The Neighbor Course Distribution Method with Gaussian Mixture Models for AIS-based Vessel Trajectory Prediction

Abstract

When operating an autonomous surface vessel (ASV) in a marine environment it is vital that the vessel is equipped with a collision avoidance (COLAV) system. This system must be able to predict the trajectories of other vessels in order to avoid them. The increasingly available automatic identification system (AIS) data can be used for this task. In this paper, we present a data-driven approach to predict vessel positions 5-15 minutes into the future using AIS data. The predictions are given as Gaussian Mixture Models (GMMs), thus the predictions give a measure of uncertainty and can handle multimodality. A nearest neighbor algorithm is applied on two different data structures. Tests to determine the accuracy and covariance consistency of both structures are performed on real data.