Driver Drowsiness and Alcohol Detection using IOT and Machine Learning

Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsiness of driver. Detecting the drowsiness and alcohol taken by the driver is one of the surest ways of measuring driver fatigue. This project uses existing prototype of drowsiness detection system and alcohol detection system. This system captures and examines the eyes of the driver and triggers an alarm when he/she is drowsy. The priority is on improving the safety of the driver. Driver fatigue often becomes a direct cause of many traffic accidents. Therefore, there is a need to develop the systems that will detect and notify a driver of her/him bad psycho-physiological condition, which could significantly reduce the number of fatigue-related car accidents. One of the technical possibilities to implement driver drowsiness detection systems is to use the vision-based approach. This report presents the currently used driver drowsiness detection systems and alcohol detection system. This system captures the human face, but mainly focuses on eyes of the driver and detects the drowsiness. Using machine learning the system detects and examines drowsiness and after detection if driver found sleepy the system triggers an alarm. This system detects the alcohol MQ6 sensors.