A Data-Driven Approach to Vessel Trajectory Prediction for Safe Autonomous Ship Operations

Abstract

Autonomous vehicles will be an integral part of future transportation systems, and the maritime industry is working towards developing methods to ensure safe autonomous ship operations. One of the major challenges in realizing autonomous ships is ensuring effective collision avoidance technologies. Autonomous vessels must have a higher degree of situation awareness to detect other vessels, predict their future intentions, and evaluate the respective collision risk. One step in achieving this goal is to predict other vessel trajectories accurately. In this paper, a data-driven approach to vessel trajectory prediction for time horizons of 5–30 minutes utilizing historical AIS data is evaluated. A clustering based Single Point Neighbor Search Method is investigated along with a novel Multiple Trajectory Extraction Method. Predictions have been conducted using these methods and compared with the Constant Velocity Method. Additionally, the Multiple Trajectory Extraction Method is utilized to evaluate estimated ship routes.