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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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AttSCNs: A Bayesian-Optimized Hybrid Model with Attention-Guided Stochastic Configuration Networks for Robust GPS

Xue-Bo Jin1, Ye-Qing Wang1, Jian-Lei Kong1

  • 1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

Entropy (Basel, Switzerland)
|November 26, 2025
PubMed
Summary

This study introduces AttSCNs, a novel framework for Internet of Vehicles (IoV) trajectory prediction. It effectively handles GPS noise and long-term dependencies, improving road safety and traffic flow.

Keywords:
Bayesian hyperparameter optimizationGPS trackingInternet of Vehiclesattention mechanismstochastic configuration networkstrajectory prediction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Trajectory prediction is vital for Internet of Vehicles (IoV) safety and efficiency.
  • Existing methods struggle with GPS noise and long-term trajectory dependencies.

Purpose of the Study:

  • To develop a probabilistic hybrid framework, AttSCNs, for robust IoV trajectory prediction.
  • To address challenges of colored noise and long-term dependencies in GPS data.

Main Methods:

  • Integration of stochastic configuration networks (SCNs) with an attention-based encoder.
  • Utilizing SCNs' stochastic neurons for adaptive noise filtering.
  • Employing Bayesian hyperparameter optimization for robust model configuration.

Main Results:

  • AttSCNs significantly outperform conventional methods on real-world GPS datasets.
  • Achieved 36.51% RMSE reduction vs. SCNs and 97.8% MAE reduction vs. Kalman filters.
  • Demonstrated 52.5% RMSE and 68.5% MAE reduction vs. LSTM, with real-time inference.

Conclusions:

  • AttSCNs offers a robust, noise-resistant solution for IoV trajectory prediction.
  • The framework enhances performance for autonomous driving and smart city systems.
  • Quantifies prediction uncertainty for improved reliability.