Trajectory prediction network for future anticipation of ships

Abstract

This work investigates the anticipation of future ship locations based on multimodal sensors. Predicting future trajectories of ships is an important component for the development of safe autonomous sailing ships on water. A core challenge towards future trajectory prediction is making sense of multiple modalities from vastly different sensors, including GPS coordinates, radar images, and charts specifying water and land regions. To that end, we propose a Trajectory Prediction Network, an end-to-end approach for trajectory anticipation based on multimodal sensors. Our approach is framed as a multi-task sequence-to-sequence network, with network components for coordinate sequences and radar images. In the network, water/land segmentations from charts are integrated as an auxiliary training objective. Since future anticipation of ships has not previously been studied from such a multimodal perspective, we introduce the Inland Shipping Dataset (ISD), a novel dataset for future anticipation of ships. Experimental evaluation on ISD shows the potential of our approach, outperforming single-modal variants and baselines from related anticipation tasks.