Intelligent Accident Detection and Alerting System based on Machine Learning over the IoT Network
Gopal Sunil Rathod, Rani Chandrabhan Tipale, Kapil Jajulwar
Abstract
Traffic accidents are increasing daily as the number of automobiles rises. An annual global death toll of 3 million and an injury toll of 55 million are reported. The absence of emergency treatment at the scene of the accidents or the lengthy responding period during the emergency effort are the main causes of deaths. We can reduce waiting time for rescue operation which has the potential to saving several people life’s by using a cognitively assistant for detecting accident and make alarm to traffic observer or rescue team. Transportation systems were gaining significant attention in industry and academics because to the increasing demand of intelligence urban centers and were seen as a way to increase traffic security in these areas. This study presented a smart accident detection model for rescue team based on Internet of Things and machine learning. An Internet of Things module is designed that can recognize an accident, gather all accidental data, including location, pressure, force, speed, and more, and transfer all the related data to the cloud. Machine learning model is utilized in the clouds to verify the IoT module data and enable the emergency module after the any accident is recognized.