VISION: Vehicle Intelligence System for Incident Observation and Navigation

Abstract

Rising vehicle traffic volume on modern highways and metropolitan corridors has significantly increased accident rates, reckless speeding, unauthorized wrong-way entries, and life-threatening transit delays for emergency responders. Conventional commercial navigation applications (e.g., Google Maps, Apple Maps, Waze) subject drivers to severe cognitive distraction by rendering dense 3D graphics, buildings, advertisements, and complex map geometries that pull the driver\'s visual gaze away from the forward roadway for seconds at a time. Furthermore, existing mobile navigation platforms lack unified priority arbitration when multiple hazards occur concurrently and frequently interpolate or fabricate unverified speed limits on unmapped roads. This paper presents VISION (Vehicle Intelligence System for Incident Observation and Navigation), an automotive digital instrument cluster HUD and incident awareness platform engineered as a high-performance web-based telematics framework for immediate operational deployment, with an architecturally decoupled roadmap for dedicated in-vehicle IoT integration. The platform pairs a cross-platform Flutter Web application with a Node.js, Express, and TypeScript edge telematics backend. The system streams live GPS coordinates, heading, and velocity from client browsers, dynamically querying OpenStreetMap (Nominatim and Overpass) and Project OSRM in parallel. A deterministic Seven-Level Alert Priority Engine evaluates simultaneous risks (wrong-way driving, no-entry restrictions, approaching emergency vehicles, overspeeding, school/hospital zones, and lane optimization), selecting the single most critical life-safety advisory for display on a dominant central message panel commanding 45%–55% of the screen. Crucially, VISION implements a strict zero-hallucination policy, explicitly displaying \"SPEED LIMIT DATA UNAVAILABLE\" when unmapped. A dynamic ambulance corridor module tracks approaching emergency vehicles within 800 meters, calculating geodesic relative bearings to issue actionable steering evasions (\"MOVE LEFT WHEN SAFE\"). An abstract VehicleDataSource layer allows the current browser GPS to be seamlessly substituted by an ESP32 hardware module with CAN-bus integration in future iterations without altering dashboard presentation logic. Experimental evaluation across ten real-world scenarios demonstrates sub-second edge arbitration, robust velocity filtering, and elimination of cockpit visual clutter.

Introduction

The text presents VISION, an intelligent, distraction-free driver assistance system designed to improve road safety by providing real-time driving alerts through a simplified Heads-Up Display (HUD).

Traditional navigation applications often distract drivers with complex maps, excessive information and unreliable speed-limit data. They also lack an effective system for prioritizing multiple road hazards and providing early warnings about approaching emergency vehicles.

To overcome these limitations, VISION integrates GPS-based vehicle monitoring, road intelligence, hazard detection and emergency vehicle tracking into a single platform.

The system has the following key features:

1. Real-Time Speed Monitoring: Uses GPS data to monitor vehicle speed and compare it with verified speed limits. If speed-limit information is unavailable, the system clearly displays this instead of generating inaccurate values.

2. Wrong-Way Detection: Identifies vehicles travelling in the wrong direction using GPS heading and road direction information.

3. Emergency Vehicle Alerts: Detects approaching ambulances within an 800-metre range and provides directional instructions such as "MOVE LEFT WHEN SAFE."

4. Seven-Level Alert Priority Engine: Prioritizes multiple safety warnings and displays only the most critical alert to minimize driver distraction.

5. Voice and Visual Alerts: Provides clear dashboard notifications and voice warnings to help drivers remain focused on the road.

6. Road Intelligence: Integrates OpenStreetMap (OSM), Nominatim, Overpass and OSRM to obtain road information, speed limits, access restrictions and upcoming road maneuvers.

7. IoT Integration: Uses a flexible VehicleDataSource architecture that supports future integration with ESP32, CAN-bus and vehicle telemetry hardware.

The system follows a modular architecture consisting of five main components: telematics ingestion, road intelligence, hazard rule engines, alert prioritization and emergency corridor management.

The literature survey highlights limitations in existing solutions, including static speed-limit databases, basic emergency vehicle notifications, unreliable road information and the absence of unified alert prioritization. VISION addresses these gaps by combining multiple safety features into one intelligent platform.

Conclusion

This paper presented VISION, a distraction-free vehicle intelligence platform designed to eliminate cockpit cognitive distraction through a non-map digital HUD. Developed as a responsive web platform, VISION captures live telematics, queries multi-source OpenStreetMap and OSRM layers via high-speed spatial grid caching, and executes deterministic priority arbitration across seven hazard tiers. The system enforces zero-hallucination speed reporting, provides vector-based emergency corridor clearances, and structures vehicle telemetry behind an abstract VehicleDataSource interface. Future Scope & IoT Hardware Roadmap: Building upon this web-based foundation, subsequent work will implement the embedded automotive IoT tier: (1) Dedicated ESP32 Hardware Integration using an ESP32-WROOM-32 microcontroller interfaced with a u-blox NEO-M8N GPS module, MPU-6050 6-axis IMU, and an MCP2515 CAN-bus transceiver to read wheel speed, brake status, and steering angle directly from vehicle onboard diagnostics; (2) Driver & Cabin Vision Monitoring via an in-cabin camera module running lightweight computer vision models (e.g., MobileNet / YOLO-Nano) to detect driver drowsiness, phone distraction, and seatbelt compliance; (3) Automated Crash Notification (eCall) utilizing 6-axis IMU accelerometer spikes (>3g) to detect collisions and dispatch emergency SMS and V2X broadcasts with exact GNSS coordinates; and (4) Cloud-Synchronized Emergency Corridors connecting active emergency vehicle broadcasts to Cloud Firestore to synchronize corridors across thousands of vehicles in real time.

References

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Copyright

Copyright © 2026 Mrs. K. Yazhini, Sabin Rahul I, Sanjay R, Senathipathi T, Sheshash S D. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.