Citation

Mohak Bhalla, Simran Gawri, Chirag Goya, Monica Bhutani. Int. J. Eng. Technol. Manage. Sci. 2022; 4(6): 102-110.

Copyright

(Copyright © 2022, International Journal of Engineering Technology and Management Sciences)

DOI

10.46647/ijetms.2022.v06i04.0019

PMID

unavailable

Abstract

Electric Vehicle Accident Alert System is a Machine Learning Integrated, IoT based real-time alerting system, operating on ESP8266 microcontroller. Our designed system acts as an alerting system in its truest sense. It uses KNN classification algorithm to analyse the GPS location of the vehicle and generate a prediction indicating whether the vehicle is in an accident-prone area or not. And, if the accident occurs, the Electric Vehicle Accident Alert System sends a SOS alert message with a link to google maps (for directions), to the emergency contact saved in the system. The whole system is based on a plug n play concept, i.e., the system would be ready to use once powered up. To provide this agility and flexibility, we have designed a registration web portal for the device which registers the user and their device with a unique UID on the Cloud. After successful registration, the system becomes ready and can be simply fitted inside a vehicle to be used. Moreover,the ML algorithm saves those GPS coordinates in its dataset to further improve the accuracy of the prediction. The whole IoT stack integrated with ML algorithm, Web Portal and a Cloud server (implemented on Google Firebase), makes our project a self-improving, agile and a user-friendly system.