Computer Vision–Centric Digital Twins for Intelligent Mobility: Simulation of Object Detection and Scene Segmentation
Author(s): Reamy Womodowe Achirobe, Derrick Appiah Osei
Publication #: 2602020
Date of Publication: 22.08.2026
Country: United States
Pages: 1-7
Published In: Volume 12 Issue 4 August-2026
Abstract
Digital twins (DTs) are rapidly emerging as a foundational paradigm for intelligent mobility systems, enabling high-fidelity virtual representations of physical transportation environments. In parallel, computer vision, particularly object detection and scene/semantic segmentation remain central to perception, decision-making, and safety in autonomous and connected vehicles. This review synthesizes recent advances at the intersection of digital twins and computer vision, with a focus on how DT-based simulation frameworks support the development, validation, and deployment of vision algorithms for intelligent mobility. Drawing on recent studies in DT-assisted autonomous driving, cooperative perception, simulation-based perception evaluation, and data-driven DT architectures, the paper reviews methodologies for generating synthetic visual data, closing the sim-to-real gap, and embedding vision models within cyber–physical feedback loops.
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