Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection

• URL: http://arxiv.org/abs/2412.17699v1
• Date: Mon, 23 Dec 2024 16:31:29 GMT
• Title: Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection
• Authors: Yikang Zhang, Chuang-Wei Liu, Jiahang Li, Yingbing Chen, Jie Cheng, Rui Fan,
• Abstract summary: Road inspection is essential for ensuring road maintenance and traffic safety. Traditional methods, which rely on manual evaluations, are labor-intensive, costly, and time-consuming. We propose a system based on Urban Digital Twin (UDT) technology for intelligent road inspection.
• Score: 8.677223673536092
• License:
• Abstract: Road inspection is essential for ensuring road maintenance and traffic safety, as road defects gradually emerge and compromise road functionality. Traditional methods, which rely on manual evaluations, are labor-intensive, costly, and time-consuming. Although data-driven approaches are gaining traction, the scarcity and spatial sparsity of road defects in the real world pose significant challenges in acquiring high-quality datasets. Existing simulators designed to generate detailed synthetic driving scenes, however, lack models for road defects. Furthermore, advanced driving tasks involving interactions with road surfaces, such as planning and control in defective areas, remain underexplored. To address these limitations, we propose a system based on Urban Digital Twin (UDT) technology for intelligent road inspection. First, hierarchical road models are constructed from real-world driving data, creating highly detailed representations of road defect structures and surface elevations. Next, digital road twins are generated to create simulation environments for comprehensive analysis and evaluation. These scenarios are subsequently imported into a simulator to enable both data acquisition and physical simulation. Experimental results demonstrate that driving tasks, including perception and decision-making, can be significantly improved using the high-fidelity road defect scenes generated by our system.

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