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[21] V. Vijayan and P. Kp, “A comparative analysis of RootSIFT and SIFT methods for drowsy features extraction,” Procedia Computer Science, vol. 171, pp. 436–445, 2020.
[22] S. P. Rajamohana, E. G. Radhika, S. Priya, and S. Sangeetha, “Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM),” Materials Today: Proceedings, vol. 45, no. 2, pp. 2897–2901, 2021.
[23] M. Doudou, A. Bouabdallah, and V. Berge-Cherfaoui, “Driver drowsiness measurement technologies: Current research, market solutions, and challenges,” International Journal of Intelligent Transportation System Research, vol. 18, no. 2, pp. 297–319, 2020.
[24] B. Kitchenham, “Procedures for performing systematic reviews,” 2004.
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[29] Dong, Z. Hu, K. Uchimura, and N. Murayama, “Driver inattention monitoring system for intelligent vehicles: A review,” IEEE Transasctions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 596–614, 2011.
[30] Y.J. Zhong, L.P. Du, K. Zhang, and X.H. Sun, “Localized energy study for analyzing driver fatigue state based on wavelet analysis,” in 2007 International Conference on Wavelet Analysis and Pattern Recognition, 2007.
[31] Z. Chen, C. Wu, M. Zhong, N. Lyu, and Z. Huang, “Identification of common features of vehicle motion under drowsy/distracted driving: A case study in Wuhan, China,” Accident Analysis and Prevention, vol. 81, pp. 251–259, 2015.
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[39] D. Artanto, M. P. Sulistyanto, I. D. Pranowo, and E. E. Pramesta, “Drowsiness detection system based on eye-closure using a low-cost EMG and ESP8266,” in 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2017.
[40] S. Murugan, J. Selvaraj, and A. Sahayadhas, “Detection and analysis: driver state with electrocardiogram (ECG),” Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 525–537, 2020.
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[42] S. Junaedi and H. Akbar, “Driver drowsiness detection based on face feature and PERCLOS,” Journal of Physics: Conference Series, vol. 1090, 2018.
[43] C. Zhang, L. Wei, and P. Zheng, “Research on driving fatigue detection based on PERCLOS,” in 2017 4th International Conference on Vehicle, Mechanical and Electrical Engineering (ICVMEE), 2017.
[44] F. You, X. Li, Y. Gong, H. Wang, and H. Li, “A real-time driving drowsiness detection algorithm with individual differences consideration,” IEEE Access, vol. 7, pp. 179396–179408, 2019.
[45] T. Soukupova and J. Cech, “Real-time eye blink detection using facial landmarks,” in 21st Computer Vision Winter Workshop, 2016.
[46] A. S. Houssaini, M. A. Sabri, H. Qjidaa, and A. Aarab, “Real-time driver’s hypovigilance detection using facial landmarks,” in 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), 2019.
[47] A. U. I. Rafid, A. R. Niloy, A. I. Chowdhury, and N. Sharmin, “A Brief Review on Different Driver’s Drowsiness Detection Techniques,” International Journal of Image, Graphics and Signal Processing (IJIGSP), vol. 12, no. 3, pp. 41–50, 2020.
[48] T. Brandt, R. Stemmer, and A. Rakotonirainy, “Affordable visual driver monitoring system for fatigue and monotony,” in 2004 IEEE lntemational Conference on Systems, Man and Cybemetics, 2004.
[49] S. Mohanty, S. V. Hegde, S. Prasad, and J. Manikandan, “Design of real-time drowsiness detection system using dlib,” in 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), 2019.
[50] M. Dreissig, M. H. Baccour, T. Schaeck, and E. Kasneci, “Driver drowsiness classification based on eye blink and head movement features using the k-NN algorithm,” arXiv [cs.CV], 2020.
[2] R. Ghoddoosian, M. Galib, and V. Athitsos, “A realistic dataset and baseline temporal model for early drowsiness detection,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.
[3] W. Deng, and R. Wu, “Real-time driver-drowsiness detection system using facial features,” IEEE Access: Practical Innovations, Open Solutions, vol. 7, pp. 118727–118738, 2019.
[4] J. M. Guo and H. Markoni, “Driver drowsiness detection using hybrid convolutional neural network and long short-term memory,” Multimedia Tools and Applications, vol. 78, no. 20, pp. 29059–29087, 2019.
[5] E. Ouabida, A. Essadike, and A. Bouzid, “Optical correlator based algorithm for driver drowsiness detection,” Optik, vol. 204, pp. 164102–164113, 2020.
[6] R. Tamanani, R. Muresan, and A. Al-Dweik, “Estimation of driver vigilance status using real-time facial expression and deep learning,” IEEE Sensors Letters, vol. 5, no. 5, pp. 1–4, 2021.
[7] M. Dua, Shakshi, R. Singla, S. Raj, and A. Jangra, “Deep CNN models-based ensemble approach to driver drowsiness detection,” Neural Computing and Application, vol. 33, no. 8, pp. 3155–3168, 2021.
[8] B. Akrout and W. Mahdi, “A novel approach for driver fatigue detection based on visual characteristics analysis,” Journal of Ambient Intelligence and Humanized Computing, 2021.
