SafeWay: Improving the safety of autonomous waypoint detection in maritime using transformer and interpolation

Detecting waypoints where vessels change their behavior (i.e., maneuvers, speed changes, etc.) is essential for optimizing vessel trajectories to increase the efficiency and safety of sailing. However, accurately detecting waypoints is challenging due to potential AIS data quality issues (i.e., missing or inaccurate messages). In this paper, we propose a five-step learning approach (SafeWay) to estimate waypoints on a given AIS trajectory. First, we interpolate trajectories to tackle AIS data quality issues. Then, we annotate historical trajectories by using an existing waypoint library that contains historical waypoints. As the historical waypoints are passage plans manually created by port operators considering sailing conditions at that time, they are not specific to other historical trajectories between the same ports. We, therefore, use a similarity metric to determine overlapping segments of historical trajectories with the historical waypoints from the waypoint library. Then, we build a transformer model to capture vessel movement patterns based on speed- and location-related features. We do not process location features directly to avoid learning location-specific context, but take into account tailored delta features. We test our approach on a real-world AIS dataset collected from the Norwegian Sea between Å lesund and Måløy and show its effectiveness in terms of a harmonic mean of purity and coverage, mean absolute error and detection rate on the task of detecting trajectory waypoints compared to a state-of-the-art approach. We also show the effectiveness of the trained model on the trajectories obtained from two other regions, the North Sea (London and Rotterdam) and the North Atlantic Ocean (Setubal and Gibraltar), on which the model has not been trained. The experiments indicate that our interpolation-enabled transformer design provides improvements in the safety of the estimated waypoints.