Enhancing road safety: machine learning-based driver drowsiness detection
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Published:
Nov 29, 2023
Keywords:
Accidents, Drivers, Drowsiness, Machine Learning
Section
Computer Science
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Abstract
Cars are a means of land transportation commonly used by humans. With cars, human activities can become more efficient and save time, especially when traveling. When driving a car, drivers must have a high level of focus and must obey the rules and prioritize safety when driving. A traffic accident is an unexpected and unintentional event involving vehicles or road users. The negative impacts of traffic accidents such as material loss, illness and death can affect the level of public health, vehicle factors and environmental factors. Of the several factors that cause accidents above, accidents are caused by humans. Accidents that occur are greatly influenced by the condition of the vehicle driver. Driver fatigue shows that sleepy drivers are the cause of road accidents. This research will develop a machine learning model to detect drowsiness in car drivers. The model will detect the driver's eyelid image and yawn condition. The data used is driver images which are collected and then processed using machine learning. The results of the study provide an overview of the level of drowsiness of car drivers.
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How to Cite
Ari Sellyana and Juni S, J. S. (2023) “Enhancing road safety: machine learning-based driver drowsiness detection”, Jurnal Mantik, 7(3), pp. 2555-2562. doi: 10.35335/mantik.v7i3.4468.
References
Abbas, Q. (2020). HybridFatigue: A real-time driver drowsiness detection using hybrid features and transfer learning. International Journal of Advanced Computer Science and Applications, 11(1), 585–593. https://doi.org/10.14569/ijacsa.2020.0110173
Ahmed, M. I. B., Alabdulkarem, H., Alomair, F., Aldossary, D., Alahmari, M., Alhumaidan, M., Alrassan, S., Rahman, A., Youldash, M., & Zaman, G. (2023). A Deep-Learning Approach to Driver Drowsiness Detection. Safety, 9(3), 65. https://doi.org/10.3390/safety9030065
Albadawi, Y., Takruri, M., & Awad, M. (2022). A Review of Recent Developments in Driver Drowsiness Detection Systems. In Sensors (Vol. 22, Issue 5). MDPI. https://doi.org/10.3390/s22052069
Altameem, A., Kumar, A., Poonia, R. C., Kumar, S., & Saudagar, A. K. J. (2021). Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning. IEEE Access, 9, 162805–162819. https://doi.org/10.1109/ACCESS.2021.3131601
Basavaraj Murdeshwar, P., Tharanath Salian, S., Reddy, S., S, S. D., & Kotari, M. (2019). Driver Drowsiness Detection using Machine Learning Approach. www.ijert.org
Chinthalachervu, R., Teja, I., Ajay Kumar, M., Sai Harshith, N., & Santosh Kumar, T. (2022). Driver Drowsiness Detection and Monitoring System using Machine Learning. Journal of Physics: Conference Series, 2325(1). https://doi.org/10.1088/1742-6596/2325/1/012057
Dipu, A., Sumbul Hossain, S., Arafat, Y., & Rafiq, F. B. (2021). Real-time Driver Drowsiness Detection using Deep Learning. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 12, Issue 7). www.ijacsa.thesai.org
Ebrahim Shaik, M. (2023). A systematic review on detection and prediction of driver drowsiness. In Transportation Research Interdisciplinary Perspectives (Vol. 21). Elsevier Ltd. https://doi.org/10.1016/j.trip.2023.100864
Fouad, I. A. (2023). A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms. Ain Shams Engineering Journal, 14(3). https://doi.org/10.1016/j.asej.2022.101895
Gade, Prof. S., Kamble, K., Sheth, A., Patil, S., & Potdar, S. (2022). Driver Drowsiness Detection Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 10(3), 2032–2036. https://doi.org/10.22214/ijraset.2022.40895
Gwak, J., Hirao, A., & Shino, M. (2020). An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing. Applied Sciences (Switzerland), 10(8). https://doi.org/10.3390/APP10082890
Hasan, M. M., Watling, C. N., & Larue, G. S. (2022). Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches. Journal of Safety Research, 80, 215–225. https://doi.org/10.1016/j.jsr.2021.12.001
Hatta Fudholi, D., Abida Nayoan, R. N., Suyuti, M., & Rahmadi, R. (2021). Deteksi Indikasi Kelelahan Menggunakan Deep Learning. In Jurnal Sains Komputer & Informatika (J-SAKTI (Vol. 5, Issue 1).
