Driver Drowsiness Detection

Driver Drowsiness Detection

"Driver Drowsiness Detection" is a real time cutting-edge system designed to enhance road safety by utilizing advanced technology to detect signs of driver fatigue in real-time.

The problem Driver Drowsiness Detection solves

Drowsy driving is a significant safety concern, contributing to numerous accidents and fatalities worldwide each year. Despite awareness campaigns and regulations, drivers still struggle to recognize and combat fatigue while behind the wheel. Our "Driver Drowsiness Detection" project addresses this problem by providing a proactive solution that:

1] Enhances Road Safety: By continuously monitoring driver behavior, the system detects early signs of drowsiness or distraction, allowing for timely intervention before accidents occur.

2] Reduces Accidents: Prompt alerts or automated safety measures prevent potential collisions caused by drowsy driving, safeguarding both drivers and pedestrians.

3] Improves Driver Awareness: Raises awareness about the dangers of drowsy driving, encouraging drivers to prioritize rest and take necessary breaks during long journeys.

4] Enhances Vehicle Efficiency: Preventing accidents and ensuring driver alertness can lead to reduced insurance costs, vehicle maintenance expenses, and overall operational efficiency for fleet owners and transportation companies.

By addressing the root cause of many road accidents—driver fatigue—our project significantly contributes to making roads safer for everyone, ultimately saving lives and reducing the economic burden associated with road accidents.

UNIQUE SELLING POINT
Future Implementation with Raspberry Pi and Camera Module:
In the future, our "Driver Drowsiness Detection" project can be seamlessly integrated with Raspberry Pi and camera modules, paving the way for cost-effective mass production. By leveraging this technology, we aim to bring cutting-edge safety features to vehicles at an affordable price point, making roads safer for everyone.

Challenges we ran into

We basically faced issues with ml library for its access and efficiency.The major issue was to detect the non fatal blinking of eyes. We used Dlib to solve this problem & as well as we made code such that it can ignore natural eye lid blinking.

Tracks Applied (1)

Ethereum Track

Integration of Driver Drowsiness Detection with Ethereum Track: Our project, "Driver Drowsiness Detection," aligns with...Read More

ETHIndia

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