Enhanced Driver Drowsiness Detection Model Using Multi-Level Features Fusion and a Long-Short-Term Recurrent Neural Network-Reference-Cited by-同舟云学术

Enhanced Driver Drowsiness Detection Model Using Multi-Level Features Fusion and a Long-Short-Term Recurrent Neural Network

Published:2024-05-28 Issue: Volume:10116 Page:338
ISSN:
Container-title:The 4th International Electronic Conference on Applied Sciences
language:
Short-container-title:

Author:

Yusuf Lawan¹,Hamada Mohammed²^ORCID,Hassan Mohammed³,Kakudi Habeebah⁴

Affiliation:

1. Department of Information and Communication Technology, Directorate of Examinations and Assessments, National Open University of Nigeria, Abuja 900108, Nigeria

2. Department of Computer Science, Faculty of Computing, University of Aizu, Aizuwakamatsu 965-0006, Japan

3. Department of Software Engineering, Faculty of Computing, Bayero University, Kano 700006, Nigeria

4. Department of Computer Science, Faculty of Computing, Bayero University, Kano 700006, Nigeria

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/56/1/338/pdf

Reference12 articles.

1. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface;Chen;Lect. Notes Comput. Sci.,2017

2. Chen, S., Wang, Z., and Chen, W. (2021). Driver drowsiness estimation based on factorized bilinear feature fusion and a long-short-term recurrent convolutional network. Information, 12.

3. Dehais, F., Duprès, A., Di Flumeri, G., Verdière, K.J., Borghini, G., Babiloni, F., and Roy, R.N. (2018, January 7–10). Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using a hybrid fNIRS-EEG passive BCI. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.

4. Garcia, I., Bronte, S., Bergasa, L.M., Almazan, J., and Yebes, J. (2012, January 3–7). Vision-based drowsiness detector for Real Driving Conditions. Proceedings of the 2012 IEEE Intelligent Vehicles Symposiu, Alcala de Henares, Spain.

5. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue;Hu;J. Saf. Res.,2020