• Priyanka Konduru
• Mrs. M. Swathi

Driver drowsiness is a significant factor contributing to road accidents, making its detection crucial for enhancing road safety. This study presents a real-time driver drowsiness detection leveraging machine learning algorithms. The proposed system utilizes a combination of physiological signals and behavioural characteristics, including eye closure rate, yawing frequency and head position, to accurately identify signs for driver fatigue. By integrating data from multiple sensors and applying advanced machine learning techniques, the system achieves high accuracy in detecting drowsiness. The result demonstrate that this approach can effectively alert drivers and prevent potential accidents, offering a promising solution for real-world implementation in modern vehicles.

Driver DrowsinessDetection, Machine learning, Algorithms, Physiological signals, Behavioural characteristics, Sensors, Accuracy, Fatigue

"DRIVER DROWSINESS DETECTION SYSTEM USING DATA SCIENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.328-332, June-2024, Available :http://www.jetir.org/papers/JETIRGL06055.pdf

"DRIVER DROWSINESS DETECTION SYSTEM USING DATA SCIENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp328-332, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06055.pdf