• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.3
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• pp.561-573
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• 2018

In order to design the ventilation in the road tunnel, it is necessary to know the ratio of average annual daily traffic by vehicle type. In general, the road tunnels are onedirectional tunnel, so the traffic of each vehicle type does not change along the tunnel. On the other hand, in the case of network road tunnels, since the connections in the tunnels are complex, the traffic of vehicle-type varies depending on the network composition of tunnels. In the studying the easy method for calculating the ratio of vehicle type for the network road tunnel are proposed with using incidence matrix.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.157-161
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• 2011

This paper explains an error process for multimedia collaboration works with session management running on vehicle network environment. This system consists of an FDA and FRA. FDA is an agent that detects an error by hooking techniques for multimedia system based on vehicle network environment with session management. FRA is a system that is suitable for recovering software error for multimedia system with session management based on vehicle network environment. This paper describes only FRA. When multiple local sessions is opened, each local session manager sends information of participant to global session manager and take current information about session of processing in network.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.79-88
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• 2015

In this paper, we examine Ethernet based vehicle network which is recently emerging technology. Current MOST for entertainment will be soon replaced with the emerging Ethernet based vehicle network. Although legacy standard Ethernet has several advantages it is not suitable for vehicle backbone network without any modification. As a result, many researches are happening on extending and modification of the Ethernet function for realtime and reliability. Time-triggered Ethernet, one of many trials known as AS6802, is investigated on the architecture and functionalities. We design the traffic model on Time-triggered Ethernet and analyse the latency of the network. We also consider the QoS requirement and environment of operating configuration for vehicle network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.773-776
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• 2009

Last of the car industry can be grouped in one-vehicle electronic equipment, the development of the network, and accordingly the communication between each of the network is important. The network currently being used in vehicles include CAN, LIN, MOST, FlexRay, etc. are used. Network of several different kinds of applications using the federation said they were also germ, which causes the driver some more convenient environment, the desire to drive a vehicle that is increasing. If vehicle for other network environments with one integrated environment to make it a gateway for research done actively, the more applications are expected to be developed. In this paper, using gateway between CAN bus used for Body Train-side control of the vehicle network and MOST provided for infotainment systems. In vehicle automatic safety control system will be designed by One of CAN Nodes car speed information sending to MOST Navigation while don't received GPS information in the tunnel.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.95-97
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• 2010

Recently, CAN network technology and MOST network are introduced in vehicle to control many electronic devices and to provide entertainment service. Many interconnected devices operate in MOST network which has ring topology such as CD-ROM(DVD), AMP, VIDEO CAMERA, VIDEO DISPLAY, GPS NAVIGATION and so on. In this paper, The input image of CAMERA in the MOST network is used for determining the movement direction of vehicle. Even though the position information was received from GPS, it is difficult to directly determine the direction of moving vehicle in certain areas such as the parallel road structure. This paper designs and implements the method to determine vehicle's direction by real-time matching between CAMERA image and object image base on image DB.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.95-98
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• 2008

The vehicle has many technique change from The requirement of the safety the energy environment and convenience dimension is an enlargement toe. This is keeping changing the paradigm of the vehicle industry rapidly. The change to be technical such brought the intelligence of the former control device. And this organizes a sensor network among each systems and makes new traffic system. This paper a standard framework based on Sensor. We call it Vehicle Management System. The VMS used MOST network and It is able to make the stability of the component swap time or vehicle order the greatest.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.219-222
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• 2009

Recently, as the techniques of vehicle and communication have improve, the techniques of in- vehicle network that is a essential part of ITS have been focused. In-vehicle networks, however, are not unified to single network. The networks are composed of several local networks because of communication speed, cost and efficiency. It is important to communicate information between the networks. Therefore, the complexity of network design for communication increases. To solve this problem, local networks need a framework for interworking between heterogeneous networks. In this paper, a framework interworking between in-vehicle networks is proposed.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.634-637
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• 2005

We propose a charge/discharge model for network based vehicle. These model include motor, alternator, lamp, brake, window brush, air conditioner, etc.. Also, we simulate these models in Matlab. The simulation results show that error range is less than 3%. So, we can adopt these model to charge/discharge simulator for network based vehicle. If this error range can be shrunk within 2%, we can use this simulator for comertial use.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.113-120
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• 2017

In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.