# Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach

arXiv: https://arxiv.org/abs/2406.02344 · HTML: https://mlsanity.com/paper/2406.02344/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction](https://mlsanity.com/paper/2109.03958/) (2021)
- [A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent](https://mlsanity.com/paper/1912.09429/) (2019)

## Built on by (most cited first, 2 of 2)

- [AIS Data-Driven Maritime Monitoring Based on Transformer: A Comprehensive Review](https://mlsanity.com/paper/2505.07374/) (2025)
- [Reliability comparison of vessel trajectory prediction models via Probability of Detection](https://mlsanity.com/paper/2508.14198/) (2025)

## Abstract (arXiv)

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.