# Reliability comparison of vessel trajectory prediction models via Probability of Detection

arXiv: https://arxiv.org/abs/2508.14198 · HTML: https://mlsanity.com/paper/2508.14198/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 19 Aug 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Spatial and social situation-aware transformer-based trajectory prediction of autonomous systems](https://mlsanity.com/paper/2406.02767/) (2024)
- [Improved context-sensitive transformer model for inland vessel trajectory prediction](https://mlsanity.com/paper/2406.02771/) (2024)
- [Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach](https://mlsanity.com/paper/2406.02344/) (2024)

## Abstract (arXiv)

This contribution addresses vessel trajectory prediction (VTP), focusing on the evaluation of different deep learning-based approaches. The objective is to assess model performance in diverse traffic complexities and compare the reliability of the approaches. While previous VTP models overlook the specific traffic situation complexity and lack reliability assessments, this research uses a probability of detection analysis to quantify model reliability in varying traffic scenarios, thus going beyond common error distribution analyses. All models are evaluated on test samples categorized according to their traffic situation during the prediction horizon, with performance metrics and reliability estimates obtained for each category. The results of this comprehensive evaluation provide a deeper understanding of the strengths and weaknesses of the different prediction approaches, along with their reliability in terms of the prediction horizon lengths for which safe forecasts can be guaranteed. These findings can inform the development of more reliable vessel trajectory prediction approaches, enhancing safety and efficiency in future inland waterways navigation.