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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.LG2025

Polynomial Chaos Expanded Gaussian Process

Dominik Polke, Tim Kösters, Elmar Ahle +1

In complex and unknown processes, global models are initially generated over the entire experimental space but often fail to provide accurate predictions in local areas. A common a…

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…

cs.LG2024

Improved context-sensitive transformer model for inland vessel trajectory prediction

Kathrin Donandt, Karim Böttger, Dirk Söffker

Physics-related and model-based vessel trajectory prediction is highly accurate but requires specific knowledge of the vessel under consideration which is not always practical. Mac…

cs.LG2024

Spatial and social situation-aware transformer-based trajectory prediction of autonomous systems

Kathrin Donandt, Dirk Söffker

Autonomous transportation systems such as road vehicles or vessels require the consideration of the static and dynamic environment to dislocate without collision. Anticipating the…