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

Autonomous shipping has recently gained much interest in the research
community. However, little research focuses on inland - and port navigation,
even though this is identified by countries such as Belgium and the Netherlands
as an essential step towards a sustainable future. These environments pose
unique challenges, since they can contain dynamic obstacles that do not
broadcast their location, such as small vessels, kayaks or buoys. Therefore,
this research proposes a navigational algorithm which can navigate an inland
vessel in a wide variety of complex port scenarios using ranging sensors to
observe the environment. The proposed methodology is based on a machine
learning approach that has recently set benchmark results in various domains:
model-based reinforcement learning. By randomizing the port environments during
training, the trained model can navigate in scenarios that it never encountered
during training. Furthermore, results show that our approach outperforms the
commonly used dynamic window approach and a benchmark model-free reinforcement
learning algorithm. This work is therefore a significant step towards vessels
that can navigate autonomously in complex port scenarios.

该研究提出了一种基于机器学习的导航算法，能够在各种复杂港口情况下通过距离传感器观察环境来导航内陆船只。结果表明，该算法优于常用的动态窗口方法和基准无模型强化学习算法，从而实现了船只在复杂港口情况下的自主导航。