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

Abstract

Digital twins in vehicle-in-the-loop (VIL) test has great practical significance for the functional development, testing and evaluation of intelligent vehicle. The study about the credibility assessment of dynamically evolving models still lacks effective approaches. In addition, it has rarely been studied in automotive tests. In this paper, a closed loop test of dynamic virtual and real-world interaction was built, and its characteristics are also analyzed. According to its characteristics and assessment methods, a credibility assessment methodology based on information entropy is proposed to reveal the degree of its own information confusion and structural relevance of different information, which involves ApEn and cross-ApEn. The algorithm has been successfully verified in experiments and it has been found that the inconsistent weight of the real and digital vehicle is an important factor on digital twins VIL tests. Furthermore, the effect of the length of series on the credibility assessment has been emphatically studied, and the results show that it has no more than 2% effect on the credibility assessment.