14 citations · 21 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models
Kathrin Donandt, Karim Böttger, Dirk Söffker
Accurate vessel trajectory prediction is necessary for save and efficient navigation. Deep learning-based prediction models, esp. encoder-decoders, are rarely applied to inland nav…
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…
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…