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AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches by Mu-Yen Chen; Min-Hsuan Fan; Li-Xiang Huang is a Computer Science article available to read on EtoBox.

What is AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches about?

In recent years, vehicular networks have become increasingly large, heterogeneous, and dynamic, making it difficult to meet strict requirements of ultralow latency, high reliability, high security, and massive connections for next generation (6G) networks. Recently, deep learning (DL ) has emerged as a powerful artificial intelligence (AI ) technique to optimize the efficiency and adaptability of vehicle and wireless communication. However, rapidly increasing absolute numbers of vehicles on the roads are leading to increased automobile accidents, many of which are attributable to drivers interacting with their mobile phones. To address potentially dangerous driver behavior, this study applies deep learning approaches to image recognition to develop an AI-based detection system that can detect potentially dangerous driving behavior. Multiple convolutional neural network (CNN )-based techniques including VGG16, VGG19, Densenet, and Openpose were compared in terms of their ability to detect and identify problematic driving.

Who reads AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches?

Author

Mu-Yen Chen; Min-Hsuan Fan; Li-Xiang Huang

Publisher

ACM

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)