Vehicle Trajectory Prediction with Gaussian Process Regression in Connected Vehicle Environment$\star$

This paper addresses the problem of long term location prediction for collision avoidance in Connected Vehicle (CV) environment where more information about the road and traffic data is available through vehicle-to-vehicle and vehicle-to-infrastructure communications. Gaussian Process Regression (GPR) is used to learn motion patterns from historical trajectory data collected with static sensors on the road. Trained models are then shared among the vehicles through connected vehicle cloud. A vehicle receives information, such as Global Positioning System coordinates, about nearby vehicles on the road using inter-vehicular communication. The collected data from vehicles together with GPR models received from infrastructure are then used to predict the future trajectories of vehicles in the scene. The contributions of this work are twofold. First, we propose the use of GPR in CV environment as a framework for long term location prediction. Second, we evaluate the effect of pre-analysis of training data via clustering in improving the trajectory pattern learning performance. Experiments using real-world traffic data collected in Los Angeles, California, US show that our proposed method improves prediction accuracy compared to the baseline kinematic models.