DriveSafe AI is a real-world inspired system designed to improve road safety by monitoring driver behavior in real time and alerting vehicle owners when risky situations occur (drowsiness, overspeeding, abnormal activity).
This project simulates how modern fleet-monitoring and driver-safety solutions are built in production environments.
Long-distance drivers often face fatigue, distraction, and sleep deprivation, which are major causes of road accidents.
Fleet owners usually have no real-time visibility into:
- Whether a driver is alert or drowsy
- What the driver is doing inside the cabin
- When to intervene before an accident happens
DriveSafe AI addresses this gap.
DriveSafe AI provides:
- Real-time driver eye monitoring
- Instant alerts to the vehicle owner
- Live video access during risky situations
- Vehicle-wise monitoring dashboard
- Alert history with detailed context
The system is designed keeping real-world constraints in mind:
- Limited internet
- Low-cost hardware
- Non-technical drivers
- Owner-centric control
- Eye-blink based detection using computer vision
- Detects prolonged eye closure indicating fatigue
- Owner can view live cabin video
- Session-limited to prevent misuse
- Separate logic for:
- Manual owner checks
- Alert-triggered access
- All alerts stored with:
- Vehicle number
- Driver details
- Alert reason (sleep, risk, speed)
- Timestamp
- One-click access to live video from alerts
- Owner dashboard supports multiple vehicles
- Easy driver reassignment per vehicle
- Clear vehicle identification
Driver Camera (Phone / Camera)
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Python (Computer Vision + Detection)
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Node.js / Express (API Layer)
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MongoDB (Alerts & Vehicle Data)
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React Dashboard (Owner View)
- React (Vite)
- REST API integration
- Real-time UI updates
- Node.js
- Express.js
- MongoDB (Atlas – Free Tier)
- Python
- OpenCV
- MediaPipe (Face & Eye landmarks)
This project intentionally documents real challenges faced during development:
- Camera resource conflicts on Windows
- Limitations of laptop webcams for live streaming
- OS-level constraints in real-time video processing
- Session-based access control design
These challenges helped in understanding production-level system limitations, not just happy-path coding.
- Solves a real transportation safety problem
- Mimics fleet management systems
- Focuses on practical engineering trade-offs
- Demonstrates backend + frontend + CV integration
- Dedicated Android driver app for GPS & speed
- Edge device support (Raspberry Pi / Jetson)
- Improved low-light detection
- Cloud video relay (WebRTC)
- AI-based risk scoring
This project was built with a learning + real-world mindset, focusing on:
- System thinking
- Debugging real constraints
- Designing owner-centric workflows
If you'd like to discuss system design, improvements, or real-world deployment scenarios, feel free to connect.