AI-DRIVEN VEHICLE EVALUATION FOR ONLINE USED VEHICLE AUCTIONS

Mykola Bielousov

doi:10.69635/ciai.2026.46

Authors

Mykola Bielousov Master of Science in Computer Science, State University of Information and Communication Technology, Kyiv, Ukraine, IEEE Senior Member (#102065992), IEEE Computer Society, Full-Stack Software Engineer, CodeSoft LLC, 1376 Stonewood Ct., San Pedro, United States Author https://orcid.org/0009-0005-6769-745X

DOI:

https://doi.org/10.69635/ciai.2026.46

Keywords:

Artificial Intelligence, Vehicle Valuation, Machine Learning, Online Auctions, Multimodal Models, Information Asymmetry

Abstract

The aim of this study is to develop and substantiate an approach to the valuation of used vehicles within online auction environments using artificial intelligence methods, taking into account the specific characteristics of the United States and European Union markets.

The study employs a comprehensive set of methods, including the analysis of contemporary scientific approaches, comparative analysis of market models, and machine learning techniques to formalize the valuation problem as a regression task. Particular attention is given to the use of multimodal models that integrate structured, semi-structured, and unstructured data, as well as to approaches for uncertainty estimation and result interpretability.

The obtained results confirm that traditional valuation methods have limited effectiveness in digital auction environments, whereas the application of AI significantly improves prediction accuracy, reduces the impact of information asymmetry, and enhances decision-making validity. It has been established that the integration of visual data and historical information is a key factor in improving model performance.

It is concluded that the use of multimodal AI approaches, combined with uncertainty estimation and interpretability, provides a solid foundation for the development of effective decision support systems in the field of online vehicle auctions.

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