# A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent

arXiv: https://arxiv.org/abs/1912.09429 · HTML: https://mlsanity.com/paper/1912.09429/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [MSTFormer: Motion Inspired Spatial-temporal Transformer with Dynamic-aware Attention for long-term Vessel Trajectory Prediction](https://mlsanity.com/paper/2303.11540/) (2023)
- [Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach](https://mlsanity.com/paper/2406.02344/) (2024)
- [Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models](https://mlsanity.com/paper/2406.02770/) (2024)
- [Unified Multimodal Vessel Trajectory Prediction with Explainable Navigation Intention](https://mlsanity.com/paper/2511.14265/) (2025)
- [Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways](https://mlsanity.com/paper/2603.04472/) (2026)

## Abstract (arXiv)

Ships, or vessels, often sail in and out of cluttered environments over the course of their trajectories. Safe navigation in such cluttered scenarios requires an accurate estimation of the intent of neighboring vessels and their effect on the self and vice-versa well into the future. In manned vessels, this is achieved by constant communication between people on board, nautical experience, and audio and visual signals. In this paper we propose a deep neural network based architecture to predict intent of neighboring vessels into the future for an unmanned vessel solely based on positional data.