[9] Y. Tsuzuki, M. Mizusako, M. Yasushi, and H. Hashimoto, “Sleepiness detection system based on facial expressions,” in IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019, pp. 6934–6939.
[10] C. Jacobé de Naurois, C. Bourdin, C. Bougard, and J.-L. Vercher, “Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness,” Accident Analysis and Prevention, vol. 121, pp. 118–128, 2018.
[11] C. B. S. Maior, M. J. das C. Moura, J. M. M. Santana, and I. D. Lins, “Real-time classification for autonomous drowsiness detection using eye aspect ratio,” Expert System with Application, vol. 158, pp. 113505–113516, 2020.
[12] Shubhi, K. Srikaran, N. Saisriram, and P. Sasikumar, “Smart driver monitoring system,” Multimedia Tools and Applications, vol. 80, no. 17, pp. 25633–25648, 2021.
[13] S. E. H. Kiashari, A. Nahvi, H. Bakhoda, A. Homayounfard, and M. Tashakori, “Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator,” Multimedia Tools and Applications, vol. 79, no. 25–26, pp. 17793–17815, 2020.
[14] J. S. Wijnands, J. Thompson, K. A. Nice, G. D. P. A. Aschwanden, and M. Stevenson, “Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks,” Neural Computing and Application, vol. 32, no. 13, pp. 9731–9743, 2020.
[15] A. A. Minhas, S. Jabbar, M. Farhan, and M. Najam ul Islam, “Smart methodology for safe life on roads with active drivers based on real-time risk and behavioral monitoring,” Journal of Ambient Intelligence and Humanized Computing, 2019.
[16] A. Quddus, A. S. Zandi, L. Prest, and F. J. E. Comeau, “Using long short term memory and convolutional neural networks for driver drowsiness detection,” Accident Analysis and Prevention, vol. 156, pp. 106107–106112, 2021.
[17] M. Y. Hossain and F. P. George, “IOT based real-time drowsy driving detection system for the prevention of road accidents,” in 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2018, pp. 190–195.
[18] S. Dari, N. Epple, and V. Protschky, “Unsupervised blink detection and driver drowsiness metrics on naturalistic driving data,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020.
[19] C. Anitha, M. K. Venkatesha, and B. S. Adiga, “A two fold expert system for yawning detection,” Procedia Computer Science, vol. 92, pp. 63–71, 2016.
[20] R. Jabbar, K. Al-Khalifa, M. Kharbeche, W. Alhajyaseen, M. Jafari, and S. Jiang, “Real-time driver drowsiness detection for android application using deep neural networks techniques,” Procedia Computer Science, vol. 130, pp. 400–407, 2018.
[21] V. Vijayan and P. Kp, “A comparative analysis of RootSIFT and SIFT methods for drowsy features extraction,” Procedia Computer Science, vol. 171, pp. 436–445, 2020.
[22] S. P. Rajamohana, E. G. Radhika, S. Priya, and S. Sangeetha, “Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM),” Materials Today: Proceedings, vol. 45, no. 2, pp. 2897–2901, 2021.
[23] M. Doudou, A. Bouabdallah, and V. Berge-Cherfaoui, “Driver drowsiness measurement technologies: Current research, market solutions, and challenges,” International Journal of Intelligent Transportation System Research, vol. 18, no. 2, pp. 297–319, 2020.
[24] B. Kitchenham, “Procedures for performing systematic reviews,” 2004.
[25] B. Kitchenham, “Guidelines for performing systematic literature reviews in software engineering,” 2007.
[26] K. Sadeghniiat-Haghighi and Z. Yazdi, “Fatigue management in the workplace,” Industrial Psychiatry Journal, vol. 24, no. 1, pp. 12–17, 2015.
[27] A. Å. Miley, G. Kecklund, and T. Åkerstedt, “Comparing two versions of the Karolinska Sleepiness Scale (KSS),” Sleep and Biological Rhythms, vol. 14, no. 3, pp. 257–260, 2016.
[28] A. Shahid, K. Wilkinson, S. Marcu, and C. M. Shapiro, “Karolinska Sleepiness Scale (KSS),” in STOP, THAT and One Hundred Other Sleep Scales, New York, NY: Springer New York, 2011, pp. 209–210.
[29] Dong, Z. Hu, K. Uchimura, and N. Murayama, “Driver inattention monitoring system for intelligent vehicles: A review,” IEEE Transasctions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 596–614, 2011.
[30] Y.J. Zhong, L.P. Du, K. Zhang, and X.H. Sun, “Localized energy study for analyzing driver fatigue state based on wavelet analysis,” in 2007 International Conference on Wavelet Analysis and Pattern Recognition, 2007.
[31] Z. Chen, C. Wu, M. Zhong, N. Lyu, and Z. Huang, “Identification of common features of vehicle motion under drowsy/distracted driving: A case study in Wuhan, China,” Accident Analysis and Prevention, vol. 81, pp. 251–259, 2015.
