Fatigue Driving Detection Based on Convolutional Neural Network Model Connected to New Energy Vehicles

Xiang Li

doi:10.47297/taposatWSP2633-456927.20250602

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Fatigue Driving Detection Based on Convolutional Neural Network Model Connected to New Energy Vehicles

Volume 6, Issue 2

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Fatigue Driving Detection Based on Convolutional Neural Network Model Connected to New Energy Vehicles
Fatigue Driving Detection Based on Convolutional Neural Network Model Connected to New Energy Vehicles
Kunming University of Science and Technology Oxbridge College
Vol. 6, Issue 2, Pages: 144-148(2025)
Published：25 February 2025
DOI：10.47297/taposatWSP2633-456927.20250602
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Xiang Li.Fatigue Driving Detection Based on Convolutional Neural Network Model Connected to New Energy Vehicles[J].Theory and Practice of Science and Technology,2025,06(02):144-148.
DOI：

Xiang Li.Fatigue Driving Detection Based on Convolutional Neural Network Model Connected to New Energy Vehicles[J].Theory and Practice of Science and Technology,2025,06(02):144-148. DOI： 10.47297/taposatWSP2633-456927.20250602.

摘要

Abstract

Fatigue driving detection based on Convolutional Neural Network (CNN) is a method that utilizes deep learning technology to monitor and identify whether a driver is in a fatigued state in real-time. This method analyzes the driver's facial features

eye status

and other information

combining multiple algorithms and techniques to achieve accurate detection and early warning of fatigue driving. This paper takes the application comparison of Yolov8 model as an example to conduct research on the idea of integrating fatigue detection systems with the Internet of Vehicles. During driving

if fatigue driving occurs

the system will make a judgment. Then

the autonomous driving system will take over the driving of the car and remind the driver to enter a rest area or stop to rest.

关键词

Keywords

references

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Li Shu, Li Siyuan & Liu Guoqing. Research on Small Target Detection Algorithm of UAV Aerial Images Based on YOLOv8. Journal of Chinese Computer Systems.

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