학술논문
DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring
Document Type
Conference
Author
Source
2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) Computing, Power and Communication Technologies (IC2PCT), 2024 IEEE International Conference on. 5:589-594 Feb, 2024
Subject
Language
Abstract
This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads.