• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.11
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• pp.924-933
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• 2013

In this paper, we propose a modeling of the broadcasting in the IEEE 802.11p MAC protocol for the VANET(Vehicular Ad hoc Networks). Due to the fact that the beacon message which is needed for the safety services is shared via broadcasting, the analytical modeling of the broadcasting is crucial for the optimum design of the services. Two characteristics specific to the IEEE 802.11p are reflected in the modeling; the time limited CCH interval caused by the channel switching between the CCH and SCH, and no retransmission of the broadcasted messages. In the proposal, we assumed no restriction on the moment of generation of the beacon messages. We allow the messages to be generated and broadcasted within the whole CCH interval. Simulation results prove the accuracy of the proposed modeling. Noticeable improvements are also observed in terms of the performance indices such as the successful delivery ratio, transmission delay, and the variation of the delay.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2532-2537
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• 2012

In recent, the researches on driver assistance systems have been actively performed with development of vehicle industry. AVM(Around View Monitoring) Systems, a part of these systems, have been researching and developing. Existing AVM systems have been developed in the forms of embedded systems or a SoC (System on Chip) to provide view around vehicle in real time. However, if Car PC is equipped with in vehicle, AVM can be developed using only software without additional cost. In this study, we implemented AVM system which provides location information by adding the informations such as latitude, longitude and speed to functions of "Car PC" based AVM system. If storing function is added to the AVM system implemented in this study which provides location information, the system with storing function can be used as AVM black box system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.73-79
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• 2014

In this paper, we proposed the mechanism which can measure traffic flow and vehicle speed on the highway as well as road by using the video and image processing to detect and track cars in a video sequence. The proposed mechanism uses the first few frames of the video stream to estimate the background image. The visual tracking system is a simple algorithm based on the sum of absolute frame difference. It subtracts the background from each video frame to produce foreground images. By thresholding and performing morphological closing on each foreground image, the proposed mechanism produces binary feature images, which are shown in the threshold window. By measuring the distance between the "first white line" mark and the "second white line"mark proceeding, it is possible to find the car's position. Average velocity is defined as the change in position of an object divided by the time over which the change takes place. The results of proposed mechanism agree well with the measured data, and view the results in real time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.582-585
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• 2018

As traffic volume increases and road networks become more complicated, identifying for accurate traffic flow and driving smooth traffic flow are a concern of many countries. There are various analytical techniques and studies which desire to study about effective traffic flow. However, the necessary activity is finding the traffic flow pattern through data visualization including location information. In this paper aim to study a real-world urban traffic trajectory and visualize a pattern of traffic flow with a simulation tool. Our experiment is installing the sensor module in 40 taxis and our dataset is generated along 24 hours and unscheduled routes. After pre-processing data, we improved an open source traffic visualize tools to suitable for our experiment. Then we simulate our vehicle trajectory data with a dots animation over a period of time, which allows clearly view a traffic flow simulation and a understand the direction of movement of the vehicle or route pattern. In addition we further propose some novel timelines to show spatial-temporal features to improve an urban environment due to the traffic flow.

• Spatial Information Research
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• v.7 no.2
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• pp.209-221
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• 1999

In this research, AVL-GIS(Automatic Vehicle Location System linked with Geographic Information System) system was developed using integration of core techniques of GIS engine written by Java language, GOS(Global Positioning System) and wireless telecommunication interfacing techniques. IDGPS(Inverted differential GPS) techniques was employed to estimate accurate position of mobile vehicle and to supervise their path from AVL-GLS control center system. Between mobile vehicle and AVL-GLS control center system which has spatial data analysis function, road network and rleate ddata base were connected wireless phone to communicate for position an dmessage in real time. The developed system from this research has more enhanced GIS functions rather than previous AVL oriented system which has MDT for message display and voice communication only. This system can support build-up application system such as fleet management like bus, taxi, truck, disaster and emergency and monitoring of transportation status for customer s order via web browser in filed of EC/CALS in low cost.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12B
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• pp.1075-1082
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• 2008

