An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and hando...

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Autores principales:	Huigang Chang, Nianwen Ning
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Cognitive Internet of Vehicles artificial intelligence autonomous driving genetic algorithm clustering mechanism Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f86e0978d4194a2189b19c1507b9671d
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id	oai:doaj.org-article:f86e0978d4194a2189b19c1507b9671d
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spelling	oai:doaj.org-article:f86e0978d4194a2189b19c1507b9671d2021-11-25T18:57:40ZAn Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles10.3390/s212275881424-8220https://doaj.org/article/f86e0978d4194a2189b19c1507b9671d2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7588https://doaj.org/toc/1424-8220Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.Huigang ChangNianwen NingMDPI AGarticleCognitive Internet of Vehiclesartificial intelligenceautonomous drivinggenetic algorithmclustering mechanismChemical technologyTP1-1185ENSensors, Vol 21, Iss 7588, p 7588 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cognitive Internet of Vehicles artificial intelligence autonomous driving genetic algorithm clustering mechanism Chemical technology TP1-1185
spellingShingle	Cognitive Internet of Vehicles artificial intelligence autonomous driving genetic algorithm clustering mechanism Chemical technology TP1-1185 Huigang Chang Nianwen Ning An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles
description	Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.
format	article
author	Huigang Chang Nianwen Ning
author_facet	Huigang Chang Nianwen Ning
author_sort	Huigang Chang
title	An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles
title_short	An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles
title_full	An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles
title_fullStr	An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles
title_full_unstemmed	An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles
title_sort	intelligent multimode clustering mechanism using driving pattern recognition in cognitive internet of vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f86e0978d4194a2189b19c1507b9671d
work_keys_str_mv	AT huigangchang anintelligentmultimodeclusteringmechanismusingdrivingpatternrecognitionincognitiveinternetofvehicles AT nianwenning anintelligentmultimodeclusteringmechanismusingdrivingpatternrecognitionincognitiveinternetofvehicles AT huigangchang intelligentmultimodeclusteringmechanismusingdrivingpatternrecognitionincognitiveinternetofvehicles AT nianwenning intelligentmultimodeclusteringmechanismusingdrivingpatternrecognitionincognitiveinternetofvehicles
_version_	1718410501741871104

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

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