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Research Progress on the Development of Fatigue Driving Detection Based on Deep Learning
- 1 School of Software Engineer, Sichuan University, Sichuan, China
* Author to whom correspondence should be addressed.
Abstract
Inattentiveness, weariness, and drowsiness are prevalent causes of car accidents; however, timely warnings can prevent these dangerous situations. Advances in artificial intelligence (AI) and computer vision have made it feasible to continuously monitor a driver's condition and alert them when their attention starts to wane. AI technology can assess a driver's level of fatigue by analyzing various facial expressions such as yawning, the closing of eyes, and head movements. Additionally, it can collect data on vehicle behavior and the driver's biological signals. The primary metric for evaluating the effectiveness of these systems is detection accuracy, which indicates how reliably the system can identify signs of driver fatigue. This study provides a comprehensive review of recent techniques for detecting fatigue driving, with a strong focus on deep learning methodologies. It highlights the application and reliability of these advanced technologies in real-world scenarios. Furthermore, the study delves into the latest issues and challenges faced in this field, including limitations in current technology and potential obstacles to widespread adoption. Finally, it proposes suggestions and outlines future research directions to enhance the effectiveness and reliability of fatigue detection systems. This expanded analysis aims to contribute to the development of safer driving environments by leveraging cutting-edge AI and computer vision technologies.
Keywords
Fatigue driving, deep learning, vehicle accidents, neural networks, image detection
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Cite this article
Guan,Z. (2024). Research Progress on the Development of Fatigue Driving Detection Based on Deep Learning. Theoretical and Natural Science,52,128-136.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MPCS 2024 Workshop: Quantum Machine Learning: Bridging Quantum Physics and Computational Simulations
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