Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Drowsiness among drivers causes many road accidents worldwide. To tackle this problem, we created a real-time driver drowsiness monitoring system that uses a Raspberry Pi to detect early signs of drowsiness. The system analyzes facial cues like prolonged eye closing, reduced blinking, and yawning to identify drowsiness in real time. When it spots these signs, it gives immediate audio alerts to help the driver stay awake. Besides monitoring the driver, the system has an accident detection feature using a tilt sensor. If it detects a sudden impact or a tilt in the vehicle, the system automatically collects the vehicle's GPS location. It then sends this information via a GSM module to registered emergency contacts. This ensures that help can arrive quickly, even if the driver cannot respond. The combination of early drowsiness detection and instant location sharing in case of an accident makes the system a reliable and low-cost solution for improving road safety.
"Accident prevention and alert system using GSM and GPS Modules", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b14-b18, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605103.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator