Machine learning and artificial intelligence techniques for detecting driver drowsiness

• Autorzy: Prathap Boppuru Rudra , Kumar Kukatlapalli Pradeep , Hussain Javid , Chowdary Cherukuri Ravindranath

Identyfikatory

DOI

10.14313/JAMRIS/2-2022/17

• Języki publikacji: EN

Abstrakty

EN

The number of automobiles on the road grows in lockstep with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car accidents. Driver drowsiness and weariness are major contributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the MultiLayer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts.

Słowa kluczowe

EN

artificial intelligence; machine learning; drowsiness detection; image processing; convolutional neural networks; AI visuals

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 2
• Strony: 64--73
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Prathap Boppuru Rudra

• Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore-560074, Karnataka, India

autor

Kumar Kukatlapalli Pradeep

• Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore-560074, Karnataka, India

autor

Hussain Javid

• Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore-560074, Karnataka, India

autor

Chowdary Cherukuri Ravindranath

• Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore-560074, Karnataka, India

Bibliografia

• [1] P. Inthanon and S. Mungsing, “Detection of Drowsiness from Facial Images in Real-Time Video Media using Nvidia Jetson Nano,” 2020 17th International Conf. Electrical Engineering/Electronics, Computer, Telecommunications and InformationTechnology (ECTI-CON), 2020, pp. 246–249, doi:10.1109/ECTI-CON49241.2020.9158235.
• [2] A. Dasgupta, D. Rahman and A. Routray, “A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 11, 2019, pp. 4045–4054, doi: 10.1109/TITS.2018.2879609.
• [3] M. Ramzan et al., “A Survey on State-of-the-Art. Drowsiness Detection Techniques,” IEEE Access,vol. 7, 2019 pp. 61904–61919, doi: 10.1109/ACCESS.2019.2914373.
• [4] K.G. Seifert, T. Jan and T. Karnahl, “Don’t Sleep and Drive – VW’s Fatigue Detection Technology,” Proc. 19th International Technical Conf. Enhanced Safety of Vehicles (ESV), 2005.
• [5] R.J. Sternberg, Cognitive Psychology, Cengage Learning Press, 2012.
• [6] I. Biederman and P. Kalocsai, “Neural and Psychophysical Analysis of Object and Face Recognition.” Face Recognition, Springer, 1998, pp. 3–25.
• [7] A. Ellis and R.M. Grieger, Handbook of Rational-Emotive Therapy, Vol. 2, Springer, 1986.
• [8] J. Qiang and X. Yang, “Real-time eye, gaze, and face pose tracking for monitoring driver vigilance,” International Journal of Real-Time Imaging, vol. 8, no. 5, 2002, pp. 357–377, doi: 10.1006/rtim.2002.0279.
• [9] D. Chauhan et al. “An effective face recognition system based on Cloud-based IoT with a deep learning model.” Microprocessors and Microsystems, vol. 81, 2021, pp. 103726.
• [10] V.J. Pillai et al., “Fixed Angle Video Frame Diminution Technique for Vehicle Speed Detection,” Annals of the Romanian Society for Cell Biology, vol. 25, no. 2, 2021, pp. 3204–3210.
• [11] S. Hu and G. Zheng, “Driver drowsiness detection with eyelid related parameters by Support Vector Machine,” Expert Systems with Applications, vol. 36, no. 4, 2009, pp. 7651–7658, doi: 10.1016/j.eswa.2008.09.030.
• [12] B.R. Prathap and K. Ramesha. “Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context.” International Journal of Digital Crime and Forensics (IJDCF), vol. 12, no. 4, 2020, pp. 1–19, doi: 10.4018/IJDCF.2020100101.
• [13] T. Hamada et al., “Detecting method for Driver’s drowsiness applicable to Individual Features,” IEEE Proc. Intelligent Transportation Systems, vol. 2, 2003, pp. 1405–1410, doi: 10.1109/ITSC.2003.1252715.
• [14] L. Barr et al., “A review and evaluation of emerging driver fatigue detection, measures and technologies,” A Report of U.S. Department of Transportation, 2009.
• [15] M. Eriksson and N.P. Papanikolopoulos, “Eyetracking for detection of driver fatigue,” IEEE Proc. Intelligent Transport Systems, 1999, pp. 314–318, doi: 10.1109/ITSC.1997.660494.
• [16] A. Eskandarian and A. Mortazavi, “Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection,” IEEE Intelligent Vehicle Symposium (IV’07), Istanbul, Turkey, 2007, pp. 553–559, doi: 10.1109/IVS.2007.4290173.
• [17] R. Grace et al., “A Drowsy Driver Detection System for Heavy Vehicles,” Digital Avionics Systems Conference, 1998. Proceedings, 17th DASC. The AIAA/IEEE/SAE, vol. 2, 1998, pp. 50–70, doi: 10.1109/DASC.1998.739878.
• [18] M.T. De Mello et al., “Sleep disorders as a Cause of Motor Vehicle Collisions.” International Journal of Preventive Medicine, vol. 4, no. 3, 2003, pp. 246–257.
• [19] M. Shahverdy et al., “Driver Behavior Detection and Classification Using Deep Convolutional Neural Networks,” Expert Systems with Applications, vol. 149, 2020, pp. 113240, doi.org/10.1016/j.eswa.2020.113240.
• [20] V.A. Valsan, P.P. Mathai and I. Babu, “Monitoring Driver’s Drowsiness Status at Night Based on Computer Vision,” 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 989-993 (2021). doi:10.1109/ICCCIS51004.2021.9397180.
• [21] J.W. Baek et al., “Real-time Drowsiness Detection Algorithm for Driver State Monitoring Systems,” 2018 Tenth International Conf. Ubiquitous and Future Networks (ICUFN), 2018, pp. 73–75, doi: 10.1109/ICUFN.2018.8436988.
• [22] Rivelli, Elizabeth. “Drowsy Driving 2021 Facts and Statistics | Bankrate.” Drowsy Driving 2021 Facts & Statistics | Bankrate, www.bankrate. com/insurance/car/drowsy-driving-statistics. Accessed 27 Jan. 2023.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).