Application of On-Board Diagnostics (OBD) Data for Vehicle Trajectory Prediction

This study explores the use of On-Board Diagnostics (OBD) data in the analysis and prediction of vehicle dynamics. Though various data sources are available for traffic data collection, these conventional approaches may not work for the complex traffic system in India, with its heterogeneity and lack of lane discipline. On-board units such as GPS and OBD are some devices, which perform independent of the traffic conditions. This study focuses on the use of OBD data along with GPS data for individual vehicle trajectory prediction. A machine learning tool, namely Long–Short-Term Memory (LSTM) model is employed and the prediction of speed and bearing for the next 1 s is done. Results obtained showed the OBD as a potential source of data that can be used for various real-time and offline applications.