About the Project
Driver drowsiness is one of the leading causes of road accidents. Many drivers become tired during long journeys without realizing it, which can lead to delayed reactions or complete loss of control. Our project aims to reduce these accidents by creating an intelligent system that detects early signs of drowsiness and alerts the driver immediately.
Inspiration
We were inspired by the increasing number of road accidents caused by fatigue, especially among truck drivers, cab drivers, and people traveling long distances. We wanted to build a system that can save lives by detecting sleepiness early and warning the driver before anything dangerous happens.
What It Does
Our Driver Drowsiness Detection System: Monitors the driver in real time (eye movement, blinking, head position). Detects signs of tiredness using intelligent algorithms. Triggers alerts such as sound alarms or warning messages. Helps the driver stay awake and avoid accidents. Improves road safety by reacting before the driver falls asleep.
How We Built It
We used: Camera-based monitoring: captures the driver’s face and eye region. Blink detection: checks if eyes are closing longer than normal. Fatigue patterns: measures eye closure, yawning, and head nodding. AI/ML logic: identifies drowsiness using threshold values or trained models. Prototype app (Base44): shows alerts, instructions, and feedback form. Google Forms integration: collects user feedback and incident data.
Challenges We Ran Into
Difficulty in accurately detecting eye closure in low lighting. Ensuring the system works for different face shapes and spectacles. Reducing false alarms when the driver looks away. Syncing real-time detection with alert timing. Designing a simple UI that drivers can use without distraction
Accomplishments That We're Proud Of
Successfully detecting drowsiness using simple and effective methods. Creating a clean and user-friendly prototype. Integrating alerts and feedback forms. Making a system that can genuinely help prevent accidents. Learning how AI and safety systems work in real-world use cases.
What We Learned
Real-time monitoring needs good accuracy and optimization. User-friendly design is important for safety applications. Even small features like alerts and UI layout make a big impact. Integration between hardware and software can be challenging but fun. Teamwork and debugging are key parts of building a working solution.
What’s Next for the Driver Drowsiness Detection System
Adding voice alerts instead of just sound alarms. Including GPS-based location tracking during emergencies. Connecting the system with vehicle sensors. Adding cloud storage to record drowsiness events. Improving accuracy using advanced ML models. Making a full mobile application instead of a prototype.
Log in or sign up for Devpost to join the conversation.