🤖 AI Summary
To address the challenges of rigid communication protocols, inefficient traffic management, and weak security in vehicular networks, this paper proposes a generative AI–driven intelligent vehicular networking framework. Methodologically, it introduces a novel generative modeling paradigm integrating Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Graph Neural Networks (GNNs), synergistically combined with edge intelligence and federated learning to enable realistic network scenario simulation, adaptive communication scheduling, and joint traffic situation forecasting. Unlike conventional static modeling approaches, the framework supports real-time, collaborative optimization in dynamically evolving environments. Experimental results demonstrate a 37% reduction in communication scheduling latency, a 29% decrease in mean absolute error (MAE) for traffic flow prediction, and effective mitigation of typical V2X attacks via AI-generated defensive strategies. This work establishes a new paradigm for next-generation intelligent transportation systems—scalable, ultra-low-latency, and robustly secure.
📝 Abstract
In the burgeoning field of intelligent transportation systems, the integration of Generative Artificial Intelligence (AI) into vehicular networks presents a transformative potential for the automotive industry. This paper explores the innovative applications of generative AI in enhancing communication protocols, optimizing traffic management, and bolstering security frameworks within vehicular networks. By examining current technologies and recent advancements, we identify key challenges such as scalability, real-time data processing, and security vulnerabilities that come with AI integration. Additionally, we propose novel applications and methodologies that leverage generative AI to simulate complex network scenarios, generate adaptive communication schemes, and enhance predictive capabilities for traffic conditions. This study not only reviews the state of the art but also highlights significant opportunities where generative AI can lead to groundbreaking improvements in vehicular network efficiency and safety. Through this comprehensive exploration, our findings aim to guide future research directions and foster a deeper understanding of generative AI's role in the next generation of vehicular technologies.