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Recognizing the tremendous improvements that the integration of generative AIcan bring to intelligent transportation systems, this article explores theintegration of generative AI technologies in vehicular networks, focusing ontheir potential applications and challenges. Generative AI, with itscapabilities of generating realistic data and facilitating advanceddecision-making processes, enhances various applications when combined withvehicular networks, such as navigation optimization, traffic prediction, datageneration, and evaluation. Despite these promising applications, theintegration of generative AI with vehicular networks faces several challenges,such as real-time data processing and decision-making, adapting to dynamic andunpredictable environments, as well as privacy and security concerns. Toaddress these challenges, we propose a multi-modality semantic-aware frameworkto enhance the service quality of generative AI. By leveraging multi-modal andsemantic communication technologies, the framework enables the use of text andimage data for creating multi-modal content, providing more reliable guidanceto receiving vehicles and ultimately improving system usability and efficiency.To further improve the reliability and efficiency of information transmissionand reconstruction within the framework, taking generative AI-enabledvehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning(DRL)-based approach is proposed for resource allocation. Finally, we discusspotential research directions and anticipated advancements in the field ofgenerative AI-enabled vehicular networks.

Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study