Machine Learning in Connected Vehicle Environments

Vehicle trajectory prediction is an important task that can ensure high safety performance in collision avoidance systems and applications of autonomous vehicles. Machine learning methods for vehicle trajectory prediction have been proposed in recent literature in an effort to increase traffic safety by accurately predicting vehicle trajectories in various traffic scenarios. This paper presents a comprehensive review of recently proposed methods of vehicle trajectory prediction that utilize machine learning (ML) techniques. Moreover, it proposes a novel neural network framework for vehicle trajectory prediction that aims to be implemented in a connected vehicles (CV) environment.