Due to limited budgets allocated for road maintenance projects in various countries, road management departments face difficulties in making scientific maintenance decisions. This paper aims to provide road management departments with more scientific decision tools and evidence. The framework proposed in this paper mainly has the following four innovative points: 1) Predicting pavement performance deterioration levels of road sections as decision basis rather than accurately predicting specific indicator values; 2) Determining maintenance route priorities based on multiple factors; 3) Making maintenance plan decisions by establishing deep reinforcement learning models to formulate predictive strategies based on past maintenance performance evaluations, while considering both technical and management indicators; 4) Determining repair section priorities according to actual and suggested repair effects. By resolving these four issues, the framework can make intelligent decisions regarding optimal maintenance plans and sections, taking into account limited funds and historical maintenance management experiences.

EyeVi, an Estonian startup, plans to build out tools to automate road data capture to improve maintenance and operations and expand into U.S. markets. EyeVi provides road service surveyors, repair crews, and municipalities with computer-vision hardware and AI-driven SaaS to map and identify road infrastructure needs. "We are developing a platform that can survey the road for about one-hundredth the cost of manual approaches today," EyeVi CEO Gaspar Anton told VentureBeat. Anton conceived of the idea about a decade ago as a driver for Google Streetview. Now EyeVi, which has raised $2 million in seed funding, is extending the same concept to support road operations and maintenance.

Scientists are building autonomous repair robots that will use AI to identify and fix potholes in UK roads. The electric, self-driving bots – which are being built by a spin-out company from the University of Liverpool called Robotiz3d – can find small cracks in the road and cover them with asphalt. Researchers say the machines, which look like a cross between a tank and a road roller, will transform road maintenance when they hit the roads in 2021, and finally offer a cost effective fix for the UK's pothole problem. Currently, no autonomous technology solutions exist to tackle potholes, which are estimated to have cost UK taxpayers more than £1 billion to fix over the last decade. Artist's impression of the autonomous road repair system, which looks part-tank, part road roller.