Data driven vessel trajectory forecasting using stochastic generative models

Abstract

In this work, we propose a data driven trajectory forecasting algorithm that utilizes both recorded historical and streaming trajectory observations. The algorithm performs Bayesian inference on a directed graph the walks on which represent stochastic change point models of trajectory classes. Parameter distributions of these models are learnt from recorded trajectories. Forecasting is then made by calculating the class -or, walk- probabilities and corresponding predictive distributions for a given stream of location and velocity observations. This approach is tailored for the maritime domain and automatic identification system (AIS) data exploitation through the use of an Ornstein-Uhlenbeck process driven stochastic process model that captures vessel motion characteristics. We demonstrate the efficacy of this approach on a real data set.