IoT-enabled Accident Detection and Emergency Alert System Using ML

This project offers a life-saving vehicle safety solution that uses real-time motion and location data to detect potential road accidents and send immediate alerts. The system comprises an accelerometer to detect sudden impact, a GPS module for accurate location tracking, and a GSM module to send automated SMS alerts to emergency contacts. Machine learning algorithms, such as SVM or decision tree models trained in Google Colab, are used to distinguish between normal driving patterns and actual accident events. The entire setup ensures swift response times and proactive rescue support, ideal for smart transportation and emergency systems.

What You’ll Receive with This Project Kit:

All Essential Project Components:
Kit includes a high-precision accelerometer, GPS module, GSM/SIM800L module, ESP32 controller, power circuitry, and accessories for vehicle mounting.

Fully Functional Source Code:
Real-time impact monitoring and emergency alert logic coded and integrated with ML inference using accident dataset-trained models from Google Colab.

Step-by-Step Working Methods:
Comprehensive user guide with sensor calibration, alert logic setup, SIM configuration, and machine learning threshold tuning.

100-Page Detailed Project Report (PDF):
Complete academic report covering problem analysis, accident detection datasets, SVM model design, hardware integration, and output validations.

Advanced Mobile/Web Alert Dashboard:
A custom interface to visualize accident locations, system status, and SMS alert logs, enhancing real-time visibility and traceability for guardians or control centers.