A pothole detector fitted beneath bin lorries is being trialled in Thurrock and York.
The technology aims to monitor road surfaces and detect problems before they become potholes.
The pothole-spotter system, mounted to refuse collection vehicles, comprises of high-definition cameras, integrated navigation system and intelligent software.
York City Council executive member for transport and planning Ian Gillies said: “We welcome investment in this pioneering technology which will hopefully allow the council to reduce the amount of money spent fixing potholes each year by repairing road surface defects before they escalate into potholes.
“The trial is also expected to identify wider efficiencies in how the council’s highways maintenance service is managed. It is expected that by using the data collected, the council will be able to respond to dangerous defects across the network quicker, hopefully leading to a reduction in the number of compensation claims.”
Thurrock council leader Rob Gledhill said: “Thurrock was selected as it is recognised by government as being ready to test innovative new techniques to improve the efficiency of local services, and for which the reliability and quality of its road network is crucial for residents and businesses alike.
“This is the first initiative of its kind – using cutting edge technology and innovation that leads to better road conditions at less cost. I am very pleased Thurrock has been chosen by the Department for Transport as a partner in this pioneering project and I look forward to sharing how it worked with colleagues in other local authorities.”
One of the consultants working with Thurrock is Gaist, whose director of innovation and research Stephen Remde said: “This project is really exciting and will capture the highest ever levels of technically advanced data that will provide us with a real insight into how roads deteriorate and defects form such as potholes, surface durability and day to day traffic volume damage.
“Computer vision technology is advancing rapidly and we seek to capitalise on new ‘Deep Learning’ data analysis techniques we have, to analyse and manage the huge volumes of video and related data that can be used to improve the safety of roads and provide more cost effective repairs.”