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Detecting Driver Drowsiness Based on Sensors: A Review
Open accessAbstract
In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.
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Citations
Cited by
A multimodal approach to estimating vigilance using EEG and forehead EOG
Zheng, W.-L., & Lu, B.-L. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5a98 · Cited by 424
“Various approaches for estimating vigilance levels have been proposed in the literature (Ji, Zhu & Lan 2004, Dong, Hu, Uchimura & Murayama 2011, Sahayadhas, Sundaraj & Murugappan 2012).”
BackgroundA Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving
Lohani, M., Payne, B. R., & Strayer, D. L. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00057 · Cited by 348
Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State
Mandal, B., Li, L., Wang, G. S., & Lin, J. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2582900 · Cited by 328
WONG, I. S., POPKIN, S., & FOLKARD, S. (2019) · Industrial Health · doi:10.2486/indhealth.sw-6 · Cited by 88
A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems
Kartsch, V. J., Benatti, S., Schiavone, P. D., Rossi, D., & Benini, L. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.11.005 · Cited by 69
Metadata
- Authors
- Sahayadhas, A., Sundaraj, K., & Murugappan, M.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2012-12-07
- Volume
- 12
- Issue
- 12
- Pages
- 16937-16953
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2025623975
- Cited by
- 782
- References
- 0
- FWCI
- 19.02
- Type
- journal article
- Language
- en
- Open access
- gold
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