Driver drowsiness detection and smart alerting using deep learning and IoT

Internet of Things - Tập 22 - Trang 100705 - 2023
Anh-Cang Phan1, Thanh-Ngoan Trieu2,3, Thuong-Cang Phan3
1Vinh Long University of Technology Education, Vinh Long, 85110, Viet Nam
2Université de Bretagne Occidentale, Brest, 29200, France

Tài liệu tham khảo

. NHTSA, https://www.nhtsa.gov/risky-driving/drowsy-driving. SleepFoundation, 2022 . NSC, https://www.nsc.org/road/safety-topics/fatigued-driver. Tefft, 2014 CDC, 2022 NHTSA, 2021 Phan, 2021, An efficient approach for detecting driver drowsiness based on deep learning, Appl. Sci., 11, 8441, 10.3390/app11188441 Wierwille, 1994 Haworth, 2006, Fatigue in motorcycle crashes: Is there an issue?, 1 L.R. Hartley, Fatigue and Driving: Driver Impairment, Driver Fatigue, and Driving Simulation, CRC Press, 1995, section 2: the epidemiology of fatigue-related crashes. Čolić, 2014, 7 Hussein, 2021, Driver drowsiness detection techniques: A survey, 45 Wong, 2019, Real-time driver alert system using raspberry pi, ECTI Trans. Electr. Eng. Electron. Commun., 17, 193, 10.37936/ecti-eec.2019172.215488 Ramos, 2019, Driver drowsiness detection based on eye movement and yawning using facial landmark analysis, Int. J. Simul.–Syst. Sci. Technol., 20 Shivani, 2020, Driver drowsiness detection system using machine learning algorithms, Int. J. Recent Technol. Eng. (IJRTE), 8, 990, 10.35940/ijrte.F7514.038620 Biswal, 2021, IoT-based smart alert system for drowsy driver detection, Wirel. Commun. Mob. Comput., 2021, 10.1155/2021/6627217 Sharma, 2021, Machine learning and deep learning applications-A vision, Glob. Transit. Proc., 2, 24, 10.1016/j.gltp.2021.01.004 He, 2020, A real-time driver fatigue detection method based on two-stage convolutional neural network, IFAC-PapersOnLine, 53, 15374, 10.1016/j.ifacol.2020.12.2357 Zhao, 2020, Driver fatigue detection based on convolutional neural networks using em-cnn, Comput. Intell. Neurosci., 2020, 10.1155/2020/7251280 Venkata, 2021, Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal, J. Neurosci. Methods, 347 Chand, 2022, CNN based driver drowsiness detection system using emotion analysis, Intell. Autom. Soft Comput., 31, 717, 10.32604/iasc.2022.020008 Rajkar, 2022, Driver drowsiness detection using deep learning, 73 Quddus, 2021, Using long short term memory and convolutional neural networks for driver drowsiness detection, Accid. Anal. Prev., 156, 10.1016/j.aap.2021.106107 V. Yarlagadda, S.G. Koolagudi, M. Kumar M V, S. Donepudi, Driver Drowsiness Detection Using Facial Parameters and RNNs with LSTM, in: 2020 IEEE 17th India Council International Conference, INDICON, 2020, pp. 1–7, http://dx.doi.org/10.1109/INDICON49873.2020.9342348. Faraji, 2021 Chaabene, 2021, Convolutional neural network for drowsiness detection using EEG signals, Sensors, 21, 10.3390/s21051734 G. Geoffroy, L. Chaari, J.-Y. Tourneret, H. Wendt, Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning, in: 29th European Signal Processing Conference (EUSIPCO 2021), Dublin, Ireland, 2021, pp. 955–959, URL:. Kitajima, 1997, Prediction of automobile driver sleepiness. 1st report. Rating of sleepiness based on facial expression and examination of effective predictor indexes of sleepiness., Trans. Jpn. Soc. Mech. Eng. Ser. C, 63, 3059, 10.1299/kikaic.63.3059 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Simonyan, 2014 C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818–2826. G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700–4708. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. Hashmi, 2020, Efficient pneumonia detection in chest Xray images using deep transfer learning, Diagnostics, 10, 10.3390/diagnostics10060417 Li, 2021 Shazia, 2021, A comparative study of multiple neural network for detection of COVID-19 on chest X-ray, EURASIP J. Appl. Signal Process., 2021, 50, 10.1186/s13634-021-00755-1 A. Mostafa, M.I. Khalil, H. Abbas, Emotion Recognition by Facial Features using Recurrent Neural Networks, in: 2018 13th International Conference on Computer Engineering and Systems, ICCES, 2018, pp. 417–422, http://dx.doi.org/10.1109/ICCES.2018.8639182. Kingman, 1999