Self-Supervised Adaptive Weighting for Cooperative Perception in V2V Communications

Liu, C; Chen, J; Chen, Y; Payton, R; Riley, M; Yang, SH

Self-Supervised Adaptive Weighting for Cooperative Perception in V2V Communications

Liu, C Chen, J Chen, Y Payton, R Riley, M Yang, SH

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Vehicles, 2023, PP, (99), pp. 1-12
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Liu, C
dc.contributor.author	Chen, J
dc.contributor.author	Chen, Y
dc.contributor.author	Payton, R
dc.contributor.author	Riley, M
dc.contributor.author	Yang, SH
dc.date.accessioned	2024-05-13T01:41:13Z
dc.date.available	2024-05-13T01:41:13Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Intelligent Vehicles, 2023, PP, (99), pp. 1-12
dc.identifier.issn	2379-8858
dc.identifier.issn	2379-8858
dc.identifier.uri	http://hdl.handle.net/10453/178867
dc.description.abstract	Perception of the driving environment is critical for collision avoidance and route planning to ensure driving safety. Cooperative perception has been widely studied as an effective approach to addressing the shortcomings of single-vehicle perception. However, the practical limitations of vehicle-to-vehicle (V2V) communications have not been adequately investigated. In particular, current cooperative fusion models rely on supervised models and do not address dynamic performance degradation caused by arbitrary channel impairments. In this paper, a self-supervised adaptive weighting model is proposed for intermediate fusion to mitigate the adverse effects of channel distortion. The performance of cooperative perception is investigated in different system settings. Rician fading and imperfect channel state information (CSI) are also considered. Numerical results demonstrate that the proposed adaptive weighting algorithm significantly outperforms the benchmarks without weighting. Visualization examples validate that the proposed weighting algorithm can flexibly adapt to various channel conditions. Moreover, the adaptive weighting algorithm demonstrates good generalization to untrained channels and test datasets from different domains.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Intelligent Vehicles
dc.relation.isbasedon	10.1109/TIV.2023.3345035
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4002 Automotive engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	Self-Supervised Adaptive Weighting for Cooperative Perception in V2V Communications
dc.type	Journal Article
utslib.citation.volume	PP
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T01:41:11Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Perception of the driving environment is critical for collision avoidance and route planning to ensure driving safety. Cooperative perception has been widely studied as an effective approach to addressing the shortcomings of single-vehicle perception. However, the practical limitations of vehicle-to-vehicle (V2V) communications have not been adequately investigated. In particular, current cooperative fusion models rely on supervised models and do not address dynamic performance degradation caused by arbitrary channel impairments. In this paper, a self-supervised adaptive weighting model is proposed for intermediate fusion to mitigate the adverse effects of channel distortion. The performance of cooperative perception is investigated in different system settings. Rician fading and imperfect channel state information (CSI) are also considered. Numerical results demonstrate that the proposed adaptive weighting algorithm significantly outperforms the benchmarks without weighting. Visualization examples validate that the proposed weighting algorithm can flexibly adapt to various channel conditions. Moreover, the adaptive weighting algorithm demonstrates good generalization to untrained channels and test datasets from different domains.

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http://hdl.handle.net/10453/178867