用检测概率评估船舶轨迹预测模型可靠性，看清复杂交通下的表现差异。
Reliability comparison of vessel trajectory prediction models via Probability of Detection
- 按交通复杂度分类测试样本，量化模型在不同场景的可靠性。
- 发现预测时长越长，模型可靠度下降明显，存在安全预测时限。
- 为内河航行安全提供可信赖的预测评估方法，适合航运系统设计者。
本文研究船舶轨迹预测（VTP），评估多种基于深度学习的方法在不同交通复杂度下的性能与可靠性。现有模型常忽略交通情景复杂性且缺乏可靠性分析，本研究引入检测概率分析，超越传统误差分布评估，对测试样本按预测时段的交通状况进行分类，获取各类别的性能指标与可靠性估计。结果揭示了各方法在不同预测时长下的优劣与可信范围，明确了安全预测的保障时限。该研究为提升内河航行安全性与效率提供了可靠的评估依据。
原文摘要 · Abstract (English)
This contribution addresses vessel trajectory prediction (VTP), focusing on the evaluation of different deep learning-based approaches. The objective is to assess model performance in diverse traffic complexities and compare the reliability of the approaches. While previous VTP models overlook the specific traffic situation complexity and lack reliability assessments, this research uses a probability of detection analysis to quantify model reliability in varying traffic scenarios, thus going beyond common error distribution analyses. All models are evaluated on test samples categorized according to their traffic situation during the prediction horizon, with performance metrics and reliability estimates obtained for each category. The results of this comprehensive evaluation provide a deeper understanding of the strengths and weaknesses of the different prediction approaches, along with their reliability in terms of the prediction horizon lengths for which safe forecasts can be guaranteed. These findings can inform the development of more reliable vessel trajectory prediction approaches, enhancing safety and efficiency in future inland waterways navigation.
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