向内陆河流船舶轨迹预测的可解释深度学习
Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
AI总结：
本文提出基于LSTM的船舶轨迹预测模型，通过引入训练好的船舶领域参数提升可解释性，实验显示模型在内陆水域中具有良好的预测性能，但权重分配存在偏差，需进一步优化注意力机制。
摘要：
在拥挤环境中准确预测船舶轨迹对于确保内陆水域交通安全至关重要。近年来深度学习的进步使复杂场景的准确性得到了提升。尽管船与船之间的意识挑战正得到日益成功的解决，但这些模型的可解释性往往被忽视，这可能会掩盖不准确的逻辑并削弱其可靠性。本研究通过引入训练好的船舶领域参数，对基于LSTM的船舶轨迹预测模型进行了考察，这些参数提供了关于交互船舶隐藏状态的注意力融合的见解。这一方法在航运领域之前已被探索，但内陆水域中多样性和复杂性的相遇允许对模型可解释性的更深入分析。所提出的模型变体的预测性能通过标准位移误差统计进行评估。此外，生成的船舶领域值的合理性也进行了分析。在5分钟预测时间范围内，模型的最终位移误差约为40米，其性能与类似研究相当。尽管船与船的注意力架构提高了预测准确性，但使用学习的船舶领域值在相遇中分配给船舶的权重偏离了预期。观察到的准确性提高因此并非完全由预测轨迹与附近船舶轨迹之间的因果关系驱动。这一发现通过其本质上可解释的设计突显了模型的解释能力。未来的工作将专注于利用该架构进行反事实分析，并整合更复杂的注意力机制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
