify its validity in actual applications. It was found that the implemented system could be used for obtaining real-time remote information such as construction simulation, tour guide, broadcasting, disaster observation, or military purpose.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.949-956
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• 2020

It's important to measure fuel consumption of vehicles. It's possible to monitor green house gas from vehicles for various traffic conditions with the measured data. It's effective to eco-drive for drivers with fuel consumption data also. There's a display of fuel consumption in the modern vehicles, but it's not useful to get the data from the display. An estimating method for fuel consumption of a vehicle is suggested in the study. It's a simple but an effective method using GPS data. The GPS data(speed, acceleration, road slope) and vehicle data(weight, frontal area, model year, certified fuel economy) is necessary to estimate the fuel consumption for the method. It calculates driving resistance force to estimate engine power. Then it estimates the necessary fuel consumption to maintain the engine power with fuel-power conversion factor. The conversion factor is corrected with certified fuel economy, model year and rated power. The precision of the methods is checked with road test data. The test driving data was measured with GPS and OBD. The error of the estimated fuel consumption for the measured one is about 1.8%. But the error is large for the 1000 and 100 data number from the total data number of about 10,000. The error is from the larger change range of the GPS data than the one of the measured fuel consumption data. But the proposed estimating method is useful to percept the fuel consumption change for better fuel economy with simple gadget like smart phone or other GPS instruments.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.116-128
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• 2017

Traffic accidents can be defined as a physical collision event of vehicles occurred instantaneously when drivers do not perceive the surrounding vehicles and roadway environments properly. Therefore, detecting the high potential events that cause traffic accidents with monitoring the interactions among the surroundings continuously by driver is the prerequisite for prevention the traffic accidents. For the analysis, basic data were collected to analyze interactions using a test vehicle which is equipped the GPS(Global Positioning System)-IMU(Inertial Measurement Unit), camera, radar and RiDAR. From the collected data, highway geometric information and the surrounding traffic situation were analyzed and then safety evaluation algorithm for driving vehicle was developed. In order to detect a dangerous event of interaction with surrounding vehicles, locations and speed data of surrounding vehicles acquired from the radar sensor were used. Using the collected data, the tangent and curve section were divided and the driving safety evaluation algorithm which is considered the highway geometric characteristic were developed. This study also proposed an algorithm that can assess the possibility of collision against surrounding vehicles considering the characteristics of geometric road structure. The methodology proposed in this study is expected to be utilized in the fields of autonomous vehicles in the future since this methodology can assess the driving safety using collectible data from vehicle's sensors.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.

• Spatial Information Research
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• v.22 no.1
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• pp.1-7
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• 2014

In this paper, we suggested plan that utilize the mobile mapping system data for constructing 3D cadastral information of roads and buildings effectively. 3D cadastral information means conflation of existing cadastral information and spatial information. It also means 3D land management that can register and manage various spatial information with land information effectively. Technically, geometry information and attribute information by image or radar scanner and location information of geographic features calculated by GPS/INS integration technology are useful for constructing 3D cadastral information included in buildings and features on the ground. As a result, the application of mobile mapping system for constructing 3D cadastral information will make a scientification and enhancing of the land information.

• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.111-116
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• 2020

Recently, many safety accidents involving children shuttle buses take place. Without a teacher for help, a safety accident occurs when the driver can't see a child who is getting off in the blind spot of both frontside and backside. A deep learning-based smart mirror allows user authentication and provides various services. Especially, It can be a role of helper for children, and prevent accidents that can occur when drivers or assistant teachers do not see them. User authentication is carried out with children's face registered in advance. Safety accidents can be prevented by an approximate sensor and a camera in frontside and backside of the bus. This study suggests a way of checking out whether children are missed in the process of getting in and out of the bus, designs a system that reduce blind spots in the front and back of the vehicle, and builds a safety system that provide various services using GPS.

• Journal of Korea Spatial Information System Society
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• v.10 no.1
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• pp.41-49
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• 2008

This paper presents a data analysis method for the taxi telematics system which generates a greate deal of location history data. By the record consist of the basic GPS receiver-generated fields, device-added fields such as taxi operation status, and framework-attached fields such as matched link Identifier and position ratio in a link, each taxi can be represented by a state diagram. The transition and the state definition enable us to efficiently extract such information as pick-up time, pick-up distance, dispatch time, and dispatch distance. The analysis result can help to verify the efficiency of a specific taxi dispatch algorithm, while the analysis framework can invite a new challenging service including future traffic estimation, trajectory clustering, and so on.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.71-78
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• 2010

The road safety depend on many road factors like vertical alignment, horizontal alignment and road cross section angle. These data are hardly to get with drawings, and the real data are differ from drawings because of road pavement overlay, etc. To get these data, so many time and cost are needed, moreover it is dangerous work in heavy traffic road. In this study we obtained the road safety data with RoSSAV(Road Safety Survey & Analysis Vehicle) of Korea Institute of Construction Technology in accordance with traffic flow, and make analysis of road safety with the vertical alignment, horizontal alignment and road cross section angle data. We derived the safety improvement method in Young-dong accident prone spot and described detail method in this paper.