• ETRI Journal
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• v.36 no.6
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• pp.968-978
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• 2014

Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision-based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego-motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a $300m{\times}500m$ space.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.700-710
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• 2009

A computer vision technique of estimating the location of an unmanned ground vehicle is proposed. Identifying the location of the unmaned vehicle is very important task for automatic navigation of the vehicle. Conventional positioning sensors may fail to work properly in some real situations due to internal and external interferences. Given a DSM(Digital Surface Map), location of the vehicle can be estimated by the registration of the DSM and multiview range images obtained at the vehicle. Registration of the DSM and range images yields the 3D transformation from the coordinates of the range sensor to the reference coordinates of the DSM. To estimate the vehicle position, we first register a range image to the DSM coarsely and then refine the result. For coarse registration, we employ a fast random sample matching method. After the initial position is estimated and refined, all subsequent range images are registered by applying a pair-wise registration technique between range images. To reduce the accumulation error of pair-wise registration, we periodically refine the registration between range images and the DSM. Virtual environment is established to perform several experiments using a virtual vehicle. Range images are created based on the DSM by modeling a real 3D sensor. The vehicle moves along three different path while acquiring range images. Experimental results show that registration error is about under 1.3m in average.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.316-319
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• 2008

Most of campus vehicle management systems, so far, have simple functions such as managing vehicle in/out or issuing parking tickets. Recently some of them use RFID tags to count total numbers of cars in the campus, excluding exact parking position management. In this paper we propose a new campus vehicle management system using wireless sensor network location management scheme. This system adopts RSSI based location management method with some performance improvement technique. According to the experimental result, this proposed scheme can be used to implement an effective campus vehicle management system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.19-27
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• 2021

In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle's location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner's sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver's privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.201-208
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• 2009

As the ubiquitous environment is created, the latest ports introduce U-Port services in managing ports generally and embody container's location identification system, port terminal management system, and advanced information exchange system etc. In particular, the location identification system for freight cars and containers provide in real time the information on the location and condition for them, and enables them to cope with an efficient vehicle operation management and its related problems immediately. However, such a system is insufficient in effectively handling with the troubles in a large-scale port including freight car's disorderly driving, parking, stop, theft, damage, accident, trespassing and controlling. In order to solve these problems, this study structures the vehicle positioning system and the image verification system unsing high resolution image compression and AVE/H.264 store and transmission technology, able to mark and identify the vehicle location on the digital map while a freight car has stayed in a port since the entry of an automatic gate, or able to identify the place of accident through image remotely.

• International Journal of Contents
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• v.7 no.4
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• pp.19-23
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• 2011

This paper proposes a segmentation method for overlapped vehicles based on analysis of the vehicle location and the spatiotemporal association information. This method can be used in an intelligent transport system. In the proposed method, occlusion is detected by analyzing the association information based on a vehicle's location in continuous images, and occlusion segmentation is carried out by using the vehicle information prior to occlusion. In addition, the size variations of the vehicle to which association tracking is applied can be anticipated by learning the variations according to the overlapped vehicles' movements. To assess the performance of the suggested method, image data collected from CCTVs recording traffic information is used, and average success rate of occlusion segmentation is 96.9%.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.194-197
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• 1999

In AVL, GIS analyze the information from the vehicles to provide commercial or other value far user. As spatial analysis functions in GIS make a new valuable information using the vehicle's position and geographic object's location, they perform an important roles to improve the management efficiency of vehicles. Most GIS however are used static data for the spatial analysis, so the research area on AVL used dynamic vehicle location has generated unsuitable result. In this study, we use GPS real time tracking data to perform spatial analysis between moving vehicle and static geographic object. The method proposed in this paper considers the driving direction of vehicle and creates the result which is located in forward of vehicle. In this paper, two spatial analysis functions, near and connectivity, are developed.

• Journal of the Korea Safety Management & Science
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• v.9 no.1
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• pp.85-93
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• 2007

Recently, RFID technology is being widely embraced in the supply chain, by manufacturers, retailers, and logistics firms. This study shows the RFID system to provide location based service for commercial vehicle by integrating tracking function of GPS with Mobile service. For this, we configured PC-based terminal for vehicle and developed application consisted of RFID and GPS manager program. In web service module which uses mobile service, commodity information acquired from each vehicle at real-time is added at database and it is analyzed in various aspects to support management decision through the Legacy system.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.208-215
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• 2013

Early iterations of the existing Global Positioning System (GPS)-based or radio lateration technique-based vehicle localization algorithms suffer from flip ambiguities, forged relative location information and location information exchange overhead, which affect the subsequent iterations. This, in turn, results in an erroneous neighbor-vehicle map. This paper proposes an extended information overlap measure (EIOM) algorithm to reduce the flip error rates by exchanging the neighbor-vehicle presence features in binary information. This algorithm shifts and associates three pieces of information in the Moore neighborhood format: 1) feature information of the neighboring vehicles from a vision-based environment sensor system; 2) cardinal locations of the neighboring vehicles in its Moore neighborhood; and 3) identification information (MAC/IP addresses). Simulations were conducted for multi-lane highway scenarios to compare the proposed algorithm with the existing algorithm. The results showed that the flip error rates were reduced by up to 50%.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.79-87
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• 1999

For its simplicity and cost effectiveness in implementation, the Inverted DGPS is well suited for some specific applications like automatic vehicle location systems, where the monitoring station needs accurate position of the vehicles in the street. In the Inverted DGPS, the user sends its GPS position and associated satellite informations to the reference station, and the corrections are made at the reference station to get differentially corrected user position. A fundamental requirement in IDGPS is that the user and the reference station must use the same satellites when the corrections are made. But in practice, it is not often satisfied. Inverted DGPS is also suffered from performance degradation as the baseline become large like DGPS. IDGPS system using multi-reference station can resolve this problem. In this paper a simple multi-reference IDGPS algorithm is proposed and some experiments and analysis are peformed. Experiment results show that IDGPS can achieve the positioning performance as accurate as the DGPS can provide.