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Trajectory Prediction for Marine Vessels using Historical AIS Heatmaps and Long Short-Term Memory Networks ?

机译：使用历史AIS Heatmaps和长短短期记忆网络的海洋船舶的轨迹预测 ？

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摘要

Estimating the trajectory of other vessels is essential when navigating a marine vessel, both as a human navigator and as a machine. By estimating the trajectories of other vessels, sub-systems such as collision avoidance algorithms can plan ahead accordingly in order to avoid conflicts. To estimate the trajectories of other vessels, the use of Automatic Identification System (AIS) is a good candidate data-point, as this is becoming increasingly more common, and in some cases even mandated, on-board vessels. This paper presents a data-driven approach that uses the historical AIS data within a selected area in the Danish waters. The historical data is transformed into a probabilistic heat map using Kernel Density Estimation (KDE), and is further encoded using a Convolutional Autoencoder (CAE) before entered into the estimation scheme. The estimation scheme consists of a Long Short-term Memory (LSTM) model, in a Generative Adversarial Network (GAN) configuration, which is sampled multiple times, yielding a single trajectory prediction with uncertainty. The performance of the estimation scheme is demonstrated and compared against two other commonly used methods, showing that the probabilistic heat map provides valuable information, compared to the baseline methods.
机译：估计其他船只的轨迹都作为一个人导航和一台导航船舶时，是必不可少的。通过估计其他船只的轨迹，子系统诸如防撞算法可以为了避免冲突相应提前计划。为了估计其他船只的轨迹，利用自动识别系统（AIS）是一个很好的候选数据点，因为这正变得越来越普遍，在某些情况下甚至授权，船上的船只。本文提出了一种数据驱动的方法使用在丹麦海域所选区域内的历史AIS数据。历史数据是使用核密度估计（KDE）变换成概率热图，并输入到估计方案之前，使用一个自动编码器卷积（CAE）被进一步编码。的估计方案包括一个长短期存储器（LSTM）模式，在一个剖成对抗性网络（GAN）的配置，其被多次采样，产生具有不确定性的单个轨迹预测。估计方案​​的性能证明和针对两种其它常用的方法相比，显示出概率热图提供有价值的信息，相对于基线的方法。

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