Enhancing Vehicular Networks with Generative AI: Opportunities and Challenges

TL;DR

This paper reviews how generative AI can transform vehicular networks by improving communication, traffic management, and security, while addressing key challenges and proposing innovative applications for future advancements.

Contribution

It introduces novel methodologies leveraging generative AI for simulating network scenarios, adaptive communication, and predictive traffic analysis in vehicular networks.

Findings

Generative AI enhances traffic prediction accuracy.

AI-driven simulation improves network robustness.

Security frameworks benefit from AI-generated threat models.

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.