Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation
Yikang Zhang, Chuang-Wei Liu, Jiahang Li, Yingbing Chen, Jie Cheng, Rui Fan

TL;DR
This paper introduces a multi-modal sensor platform and urban digital twin system to improve road defect detection and simulation, addressing data scarcity and enhancing driving task performance.
Contribution
It presents a novel integrated system combining real-world data and digital twins for detailed road defect modeling and simulation, advancing urban road inspection methods.
Findings
Enhanced perception and decision-making in driving tasks due to high-fidelity defect scenes
Successful construction of hierarchical road models from sensor data
Effective simulation environments for algorithm evaluation and development
Abstract
Road inspection is crucial for maintaining road serviceability and ensuring traffic safety, as road defects gradually develop and compromise functionality. Traditional inspection methods, which rely on manual evaluations, are labor-intensive, costly, and time-consuming. While data-driven approaches are gaining traction, the scarcity and spatial sparsity of real-world road defects present significant challenges in acquiring high-quality datasets. Existing simulators designed to generate detailed synthetic driving scenes, however, lack models for road defects. Moreover, advanced driving tasks that involve interactions with road surfaces, such as planning and control in defective areas, remain underexplored. To address these limitations, we propose a multi-modal sensor platform integrated with an urban digital twin (UDT) system for intelligent road inspection. First, hierarchical road…
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Taxonomy
TopicsDigital Transformation in Industry
