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.
Abstract (translated)
在拥挤环境中准确预测船舶轨迹对于确保内陆水道交通的安全至关重要。近年来，深度学习技术的进步承诺即使在复杂场景下也能提高准确性。虽然解决船只间感知的问题已经取得了不少进展，但这些模型的可解释性往往被忽视，这可能会掩盖其逻辑上的不准确，并削弱对其可靠性的信心。 这项研究考察了一种基于LSTM（长短期记忆网络）的船舶轨迹预测模型，在该模型中引入了训练过的船域参数，以提供关于相互作用船只隐藏状态的关注机制融合的见解。这种方法在海事航运领域已被探索过，但内陆水道遭遇情况的多样性和复杂性允许对模型解释性的更深层次分析。 通过使用标准位移误差统计方法评估提出的模型变体的预测性能，并进一步分析生成的船域值的合理性。最终，在5分钟预测时间范围内，该模型以约40米的平均位移误差表现出了与类似研究相当的水平。虽然船舶间注意力架构提升了预测精度，但使用学习到的船域值为遭遇中的船只分配权重时却偏离了预期。 观察到的准确性改进并非完全由预测轨迹与其附近船只轨迹之间的因果关系驱动。这一发现突显了该模型通过其固有的可解释性设计来展示其解译能力。未来的研究将侧重于利用架构进行反事实分析，并引入更复杂的注意力机制。
URL
https://arxiv.org/abs/2603.04472
