Reliability comparison of vessel trajectory prediction models via Probability of Detection

• URL: http://arxiv.org/abs/2508.14198v1
• Date: Tue, 19 Aug 2025 18:43:56 GMT
• Title: Reliability comparison of vessel trajectory prediction models via Probability of Detection
• Abstract summary: This contribution addresses vessel trajectory prediction (VTP), focusing on the evaluation of different deep learning-based approaches.<n>The objective is to assess model performance in diverse traffic complexities and compare the reliability of the approaches.
• Score: 2.287415292857564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• BER-PEF: Unified Human Mobility Predictability Evaluation via Bayes Error Rate Estimation [18.497151729519267]
  We present BER-PEF, a Bayes-error-rate-based framework that converts BER estimation into mobility predictability estimation.<n>The framework maps symbolic sequences, numeric trajectories, contextual features, and learned representations into a common feature--label space.<n> Experiments on Foursquare NYC and TKY, GeoLife, and T-Drive show that several BER-based estimators achieve lower reference discrepancy than existing predictability methods.
  arXiv  Detail & Related papers  (2026-09-03T12:19:56Z)
• RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting [29.01605113478814]
  We propose RIPCN, a Road Impedance Principal Component Network that integrates domain-specific transportation theory withtemporal principal component learning for PTFF.<n> RIPCN captures traffic transfer patterns driven by congestion level and flow variability, revealing direct causes of uncertainty and enhancing both reliability and interpretability.
  arXiv  Detail & Related papers  (2025-12-25T14:08:50Z)
• Scenario-aware Uncertainty Quantification for Trajectory Prediction with Statistical Guarantees [16.41262478059784]
  We propose a novel scenario-aware uncertainty quantification framework to provide the predicted trajectories with prediction intervals and reliability assessment.<n>We evaluated our framework using the real-world nuPlan dataset, demonstrating its effectiveness in scenario-aware uncertainty quantification and reliability assessment.
  arXiv  Detail & Related papers  (2025-12-05T12:54:39Z)
• Confidence Aware Learning for Reliable Face Anti-spoofing [52.23271636362843]
  We propose a Confidence Aware Face Anti-spoofing model, which is aware of its capability boundary.<n>We estimate its confidence during the prediction of each sample.<n>Experiments show that the proposed CA-FAS can effectively recognize samples with low prediction confidence.
  arXiv  Detail & Related papers  (2024-11-02T14:29:02Z)
• Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach [54.429396802848224]
  This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
  arXiv  Detail & Related papers  (2024-03-10T04:16:04Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Driving [0.9217021281095907]
  In this paper, we assess the state of the art in pedestrian trajectory prediction within the context of generating single trajectories. The evaluation is conducted on the widely-used ETH/UCY dataset where the Average Displacement Error (ADE) and the Final Displacement Error (FDE) are reported.
  arXiv  Detail & Related papers  (2023-08-09T19:21:50Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability [7.489793155793319]
  Uncertainty estimation provides the user with augmented information about the model's confidence in its predicted outcome. There is a thin consensus around the different types of uncertainty that one can gauge in machine learning models. This work aims to cover this lack of research by reviewing different techniques and metrics of uncertainty available in the literature.
  arXiv  Detail & Related papers  (2022-10-28T10:49:55Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Probabilistic Symmetry for Multi-Agent Dynamics [18.94585103009698]
  We propose a novel deep dynamics model, Probabilistic Equivariant Continuous COnvolution (PECCO) for probabilistic prediction of multi-agent trajectories. PECCO shows significant improvements in accuracy and calibration compared to non-equivariant baselines.
  arXiv  Detail & Related papers  (2022-05-04T07:51:08Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification [0.13999481573773068]
  Ensemble forecasting is, so far, the most successful approach to produce relevant forecasts along with an estimation of their uncertainty. Main limitations of ensemble forecasting are the high computational cost and the difficulty to capture and quantify different sources of uncertainty. In this work proof-of-concept model experiments are conducted to examine the performance of ANNs trained to predict a corrected state of the system and the state uncertainty using only a single deterministic forecast as input.
  arXiv  Detail & Related papers  (2021-11-29T16:52:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.