Task Assignment Strategy for Platoons in Cooperative Driving

Li, C; Zhang, Y; Zhang, Y; Mao, G; Jia, C

Task Assignment Strategy for Platoons in Cooperative Driving

Li, C Zhang, Y Zhang, Y Mao, G

Jia, C

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Publication Type:: Journal Article
Citation:: Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2020, 42, (1), pp. 65-73
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Li, C
dc.contributor.author	Zhang, Y
dc.contributor.author	Zhang, Y
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949
dc.contributor.author	Jia, C
dc.date.accessioned	2021-05-31T08:12:38Z
dc.date.available	2021-05-31T08:12:38Z
dc.date.issued	2020-01-01
dc.identifier.citation	Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2020, 42, (1), pp. 65-73
dc.identifier.issn	1009-5896
dc.identifier.uri	http://hdl.handle.net/10453/149312
dc.description.abstract	Autonomous vehicles are equipped with multiple on-board sensors to achieve self-driving functions. However, a tremendous amount of data is generated by autonomous vehicles, which significantly challenges the real-time task processing. Through multiple-vehicle cooperation, which makes the best of vehicle onboard computing resources, autonomous and cooperative driving becomes a promising candidate to solve the aforementioned problem. In this case, it is vital for autonomous and cooperative driving to form a driving platoon and allocate driving tasks efficiently. In this paper, a more general analytical model is developed based on G/G/1 queueing theory to model the topology of platoons. Next, Support Vector Machine (SVM) method is adopted to classify the “idle” and “busy” categories of the vehicles in the platoon based on their computing load and task processing capacity. Finally, based on the analysis above, an efficient task balancing strategy of platoons in autonomous and cooperative driving called Classification based Greed Balancing Strategy (C-GBS) is proposed, in order to balance the task burden among vehicles and cooperate more efficiently. Extensive simulations demonstrate that the proposed technique can reduce the processing delay of driving tasks in platoons with high computing load, which will improve the processing efficiency in autonomous vehicles.
dc.language	en
dc.relation.ispartof	Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology
dc.relation.isbasedon	10.11999/JEIT190557
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Networking & Telecommunications
dc.title	Task Assignment Strategy for Platoons in Cooperative Driving
dc.type	Journal Article
utslib.citation.volume	42
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-31T08:12:36Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	1

Abstract:

Autonomous vehicles are equipped with multiple on-board sensors to achieve self-driving functions. However, a tremendous amount of data is generated by autonomous vehicles, which significantly challenges the real-time task processing. Through multiple-vehicle cooperation, which makes the best of vehicle onboard computing resources, autonomous and cooperative driving becomes a promising candidate to solve the aforementioned problem. In this case, it is vital for autonomous and cooperative driving to form a driving platoon and allocate driving tasks efficiently. In this paper, a more general analytical model is developed based on G/G/1 queueing theory to model the topology of platoons. Next, Support Vector Machine (SVM) method is adopted to classify the “idle” and “busy” categories of the vehicles in the platoon based on their computing load and task processing capacity. Finally, based on the analysis above, an efficient task balancing strategy of platoons in autonomous and cooperative driving called Classification based Greed Balancing Strategy (C-GBS) is proposed, in order to balance the task burden among vehicles and cooperate more efficiently. Extensive simulations demonstrate that the proposed technique can reduce the processing delay of driving tasks in platoons with high computing load, which will improve the processing efficiency in autonomous vehicles.

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http://hdl.handle.net/10453/149312