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https://hdl.handle.net/20.500.14094/0100481871
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2025-05-02
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0100481871 (fulltext)
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メタデータID
0100481871
アクセス権
open access
出版タイプ
Version of Record
タイトル
Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV
著者
Alowish, Mazen ; Shiraishi, Yoshiaki ; Mohri, Masami ; Morii, Masakatu
著者名
Alowish, Mazen
著者ID
A0372
研究者ID
1000070351567
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=210a85879e6855c4520e17560c007669
著者名
Shiraishi, Yoshiaki
白石, 善明
シライシ, ヨシアキ
所属機関名
工学研究科
著者名
Mohri, Masami
著者ID
A0450
研究者ID
1000000220038
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=c99ab490e69627de520e17560c007669
著者名
Morii, Masakatu
森井, 昌克
モリイ, マサカツ
所属機関名
工学研究科
言語
English (英語)
収録物名
Future Internet
巻(号)
14(1)
ページ
12
出版者
MDPI
刊行日
2022-01
公開日
2023-04-24
抄録
The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.
キーワード
clustering
driver behavior analysis
alert message dissemination
personalized assistance
E-RSU
fog computing
カテゴリ
工学研究科
学術雑誌論文
権利
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
関連情報
DOI
https://doi.org/10.3390/fi14010012
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資源タイプ
journal article
eISSN
1999-5903
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