• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.45-56
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• 2013

The precise identification of vehicle positions, known as the vehicle localization problem, is an important requirement for building intelligent vehicle ad-hoc networks (VANETs). To solve this problem, two categories of solutions are proposed: stand-alone and data fusion approaches. Compared to stand-alone approaches, which use single information including the global positioning system (GPS) and sensor-based navigation systems with differential corrections, data fusion approaches analyze the position information of several vehicles from GPS and sensor-based navigation systems, etc. Therefore, data fusion approaches show high accuracy. With the position information on a set of vehicles in the preprocessing stage, data fusion approaches is used to estimate the precise vehicular location in the local map building stage. This paper proposes an efficient local map building scheme, which increases the accuracy of the estimated vehicle positions via V2V communications. Even under the low ratio of vehicles with communication modules on the road, the proposed local map building scheme showed high accuracy when estimating the vehicle positions. From the experimental results based on the parameters of the practical vehicular environments, the accuracy of the proposed localization system approached the single lane-level.

• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.409-414
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• 2005

For an intelligent vehicle driving which uses magnetic markers and magnetic sensors, we can get every kind of road information while moving the vehicle if we use the code that is encoded with N, S pole direction of markers. If we make it an only aim to move the vehicle, it becomes easy to control the vehicle the more we put markers close. By the way, to recognize the direction of a marker pole it is much better that the markers have no effect each other. To get road informations and move the vehicle autonomously we propose the methods of arranging magnetic sensors and algorithm of recognizing the position of the vehicle with those sensors. We verified the effectiveness of the methods with computer simulation.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.15-23
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• 2020

In this paper, we propose a new method to detect traffic accidents in video from vehicle-mounted cameras (vehicle black box). We use the distance between vehicles to determine whether an accident has occurred. To calculate the position of each vehicle, we use object detection and tracking method. By the way, in a crowded road environment, it is so difficult to decide an accident has occurred because of parked vehicles at the edge of the road. It is not easy to discriminate against accidents from non-accidents because a moving vehicle and a stopped vehicle are mixed on a regular downtown road. In this paper, we try to increase the accuracy of the vehicle accident detection by using not only the motion of the surrounding vehicle but also ego-motion as the input of the Recurrent Neural Network (RNN). We improved the accuracy of accident detection compared to the previous method.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Journal of Astronomy and Space Sciences
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• v.19 no.4
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• pp.351-366
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• 2002

In this study, we developed low cost INS and (D)GPS integration for continuous attitude and position and utilized it for the determination of exterior orientation parameters of image sensors which are equipped in 4S-Van. During initial alignment process, the heading information was extracted from twin GPS and fine alignment with Kalman filter was performed for the determination of roll and pitch. Simulation and van test were performed for the performance analysis. Based on simulation result, roll and pitch error is around 0.01-0.03 degrees and yaw error around 0.1 degrees. Based on van test, position error in linear road is around 10 cm and curve around 1 m. Using direct georeferencing method, the image sensor's orientation and position information can be acquired directly from (D)GPS/INS integration. 4S-Van achieved 3D spatial data using (D)GPS/INS and image data can be applied to the spatial data integration and application such as contemporary digital map update, road facility management and Video GIS DB.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.975-981
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• 2006

This paper shows a feasibility analysis results on RFID for car positioning system. Usually, a car navigation is mainly based on GPS combined with map-matching. However, in the case of poor visibility of satellites, GPS can not supply accurate position information continuously. In recent years, RFID has been considered to be one of key technologies in positioning and localization area. But its application and research results in the area of vehicular positioning are not popular. RFID system consists of tag, reader, antenna and software such as drivers and middleware. The main function of RFID system in a vehicular positioning is to retrieve ID recorded position information from tags which set on the center of road. We propose a positioning method for vehicles using RFID and we present some indoor and outdoor experiment results to show that the proposed method is available in vehicle operational environments.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.176-190
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• 2002

According as vehicles increases, system such as Advanced Drivers Assistance System(ADAS ) to inform forward situation to driver is required. In this paper, we proposes method to detect forward vehicles and lane from sequential color images by basis process to inform forward situation to driver. We detect a front vehicle using that shadow area exists on part under vehicles and that road area occupies many parts even if road traffic is confused. We detect lane information using that lane part is white order by reverse characteristic of shadow area. This method shows good result in case road is confused or there is direction indication to road. HSV color space is selected for color modeling. This method uses saturation component and value component in HSV color model to detect vehicles and lane. It uses statistics features of HSV component and position to know whether detected vehicles area is vehicles such as vehicles previous frame. To verify the effects of the proposed method, we capture the road images with notebook and CCD camera for PC and Present the results such as processing time, accuracy and vehicles detection against the images.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.213-220
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• 1995

Map Matching is the method for correcting the current position from dead reckoning in Car Navigation System. In this paper, we proposed the new map matching algorithm that can correct the positioning error caused by sensors and digital map data around the cross road area. To do this, first we set the error boundary of the cross road area by combining the relative error of moving distance and the absolute error of road length, second, we find out the starting point of turning within the determined error boundary of the cross point area, third, we compare the turning angle of the car to the angle of each possible road, and the last, we decide the matched road. We used wheel sensor as a speed sensor and used optical fiber gyro as a directional sensor, and assembled the sensors to the notebook computer. We testified our algorithm by driving the Daejeon area-which is a part of south Korea-as a test area. And we proved the efficiency by doing that.

• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.438-447
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• 2011

geoContents have been closely related with personal life since the Google Earth and Street View by Google and the Road View by Daum were introduced. So, Location-based content, which is referred to geoContents, involving geometric spacial information and location-based image information is a sharp rise in demand. A mobile mapping system used in the area of map upgrade and road facility management has been having difficulties in satisfying the demand in the cost and time for obtaining these kinds of contents. This paper addresses geoData acquisition and processing system for producing panoramic images. The system consists of 3 devices: the first device is 3 GPS receivers for acquiring location information which is including position, attitude, orientation, and time. The second is 6 cameras for image information. And the last is to synchronize the both data. The geoData acquired by the proposed system and the method for authoring geoContents which are referred to a panoramic image with position, altitude, and orientation will be used as an effective way for establishing the various location-based content and providing them service area.

• 전자공학회논문지 IE
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• v.44 no.1
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• pp.27-32
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• 2007

This study was to suggest the predictive model of propagation, considering the effect by the multipath waves produced by the sending and receiving vehicles' left/right reflectors and the adjacent vehicles when the communication between the vehicles on the one-way two-lanes road in the urban city with a lot of traffic jams. Then, the radius of curved road was 600[m], the length of curved roads $52.4\sim471.2[m]$, and the bridge's pier of road was $5o\sim45o$. Also, it was simulated by changing the receiving vehicle located on the curved road's gap from minimum 3.3[m] to maximum 29.5[m], corresponding to the change of distance of the bridge's pier of road and curved road. As a result of this research above, in case of $5o\sim15o$ bridge's pier of road, it was within l[dB] regardless of the receiving vehicle's position on the curved road in case of propagation path loss. In case of $15o\sim45o$, it was approximately $1\sim8[dB]$ as the bridge's pier of road is changed. And, in case of propagation path, it found out that it was changed to $0.4\sim120[m]$ according to the change of bridge's pier of road. Then, the delay time of propagation was 400[nsec] as it produced 120[m] in the difference of propagation path.