Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving

In a dynamic autonomous driving environment, artificial intelligence-generated content (AIGC) technology can supplement vehicle perception and decision making by leveraging models' generative and predictive capabilities, and has the potential to enhance motion planning, trajectory prediction, and traffic simulation. This article proposes a cloud-edge-terminal collaborative architecture to support AIGC for autonomous driving. By delving into the unique properties of AIGC services, this article attempts to construct mutually supportive AIGC and network systems for autonomous driving, including communication, storage, and computation resource allocation schemes to support AIGC services, and leverage AIGC to assist system design and resource management.

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Publication Details

Journal
IEEE Wireless Communications
Published
2024-08-01
DOI
https://doi.org/10.1109/mwc.004.2300572
Citations
19
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
5.04

Funders

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article

Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving

Zhiwei Wei, Xiayi Wang, Boxun Liu, Jianan Zhang et al.
19 citations
IEEE Wireless Communications
Traffic Prediction and Management Techniques
5.04
article

Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving

Zhiwei Wei, Xiayi Wang, Boxun Liu, Jianan Zhang, Yu Yong, Rongqing Zhang
article en
19 citations

Abstract

In a dynamic autonomous driving environment, artificial intelligence-generated content (AIGC) technology can supplement vehicle perception and decision making by leveraging models' generative and predictive capabilities, and has the potential to enhance motion planning, trajectory prediction, and traffic simulation. This article proposes a cloud-edge-terminal collaborative architecture to support AIGC for autonomous driving. By delving into the unique properties of AIGC services, this article attempts to construct mutually supportive AIGC and network systems for autonomous driving, including communication, storage, and computation resource allocation schemes to support AIGC services, and leverage AIGC to assist system design and resource management.

IEEE Wireless CommunicationsVol. 31(4)
Tongji University (CN), Peking University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 4%
Traffic Prediction and Management Techniques
5.04
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Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving — Zhiwei Wei, Xiayi Wang, et al. · IEEE Wireless Communications (2024) | TGRS Research Map | TGRS