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Credibility Assessment for Digital Twins in Vehicle-in-the-Loop Test Based on Information Entropy.

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  • 1Xiamen Product Quality Supervision and Inspection Institute, Xiamen 361023, China.

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This study introduces a novel credibility assessment methodology for digital twins in vehicle-in-the-loop (VIL) testing. The proposed information entropy-based approach effectively evaluates model dynamics and identifies key factors affecting test accuracy.

Keywords:
approximate entropycross approximate entropydigital twinsinformation entropyvehicle-in-the-loop test

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Area of Science:

  • Intelligent transportation systems
  • Automotive engineering
  • Cyber-physical systems

Background:

  • Digital twins are crucial for intelligent vehicle development and testing.
  • Effective credibility assessment for dynamically evolving models in automotive tests is lacking.
  • Vehicle-in-the-loop (VIL) testing requires robust methods to evaluate virtual-real world interactions.

Purpose of the Study:

  • To develop a credibility assessment methodology for digital twins in VIL tests.
  • To analyze the characteristics of closed-loop dynamic virtual and real-world interaction tests.
  • To identify factors influencing the accuracy and reliability of digital twin VIL tests.

Main Methods:

  • Constructed a closed-loop test for dynamic virtual and real-world interaction.
  • Proposed an information entropy-based credibility assessment methodology.
  • Utilized Approximate Entropy (ApEn) and cross-ApEn to quantify information confusion and relevance.

Main Results:

  • The proposed algorithm was successfully verified through experiments.
  • Inconsistent weighting between real and digital vehicles was identified as a significant factor impacting VIL test credibility.
  • The length of the time series data demonstrated a minimal effect (≤2%) on credibility assessment outcomes.

Conclusions:

  • The information entropy-based methodology provides an effective approach for assessing digital twin credibility in VIL tests.
  • Understanding and managing weighting inconsistencies is vital for reliable automotive VIL testing.
  • The developed method offers a valuable tool for enhancing the functional development and evaluation of intelligent vehicles.