Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
arXiv:2603.04472v1 Announce Type: new 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 usi
arXiv:2603.04472v1 Announce Type: new 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 of nearby ships. This finding underscores the model's explanatory capabilities through its intrinsically interpretable design. Future work will focus on utilizing the architecture for counterfactual analysis and on the incorporation of more sophisticated attention mechanisms.
Executive Summary
This article presents an LSTM-based vessel trajectory prediction model that incorporates ship domain parameters to enhance explainability and accuracy in inland waterways traffic prediction. The study evaluates the model's performance using standard displacement error statistics and analyzes the plausibility of generated ship domain values. While the model achieves comparable performance to similar studies, the weights assigned to vessels in encounters using learned ship domain values deviate from expectations, suggesting that the accuracy improvements are not entirely driven by causal relationships between predicted trajectories and nearby ships. This finding underscores the model's explanatory capabilities through its intrinsically interpretable design. Future work will focus on utilizing the architecture for counterfactual analysis and incorporating more sophisticated attention mechanisms.
Key Points
- ▸ The study proposes an LSTM-based vessel trajectory prediction model that incorporates ship domain parameters to enhance explainability and accuracy in inland waterways traffic prediction.
- ▸ The model achieves comparable performance to similar studies with a final displacement error of around 40 meters in a 5-minute prediction horizon.
- ▸ The weights assigned to vessels in encounters using learned ship domain values deviate from expectations, suggesting that accuracy improvements are not entirely driven by causal relationships between predicted trajectories and nearby ships.
Merits
Strength
The study provides a novel approach to incorporating explainability in deep learning models for vessel trajectory prediction, addressing a critical gap in the literature.
Strength
The use of ship domain parameters enhances the model's interpretability and provides insight into the attention-based fusion of interacting vessels' hidden states.
Demerits
Limitation
The study's findings suggest that the accuracy improvements are not entirely driven by causal relationships between predicted trajectories and nearby ships, which may undermine the confidence in the model's reliability.
Limitation
The study's evaluation metrics and methods may not be directly applicable to other inland waterways scenarios, limiting the generalizability of the results.
Expert Commentary
While the study makes significant contributions to the field of explainable AI in vessel trajectory prediction, it is essential to note that the accuracy improvements observed may not be entirely driven by causal relationships between predicted trajectories and nearby ships. This finding highlights the need for further research on the interpretability of deep learning models and the development of more sophisticated attention mechanisms. Future work should focus on utilizing the proposed architecture for counterfactual analysis and exploring the potential of more advanced attention mechanisms to improve model performance.
Recommendations
- ✓ Researchers should prioritize the development of more advanced attention mechanisms to improve the performance of vessel trajectory prediction models in complex scenarios.
- ✓ Policymakers should prioritize the development of explainable AI models in critical domains, such as transportation and safety, to ensure public trust and confidence.