The Australian state of New South Wales (NSW) has disclosed a new AI (artificial intelligence) technology that will automate and reform road maintenance and repair in the state.
The project, which was unveiled on Tuesday will finance a 2.9-million-Australian dollar ($1.96-million) trial from Asset AI – an AI company, which is responsible for the sensor installations on 32 public buses with routes throughout greater Sydney.
AI helps these sensors in combining visual data with local weather conditions and forecasts the rate of depreciation of the roads in the city. Theoretically, this implies that it will be able to warn the maintenance teams before the potholes or other damages on the road pose a risk to traffic.
Victor Dominello – NSW Minister for Customer Service and Digital Government stated that always the presence of cracks, and potholes can be witnessed on the road, yet with such smart technologies, predicting depreciation, and streamlining maintenance are possible, and better results can be obtained swiftly.
At the moment, road damage and defects are determined based on reports from residents. However, during months of heavy rain, such as those experienced in Sydney, roads frequently deteriorate faster than can be sufficiently monitored. The technology would generate pothole “heatmaps” across the city, indicating which areas are in desperate need of repair.
Canterbury Bankstown, an area in southwest Sydney experienced a pre-trial of the technology, and its Mayor, Khal Asfour stated that the AI technology has been helpful in enhancing the safety of the road in the area. We do a road audit every four years, and it is very costly. Instead, we will be able to do it on a weekly basis thanks to this new technology, Asfour added.
Asset AI makes use of predictive analysis to enhance the maintenance of roads by anticipating the risk to the community instead of simply disclosing the road assets condition which is great news for the residents.
Aside from the initial trials in Sydney, the project would like to collect road data along 100 kilometers of rural roads in the state, which are particularly expensive and difficult to maintain.
