Fatigue related road accidents are common and among major social concerns. Accidents affects the society in various ways ranging from property damage to at times death and also there are several factors that are the root cause for accidents. High Speed, jumping signals, drowsiness and so on. People become drowsy when they are tired. Statistics related to drowsiness accidents are very high. Driver drowsiness systems can be used for drowsiness detection and hence alert the driver in advance and thus helps in preventing accidents caused by driver drowsiness. Early detection can prevent accidents due to drowsiness. There are several drowsiness detection techniques proposed by several researchers. Hence in this paper we introduce several existing methods for early driver’s sleepiness detection and also find out their benefits and shortcomings.
REFERENCES
1.
M. of Road Transport and H. Transport
, “Road Accidents in India-Ministry of Road Transport and Highways Transport Research wing(Morth) report
” 2018
. [Online].Available: https://morth.nic.in/road-accident-in-india.2.
J.-R. T. Mkhuseli
Ngxande
and M.
Burke
, Driver drowsiness detection using Behavioral measures and machine learning techniques: A reviewof state-of-art techniques. 978-1-5386-2313-8/17/©2017
IEEE
.3.
Detection and Prevention: Drowsy Driving – Stay Alert
, Arrive Alive Available: http://drowsydriving.org/about/detectionand-prevention/. [Accessed: 22-Jun-2017].4.
R.
Knipling
and P.
Rau
, PERCLOS: A Valid Psychophysiological Measure of Alertness As Assessed by Psychomotor Vigilance
”, volume =.5.
S. M. A. A. I. M. I. Muhammad
Ramzan
, Hikmat Ullah
Khan
and A.
Mahmood
, A Survey on State-of-the-Art DrowsinessDetection Techniques Received March 25, 2019, accepted April 26, 2019, date of publication May 1, 2019, date of current version May 23, 2019. Digital Object Identifier
. 2019
IEEE
.6.
S. P. L.
Barr
and H.
Howarth
, An evaluation of emerging driver fatigue detection measures and technologies Federal Motor Carrier Saf. Admin., Washington, DC, USA
, Tech. Rep. FMCSA-RRR-09-005
, 2009
.7.
N.
Dalal
and B.
Triggs
, Histograms of Oriented Gradients for Human Detection Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05
), 2005
.8.
R.
Ahmad
and J. N.
Borole
, Drowsy driver identification using eye blink detection
Int. J. Comput. Sci. Inf. Technol.
, Vol. 6
, no. 1
, pp. 270
– 274
, Jan. 2015
.9.
Y.-F. L. T.-L. L. Jun-Juh
Yan
, Hang-Hong
Kuo
, Realtime Driver Drowsiness Detection System Based on PERCLOS and Grayscale ImageProcessing 978-1-5090-3071-2/16 © 2016
IEEE
DOI .10.
M. J. N. G. J.
AL-Anizy
and M. M.
Razooq
, Automatic Driver Drowsiness Detection Using Haar Algorithm and Support Vector MachineTechniques
Asian Journal Appl. Sci.
, 2015
.11.
L. G. F.
Zhang
, J.
Su
and Z.
Xiao
, “Driver Fatigue Detection based on Eye State Recognition 2017 International Conference on Machine Vision and Information Technology, 978-1-5090-4993-6/17 © 2017
IEEE
DOI .12.
A. B. S. Maryam Hashemi. Alireza
Mirrashid
, Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network SN Computer Science
(2020
) 1
:289
.13.
S. Z.-A. A. S. Bahjat
Fatima
, Ahmad R.
Shahid
and H.
Ramzan
, Driver Fatigue Detection Using Viola Jones and Principal Component
AnalysisAPPLIED ARTIFICIAL INTELLIGENCE
.14.
M. B.N,
Facial Features Monitoring for Real Time Drowsiness Detection 2016
12th International Conference on Innovations in Information Technology (IIT
), 978-1-5090-5343-8/16/ ©2016
IEEE
.15.
S. Y.-C. S. J. J. Bhargava
Reddy
, Ye-Hoon
Kim
, Real-time Driver Drowsiness Detection for Embedded System Using Model Compression ofDeep Neural Networks © 2017
IEEE
.16.
J.-M. G. H.
Markoni
, Driver drowsiness detection using hybrid convolutional neural network and long short-term memory Received: 28 February 2018 /Revised: 20 May 2018 /Accepted: 3 July 2018 Springer Science+Business Media, LLC, part of Springer Nature
2018
.17.
W.
Deng
and R.
WU
, Real-Time Driver-Drowsiness Detection System Using Facial Features Digital Object Identifier
.18.
N. B. K. S. S. K. S. V. Venkatrama Phani Kumar
Sistla
, Venkata K.K.
Kolli
, Stacked Ensemble Classification Based Real-Time DriverDrowsiness Detection
.Received: 10 January 2020 Accepted: 11 April 2020
.19.
A. A. N. N. Isha
Gupta
, Novesh
Garg
and B.
Verma
, Real-Time Driver’s Drowsiness Monitoring Based on Dynamically Varying ThresholdProceedings of 2018 Eleventh International Conference on Contemporary Computing (IC3
), 2-4 August, 2018
, Noida, India
, 978-15386-6835-1/18/ ©2018 IEEE
.20.
C.-H. J. In-Ho
Choi
and Y.-G.
