The proposed project aims for the development to market (TRL8-9) of EGNSS-based integrated low-cost sensor technologies and artificial-intelligence-driven open-architecture software solution (machine learning (ML) and machine vision (MV)), for the detection, classification, and georeferencing of roadway pavement surface anomalies and for the low-cost assessment of roadway pavements using participatory sensing. The proposed system is of practical importance since it provides continuous information about roadway pavement surface anomalies which are valuable for efficiently monitoring the transport infrastructure and for public safety. The vision for roadway condition assessment by utilization of smartphone-like technology is set in parallel with the hypothesis that such technology can be used for crowd-sourced data collection and analysis in GIS-based pavement management systems (PMS), and that the developed technology and related transport informatics are disruptive technologies that have the potential to reshape the transport and infrastructure O&M industries through the project objectives discussed in the proposal.
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