AI-Based Real-Time Emergency Call Classification and Dispatch

Overview

The AI-Based Real-Time Emergency Call Classification and Dispatch System is a cutting-edge solution designed to revolutionize emergency response for automobile-related incidents. Traditional emergency response systems rely on manual call handling, which can lead to delays, misclassification, and inefficient resource allocation. This project leverages Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) to streamline emergency call classification and automate dispatching, significantly improving response times and resource efficiency.

Why This System is Needed

1. Delayed Responses in Traditional Systems:

   • Emergency calls are manually handled, increasing the time required to classify and dispatch the correct responders.
   • High call volumes lead to extended wait times and bottlenecks in emergency response centers.

2. Risk of Human Error:

   • Manual classification of emergencies is prone to errors, leading to misallocation of resources.
   • Distress calls may be misinterpreted due to lack of contextual awareness.

3. Handling Prank and Non-Emergency Calls:

   • Many emergency call centers face challenges in filtering out prank calls or non-emergency situations, consuming valuable resources.

4. Lack of Integration with Modern Vehicle Systems:

   • Existing accident detection systems in vehicles (e.g., OnStar, Tesla API) lack intelligent emergency classification and automated dispatching.
   • Emergency response systems often operate in isolation rather than leveraging smart city infrastructure.

How This System Solves the Problem

• Automates Emergency Call Handling: The system utilizes an AI-powered virtual assistant to engage with callers and extract crucial information in real-time.
• Speech-to-Text and NLP Classification: Converts voice calls into structured text, analyzes them using NLP, and categorizes emergencies based on severity and type.
• Real-Time Prioritization & Dispatch: Automatically assigns severity scores to incidents and dispatches the nearest available emergency responders.
• Seamless Integration with Vehicle Systems: Connects with vehicle APIs to detect and classify emergencies automatically, even if the driver is incapacitated.
• Smart Filtering of Calls: Uses AI to distinguish genuine emergencies from non-emergency or prank calls, reducing the burden on human operators.

This AI-driven approach ensures faster response times, improved emergency classification accuracy, and optimal utilization of emergency resources, ultimately saving lives and reducing inefficiencies in emergency management systems.

Key Features

• Automated Emergency Call Handling: AI-powered assistant processes distress calls and extracts key details.
• Real-Time Speech-to-Text Conversion: Converts emergency calls into structured text for analysis.
• Natural Language Processing (NLP) for Classification: Determines the severity and type of emergency.
• Automated Dispatch System: Directs emergency responders (police, fire, medical) to the location.
• Geolocation Tracking: Ensures accurate dispatch and real-time location updates.
• Web-Based Dashboard: Provides live monitoring and status updates.
• Integration with Vehicle Emergency Systems: Compatible with OnStar, Tesla API, and other vehicle distress systems.

System Architecture

The system consists of five core modules:

1. User Interaction Module
   • AI-driven conversation interface for callers.
   • Collects emergency details dynamically.
2. AI Processing Module
   • Context-aware NLP engine for analyzing caller input.
   • Extracts emergency type, severity, and key information.
3. Real-Time Data Management Module
   • Ensures structured storage and dashboard updates.
   • Provides emergency responders with necessary insights.
4. Classification and Prioritization Module
   • Categorizes emergencies (Medical, Fire, Police).
   • Assigns severity scores for optimized response.
5. Dispatch and Resource Allocation Module
   • Identifies the nearest available emergency response team.
   • Automates deployment based on priority ranking.

Technology Stack

• Backend: Node.js, Python (FastAPI/Flask)
• Frontend: HTML, CSS, JavaScript
• AI & NLP: OpenAI's Pretrained Models, Speech-to-Text APIs
• Database: PostgreSQL / Firebase for real-time data management
• Deployment: Docker, Kubernetes (Optional)

Installation & Setup

Prerequisites

Ensure you have the following installed:

• Python (>=3.8)
• Node.js (>=14.x)
• PostgreSQL (or Firebase, if cloud-based)
• Docker (if deploying in a containerized environment)

Steps to Install

1. Clone the Repository

   git clone https://github.com/your-repo/ai-emergency-dispatch.git
   cd ai-emergency-dispatch

2. Set Up Backend

   cd backend
   pip install -r requirements.txt
   python app.py

3. Set Up Frontend

   cd frontend
   npm install
   npm start

4. Database Configuration
   • Update config.json with PostgreSQL credentials.
   • Run migrations (if applicable):

     python manage.py migrate

5. Run the System

   docker-compose up  # If using Docker

Usage Guide

• Web Dashboard: View and manage live emergency call classifications.
• AI Call Handler: Handles emergency call processing and NLP-based classification.
• Dispatch System: Automates resource allocation and nearest responder assignment.

Future Enhancements

• Multilingual Support: Enable emergency classification in multiple languages.
• Predictive Analytics: AI-based trend detection for proactive emergency response.
• Mobile App Integration: Enable one-tap emergency reporting from mobile devices.
• Smart City & IoT Integration: Connect with city-wide emergency networks.

Contact

For queries, reach out at: ankitpatil.cs22@rvce.edu.in