A System for Driver Drowsiness Detection Using Deep Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5015-7_7
Reference9 articles.
1. Altameem A, Kumar A, Poonia RC (2021) Early identification and detection of driver drowsiness by hybrid machine learning. IEEE Access 9:162805–162819. https://doi.org/10.1109/ACCESS.2021.3131601
2. Kassem HA, Chowdhury M, Abawajy JH (2021) Drivers fatigue level prediction using facial, and head behavior information. IEEE Access 9:121686–121697. https://doi.org/10.1109/ACCESS.2021.3108561
3. Bartolacci C, Scarpelli S, Atri A, Gorgoni M, Annarumma L, Cloos C, Giannini AM, De Gennaro L (2020) The influence of sleep quality, vigilance, and sleepiness on driving-related cognitive abilities. Brain Sci (MDPI Open Access) 10(6):327. https://doi.org/10.3390/brainsci10060327
4. Dasgupta A, Rahman D, Routray A (2019) A smartphone-based drowsiness detection and warning system for automotive drivers. IEEE Trans Intell Transp Syst 20:4045–4054. https://doi.org/10.1109/TITS.2018.2879609
5. Guede-Fernandez F, Fernandez-Chimento M, Ramos-Castro J, Garcia-Gonzalez MA (2019) Driver drowsiness detection based on respiratory signal analysis. IEEE Access 7:81826–81838. https://doi.org/10.1109/access.2019.2924481
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