Efficient Machine Learning-Based Drowsiness Detection for Enhanced Driving Safety: Real-Time Implementation


  • Pradeep Laxkar, Preetishree Patnaik, Samta Kathuria, Manish Tiwari, Nilesh Jain, Bal Krishna Sharma, Parth Gautam


Eye Aspect Ratio (EAR), Drowsiness, Drowsiness Detection System, Machine Learning, Mouth Aspect Ratio (MAR), Real Time.


In today’s rapid time changing era, the count of road accidents is increasing day by day because of sleeping disorders and drowsiness. Technical enhancement in each and every area of day-to-day life, also demands the technically enhanced driving cars which detect drowsiness in driver with more accuracy and efficiency. This study presents a real-time drowsiness detection system for drivers, by blending the power of machine learning techniques to analyze facial features like Pupil of eye, EAR, MAR and NLR, considering the system (Car) watch, GPS system as well as utilizing the Advanced Driver Assistance Systems (ADAS) of smart cars. The system employs OpenCV and Dlib to extract eye, mouth aspect ratios and nose length ratio from video frames with the other gained feature of smart cars. The data undergoes standard scaling preprocessing before training a deep neural network for binary classification of drowsy and non-drowsy states. The model architecture comprises four dense layers with dropout and L2 regularization, ending in a softmax activation. Stratified K-Fold cross-validation is utilized for data splitting, and the model is compiled using the Adam optimizer and categorical cross-entropy loss, incorporating an early stopping callback to mitigate overfitting. The proposed system demonstrates exceptional performance, achieving more than 99% accuracy, 0.993 recall, and 0.991 F1 score in real-time drowsiness detection. These results hold potential for enhancing road safety and reducing fatigue-related accidents by accurately identifying drowsiness in drivers. With a capacity to detect drowsiness in real-time at a level of high accuracy, the proposed system has an immense potential to increase road safety and prevent accidents related to fatigue.


Download data is not yet available.


H Nawaz Shariff, Likith M, Harshithaling S, Shreya S R, and Dr Madhu B K, “Driver Drowsiness Detection System for Accident Prevention,” Int. J. Adv. Res. Sci. Commun. Technol., 2022, doi: 10.48175/ijarsct-5838.

A. Abidi, K. Ben Khalifa, R. Ben Cheikh, C. A. Valderrama Sakuyama, and M. H. Bedoui, “Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches,” Neural Process. Lett., 2022, doi: 10.1007/s11063-022-10858-x.

K. Fujiwara et al., “Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG,” IEEE Trans. Biomed. Eng., 2019, doi: 10.1109/TBME.2018.2879346.

I. A. Fouad, “A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms,” Ain Shams Eng. J., 2023, doi: 10.1016/j.asej.2022.101895.

V. V. Priya and M. Uma, “EEG based Drowsiness Prediction Using Machine Learning Approach,” Webology, 2021, doi: 10.14704/web/v18i2/web18351.

S. Rios-Aguilar, J. L. M. Merino, A. Millán Sánchez, and Á. Sánchez Valdivieso, “Variation of the Heartbeat and Activity as an Indicator of Drowsiness at the Wheel Using a Smartwatch,” Int. J. Interact. Multimed. Artif. Intell., 2015, doi: 10.9781/ijimai.2015.3313.

Z. Shameen, M. Z. Yusoff, M. N. M. Saad, A. S. Malik, and M. Muzammel, “Electroencephalography (EEG) based drowsiness detection for drivers: A review,” ARPN Journal of Engineering and Applied Sciences. 2018.

M. N. Azadani and A. Boukerche, “Driving Behavior Analysis Guidelines for Intelligent Transportation Systems,” IEEE Trans. Intell. Transp. Syst., 2022, doi: 10.1109/TITS.2021.3076140.

R. Chinthalachervu, I. Teja, M. Ajay Kumar, N. Sai Harshith, and T. Santosh Kumar, “Driver Drowsiness Detection and Monitoring System using Machine Learning,” in Journal of Physics: Conference Series, 2022. doi: 10.1088/1742-6596/2325/1/012057.

