IoT and ML for Real-Time Vehicle Accident Detection Using Adaptive Random Forest

In this study, the research work comes up with a framework that combines IoT with machine learning solutions to improve the real-time detection of vehicle accidents and, at the same time, predict the likelihood of an accident. The framework uses IoT sensors placed on the vehicles to gather a variety of information; speed, acceleration, brake force, and deviation from the lane. The patient data is wirelessly sent to a central system and processed to a statistical application of Adaptive Random Forest model. The next algorithm of Adaptive Random Forest is applied to analyze the given real-time data, which looks for outliers and calculates probable accident situations. Applying IoT that collects real-time data, and machine learning that predicts the risk, the proposed framework should enhance road safety by mitigating risks on the spot. This evidently enables simple and direct detection of an accident occurrence while at the same time giving insight on areas of risk or vulnerability, thus minimizing on the number of vehicle accidents.