Jain, M., Bhagerathi, B., & C N, Dr. S. (2021). Real-Time Driver Drowsiness Detection using Computer Vision. International Journal of Engineering and Advanced Technology, 11(1), 109–113. https://doi.org/10.35940/ijeat.A3159.1011121
Magán, E., Sesmero, M. P., Alonso-Weber, J. M., & Sanchis, A. (2022). Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images. Applied Sciences (Switzerland), 12(3). https://doi.org/10.3390/app12031145
Nor Shahrudin, N. S., & Sidek, K. A. (2020). Driver drowsiness detection using different classification algorithms. Journal of Physics: Conference Series, 1502(1). https://doi.org/10.1088/1742-6596/1502/1/012037
Pachouly, S., Bhondve, N., Dalvi, A., Dhande, V., Bhamare, N., Professor, A., & Student, # B E. (2020). DRIVER DROWSINESS DETECTION USING MACHINE LEARNING WITH VISUAL BEHAVIOUR. In International Journal of Creative Research Thoughts (Vol. 8, Issue 6). www.ijcrt.org
Padamata, B. K., Jhansi, D., & Singothu, R. (2020). A Machine Learning Approach for Driver Drowsiness Detection. International Journal of Engineering Research and Applications Www.Ijera.Com, 10(11), 58–65. https://doi.org/10.9790/9622-1011015865
Phan, A. C., Nguyen, N. H. Q., Trieu, T. N., & Phan, T. C. (2021). An efficient approach for detecting driver drowsiness based on deep learning. Applied Sciences (Switzerland), 11(18). https://doi.org/10.3390/app11188441
Reddy Chirra, V. R., Uyyala, S. R., & Kishore Kolli, V. K. (2019). Deep CNN: A machine learning approach for driver drowsiness detection based on eye state. Revue d’Intelligence Artificielle, 33(6), 461–466. https://doi.org/10.18280/ria.330609
Reddy, S., Kotari, M., Basavaraj Murdeshwar, P., Tharanath Salian, S., & S, S. D. (2019). Driver Drowsiness Detection using Machine Learning Approach Formulation of a framework for Security Mechanisms in the context of Adaptive Distributed Systems View project Driver Drowsiness Detection using Machine Learning Approach. In Article in International Journal of Engineering Research and Applications. www.ijert.org
Thakur, R., Shivam, Raj, S., & Pandey, S. (2022). Driver Drowsiness Detection System Using Machine Learning. Advances in Transdisciplinary Engineering, 27, 31–38. https://doi.org/10.3233/ATDE220718
Titare, S., Chinchghare, S., & Hande, K. N. (2021). Driver Drowsiness Detection and Alert System. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 583–588. https://doi.org/10.32628/cseit2173171
Umut, ?., Aki, O., Uçar, E., & Öztürk, L. (2017). DETECTION OF DRIVER SLEEPINESS AND WARNING THE DRIVER IN REAL-TIME USING IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES. Advances in Science and Technology Research Journal, 11(2), 95–102. https://doi.org/10.12913/22998624/69149
Viswapriya, S. E., & Balabalaji, S. (2021). A Machine-Learning Approach for Driver-Drowsiness Detection based on Eye-State. www.ijert.org
Ahmed, M. I. B., Alabdulkarem, H., Alomair, F., Aldossary, D., Alahmari, M., Alhumaidan, M., Alrassan, S., Rahman, A., Youldash, M., & Zaman, G. (2023). A Deep-Learning Approach to Driver Drowsiness Detection. Safety, 9(3), 65. https://doi.org/10.3390/safety9030065
Albadawi, Y., Takruri, M., & Awad, M. (2022). A Review of Recent Developments in Driver Drowsiness Detection Systems. In Sensors (Vol. 22, Issue 5). MDPI. https://doi.org/10.3390/s22052069
Altameem, A., Kumar, A., Poonia, R. C., Kumar, S., & Saudagar, A. K. J. (2021). Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning. IEEE Access, 9, 162805–162819. https://doi.org/10.1109/ACCESS.2021.3131601
Basavaraj Murdeshwar, P., Tharanath Salian, S., Reddy, S., S, S. D., & Kotari, M. (2019). Driver Drowsiness Detection using Machine Learning Approach. www.ijert.org
Chinthalachervu, R., Teja, I., Ajay Kumar, M., Sai Harshith, N., & Santosh Kumar, T. (2022). Driver Drowsiness Detection and Monitoring System using Machine Learning. Journal of Physics: Conference Series, 2325(1). https://doi.org/10.1088/1742-6596/2325/1/012057
Dipu, A., Sumbul Hossain, S., Arafat, Y., & Rafiq, F. B. (2021). Real-time Driver Drowsiness Detection using Deep Learning. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 12, Issue 7). www.ijacsa.thesai.org
Ebrahim Shaik, M. (2023). A systematic review on detection and prediction of driver drowsiness. In Transportation Research Interdisciplinary Perspectives (Vol. 21). Elsevier Ltd. https://doi.org/10.1016/j.trip.2023.100864
Fouad, I. A. (2023). A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms. Ain Shams Engineering Journal, 14(3). https://doi.org/10.1016/j.asej.2022.101895
Gade, Prof. S., Kamble, K., Sheth, A., Patil, S., & Potdar, S. (2022). Driver Drowsiness Detection Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 10(3), 2032–2036. https://doi.org/10.22214/ijraset.2022.40895
Gwak, J., Hirao, A., & Shino, M. (2020). An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing. Applied Sciences (Switzerland), 10(8). https://doi.org/10.3390/APP10082890
Hasan, M. M., Watling, C. N., & Larue, G. S. (2022). Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches. Journal of Safety Research, 80, 215–225. https://doi.org/10.1016/j.jsr.2021.12.001
Hatta Fudholi, D., Abida Nayoan, R. N., Suyuti, M., & Rahmadi, R. (2021). Deteksi Indikasi Kelelahan Menggunakan Deep Learning. In Jurnal Sains Komputer & Informatika (J-SAKTI (Vol. 5, Issue 1).
