In order to ensure that autonomous driving vehicles can make appropriate driving decisions based on the surrounding situation, motion prediction algorithms are used to generate the driving decision output, which will then be used for guiding the trajectory of the vehicle. In general, the output of the motion prediction algorithm is a series that contains the predicted information for the future movement of the vehicle. A traditional approach is using a physics-based model to generate the acceleration prediction series. However, such an approach requires lots of mathematical computation but is only capable to be effective in specific driving scenarios.

To solve that kind of issue, we proposed a data-driven approach by running four different kinds of machine learning models to generate the prediction output series. The results show that the auto-regressive (AR) model has the best prediction performance compared with traditional physics-based models, with a 14.32% improvement on average for the ADE (average displacement error) evaluation metric and 5.93% improvement on average for the FDE (final displacement error) evaluation metric.