Detection of Driver Drowsiness Using Adaptive Eye Characteristic Ratio for Enhanced Road Safety

Driver drowsiness is a major cause of road accidents worldwide, leading to significant loss of life and property. This paper presents a Detection of Driver Drowsiness Using Adaptive Eye Characteristic Ratio for Enhanced Road Safety system that monitors the driver's alertness in real time and provides immediate warnings upon detecting signs of fatigue. The proposed system utilizes a Raspberry Pi as the central processing unit and a USB camera to continuously capture the driver's facial and eye movements. An Adaptive Eye Characteristic Ratio (AECR) technique is employed to analyze eye-opening and eye-closing patterns for accurate drowsiness detection.When drowsiness is detected, a buzzer and speaker generate audible alerts to awaken the driver and prevent potential accidents. An ultrasonic sensor is integrated to measure the distance between the vehicle and nearby obstacles, enhancing overall driving safety. A GPS module provides real-time location tracking, enabling the system to identify and transmit the vehicle's current position when required. An LCD display presents system status, drowsiness alerts, obstacle information, and location-related data to the driver. By combining computer vision, real-time monitoring, obstacle detection, and location tracking, the proposed system offers a reliable and cost-effective solution for reducing accidents caused by driver fatigue and improving road safety.