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

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.