🔒 PATENT PENDING & PRIVATE REPOSITORY Please Note: The source code for this project is currently maintained in a private repository due to ongoing intellectual property protection and publication in the Indian Patent Office (IPO) Journal. This showcase repository documents the system architecture, mathematical methodologies, and data pipelines utilized in the project.
Road traffic injuries in densely populated areas are frequently exacerbated by unmarked or poorly visible traffic calming measures (speed humps). Traditional detection systems rely on expensive vehicle hardware or continuous cloud connectivity.
This project introduces a sensor-independent, mobile-first edge computing architecture built in Flutter. By combining continuous GPS telemetry, mathematical proximity calculations, and local offline data persistence, the system provides real-time audio-visual hazard alerts to drivers without requiring internet access or vehicle retrofitting.
[ Mobile GPS Sensor ] ──(Live Coordinates)──> [ Haversine Proximity Engine ]
│
[ OpenStreetMap (OSM) Nodes ] ──(Pre-loaded)──> [ SQLite Local Database ]
│
[ Text-to-Speech (TTS) ] <──(Proximity Alert)────────────┘
Instead of relying on hardware accelerometers which suffer from mechanical vibration noise across different vehicle types, this system extracts high-frequency GPS streams directly from the mobile device. This approach guarantees hardware independence and immediate scalability across any consumer smartphone.
The core computational engine utilizes the Haversine formula to calculate the great-circle distance between the user's live coordinates and the cached coordinates of known road humps. This allows the system to deterministically trigger alerts when the proximity threshold is breached.
Because cellular data drops in rural areas or tunnels, the application operates on a strict Offline-First paradigm.
- Local Database: Geospatial nodes and hazard coordinates are stored natively on the device using SQLite.
- Zero Cloud Dependency: The proximity engine executes entirely on the local device (Edge Computing), ensuring zero-latency alerts and functionality even in dead zones.
To minimize driver distraction, the application bypasses standard UI visual checks by utilizing native Text-to-Speech (TTS). When the SQLite database confirms an upcoming hazard within the Haversine radius, the system forces a hands-free auditory warning.
- Frontend Framework: Flutter (Dart) deployed as a local Android APK.
- Geospatial Data: OpenStreetMap (OSM) coordinate structures.
- Local Persistence Layer: SQLite (Offline relational data caching).
- Mathematics: Applied spherical trigonometry (Haversine Formula).
- Hardware Integration: Native GPS / Location Services, Text-to-Speech (TTS) engines.
- Deployment: Executed locally as an Edge-Computing application to ensure real-time latency limits.
- Lead Developer / System Architect: Chetana Srinivasa Murthy
- Development Team: Amrutha Shindhe C, Chandana, Haripriya KP
- Project Guides: Dr. Shantakumar B Patil (Professor and Associate Dean of Research), Mr. Manoj Chunangat (Co-Guide)
- Institution: Sai Vidya Institute of Technology, Department of Computer Science and Engineering
The system was initially conceptualized and engineered as a localized pilot for our college campus. Navigating institutional roadways safely requires hyper-local geographic data that mainstream mapping services (like Google Maps) often fail to track or update accurately.
To ensure scalability, the architecture includes a decentralized user-reporting module allowing drivers to log new or unmarked road humps dynamically.
To prevent data pollution (vandalism, duplicate entries, or inaccurate GPS drift coordinates), the system utilizes an Administrative Verification Gateway:
- User Submission: Drivers upload raw coordinate streams via the client application.
- Staging Queue: Data points are held in a separate staging table inside the architecture.
- Manual Validation: System administrators manually verify the coordinates against physical campus routes before promoting the nodes to the master SQLite production database, ensuring absolute data integrity.