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