Time Series-based Driving Event Recognition for Two Wheelers

Abstract

Classification of a motorcycle's driving events can provide deep insights to detect issues related to driver safety. In order to perform the above, we developed a hardware system with 3-D accelerometer/gyroscope sensors that can be deployed on a motorcycle. The data obtained from these sensors is used to identify various driving events. We firstly investigated several machine learning (ML) models to classify driving events. However, in this process, we identified that though the overall accuracy of these traditional ML models is decent enough, the class-wise accuracy of these models is poor. Hence, we have developed time-series-based classification algorithms using LSTM and Bi-LSTM to classify various driving events. The experiments conducted have demonstrated that the proposed models have surpassed the state-of-the-art models in the context of driving event recognition with better class-wise accuracies. We have also deployed these models on an edge device (Raspberry Pi) with similar prediction accuracies. The experiments demonstrated that the proposed Bi-LSTM model showed a minimum of 86% accuracy in the case of a Left Turn (LT) event and a maximum of 99% accuracy for the event Stop (ST) in class-wise prediction when implemented on Raspberry Pi for a two wheeler driving dataset.