A greedy forwarding algorithm is one of the most suitable solutions for routing in vehicular ad-hoc networks. Compared to conventional routing protocols for mobile ad-hoc networks, greedy forwarding based routing protocols maintain only local information of neighbors instead of per-destination routing entries, and thus they show better performance in highly-mobile vehicular ad-hoc networks. With greedy forwarding, each node learns its geographical position and periodically broadcasts a beacon message including its position information. Based on the position information, each node selects a neighbor node located closest to the destination node as the next forwarder. One of the most serious problems in greedy forwarding is the lost link problem due to the mobility of nodes. In this paper, we propose a new algorithm to reduce the lost link problem. The proposed algorithm aims to find an efficient and stable routing path by taking account of the position of neighbors and the last beacon reception time. Our simulation results show that the proposed algorithm outperforms the legacy greedy algorithm and its variants.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.100-108
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• 2016

In this paper, We propose a method which prevents severe traffic jam by controlling traffic signal by itself based on image-processed information and fuzzy logic. The detailed idea of this method is first to let a closed monitoring camera gather the number of cars which show the flow of traffic the designated roads which are commonly considered to have traffic. After executing the image processing method on each image gathered from the monitoring camera, this system determines the changing timing of traffic signal based on fuzzy logic. Also, this image processing method shows good performance in real road environment because the setup background image which used in this system is designed to be updated in real time. All of good points mentioned above would lead driver and users to cost efficient and time efficient results by preventing the increase of the number of traffic on road in advance with the automatic traffic signal controlling algorithm based on the fuzzy logic.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.697-709
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• 2011

For improvement of wireless Internet service quality at vehicle's moving speed, it is advised to reduce the service disruption time by reducing the handover frequency on vehicle's moving path. Particularly, it is advantageous to avoid the handover to cell whose dwell time is short or can be ignored in terms of service continuity and average throughput. This paper proposes the handover scheme that is suitable for vehicle in order to improve the wireless Internet service quality. In the proposed scheme, the handover process continues to be learned before being modeled to Discrete-Time Markov Chain (DTMC). This modeling reduces the handover frequency by preventing the handover to cell that could provide service sufficiently to passenger even when vehicle passed through the cell but there was no need to perform handover. In order to verify the proposed scheme, we observed the average number of handovers, the average RSSI and the average throughput on various moving paths that vehicle moved in the given urban environment. The experiment results confirmed that the proposed scheme was able to provide the improved wireless Internet service to vehicle that moved to some degree of consistency.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.29-36
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• 2019

Unmanned Autonomous Vehicle's driving road in the national defense includes not only paved roads, but also unpaved roads which have rough and unexpected changes. This Unmanned Autonomous Vehicles monitor and recon rugged or remote areas, and defend own position, they frequently encounter environments roads of various and unpredictable. Thus, they need additional learning to drive in this environment, we propose a Additional Learning Nearest Neighbor (ALNN) which is modified from Approximate Nearest Neighbor to allow for quick learning while avoiding the 'Forgetting' problem. In addition, since the Execution speed of the ALNN algorithm decreases as the learning data accumulates, we also propose a solution to this problem using GPU parallel processing based on OpenGL Shader Language. The ALNN based on GPU algorithm can be used in the field of national defense and other similar fields, which require frequent and quick application of additional learning in real-time without affecting the existing learning data.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.699-708
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• 2021

In this paper, a steering control and speed control algorithm was presented for autonomous driving based on two vision sensors and road speed sign board. A car speed control algorithm was developed to recognize the speed sign by using TensorFlow, a deep learning program provided by Google to the road speed sign image provided from vision sensor B, and then let the car follows the recognized speed. At the same time, a steering angle control algorithm that detects lanes by analyzing road images transmitted from vision sensor A in real time, calculates steering angles, controls the front axle through PWM control, and allows the vehicle to track the lane. To verify the effectiveness of the proposed algorithm's steering and speed control algorithms, a car's prototype based on the Python language, Raspberry Pi and OpenCV was made. In addition, accuracy could be confirmed by verifying various scenarios related to steering and speed control on the test produced track.