Affiliation:
1. Department of Engineering Science, National Cheng Kung University, Taiwan
2. Department of Information Management, National Taichung University of Science and Technology, Taiwan
Abstract
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.
Funder
Ministry of Science and Technology (MOST), Taiwan, R.O.C.
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Management Information Systems
Cited by
8 articles.
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