Driving behavior classification using smartphone sensor data

The need for driver behavior monitoring systems has increased due to the significant amount of accidents that are brought on by human mistakes. These systems have the potential to lower accident rates and increase overall road safety by offering real-time monitoring and analysis of driving behavior. Based on the data collected from passengers' smartphones, we propose a novel analysis framework for classifying driving behavior in this thesis. Our mobile phone application was used to collect the data, which was then subjected to machine learning algorithms for processing. We utilized several Machine Learning (ML) classification techniques, with a particular emphasis on developing a Long Short Term Memory (LSTM) algorithm for increased accuracy and sequence-based prediction. The outcomes show how successfully the suggested method classifies driving behavior using the data obtained from smartphone applications. After having a successful result with LSTM, instead of collecting all data from users into one area, we developed a federated learning algorithm to train and test each data on users' phones. The results of the study show that federated learning is useful for the classification of driver behavior and thus increases accuracy.