Accident Mitigation System with Drowsiness Detection: A Machine Learning and Iot with Hybrid Approach

Abstract

Accidents are unavoidable with population growth around the world. There have been numerous researches conducted to preserve both life and morals. Drowsiness and fatigue have been consistently identified as significant causes of accidents. Instead of relying on limited methods to detect drowsiness and tiredness, this study incorporates deep learning in conjunction with IoT. This study focuses on developing a prototype to minimize road accidents due to drowsiness, fatigue, carelessness, and other reasons. The CNN algorithm handled drowsiness detection; drivers will be notified as soon as they fall asleep. This study takes a novel approach by combining machine learning with drunk avoidance, direction control, speed control, and distance preservation. When paired with proper guidance, the said hybrid approach would produce the best solution to the accident issues without suspects.