Kim
, Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model
Appl. Sci.
2016
, 6
, 137
; doi:.21.
C. A. Omar
Rigane
, Karim
Abbes
and M.
Masmoudi
, A Fuzzy Based Method for Driver Drowsiness Detection 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications
.22.
P.
Viola
and M. J.
Jones
, Robust Real-Time Face Detection International Journal of Computer Vision
57
(2
), 137154
, 2004
.23.
A.
Kumar
and R.
Patra
, Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning 978-1-5386-3527-8/18/©2018
IEEE
.24.
b. M. K. W. A. Y. M. J. S. J. Rateb
Jabbara
, Khalifa
Al-Khalifa
, Real-time Driver Drowsiness Detection for Android Application Using DeepNeural Networks Techniques The 9th International Conference on Ambient Systems, Networks, and Technologies (ANT 2018
),18770509 © 2018
The Authors. Published by Elsevier B.V l
.25.
G. B. H. O. S. F. K. W.
Han
, Y.
Yang
and C.
Denk
, Driver Drowsiness Detection Based on Novel Eye Openness Recognition Method andUnsupervised Feature Learning Proc.-2015 IEEE Int. Conf. Syst. Man, Cybern. SMC 2015
, no. September, pp. 1470
–1475
, 2016
.26.
N.
Kurian
and D.
Rishikesh
, Real time based driver’s safeguard system by analyzing human physiological signals
Int. J. Eng. Trends Technol
, Vol. 4
, pp. 41
–45
, 2013
.27.
K.
Singh
and R.
Kaur
, Physical and Physiological Drowsiness Detection Methods
Vol. 2
, no. 9
, pp. 35
–43
, 2013
.28.
J. T. et al,
Autonomic activity during human sleep as a function of time and sleep stage J. Sleep Res.
, Vol. 10
, no. 4
, pp. 253
–264
, 2001
. ().29.
H. R. C. H. R. C. C. K. W. Kwok Tai
Chui
, Kim Fung
Tsang
, An Accurate ECG Based Transportation Safety Drowsiness Detection Scheme DOI , IEEE
, 2016
.Transactions on Industrial Informatics
.30.
M. B. K. A. F. K. D. M.
Mozumdar
, Real-Time Driver Drowsiness Detection Using Wavelet Transform and Ensemble Logistic Regression Received: 18 January 2018 / Revised: 12 November 2018 /Accepted: 3 January 2019 Springer Science+Business Media, LLC, part of Springer Nature
2019
, International Journal of Intelligent Transportation Systems Research
.31.
K. T. C. et al,
An accurate ECG-based transportation safety drowsiness detection scheme
,” IEEE Trans. Ind. Inform.
, Vol. 12
, no. 4
, pp. 1438
–1452
, Aug. 2016
. ().32.
B. G. L. et al,
Real-time physiological and vision monitoring of vehicle drivers for non-intrusive drowsiness detection IET Commun.
, Vol. 5
, no. 17
, pp. 2461
–2469
, 2011
. ().33.
K. K. Y. S. T. Y. T. H. M. K. M. K. Koichi
Fujiwara
, Erika
Abe
, Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation
With EEG IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
, Vol. 66
, NO. 6
, JUNE 2019
.34.
K. F. et al,
Epileptic seizure prediction based on multivariate statistical process control of heart rate variability features IEEE Trans. Biomed. Eng.
, Vol. 63
, no. 6
, pp. 1321
–1332
, June. 2016
. ().35.
Y. A. O. A. Umit
Budak
, Varun
Bajaj
and A.
Sengur
, An Effective Hybrid Model for EEG-Based Drowsiness Detection
IEEE SENSORS JOURNAL
, Vol. 19
, NO. 17
, SEPTEMBER 1, 2019
.36.
X. Z. a. S. Y. Chao
Zhang
, Xiaopei
Wu
, Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications Digital Object Identifier , 2169
–3536
2019
IEEE
.37.
J.-F. C. A.
Belouchrani
, K.
Abed-Meraim
and E.
Moulines
, A blind source separation technique using second-order statistics
IEEE Trans. Signal Process.
, Vol. 45
, no. 2
, pp. 434
–444
, Feb. 1997
, doi: .38.
G. S.-S. D. Prakash
Choudhary
, Rahul
Sharma
, A Survey Paper On Drowsiness Detection Alarm System for Drivers International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056 Volume: 03
Issue: 12
— Dec-2016
.39.
R.
Fairclough
, S.H.
Graham
, Impairment of driving performance caused by sleep deprivation or alcohol: A comparative study
J. Hum. Factors Ergon.
1999
, 41
, 118
–128
.40.
A. E. Sadegh
Arefnezhad
, Sajjad
Samiee
and A.
Nahvi
, Driver Drowsiness Detection Based on Steering Wheel Data Applying AdaptiveNeuro-Fuzzy Feature Selection
, Sensors
2019
, 19
, 943
; doi:.41.
T. P. B. A. A. K. G.
Ingre
, M.
AKerstedt
, Subjective sleepiness, simulated driving performance and blink duration: Examining individualdifferences
J. Sleep Res.
2006
, 15
, 47
–53
.42.
S. D. Yashika
Katyall
, Suhas
Alur
, Safe Driving By Detecting Lane Discipline and Driver Drowsiness 2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT
).
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