Enhancing Vehicular Networks with Generative AI: Opportunities and Challenges
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908476046835712 |
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| author | David, Teef Muhammad, Kassi Nassisid, Kevin Farus, Bronny |
| author_facet | David, Teef Muhammad, Kassi Nassisid, Kevin Farus, Bronny |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Vehicular Networks with Generative AI: Opportunities and Challenges David, Teef Muhammad, Kassi Nassisid, Kevin Farus, Bronny Networking and Internet Architecture Graphics 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. |
| title | Enhancing Vehicular Networks with Generative AI: Opportunities and Challenges |
| topic | Networking and Internet Architecture Graphics |
| url | https://arxiv.org/abs/2407.11020 |