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)
Technologies Used
- ESP32
- React Native (Expo)
- Spring Boot
- PostgreSQL
- AWS
Table of Contents
- Introduction
- Solution Architecture
- Hardware & Software Designs
- Testing
- Detailed budget
- Conclusion
- Links
Introduction
Motorcycle riders face significantly higher risks compared to car drivers due to limited situational awareness, lack of advanced driver-assistance systems, and delayed emergency response. RoadEye is a smart motorcycle safety system designed to enhance rider awareness while minimizing distraction.The system combines embedded sensing, real-time data processing, mobile intelligence, and cloud-based services to deliver critical information through HUD visuals, audio alerts, and haptic feedback, ensuring safer riding without cognitive overload.
Problem Statement
Key challenges faced by motorcycle riders include:
- Blind spots caused by helmet design
- Poor visibility in rain, fog, and low-light conditions
- Distraction caused by mobile navigation
- Lack of real-time collision awareness
- Delayed emergency response after accidents
There is a need for a hands-free, real-time, intelligent safety system that improves awareness and ensures rapid emergency handling.
Solution Architecture
Description
The RoadEye system follows a distributed smart system architecture consisting of three main components:
1. Helmet Unit (User Interface Layer)
- Acts as the primary rider interaction interface
- Displays alerts using a heads-up display (HUD)
- Provides audio and haptic feedback
- Renders real-time alerts received from the bike module
2. Bike Module (Sensing & Detection Layer)
- Core data acquisition unit
- Collects environmental and motion data
- Performs edge-level processing
- Sends processed alerts to the helmet
3. Mobile Application (Intelligence Layer)
- Performs high-level processing and analytics
- Stores ride history
- Manages user preferences and emergency communication
Data Flow
- Sensors → Bike Module
- Bike Module → Helmet (real-time alerts)
- Helmet ↔ Mobile App (data synchronization & configuration)
- Crash event → Mobile App → Emergency contacts
Hardware and Software Designs
🧱 Hardware Design
🔹 1. Helmet Unit Hardware
Processing Unit
- ESP32 microcontroller
- Handles sensor input, communication, and alert generation
Display System (HUD)
- TFT micro-display
- Fresnel lens
- Reflective combiner
Purpose
- Create a virtual distant image
- Reduce eye strain and distraction
Audio System
- Stereo speakers integrated into helmet padding
Haptic Feedback
- Small vibration motors near the ears
- Used for collision and warning alerts
Sensors
- 9-Axis IMU – head motion and crash detection
- Hall effect sensor – buckle detection
- Capacitive sensor – helmet wear detection
Power System
- Li-Po battery
- USB-C charging module
- Voltage regulation
🔹 2. Bike Module Hardware
Processing Unit
- ESP32 microcontroller
Distance Sensors
- Ultrasonic / ToF sensors (rear and sides)
- Used to detect approaching vehicles
Environmental Sensors
- Temperature
- Humidity
- Pressure
- Ambient light
Motion Sensors
- 9-Axis IMU – tilt, braking, crash detection
- Vibration sensor – road condition analysis
Anti-Theft Function
- Detects movement when parked
- Sends alert to mobile application
🔹 3. Communication
- WiFi-based communication
- Optional Bluetooth pairing for authentication
💻 Software Design
🔹 1. Embedded Software (Helmet & Bike)
- Sensor data filtering and noise reduction
- Threshold-based event detection
- Priority-based alert handling
- Power management using sleep modes
🔹 2. Mobile Application
- Dashboard displaying speed, alerts, and environment data
- Ride analytics and statistics
- Emergency contact management
- Device pairing and configuration
🔹 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
- 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 hardware, firmware, mobile software, and cloud services into a unified platform.
Key achievements include:
- Fully functional smart helmet system
- Reliable collision and crash detection
- Automated emergency alert system
- Minimal rider distraction through optimized human–machine interaction
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