Accurate vessel trajectory prediction is necessary for save and efficient
navigation. Deep learning-based prediction models, esp. encoder-decoders, are
rarely applied to inland navigation specifically. Approaches from the maritime
domain cannot directly be transferred to river navigation due to specific
driving behavior influencing factors. Different encoder-decoder architectures,
including a transformer encoder-decoder, are compared herein for predicting the
next positions of inland vessels, given not only spatio-temporal information
from AIS, but also river specific features. The results show that the
reformulation of the regression task as classification problem and the
inclusion of river specific features yield the lowest displacement errors. The
standard LSTM encoder-decoder outperforms the transformer encoder-decoder for
the data considered, but is computationally more expensive. In this study for
the first time a transformer-based encoder-decoder model is applied to the
problem of predicting the ship trajectory. Here, a feature vector using the
river-specific context of navigation input parameters is established. Future
studies can built on the proposed models, investigate the improvement of the
computationally more efficient transformer, e.g. through further
hyper-parameter optimization, and use additional river-specific information in
the context representation to further increase prediction accuracy.

通过对内河航行特定因素的影响，比较了不同的编码器 - 解码器架构，包括变压器编码器 - 解码器，用于预测内河船只的下一个位置，并且结果表明，将回归任务重新定义为分类问题并引入内河特定特征能够产生最低的位移误差。

编码器 - 解码器模型用于短期内河船舶轨迹预测

Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder  Models

Physics-related and model-based vessel trajectory prediction is highly
accurate but requires specific knowledge of the vessel under consideration
which is not always practical. Machine learning-based trajectory prediction
models do not require expert knowledge, but rely on the implicit knowledge
extracted from massive amounts of data. Several deep learning (DL) methods for
vessel trajectory prediction have recently been suggested. The DL models
developed typically only process information about the (dis)location of vessels
defined with respect to a global reference system. In the context of inland
navigation, this can be problematic, since without knowledge of the limited
navigable space, irrealistic trajectories are likely to be determined. If
spatial constraintes are introduced, e.g., by implementing an additional
submodule to process map data, however, overall complexity increases. Instead
of processing the vessel displacement information on the one hand and the
spatial information on the other hand, the paper proposes the merging of both
information. Here, fairway-related and navigation-related displacement
information are used directly. In this way, the previously proposed
context-sensitive Classification Transformer (CSCT) shows an improved spatial
awareness. Additionally, the CSCT is adapted to assess the model uncertainty by
enabling dropout during inference. This approach is trained on different inland
waterways to analyze its generalizability. As the improved CSCT obtains lower
prediction errors and enables to estimate the trustworthiness of each
prediction, it is more suitable for safety-critical applications in inland
navigation than previously developed models.

通过合并船舶位移信息和空间信息，本研究提出了一种上下文敏感的分类转换器，用于改进船舶轨迹预测的空间感知能力，并通过在推理期间启用辍学来评估模型不确定性。该方法在不同内陆水道上进行了训练，分析了其可推广性，显示出较低的预测误差和对每个预测的可信度估计，更适用于内陆航行中的安全关键应用。

改进的上下文敏感转换器模型用于内陆船舶轨迹预测

Improved context-sensitive transformer model for inland vessel  trajectory prediction

Using data sources beyond the Automatic Identification System to represent
the context a vessel is navigating in and consequently improve situation
awareness is still rare in machine learning approaches to vessel trajectory
prediction (VTP). In inland shipping, where vessel movement is constrained
within fairways, navigational context information is indispensable. In this
contribution targeting inland VTP, Gaussian Mixture Models (GMMs) are applied,
on a fused dataset of AIS and discharge measurements, to generate multi-modal
distribution curves, capturing typical lateral vessel positioning in the
fairway and dislocation speeds along the waterway. By sampling the probability
density curves of the GMMs, feature vectors are derived which are used,
together with spatio-temporal vessel features and fairway geometries, as input
to a VTP transformer model. The incorporation of these distribution features of
both the current and forthcoming navigation context improves prediction
accuracy. The superiority of the model over a previously proposed transformer
model for inland VTP is shown. The novelty lies in the provision of
preprocessed, statistics-based features representing the conditioned spatial
context, rather than relying on the model to extract relevant features for the
VTP task from contextual data. Oversimplification of the complexity of inland
navigation patterns by assuming a single typical route or selecting specific
clusters prior to model application is avoided by giving the model access to
the entire distribution information. The methodology's generalizability is
demonstrated through the usage of data of 3 distinct river sections. It can be
integrated into an interaction-aware prediction framework, where insights into
the positioning of the actual vessel behavior in the overall distribution at
the current location and discharge can enhance trajectory prediction accuracy.

使用高斯混合模型对内陆航运中的船舶轨迹进行预测，在预测中整合了自动识别系统和泄放测量数据，以提高轨迹预测的准确性。