High performance road lane inspection for safer cycling
The target product is an imaging system delivering low-cost, automated inspection of cycleways, footways and the near kerb lanes on cycling permissible roads (515,600 lane-miles in the UK).
It will support prioritizing road maintenance towards safer cycling. One unique capability is the identification of potholes that become hidden by rain ponding. Whereas these may damage a vehicle wheel they can lead to serious injury or death for cyclists. Cycling is recognised as green, sustainable, and health beneficial, a transportation lifeline in the COVID-19 crisis, prompting the UK government to invest £2 billion. UK DOT has stated, “we want to make cycling and walking the natural choices for shorter journeys, or as part of a long journey”.
However, whilst available cycling miles contribute to leisure and tourism, a 2019 DOT survey records that 67% of adults view roads to be too dangerous for cycling. According to a 2018 estimate, the size of the National Cycle Network (NCN) of the UK is estimated at 16,670 lane-miles, increased exponentially from 502 miles (1995).
Furthermore, considering all available cycling miles, UK has approximately 515,600 miles. Many of Europe’s countries have similarly large cycleways and more than 12 million total cycling lane-miles. This presents a substantial export opportunity to bodies responsible for the track, path, and road maintenance.
While governments are making huge investments, this vast network will require regular inspection to ensure safety and a good level of service to the public. Inspecting networks is vital to get a thorough understanding of their ongoing condition, so appropriate maintenance activities can be prioritised and executed. Cycleways and footways are currently inspected manually, which is a slow and inaccurate process.
The present proposal is for an automated system, consisting of cameras and other sensors in conjunction with AI and software, to scan, detect, and measure defects, quantifying the overall surface condition. The innovation will provide accurate, consistent, and quantified distress measurement 10 times faster than manual surveys at 10% of the existing cost. Furthermore, it will displace the use of expensive road vehicle deployed laser scanning methods on all paved-surfaces in respect to cycle use. It will give high levels of automation with real-time overseeing for quality control, and completely digitize measurements that can seamlessly integrate with maintenance planning software.
StradaImaging will collaborate with Aston University who will deliver a characterisation basis for the captured data such as cracks, depressions, potholes, and rain ponding.