VANET: A Machine Learning Approach
Emerging automotive networks should make the automotive work more secure, greener, and more efficient, and clear the way for autonomous driving to the arrival of the 5G cell. Various new irritants arise due to high sequence factors in vehicle conditions and that is why reconsideration of remote desi...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2024
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/87406 |
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| Sumario: | Emerging automotive networks should make the automotive work more secure, greener, and more efficient, and clear the way for autonomous driving to the arrival of the 5G cell. Various new irritants arise due to high sequence factors in vehicle conditions and that is why reconsideration of remote design approaches is also considered. The smart vehicle of the future, which are the backbone of high-performance multi-purpose networks, are gradually being given more advanced sensors and continue to produce information volumes. Machine learning (ML), as an integral part of artificial intelligence (AI), has recently been used in wireless networks to provide continuous information to deal with culturally challenging issues. In this chapter, we examine the recent progress in the use of machine learning in automotive networks and try to pay close attention to this emerging domain. We began to differentiate the obvious characteristics of multi-functional vehicle networks and promote the use of a learning machine to deal with subsequent difficulties. |
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| ISBN: | 978-1-80356-872-0 |
| DOI: | 10.5772/intechopen.109349 |