3 papers
cs.LG2026
Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
Tom Legel, Dirk Söffker, Roland Schätzle +1
Accurate predictions of ship trajectories in crowded environments are essential to ensure safety in inland waterways traffic. Recent advances in deep learning promise increased acc…
cs.LG2025
Reliability comparison of vessel trajectory prediction models via Probability of Detection
Zahra Rastin, Kathrin Donandt, Dirk Söffker
This contribution addresses vessel trajectory prediction (VTP), focusing on the evaluation of different deep learning-based approaches. The objective is to assess model performance…
cs.LG2024
Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach
Kathrin Donandt, Dirk Söffker
Using data sources beyond the Automatic Identification System to represent the context a vessel is navigating in and consequently improve situation awareness is still rare in machi…