OTFS-Assisted Sensing Adaptive Cruise Control for Highways: A Reinforcement Learning Approach

Liu, Y; Zhang, X; Wu, J; Cheng, Q

OTFS-Assisted Sensing Adaptive Cruise Control for Highways: A Reinforcement Learning Approach

Liu, Y Zhang, X

Wu, J Cheng, Q

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Open Journal of Vehicular Technology, 2025, PP, (99), pp. 1-10
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Zhang, X https://orcid.org/0009-0004-5034-0959
dc.contributor.author	Wu, J
dc.contributor.author	Cheng, Q
dc.date.accessioned	2025-07-31T05:23:25Z
dc.date.available	2025-07-31T05:23:25Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Open Journal of Vehicular Technology, 2025, PP, (99), pp. 1-10
dc.identifier.issn	2644-1330
dc.identifier.issn	2644-1330
dc.identifier.uri	http://hdl.handle.net/10453/188925
dc.description.abstract	In this paper, we propose a novel channel estimation approach and driving decision method for adaptive cruise control (ACC) systems for vehicular networks, leveraging the properties of deep learning, reinforcement learning, and orthogonal time frequency space (OTFS) modulation. To achieve that, we propose to leverage deep learning (DL) to estimate motion parameters. Subsequently, we develop a reinforcement learning method to process the obtained target motion information to enable adaptive vehicle-following strategies. This ensures robust decision-making and precise control under dynamic and uncertain driving conditions, achieving superior performance in terms of both accuracy and reliability.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Open Journal of Vehicular Technology
dc.relation.isbasedon	10.1109/OJVT.2025.3574223
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4002 Automotive engineering
dc.title	OTFS-Assisted Sensing Adaptive Cruise Control for Highways: A Reinforcement Learning Approach
dc.type	Journal Article
utslib.citation.volume	PP
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-31T05:23:24Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

In this paper, we propose a novel channel estimation approach and driving decision method for adaptive cruise control (ACC) systems for vehicular networks, leveraging the properties of deep learning, reinforcement learning, and orthogonal time frequency space (OTFS) modulation. To achieve that, we propose to leverage deep learning (DL) to estimate motion parameters. Subsequently, we develop a reinforcement learning method to process the obtained target motion information to enable adaptive vehicle-following strategies. This ensures robust decision-making and precise control under dynamic and uncertain driving conditions, achieving superior performance in terms of both accuracy and reliability.

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http://hdl.handle.net/10453/188925