• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.301-308
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• 2008

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.211-220
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.801-802
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• 2010

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.95-101
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• 2016

Vehicular communications is system which can be applied for transmission of various safety messages or Intelligent Transportation Systems(ITS) applications by combining vehicle/road technology with Information and Communication Technology(ICT). In recent years, a variety of ITS services are available such as driving information, road conditions, V2X messages as well as navigation and traffic jams notification. In general, vehicular communications can be used for vehicle-to-vehicle and vehicle-to-infrastructure communication by adopting IEEE802.11p/1609 standard which is commonly known as wireless access in vehicular environments. In this paper, WAVE communication standard based on the IEEE802.11p is explained and signal characteristics in WAVE communication is introduced. Also, The H/W and S/W characteristics in Road Side Station and On Board Equipment for the Vehicle to Everything communication are analyzed. Received Signal Strength which is power of receiving signal of communication equipment is measured in test road to estimate the real WAVE communication's performance. It is shown that the implemented WAVE communication technology is satisfactory to provide ITS services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5272-5277
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• 2011

By mobile technique and vehicle industry development, vehicle service industry based on Intelligent network as telematics service will be more raised. Most of countries expect its industrial market so that many researches and standardization are being advanced. For the telematics service, the vehicle safety technique will be very important so that its research should be expected. Hence, in this paper, we propose a design mechanism for the alerting car accident to identify or avoid dangerous accident by the network of cars.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.53-64
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• 2013

The car-following model is one of core models in Advanced Vehicle & Highway Systems (AVHS). The car-following model has been developed in aspects such as human factor and reduction error rates. However, the consideration of safety depending on weather condition has not been completed yet. In this paper, therefore, changes of driving condition for car-following due to different road condition were dealt with, and optimal safety distance corresponding to road condition such as dry, wet and snowy were computed. The GMIT(GM Model with Instantaneous T) model was picked over for simulation of adaptive cruise control applied the suggested optimal safety distance. As the results, the 1.7 times longer safety distance was required for wet road condition than dry road condition, and the 5.6 times longer safety distance was required for snowy road condition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.1-14
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• 2017

Recently, the automobile industry is rapidly changing due to autonomous vehicles based on advanced ICT technology. As a result, studies related to autonomous vehicles have also been actively conducted. However, most studies are focusing on the autonomous driving technology so that the prediction of changes in road traffic safety and associated legal system due to the introduction of autonomous vehicles are lacking. The purpose of this study is to suggest improvement methods and priorities of road traffic safety projects according to the introduction of autonomous vehicles. As a result of the AHP analysis using the results of the questionnaire surveyed for autonomous driving car experts, it was analyzed that revision of the traffic safety inspection law and development of education system for autonomous driving motor drivers and operators should be given top priority.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.92-107
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• 2023

Recently, along with the continuous increase in the supply of electric vehicles, electric vehicle fire accidents are also showing a rapidly increasing trend. Electric vehicle fires last for a long time compared to fires in internal combustion engine vehicles and have problems with the risk of secondary explosions and the generation of large amounts of smoke. In particular, electric vehicle fires in underground roads, which are semi-enclosed spaces, may amplify the problems of existing electric vehicle fires. On the other hand, there are no domestic response guidelines for electric vehicle fires occurring inside underground roads. Therefore, an awareness of fire accidents was confirmed through a survey of the general public, and electric vehicle fire characteristics and primary considerations were derived from stakeholders related to electric vehicle fires in underpasses. Through this, the guidelines for responding to electric vehicle fires on underground roads were established.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.365-369
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• 2012

M2M has shown the advantages of better coverage and lower network deployment cost. Intelligent vehicle section shows severe changes in position between vehicles and has numerous large scales of networks in its components, therefore, it is required to provide safety by exchanging information between vehicles equipped with wireless communication function via biometric information in VANET(Vehicular Ad hoc Network). This thesis is to propose scheme that mutually authenticates between vehicles by composing vehicle movement as biometric information.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.