Jain, M., Bhagerathi, B., & C N, Dr. S. (2021). Real-Time Driver Drowsiness Detection using Computer Vision. International Journal of Engineering and Advanced Technology, 11(1), 109–113. https://doi.org/10.35940/ijeat.A3159.1011121
Magán, E., Sesmero, M. P., Alonso-Weber, J. M., & Sanchis, A. (2022). Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images. Applied Sciences (Switzerland), 12(3). https://doi.org/10.3390/app12031145
Nor Shahrudin, N. S., & Sidek, K. A. (2020). Driver drowsiness detection using different classification algorithms. Journal of Physics: Conference Series, 1502(1). https://doi.org/10.1088/1742-6596/1502/1/012037
Pachouly, S., Bhondve, N., Dalvi, A., Dhande, V., Bhamare, N., Professor, A., & Student, # B E. (2020). DRIVER DROWSINESS DETECTION USING MACHINE LEARNING WITH VISUAL BEHAVIOUR. In International Journal of Creative Research Thoughts (Vol. 8, Issue 6). www.ijcrt.org
Padamata, B. K., Jhansi, D., & Singothu, R. (2020). A Machine Learning Approach for Driver Drowsiness Detection. International Journal of Engineering Research and Applications Www.Ijera.Com, 10(11), 58–65. https://doi.org/10.9790/9622-1011015865
Phan, A. C., Nguyen, N. H. Q., Trieu, T. N., & Phan, T. C. (2021). An efficient approach for detecting driver drowsiness based on deep learning. Applied Sciences (Switzerland), 11(18). https://doi.org/10.3390/app11188441
Reddy Chirra, V. R., Uyyala, S. R., & Kishore Kolli, V. K. (2019). Deep CNN: A machine learning approach for driver drowsiness detection based on eye state. Revue d’Intelligence Artificielle, 33(6), 461–466. https://doi.org/10.18280/ria.330609
Reddy, S., Kotari, M., Basavaraj Murdeshwar, P., Tharanath Salian, S., & S, S. D. (2019). Driver Drowsiness Detection using Machine Learning Approach Formulation of a framework for Security Mechanisms in the context of Adaptive Distributed Systems View project Driver Drowsiness Detection using Machine Learning Approach. In Article in International Journal of Engineering Research and Applications. www.ijert.org
Thakur, R., Shivam, Raj, S., & Pandey, S. (2022). Driver Drowsiness Detection System Using Machine Learning. Advances in Transdisciplinary Engineering, 27, 31–38. https://doi.org/10.3233/ATDE220718
Titare, S., Chinchghare, S., & Hande, K. N. (2021). Driver Drowsiness Detection and Alert System. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 583–588. https://doi.org/10.32628/cseit2173171
Umut, ?., Aki, O., Uçar, E., & Öztürk, L. (2017). DETECTION OF DRIVER SLEEPINESS AND WARNING THE DRIVER IN REAL-TIME USING IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES. Advances in Science and Technology Research Journal, 11(2), 95–102. https://doi.org/10.12913/22998624/69149
Viswapriya, S. E., & Balabalaji, S. (2021). A Machine-Learning Approach for Driver-Drowsiness Detection based on Eye-State. www.ijert.org
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