Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academ...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Omar Aboulola, Mashael Khayyat, Basma Al-Harbi, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Heba Fasihuddin, Majid H. Alsulami
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	internet of connected vehicles (IoCV) edge node driver behavior analysis hidden Markov model (HMM) tri-agent-based soft actor critic (TA-SAC) recommendations Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/5378b03b131c4edfb2640a1a5733a15a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5378b03b131c4edfb2640a1a5733a15a
record_format	dspace
spelling	oai:doaj.org-article:5378b03b131c4edfb2640a1a5733a15a2021-11-11T15:24:30ZMultimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning10.3390/app1121104622076-3417https://doaj.org/article/5378b03b131c4edfb2640a1a5733a15a2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10462https://doaj.org/toc/2076-3417The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.Omar AboulolaMashael KhayyatBasma Al-HarbiMohammed Saleh Ali MuthannaAmmar MuthannaHeba FasihuddinMajid H. AlsulamiMDPI AGarticleinternet of connected vehicles (IoCV)edge nodedriver behavior analysishidden Markov model (HMM)tri-agent-based soft actor critic (TA-SAC)recommendationsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10462, p 10462 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	internet of connected vehicles (IoCV) edge node driver behavior analysis hidden Markov model (HMM) tri-agent-based soft actor critic (TA-SAC) recommendations Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	internet of connected vehicles (IoCV) edge node driver behavior analysis hidden Markov model (HMM) tri-agent-based soft actor critic (TA-SAC) recommendations Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Omar Aboulola Mashael Khayyat Basma Al-Harbi Mohammed Saleh Ali Muthanna Ammar Muthanna Heba Fasihuddin Majid H. Alsulami Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning
description	The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.
format	article
author	Omar Aboulola Mashael Khayyat Basma Al-Harbi Mohammed Saleh Ali Muthanna Ammar Muthanna Heba Fasihuddin Majid H. Alsulami
author_facet	Omar Aboulola Mashael Khayyat Basma Al-Harbi Mohammed Saleh Ali Muthanna Ammar Muthanna Heba Fasihuddin Majid H. Alsulami
author_sort	Omar Aboulola
title	Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning
title_short	Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning
title_full	Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning
title_fullStr	Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning
title_full_unstemmed	Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning
title_sort	multimodal feature-assisted continuous driver behavior analysis and solving for edge-enabled internet of connected vehicles using deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5378b03b131c4edfb2640a1a5733a15a
work_keys_str_mv	AT omaraboulola multimodalfeatureassistedcontinuousdriverbehavioranalysisandsolvingforedgeenabledinternetofconnectedvehiclesusingdeeplearning AT mashaelkhayyat multimodalfeatureassistedcontinuousdriverbehavioranalysisandsolvingforedgeenabledinternetofconnectedvehiclesusingdeeplearning AT basmaalharbi multimodalfeatureassistedcontinuousdriverbehavioranalysisandsolvingforedgeenabledinternetofconnectedvehiclesusingdeeplearning AT mohammedsalehalimuthanna multimodalfeatureassistedcontinuousdriverbehavioranalysisandsolvingforedgeenabledinternetofconnectedvehiclesusingdeeplearning AT ammarmuthanna multimodalfeatureassistedcontinuousdriverbehavioranalysisandsolvingforedgeenabledinternetofconnectedvehiclesusingdeeplearning AT hebafasihuddin multimodalfeatureassistedcontinuousdriverbehavioranalysisandsolvingforedgeenabledinternetofconnectedvehiclesusingdeeplearning AT majidhalsulami multimodalfeatureassistedcontinuousdriverbehavioranalysisandsolvingforedgeenabledinternetofconnectedvehiclesusingdeeplearning
_version_	1718435365370462208

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

Ejemplares similares