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http://dx.doi.org/10.17661/jkiiect.2021.14.2.134

A Design of the Emergency-notification and Driver-response Confirmation System(EDCS) for an autonomous vehicle safety  

Son, Su-Rak (Catholic Kwandong University, Department of Computer Engineering)
Jeong, Yi-Na (Catholic Kwandong University, Department of Software)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.2, 2021 , pp. 134-139 More about this Journal
Abstract
Currently, the autonomous vehicle market is commercializing a level 3 autonomous vehicle, but it still requires the attention of the driver. After the level 3 autonomous driving, the most notable aspect of level 4 autonomous vehicles is vehicle stability. This is because, unlike Level 3, autonomous vehicles after level 4 must perform autonomous driving, including the driver's carelessness. Therefore, in this paper, we propose the Emergency-notification and Driver-response Confirmation System(EDCS) for an autonomousvehicle safety that notifies the driver of an emergency situation and recognizes the driver's reaction in a situation where the driver is careless. The EDCS uses the emergency situation delivery module to make the emergency situation to text and transmits it to the driver by voice, and the driver response confirmation module recognizes the driver's reaction to the emergency situation and gives the driver permission Decide whether to pass. As a result of the experiment, the HMM of the emergency delivery module learned speech at 25% faster than RNN and 42.86% faster than LSTM. The Tacotron2 of the driver's response confirmation module converted text to speech about 20ms faster than deep voice and 50ms faster than deep mind. Therefore, the emergency notification and driver response confirmation system can efficiently learn the neural network model and check the driver's response in real time.
Keywords
Autonomous vehicles; Hidden Markov Model; Tacotron2; Speech recognition; Speech to Text; Text to Speech;
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Times Cited By KSCI : 1  (Citation Analysis)
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