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Emergency vehicle priority signal system based on deep learning using acoustic data  

Lee, SoYeon (순천향대학교 일반대학원 소프트웨어융합학과)
Jang, Jae Won (대구가톨릭대학교 모바일소프트웨어전공)
Kim, Dae-Young (순천향대학교 컴퓨터소프트웨어공학과)
Publication Information
Journal of Platform Technology / v.9, no.3, 2021 , pp. 44-51 More about this Journal
Abstract
In general, golden time refers to the most important time in the initial response to accidents such as saving lives or extinguishing fires. The golden time varies from disaster to disaster, but is aimed at five minutes in terms of fire and first aid. However, for the actual site, the average dispatch time for ambulances is 9 minutes and the average transfer time is 17.6 minutes, which is quite large compared to the golden time. There are various causes for this delay, but the main cause is traffic jams. In order to solve the problem, the government has established emergency car concession obligations and secured golden time to prioritize ambulances in places with the highest accident rate, but it is not a solution in rush hour when traffic is increasing rapidly. Therefore, this paper proposed a deep learning-based emergency vehicle priority signal system using collected sound data by installing sound sensors on traffic lights and conducted an experiment to classify frequency signals that differ depending on the distance of the emergency vehicle.
Keywords
Deep learning; traffic control system; sound-based learning; emergency vehicle; intelligent traffic system;
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