Kernel density estimation for target trajectory prediction

Abstract

This paper proposes the use of a kernel density estimation to measure similarities between trajectories. The similarities are then used to predict the future locations of a target. For a given environment with a history of previous target trajectories, the goal is to establish a probabilistic framework to predict the future trajectory of currently observed targets based on their recent moves. Instead of clustering trajectories into groups, we calculate the similarity between a given test trajectory and the set of all past trajectories in a dataset. Next, we use a weighted mechanism for prediction, that can be used in target tracking and collision avoidance applications. The proposed method is compared with two other commonly used similarity models (PCA and LCSS) over a dataset of simulated trajectories, and two datasets of real observations. Results show that the proposed method significantly outperforms the existing models for those datasets and experimental settings.