Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
#deep learning #ship trajectory #inland waterways #explainable AI #maritime traffic #prediction model #safety
📌 Key Takeaways
- The article introduces an explainable deep learning approach for predicting ship trajectories in inland waterways.
- It addresses the 'black box' nature of traditional deep learning models by enhancing interpretability.
- The method aims to improve safety and efficiency in maritime traffic management through transparent predictions.
- Research focuses on inland waterways, which present unique challenges compared to open-sea navigation.
📖 Full Retelling
arXiv:2603.04472v1 Announce Type: cross
Abstract: Accurate predictions of ship trajectories in crowded environments are essential to ensure safety in inland waterways traffic. Recent advances in deep learning promise increased accuracy even for complex scenarios. While the challenge of ship-to-ship awareness is being addressed with growing success, the explainability of these models is often overlooked, potentially obscuring an inaccurate logic and undermining the confidence in their reliabilit
🏷️ Themes
Explainable AI, Maritime Safety, Trajectory Prediction
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Original Source
--> Computer Science > Machine Learning arXiv:2603.04472 [Submitted on 4 Mar 2026] Title: Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways Authors: Tom Legel , Dirk Söffker , Roland Schätzle , Kathrin Donandt View a PDF of the paper titled Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways, by Tom Legel and Dirk S\"offker and Roland Sch\"atzle and Kathrin Donandt View PDF HTML Abstract: Accurate predictions of ship trajectories in crowded environments are essential to ensure safety in inland waterways traffic. Recent advances in deep learning promise increased accuracy even for complex scenarios. While the challenge of ship-to-ship awareness is being addressed with growing success, the explainability of these models is often overlooked, potentially obscuring an inaccurate logic and undermining the confidence in their reliability. This study examines an LSTM-based vessel trajectory prediction model by incorporating trained ship domain parameters that provide insight into the attention-based fusion of the interacting vessels' hidden states. This approach has previously been explored in the field of maritime shipping, yet the variety and complexity of encounters in inland waterways allow for a more profound analysis of the model's interpretability. The prediction performance of the proposed model variants are evaluated using standard displacement error statistics. Additionally, the plausibility of the generated ship domain values is analyzed. With an final displacement error of around 40 meters in a 5-minute prediction horizon, the model performs comparably to similar studies. Though the ship-to-ship attention architecture enhances prediction accuracy, the weights assigned to vessels in encounters using the learnt ship domain values deviate from the expectation. The observed accuracy improvements are thus not entirely driven by a causal relationship between a predicted trajectory and the trajectories...
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