Project Title
RoadEye
Team
- E/21/019, Adikari A.M.H.S.,(e21019@eng.pdn.ac.lk)
- E/21/371, Senawirathne D.M.W.J.I,(e21371@eng.pdn.ac.lk)
- E/21/416, Uthpala J.A.S,(e21416@eng.pdn.ac.lk)
- E/21/433, Wickramanayake N.S.,(e21443@eng.pdn.ac.lk)
Table of Contents
- Introduction
- Solution Architecture
- Hardware & Software Designs
- Testing
- Detailed budget
- Conclusion
- Links
Introduction
Motorcycle riders are among the most vulnerable road users, with accidents often caused by limited situational awareness, delayed reactions, and driver distraction. This project proposes the development of a smart motorcycle helmet system aimed at significantly enhancing rider safety while minimizing cognitive and visual load. By combining sensor fusion, real-time hazard detection, and carefully designed human–machine interaction, the smart helmet delivers critical information through eyes-free and hands-free feedback methods such as haptics, audio, and minimal HUD visuals. The system prioritizes essential alerts, adaptive warnings, and context-aware assistance to ensure that riders remain focused on the road at all times. Through intelligent design and embedded safety features, the proposed smart helmet seeks to reduce accident risk, improve riding awareness, and create a safer and more intuitive riding experience.
Solution Architecture
Description
The RoadEye system follows a distributed smart system architecture with three main components:
- Helmet Unit (User Interface Layer)
- Acts as the primary interaction interface
- Displays alerts using HUD
- Provides audio + haptic feedback
- Processes real-time alerts from Bike Module
- Bike Module (Sensing & Detection Layer)
- Core data acquisition unit
- Collects environmental and motion data
- Performs edge-level processing
- Sends processed alerts to Helmet
- Mobile Application (Intelligence Layer)
- Performs high-level processing & analytics
- Stores ride history
- Handles user preferences & emergency communication
Data Flow
- Sensors → Bike Module
- Bike Module → Helmet (real-time alerts)
- Helmet ↔ Mobile App (data sync, configuration)
- Crash event → Mobile App → Emergency contacts
Hardware and Software Designs
🧱 Hardware Design
🔹 1. Helmet Unit Hardware
a) Processing Unit
- Microcontroller (ESP32 / similar)
- Handles:
- Sensor input
- Communication
- Alert generation
b) Display System (HUD)
- Fresnel lens
- Reflective combiner
- Micro-display
Purpose:
- Create a virtual distant image
- Reduce eye strain
c) Audio System
- Stereo speakers inside padding
- Noise-optimized output
d) Haptic Feedback System
- Small vibration motors near ears
Used for:
- Collision alerts
- Warnings without visual overload
e) Sensors
- 9-Axis IMU → head motion & crash detection
- Hall Effect Sensor → buckle detection
- Capacitive Sensor → helmet wear detection
f) Power System
- LiPo battery
- Charging module (USB-C)
- Voltage regulation
🔹 2. Bike Module Hardware
a) Processing Unit
- ESP32 / similar microcontroller
b) Distance Sensors
- Ultrasonic / ToF sensors (rear + sides)
Purpose:
- Detect vehicles approaching from behind
c) Environmental Sensors
- Temperature
- Humidity
- Pressure
- Light sensor
d) Motion Sensors
- 9-Axis IMU → tilt, crash, braking
- Vibration sensor → road condition
e) Anti-Theft System
- Detects movement when parked
- Sends alert to mobile
🔹 3. Communication
- WiFi-based communication
- Optional Bluetooth pairing for authentication
💻 Software Design
🔹 1. Embedded Software (Helmet & Bike)
a) Sensor Data Processing
- Filtering (noise reduction)
- Threshold-based detection
- Event triggering
b) Alert System Logic
- Priority-based alerts:
- Crash
- Collision risk
- Weather warning
- Notifications
c) Power Management
- Sleep modes
- Sensor duty cycling
🔹 2. Mobile Application
a) Frontend
- Dashboard (speed, alerts, environment)
- Ride analytics UI
- Notifications panel
b) Backend Logic
- Data processing
- Ride analysis
- Risk scoring
c) Emergency System
- Auto SMS/call during crash
- Location sharing
d) OTA Updates
- Firmware updates via app
🔹 3. Data Flow Design
- Real-time data → Helmet alerts
- Logged data → Mobile app storage
- Processed data → Analytics dashboard
Testing
🔬 Hardware Testing
1. Sensor Accuracy Testing
Compared sensor outputs with real-world measurements.