[32] M. Ingre, T. Akerstedt, B. Peters, A. Anund, and G. Kecklund, “Subjective sleepiness, simulated driving performance and blink duration: examining individual differences,” Journal of Sleep Research, vol. 15, no. 1, pp. 47–53, 2006.
[33] A. D. McDonald, J. D. Lee, C. Schwarz, and T. L. Brown, “A contextual and temporal algorithm for driver drowsiness detection,” Accident Analysis and Prevention, vol. 113, pp. 25–37, 2018.
[34] A. Mittal, K. Kumar, S. Dhamija, and M. Kaur, “Head movement-based driver drowsiness detection: A review of state-of-art techniques,” in 2016 IEEE International Conference on Engineering and Technology (ICETECH), 2016.
[35] G. Geoffroy, L. Chaari, J.-Y. Tourneret, and H. Wendt, “Drowsiness detection using joint EEG-ECG data with deep learning,” in 2021 29th European Signal Processing Conference (EUSIPCO), 2021.
[36] M. Awais, N. Badruddin, and M. Drieberg, “A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability,” Sensors (Basel), vol. 17, no. 9, 2017.
[37] A. Sahayadhas, K. Sundaraj, and M. Murugappan, “Detecting driver drowsiness based on sensors: a review,” Sensors (Basel), vol. 12, no. 12, pp. 16937–16953, 2012.
[38] A. T. Satti, J. Kim, E. Yi, H.-Y. Cho, and S. Cho, “Microneedle array electrode-based wearable EMG system for detection of driver drowsiness through steering wheel grip,” Sensors (Basel), vol. 21, no. 15, p. 5091, 2021.
[39] D. Artanto, M. P. Sulistyanto, I. D. Pranowo, and E. E. Pramesta, “Drowsiness detection system based on eye-closure using a low-cost EMG and ESP8266,” in 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2017.
[40] S. Murugan, J. Selvaraj, and A. Sahayadhas, “Detection and analysis: driver state with electrocardiogram (ECG),” Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 525–537, 2020.
[41] M. Hashemi, A. Mirrashid, and A. B. Shirazi, “Driver safety development real time driver drowsiness detection system based on convolutional neural network,” arXiv [eess.IV], 2020.
[42] S. Junaedi and H. Akbar, “Driver drowsiness detection based on face feature and PERCLOS,” Journal of Physics: Conference Series, vol. 1090, 2018.
[43] C. Zhang, L. Wei, and P. Zheng, “Research on driving fatigue detection based on PERCLOS,” in 2017 4th International Conference on Vehicle, Mechanical and Electrical Engineering (ICVMEE), 2017.
[44] F. You, X. Li, Y. Gong, H. Wang, and H. Li, “A real-time driving drowsiness detection algorithm with individual differences consideration,” IEEE Access, vol. 7, pp. 179396–179408, 2019.
[45] T. Soukupova and J. Cech, “Real-time eye blink detection using facial landmarks,” in 21st Computer Vision Winter Workshop, 2016.
[46] A. S. Houssaini, M. A. Sabri, H. Qjidaa, and A. Aarab, “Real-time driver’s hypovigilance detection using facial landmarks,” in 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), 2019.
[47] A. U. I. Rafid, A. R. Niloy, A. I. Chowdhury, and N. Sharmin, “A Brief Review on Different Driver’s Drowsiness Detection Techniques,” International Journal of Image, Graphics and Signal Processing (IJIGSP), vol. 12, no. 3, pp. 41–50, 2020.
[48] T. Brandt, R. Stemmer, and A. Rakotonirainy, “Affordable visual driver monitoring system for fatigue and monotony,” in 2004 IEEE lntemational Conference on Systems, Man and Cybemetics, 2004.
[49] S. Mohanty, S. V. Hegde, S. Prasad, and J. Manikandan, “Design of real-time drowsiness detection system using dlib,” in 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), 2019.
[50] M. Dreissig, M. H. Baccour, T. Schaeck, and E. Kasneci, “Driver drowsiness classification based on eye blink and head movement features using the k-NN algorithm,” arXiv [cs.CV], 2020.
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Affiliations
Femilia Hardina Caryn
Universitas Indonesia
Laksmita Rahadianti
Universitas Indonesia
Driver Drowsiness Detection Based on Drivers’ Physical Behaviours: A Systematic Literature Review
Abstract
One of the most common causes of traffic accidents is human error. One such factor involves the drowsy drivers that do not focus on the road before them. Driver drowsiness often occurs due to fatigue in long distances or long durations of driving. The signs of a drowsy driver may be detected based on one out of three types of tests; i.e., performance test, physiological test, and behavioural test. Since the physiological and performance tests are quite difficult and expensive to implement, the behavioural test is a good choice to use for detecting early drowsiness. Behaviour-based driver drowsiness detection has been one of the hot research topics in recent years and is still increasingly developing. There are many approaches for behavioural driver drowsiness detection, such as Neural Networks, Multi Layer Perceptron, Support Vector Machine, Vander Lugt Correlator, Haar Cascade, and Eye Aspect Ratio. Therefore, this study aims to conduct a systematic literature review to elaborate on the development and research trends regarding driver drowsiness detection. We hope to provide a good overview of the current state of research and offer the research potential in the future.