AIS-based Vessel Trajectory Prediction for ASV Collision Avoidance

Abstract

The automotive industry has already taken a big step towards fully autonomous vehicles. This trend is now spreading to the maritime industry with development of autonomous surface vessels (ASVs). A reliable collision avoidance (COLAV) system is essential in this context. In order to avoid collision with other vessels, one must predict the future trajectories of nearby vessels. The constant velocity model (CVM) is the prevailing approach for trajectory prediction in today s COLAV systems. The main purpose of this thesis is to investigate to what extent it is possible to predict future vessel trajectories based on historical automatic identification system (AIS) data for prediction horizons up to about 15 minutes. A survey of other relevant prediction methods are also presented.

Two new, AIS based methods for vessel trajectory prediction are developed and tested: the single point neighbor search (SPNS) method and the neighbor course distribution method (NCDM). Three speed prediction methods are also tested: the straightforward constant speed method, a method using the median speed of the predicted state s close neighbors (CNs) and lastly a linear transition in predicted time between the two former methods.

The SPNS method is compared to a CVM approach and yields significantly better results on curved trajectories, in terms of lower average and median path and trajectory errors. However, a major part of vessels transit time is spent on straight line trajectories. The SPNS algorithm shows also good path predicting capabilities on close to straight line trajectories, although the CVM method yields the lowest errors in such environments. The SPNS algorithm outputs a single predicted trajectory which tends to follow the most AIS-dense sea lane ahead. Hence, it does neither facilitate any uncertainty measure nor the possibility to suggest multiple possible route choices. The more computational demanding NCDM algorithm, which outputs multiple predicted trajectories, does better facilitate prediction uncertainty and it is also capable of dividing the predicted trajectories into multiple branching sea lanes. Its predicted positions at certain time instants are clustered with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The predictions are further statistically evaluated with respect to the distances to the nearest cluster centers.

Lastly, a computation time analysis is presented. The computational time is reduced from an earlier version of the algorithm by storing data in a k-d tree. However, the NCDM algorithm with the tested decision parameters is not practically feasible in real-time. Several suggestions to reduce the computation time are given.