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.

翻译：认识到生成式人工智能的集成能为智能交通系统带来巨大改进，本文探讨了生成式人工智能技术在车联网中的集成，重点关注其潜在应用和挑战。生成式人工智能凭借其生成逼真数据和促进高级决策过程的能力，与车联网结合后增强了多种应用，例如导航优化、交通预测、数据生成和评估。尽管有这些前景广阔的应用，生成式人工智能与车联网的集成仍面临若干挑战，如实时数据处理与决策、适应动态和不可预测的环境，以及隐私和安全问题。为应对这些挑战，我们提出了一种多模态语义感知框架，以提升生成式人工智能的服务质量。通过利用多模态和语义通信技术，该框架能够使用文本和图像数据创建多模态内容，为接收车辆提供更可靠的引导，最终提高系统的可用性和效率。为进一步提升框架内信息传输与重建的可靠性和效率，以基于生成式人工智能的车对车通信为案例研究，提出了一种基于深度强化学习的资源分配方法。最后，我们讨论了生成式人工智能赋能车联网领域的潜在研究方向和预期进展。