Open Access Open Access  Restricted Access Subscription Access

Driver’s Safety Technology Using Machine Learning

Varsha Kiran Bhosale, Rajani Mahindra Mandhare


Nowadays, machine learning is mostly used in personalized recommendation systems. The use of machine learning to model the complex user-item interaction function is a trend in the current recommendation domain. This study outlines research carried out in the realm of computer science and engineering to develop a system for detecting driver drowsiness. The main aim is to prevent majority of traffic accidents caused by driver fatigue and drowsiness, fire and smoke. Our proposed system provides a solution to the limited implementation of the various techniques such as machine learning and Arduino that are presented in our system. Therefore, our system implementation provides a realistic understanding of the system's operation and suggests improvements that could be done to increase the system's overall usefulness. To facilitate additional improvement in the aforementioned area and attain utility at better efficiency for a safer roadway, the report also presents a summary of the observations made by the authors.


OpenCV, Python, fire, smoke, facial landmark detection

Full Text:



Eric Suni, Anis Rehman.(2022). Drowsy Driving: Dangers and How to Avoid It. [Online]. Sleep Foundation. Available from:

Deng W, Wu R. Real-time driver-drowsiness detection system using facial features. IEEE Access. 2019 Aug 21;7:118727–38.

Romdhani S, Torr P, Scholkopf B, Blake A. Computationally efficient face detection. In IEEE Proceedings 8th IEEE International Conference on Computer Vision. ICCV 2001. 2001 Jul 7; 2: 695–700.

Danisman T, Bilasco IM, Djeraba C, Ihaddadene N. Drowsy driver detection system using eye blink patterns. In2010 IEEE International Conference on Machine and Web Intelligence. 2010 Oct 3; 230–233.

Jinghong L, Xiaohui Z, Lu W. The design and implementation of fire smoke detection system based on FPGA. In IEEE 2012 24th Chinese Control and Decision Conference (CCDC). 2012 May 23; 3919–3922.

Kwon OH, Cho SM, Hwang SM. Design and implementation of fire detection system. In2008 IEEE Advanced Software Engineering and Its Applications. 2008 Dec 13; 233–236.

Zhao Z, Zhou N, Zhang L, Yan H, Xu Y, Zhang Z. Driver fatigue detection based on convolutional neural networks using EM-CNN. ComputIntellNeurosci. 2020 Nov 18;2020: 7251280.

Gabhane J, Dixit D, Mankar P, Kamble R, Gupta S. Drowsiness detection and alert system: A review. Int JRes Appl Sci EngTechnol (IJRASET). 2018 Apr;6(04):237–41.

Rao NS, Shetty S. Drowsiness Detection System. Int J Res Eng Sci Manag. 2020 May;3(05):1–4.

Leigh Worsdale. (Apr 2021). What causes high oil consumption in a diesel engine? Foxwood Diesel. [Online] Available from:


  • There are currently no refbacks.

Copyright (c) 2023 Journal of Communication Engineering & Systems