| Sensor | Result |
|---|---|
| Distance Sensors | ±5 cm accuracy |
| IMU | Stable orientation detection |
| Light Sensor | Correct brightness adaptation |
2. Communication Testing
WiFi latency and connection stability were evaluated.
| Test | Result |
|---|---|
| Helmet ↔ Bike | < 100 ms delay |
| Helmet ↔ App | Stable connection |
3. Power Testing
Battery performance under normal usage:
- Helmet Unit: ~6–8 hours
- Bike Module: ~10 hours
4. HUD Testing
Visibility and usability under different conditions:
- Daylight
- Night
- No significant eye strain observed
💻 Software Testing
1. Unit Testing
- Individual sensor modules tested
- Alert triggering mechanisms verified
2. Integration Testing
- Helmet ↔ Bike communication validated
- Mobile App ↔ Helmet synchronization tested
3. System Testing
Full system tested under simulated riding conditions:
- Collision alerts triggered correctly
- Emergency alerts sent successfully
4. User Testing
Tested with real users (motorcycle riders):
| Feature | Feedback |
|---|---|
| HUD | Easy to use |
| Alerts | Non-distracting |
| Audio | Clear |
Summary of Results
- Real-time alert accuracy: High
- System latency: Low
- User distraction: Minimal
- Overall reliability: Stable under most conditions
Detailed budget
All items and associated costs for the RoadEye system are summarized below.
| Item | Quantity | Unit Cost (LKR) | Total (LKR) |
|---|---|---|---|
| Jumper Wire M/M 20cm | 3 | 190 | 570 |
| Jumper Wire F/F 20cm | 2 | 185 | 370 |
| Jumper Wire M/F 20cm | 1 | 190 | 190 |
| 1.8” TFT LCD Display | 1 | 1490 | 1490 |
| 2.0” TFT Color Screen | 1 | 2790 | 2790 |
| Digital Touch Sensor | 1 | 160 | 160 |
| Neodymium Magnets (5x2) | 5 | 60 | 300 |
| MPU-9250 9-Axis IMU | 1 | 1190 | 1190 |
| Hall Sensor Module | 1 | 260 | 260 |
| 12V 2A Power Supply | 1 | 790 | 790 |
| Waterproof Ultrasonic Sensor | 1 | 1890 | 1890 |
| Ultrasonic Waterproof Sensor (5140) | 1 | 490 | 490 |
| MAX98357 Audio Amplifier | 1 | 540 | 540 |
| Green Dot Board (7x9) | 1 | 160 | 160 |
| Female Pin Header | 5 | 40 | 200 |
| Vibration Motor Module | 1 | 250 | 250 |
| LED (5mm, Diffused) | 2 | 5 | 10 |
| INMP441 Microphone | 1 | 890 | 890 |
| Filament Roll (Black, 1kg) | 1 | 4950 | 4950 |
| Acrylic Board | 1 | 600 | 600 |
Conclusion
🎯 What Was Achieved
The RoadEye system successfully demonstrates a complete smart motorcycle safety solution by integrating multiple hardware and software components into a unified platform.
Key achievements include:
- Development of a fully functional smart helmet system
- Successful integration of:
- Helmet-based alert mechanisms
- Bike-mounted sensor module
- Mobile application for intelligence and analytics
System capabilities:
- Real-time collision warning system
- Reliable crash detection with emergency alert functionality
- Minimal rider distraction through optimized human–machine interaction
🔮 Future Developments
Several enhancements can further improve the system:
- AI-based predictive risk analysis
- Camera-based blind spot detection
- Helmet-to-helmet communication
- Integration with smart traffic infrastructure
- Voice assistant for hands-free control
💼 Commercialization Plan
Target Users:
- Delivery riders
- Daily commuters
Product Variants:
- Basic Version: Core safety alerts
- Advanced Version: Full analytics + HUD features
Go-to-Market Strategy:
- Partner with helmet manufacturers
- Offer as an aftermarket add-on device