Tremendous challenges remain in terms of data quality, repeatability and accuracy, and related high costs in pavement data collection and processing. Out of the four major pavement evaluation categories (function, condition, structure, and safety), this proposal focuses on developing innovative Artificial intelligence (AI) solutions to condition and safety evaluation based on prior AI work (CrackNet) by the research team members since 2015. In addition, based on the preliminary research by the team on using a non-contact 0.1-mm resolution 3D laser imaging sensor (Safety Sensor), the proposal will improve DL based Super-Resolution techniques to reconstruct pavement surfaces at 0.1-mm resolution in 3D so that highway speed survey of pavement micro- and macro-texture, and friction can be a reality.
The goal of this project is to use artificial intelligence (AI) to improve the data quality and accuracy, as well as lower the costs, of pavement condition and safety evaluations.
Benefits: The outcome of the project will help highway agencies reduce costs and have higher accuracy and precision in evaluating pavement surface condition and safety.
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