Driver Drowsiness and Distraction Alert System Using AI-Based Behaviour Analytics

Driver drowsiness and distraction are major causes of road traffic accidents, leading to significant loss of life and injuries. Factors such as fatigue, prolonged eye closure, reduced blinking rate, and driver distraction negatively impact reaction time and cognitive performance. This paper presents a Driver Drowsiness and Distraction Alert System based on AI-driven behavior analytics. The proposed system continuously monitors the driver’s state in real time. An ESP32-CAM module is used for real-time video capture and eye movement analysis using computer vision techniques. An IR eye blink sensor enhances detection accuracy under low-light conditions. An Arduino Nano acts as the system controller, while an ESP8266 (NodeMCU) enables IoT-based communication. Audio-visual alerts, including a buzzer and LED indicators, are triggered when abnormal driver behavior is detected. The system is efficient, non-intrusive, and suitable for real-time applications. Experimental results demonstrate improved detection accuracy, reduced false alarm rates, and faster response times, contributing to enhanced road safety.