Generative AI for the Internet of Vehicles: A Review of Advances in Training, Decision-Making, and Security

Xiaoming Yuan (), Xinling Zhang, Aiwen Wang, Jiaxin Zhou, Yingying Du, Qingxu Deng and Lei Liu
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Xiaoming Yuan: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Xinling Zhang: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Aiwen Wang: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Jiaxin Zhou: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Yingying Du: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Qingxu Deng: Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Lei Liu: Xidian Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China

Mathematics, 2025, vol. 13, issue 17, 1-23

Abstract: Generative artificial intelligence (GAI) has emerged as a transformative force in the Internet of Vehicles (IoV), addressing limitations of traditional AI such as reliance on large labeled datasets and narrow task applicability. This paper aims to systematically review recent advances in applying GAI to the IoV, with a focus on training, decision-making, and security. We begin by introducing the fundamental concepts of vehicular networks and GAI, laying the groundwork for readers to better understand the subsequent sections. Methodologically, we adopt a structured literature review, covering developments in synthetic data generation, dynamic scene reconstruction, traffic flow prediction, anomaly detection, communication management, and resource allocation. In particular, we integrate multimodal GAI capabilities with 5G/6G-enabled edge computing to support low-latency, reliable, and adaptive vehicular network services. Our synthesis identifies key technical challenges, including lightweight model deployment, privacy preservation, and security assurance, and outlines promising future research directions. This review provides a comprehensive reference for advancing intelligent IoV systems through GAI.

Keywords: Internet of Vehicles (IoV); generative AI (GAI); Intelligent Transportation System (ITS); resource allocation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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