I. Jahan et al., “4D: A Real-Time Driver Drowsiness Detector Using Deep Learning,” Electron., 2023, doi: 10.3390/electronics12010235.

R. Chandana and J. Sangeetha, “Drowsiness Detection for Automotive Drivers in Real-Time,” in Lecture Notes in Networks and Systems, 2023. doi: 10.1007/978-981-19-8563-8_17.

J. M. Guo and H. Markoni, “Driver drowsiness detection using hybrid convolutional neural network and long short-term memory,” Multimed. Tools Appl., 2019, doi: 10.1007/s11042-018-6378-6.

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 Syst. Appl., 2020, doi: 10.1016/j.eswa.2020.113505.

P. Rasna and M. B. Smithamol, “SVM-Based Drivers Drowsiness Detection Using Machine Learning and Image Processing Techniques,” in Advances in Intelligent Systems and Computing, 2021. doi: 10.1007/978-981-15-6353-9_10.

M. Saradadevi, “Driver Fatigue Detection Using Mouth and Yawning Analysis,” Int. J. Comput. Sci. Netw. Secuirity, 2008.

A. Swathi et al., “Driver Drowsiness Monitoring System Using Visual Behavior And Machine Learning,” in 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2022, 2022. doi: 10.1109/IMPACT55510.2022.10029275.

Y. Albadawi, A. AlRedhaei, and M. Takruri, “Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features,” J. Imaging, 2023, doi: 10.3390/jimaging9050091.

M. A. Noor Reza, E. A. Zaki Hamidi, N. Ismail, M. R. Effendi, E. Mulyana, and W. Shalannanda, “Design a Landmark Facial-Based Drowsiness Detection Using Dlib And Opencv For Four-Wheeled Vehicle Drivers,” in Proceeding of 15th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2021, 2021. doi: 10.1109/TSSA52866.2021.9768278.

W. Mellouk and W. Handouzi, “Facial emotion recognition using deep learning: Review and insights,” in Procedia Computer Science, 2020. doi: 10.1016/j.procs.2020.07.101.

X. Liu, X. Cheng, and K. Lee, “GA-SVM-Based Facial Emotion Recognition Using Facial Geometric Features,” IEEE Sens. J., 2021, doi: 10.1109/JSEN.2020.3028075.

Mr. Pradeep V, Namratha, Nisha Tellis, Shravya, and Vshker Mayengbam, “A Review on Eye Aspect Ratio Technique,” Int. J. Adv. Res. Sci. Commun. Technol., 2023, doi: 10.48175/ijarsct-7843.

V. Uma Maheswari, R. Aluvalu, M. V. V. Prasad Kantipudi, K. K. Chennam, K. Kotecha, and J. R. Saini, “Driver Drowsiness Prediction Based on Multiple Aspects Using Image Processing Techniques,” IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3176451.

T. Bafna and J. P. Hansen, “Mental fatigue measurement using eye metrics: A systematic literature review,” Psychophysiology. 2021. doi: 10.1111/psyp.13828.

J. G. Gaspar, C. Carney, E. Shull, and W. J. Horrey, “Mapping drivers’ mental models of adaptive cruise control to performance,” Transp. Res. Part F Traffic Psychol. Behav., 2021, doi: 10.1016/j.trf.2021.07.012.

R. N. Ashlin Deepa, D. R. Sai Rakesh Reddy, K. Milind, Y. Vijayalata, and K. Rahul, “Drowsiness Detection Using IoT and Facial Expression,” in Cognitive Science and Technology, 2023. doi: 10.1007/978-981-19-2358-6_61.

J. S. Bajaj, N. Kumar, R. K. Kaushal, H. L. Gururaj, F. Flammini, and R. Natarajan, “System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures,” Sensors, 2023, doi: 10.3390/s23031292.

R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, and K. Barkaoui, “Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application,” in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020, 2020. doi: 10.1109/ICIoT48696.2020.9089484.




How to Cite

Pradeep Laxkar. (2024). Efficient Machine Learning-Based Drowsiness Detection for Enhanced Driving Safety: Real-Time Implementation. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1761–1771. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5747



Research Article