STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Open Source Developments Review Article

DRIVE-DEFENDER: A Driver Safety-oriented Alcohol and Drowsiness Detection System

  1. Nitin Puli School of Computer Science and Engineering, Lovely Professional University
  2. Shivam Kumar School of Computer Science and Engineering, Lovely Professional University
  3. Md Moniruzzaman Piyash School of Computer Science and Engineering, Lovely Professional University
  4. Karandeep Kaur School of Computer Science and Engineering, Lovely Professional University

Abstract

DRIVE-DEFENDER presents a pioneering approach in driver safety through the development of a real-time machine learning system for alcohol detection. The detrimental impact of alcohol-impaired driving on road safety necessitates efficient detection mechanisms. Current methodologies are often hindered by their cost, invasiveness, and reliance on specialized sensors. DRIVE-DEFENDER utilizes a camera for recording the face of the driver in real time, employing image processing techniques to identify facial landmarks. These landmarks serve as inputs for the calculation of features indicative of alcohol impairment, such as changes in facial expression and coordination. Machine learning algorithms, coupled with adaptive thresholding mechanisms, enable accurate detection of alcohol intoxication without the need for expensive hardware or causing distractions to the driver. The proposed approach offers a cost-effective, non-intrusive solution for enhancing driving safety in real-time by promptly identifying and mitigating the risks associated with alcohol-impaired driving.

Keywords

References (45)

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