Mix&Fix-Net: A Dual-Stage Trajectory Prediction Model for AIS and Vision-Derived Vessel Data

• URL: http://arxiv.org/abs/2608.19580v1
• Date: Thu, 20 Aug 2026 02:51:40 GMT
• Title: Mix&Fix-Net: A Dual-Stage Trajectory Prediction Model for AIS and Vision-Derived Vessel Data
• Abstract summary: Vessel trajectory prediction is critical for maritime safety and accident prevention.<n>Most existing trajectory prediction models rely on Automatic Identification System (AIS) data.<n>Small vessels mostly operate without AIS, resulting in a significant monitoring gap.<n>We propose Mix&Fix-Net, a dual-stage mixer-based trajectory prediction model.
• Score: 13.876236917728505
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vessel trajectory prediction is critical for maritime safety and accident prevention. While most existing trajectory prediction models rely on Automatic Identification System (AIS) data due to its precision and availability, small vessels mostly operate without AIS, resulting in a significant monitoring gap. To address this, we propose Mix&Fix-Net, a dual-stage mixer-based trajectory prediction model designed to handle vessel trajectory time-series data derived from both AIS and (non-AIS) vision data. Our architecture integrates a Primary Trajectory Predictor with a Residual Trajectory Adjuster, enabling more refined trajectory prediction. Additionally, we introduce a new video-based dataset derived from webcam streams, from which vessel trajectories are extracted to represent non-AIS data. Extensive evaluations on both AIS and non-AIS datasets across six metrics (mean squared error, mean absolute error, symmetric mean absolute percentage error, final displacement error, Frechet distance, and average Euclidean distance) demonstrate that Mix&Fix-Net consistently outperforms existing baselines across most metrics and datasets.

Related papers

• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Cross-Modal Reconstruction Pretraining for Ramp Flow Prediction at Highway Interchanges [30.274689865122056]
  STDAE is a two-stage framework that leverages cross-modal reconstruction pretraining.<n>STDAE-GWNET consistently outperforms thirteen state-of-the-art baselines.<n>This demonstrates its effectiveness in overcoming detector scarcity and its plug-and-play potential for diverse forecasting pipelines.
  arXiv  Detail & Related papers  (2025-10-03T15:26:56Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry [27.62788405443008]
  We propose a novel plug-and-play module featuring the Inertial Prior Network (IPNet)<n>IPNet infers an IMU bias prior by implicitly capturing the motion characteristics of specific platforms.<n>In this work, we first directly infer the biases prior only using the raw IMU data using a sliding window approach.
  arXiv  Detail & Related papers  (2025-03-16T14:45:19Z)
• Unified Human Localization and Trajectory Prediction with Monocular Vision [64.19384064365431]
  MonoTransmotion is a Transformer-based framework that uses only a monocular camera to jointly solve localization and prediction tasks.<n>We show that by jointly training both tasks with our unified framework, our method is more robust in real-world scenarios made of noisy inputs.
  arXiv  Detail & Related papers  (2025-03-05T14:18:39Z)
• ASTRA: A Scene-aware TRAnsformer-based model for trajectory prediction [15.624698974735654]
  ASTRA (A Scene-aware TRAnsformer-based model for trajectory prediction) is a light-weight pedestrian trajectory forecasting model.<n>We utilise a U-Net-based feature extractor, via its latent vector representation, to capture scene representations and a graph-aware transformer encoder for capturing social interactions.
  arXiv  Detail & Related papers  (2025-01-16T23:28:30Z)
• SSTP: Efficient Sample Selection for Trajectory Prediction [38.92588125424176]
  Training advanced trajectory prediction models on existing large-scale datasets is time-consuming and computationally expensive.<n>We propose the SSTP framework, which constructs a compact yet density-balanced dataset tailored to trajectory prediction.<n> Experiments show that SSTP achieves comparable performance to full-dataset training using only half the data.
  arXiv  Detail & Related papers  (2024-09-25T22:00:11Z)
• Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach [2.498836880652668]
  In inland shipping, where vessel movement is constrained within fairways, navigational context information is indispensable. In this contribution, a fused dataset of AIS and discharge measurements is applied to generate multi-modal distribution curves. The incorporation of these distribution features improves prediction accuracy.
  arXiv  Detail & Related papers  (2024-06-04T14:20:10Z)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs [56.48498624951417]
  This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals. We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches. The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
  arXiv  Detail & Related papers  (2021-04-27T18:17:42Z)
• BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation [28.10445924083422]
  BiTraP is a goal-conditioned bi-directional multi-modal trajectory prediction method based on the CVAE. BiTraP generalizes to both first-person view (FPV) and bird's-eye view (BEV) scenarios and outperforms state-of-the-art results by 10-50%.
  arXiv  Detail & Related papers  (2020-07-29T02:40:17Z)

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.