Smart Life Saving Navigation System for Emergency Vehicles

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Smart Life Saving Navigation System for Emergency Vehicles

Deva Priya, M. / Christy Jeba Malar, A. / Lavanya, G. / Vishnu Varthan, L. R. / Balamurugan, A.

Autonomous vehicles will establish their predominance in the near future and humans will experience a hassle-free travel as these vehicles do not demand human intervention. Autonomy diminishes disasters that may happen due to driver’s negligence. Emergency Vehicles (EVs) play a vital role in saving lives. An unobstructed path is to be provided to them to ease the mobility of EVs along busy roads. In this paper, the EVs are identified using Deep Learning (DL) based algorithms. Though they are driven by Neural Networks (NNs), there are some situations in which they have to mimic a human. The ability to perceive and respond to EVs is addressed in this paper. Self-Driving Vehicles (SDVs) are to be incorporated with the knowledge of a fast approaching EV using algorithms like Convolutional Neural Network (CNN), Fast Region-based Convolutional Network (Fast R-CNN) and You Only Look Once (YOLO) algorithm. It is seen that YOLO offers better results.

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Dokumentinformationen

Titel :

Smart Life Saving Navigation System for Emergency Vehicles

Weitere Titelangaben:

Lecture Notes in Intelligent Transportation and Infrastructure

Beteiligte:

Ben Ahmed, Mohamed (Herausgeber:in) / Boudhir, Anouar Abdelhakim (Herausgeber:in) / Santos, Domingos (Herausgeber:in) / El Aroussi, Mohamed (Herausgeber:in) / Karas, İsmail Rakıp (Herausgeber:in) / Deva Priya, M. (Autor:in) / Christy Jeba Malar, A. (Autor:in) / Lavanya, G. (Autor:in) / Vishnu Varthan, L. R. (Autor:in) / Balamurugan, A. (Autor:in)

Kongress:

The Proceedings of the Third International Conference on Smart City Applications ; 2019 ; Casablanca, Morocco October 02, 2019 - October 04, 2019

Erschienen in:

Innovations in Smart Cities Applications Edition 3 ; Kapitel : 66 ; 921-932

Lecture Notes in Intelligent Transportation and Infrastructure

Erscheinungsdatum :

2020-02-01

Format / Umfang :

12 pages

ISBN :

978-3-030-37629-1 , 978-3-030-37628-4

ISSN :