10171012Surface Rating and Severity Characterisation of roads via AI navigation (SR-ScanAI)ClosedFast Start ResponseInnovate UK**Overview** SR-ScanAI is an innovative research and development project focused on transforming how road surface conditions are monitored and maintained. The project aims to create an AI-driven, cost-effective, and vehicle-mounted system capable of automating the assessment of pavement conditions in line with internationally recognised standards. **The Challenge** Road surface maintenance is critical to public safety, transportation efficiency, and infrastructure longevity. However, many roads are still inspected manually, an approach that is time-consuming, inconsistent, and costly. While advanced survey technologies do exist, they are often too expensive or complex for widespread use, particularly on local or rural networks. This creates a growing need for smarter, scalable solutions that can improve coverage while reducing costs. **The Solution** SR-ScanAI addresses this challenge by developing a low-cost, AI-enabled sensing system that can be mounted on standard vehicles. The system captures high-resolution video and sensor data during normal driving, and uses computer vision and machine learning to automatically detect, classify, and quantify common pavement issues such as cracks, potholes, and surface wear. The resulting data supports the generation of reliable, repeatable assessments compatible with the Pavement Condition Index (PCI) method outlined in ASTM-D6433\. **Key Features** * **AI-driven analysis**: Deep learning models trained to recognise and interpret pavement defects. * **Compatibility with international standards**: Aligned with PCI methodology for structured, comparable results. * **Low-cost deployment**: Built using off-the-shelf hardware to ensure affordability and scalability. * **Non-disruptive operation**: Surveys conducted passively during regular vehicle use, no road closures or specialist vehicles required. **Impact,** **Economic and Social Benefits** Poor road conditions cost the UK economy an estimated **£2.5billion/year** in vehicle damage, accidents, and delays. By enabling earlier detection and more efficient repair planning, SR-ScanAI can help reduce these costs significantly, enabling smarter and timely investment in road maintenance, supporting connectivity for businesses, lower carbon emissions from smoother traffic flow, and enhanced safety for road users. SR-ScanAI will become an ally of local authorities, transport agencies, and maintenance providers managing road assets, by helping improve decision-making, reduce emergency repairs, and enable more efficient use of public funds. It aims to contribute to: * Better planning and prioritisation of repairs * Lower infrastructure lifecycle costs * Safer and smoother road journeys * Broader access to modern condition monitoring (incl. remote/lower-traffic areas) **Next Steps** The project will proceed through system prototyping, data collection, AI-model development, and real-world validation. Engagement with road operators and contractors will ensure the system meets real-world operational needs and integrates effectively into existing maintenance workflows.