Método Eye tracking en la detección de somnolencia en conductores: Una revisión de literatura
DOI:
https://doi.org/10.32870/recibe.v15i1.439Keywords:
Eye tracking, trackingsomnolencia, rede neuronal, neuronaldeep learningAbstract
In the present study, a literature review was conducted on the use of the Eye-tracking method (ET) for detecting, obtaining, and analyzing driver fatigue profiles, one of the most prevalent risk factors among all kinds of drivers, which increases the likelihood of causing accidents. To carry out this review, the PRISMA 2020 methodology was employed, enabling a thorough investigation through the identification, screening, eligibility assessment, and inclusion of articles addressing driver drowsiness and fatigue detection. As a result, a two‑way table was developed summarizing the consulted studies, drawn from scientific articles published in high impact journals, limited to those with recent publication dates from 2019 to 2025, relevant and up to date data, and empirical evidence contributions related to the study’s focus.References
AAA Foundation for Traffic Safety, Drowsy driving in fatal crashes, United States, 2017–2021
(Research Brief, marzo 2024). Disponible en: https://aaafoundation.org/wpcontent/uploads/2024/03/202304-AAAFTS-Drowsy-Driving-Countermeasures.pdf
A. Z. Ablahd, A. Q. Aloraibi y S. A. Dawwod, “Driver drowsiness detection,” Scalable Computing:
Practice and Experience, vol. 25, no. 5, 2024. https://doi.org/10.12694/scpe.v25i5.3046
M. I. B. Ahmed, H. Alabdulkarem, F. Alomair, D. Aldossary, M. Alahmari, M. Alhumaidan, S.
Alrassan, A. Rahman, M. Youldash y G. Zaman, “A deep-learning approach to driver drowsiness
detection,” Safety, vol. 9, no. 3, p. 65, 2023. https://doi.org/10.3390/safety9030065
H. Beles, T. Vesselenyi, A. Rus, T. Mitran, F. B. Scurt y B. A. Tolea, “Driver drowsiness multi-method
detection for vehicles with autonomous driving functions,” Sensors, vol. 24, no. 5, p. 1541, 2024.
https://doi.org/10.3390/s24051541
V. Chirra, U. S. Reddy y V. KishoreKolli, “Deep CNN: A machine learning approach for driver
drowsiness detection based on eye state,” Revue d’Intelligence Artificielle, vol. 33, pp. 461–466,
https://doi.org/10.18280/ria.330609
C. Dewi, R.-C. Chen, C.-W. Chang, S.-H. Wu, X. Jiang y H. Yu, “Eye aspect ratio for real-time
drowsiness detection to improve driver safety,” Electronics, vol. 11, no. 19, p. 3183, 2022.
https://doi.org/10.3390/electronics11193183
Ł. Dziuda et al., “Evaluation of a fatigue detector using eye closure-associated indicators acquired from
truck drivers in a simulator study,” Sensors, vol. 21, no. 19, p. 6449, 2021.
https://doi.org/10.3390/s21196449
R. Florez, F. Palomino-Quispe, R. J. Coaquira-Castillo, J. C. Herrera-Levano, T. Paixão y A. B. Alvarez,
“A CNN-based approach for driver drowsiness detection by real-time eye state identification,”
Applied Sciences, vol. 13, no. 13, p. 7849, 2023. https://doi.org/10.3390/app13137849
A. Giorgi et al., “Driving fatigue onset and visual attention: An electroencephalography-driven analysis
of ocular behavior in a driving simulation task,” Behavioral Sciences, vol. 14, no. 11, p. 1090, 2024.
https://doi.org/10.3390/bs14111090
O. F. Hassan, A. F. Ibrahim, A. Gomaa, M. A. Makhlouf y B. Hafiz, “Real-time driver drowsiness
detection using transformer architectures: A novel deep learning approach,” Scientific Reports, vol.
, p. 17493, 2025. https://doi.org/10.1038/s41598-025-02111-x
Instituto Nacional de Estadística y Geografía (INEGI), “Accidentes de tránsito,” Recuperado el 26 de
octubre de 2025. Disponible en: https://www.inegi.org.mx/temas/accidentes/
S. S. Jasim, A. K. Abdul Hassan y S. Turner, “Driver drowsiness detection using Gray Wolf optimizer
based on face and eye tracking,” ARO – The Scientific Journal of Koya University, vol. 10, no. 1, pp.
–56, 2022. https://doi.org/10.14500/aro.10928
D. Kim, H. Park, T. Kim, W. Kim y J. Paik, “Real time driver monitoring system with facial landmark
based eye closure detection and head pose recognition,” Scientific Reports, vol. 13, p. 18264, 2023.
https://doi.org/10.1038/s41598-023-44955-1
S. Kumar Satapathy, H. Mori, K. Naik y N. S. Rajput, “Automated drowsiness detection for driver
safety: A deep learning-based approach,” en 2023 International Conference on Computational
Intelligence and Sustainable Engineering Solutions (CISES), pp. 774–779, 2023.
https://doi.org/10.1109/CISES58720.2023.10183506
Y. Li et al., “A CNN based wearable system for driver drowsiness detection,” Sensors, vol. 23, no. 7,
p. 3475, 2023. https://doi.org/10.3390/s23073475
C2-19
Organización Mundial de la Salud, Traumatismos causados por el tránsito, 2023.
https://www.who.int/es/news-room/fact-sheets/detail/road-traffic-injuries
T.-C. Phan, A.-C. Phan y N.-H.-Q. Nguyen, “A novel approach of drowsiness levels detection using
Vis Net combined with facial emotion,” Systems and Soft Computing, vol. 7, p. 200288, 2025.
https://doi.org/10.1016/j.sasc.2025.200288
F. Safarov, F. Akhmedov, A. B. Abdusalomov, R. Nasimov y Y. I. Cho, “Real-time deep learningbased drowsiness detection: Leveraging computer vision and eye blink analyses for enhanced road
safety,” Sensors, vol. 23, no. 14, p. 6459, 2023. https://doi.org/10.3390/s23146459
W. Sun, Y. Wang, B. Hu y Q. Wang, “Exploration of eye fatigue detection features and algorithm
based on eye-tracking signal,” Electronics, vol. 13, no. 10, p. 1798, 2024.
https://doi.org/10.3390/electronics13101798
C. Z. Vega-Valero, “Una propuesta de adaptación del cuestionario de síntomas de fatiga laboral,”
Revista de Psicología y Ciencias del Comportamiento de la Unidad Académica de Ciencias Jurídicas
y Sociales, vol. 10, no. 1, pp. 74–86, 2019. https://doi.org/10.29059/rpcc.20190602-82
T. Yamanaka y Y. Hirata, “Detection of human drowsiness by the vestibulo-ocular reflex
compensating for heartbeat-induced head perturbations,” Scientific Reports, vol. 14, p. 23737, 2024.
https://doi.org/10.1038/s41598-024-74202-0
F. You, Y. B. Gong, X. L. Li y H. W. Wang, “R2DS: A novel hierarchical framework for driver fatigue
detection in mountain freeway,” Mathematical Biosciences and Engineering, vol. 17, no. 4, pp. 3356–
, 2020. https://doi.org/10.3934/mbe.2020190
Q. Zhuang, K. Zhang, J. Wang y Q. Chen, “Driver fatigue detection method based on eye states with
pupil and iris segmentation,” IEEE Access, vol. 8, pp. 173440–173449, 2020.
https://doi.org/10.1109/ACCESS.2020.3025818
