Embedded Vision-Based Driver Drowsiness Detection and Real-Time Alert System

Objective: To design and implement an embedded vision system that accurately detects driver drowsiness in real time and issues timely alerts to prevent road accidents.

India faces a significant challenge with road safety, with thousands of accidents annually attributed to driver fatigue and drowsiness, particularly among commercial vehicle drivers and long-haul transport operators. Early detection of drowsiness can save lives and reduce the economic losses associated with such incidents.

This project proposes an embedded vision system utilizing a low-power edge-AI platform to continuously monitor the driver’s facial features and eye patterns, analyzing visual cues to detect early signs of drowsiness. Upon detection, the system immediately issues multi-modal alerts (audio, haptic, visual) to awaken the driver and can optionally log incident data for fleet monitoring.

The working prototype will feature real-time video analysis using a camera module, on-device AI inference with a microcontroller (e.g., Raspberry Pi or NVIDIA Jetson Nano), and a responsive alert mechanism. Key deliverables include the hardware prototype, embedded software, a validated detection algorithm trained on Indian driver datasets, system integration, and a live demonstration to the examiner panel.

Impact: The system addresses a critical road-safety challenge in India with a scalable, cost-effective solution suitable for integration into commercial vehicles, public transport, and personal cars, aligning with the Indian government’s push for intelligent transport systems (ITS).