SBIR Phase I: AI-enabled Affordable and Autonomous Road Condition Assessment System

The broader/commercial impact of this Small Business Innovation Research Phase I project is to develop a low-cost, vehicle-mounted sensing approach that can be deployed widely to collect street-level data and produce practical road condition ratings that help agencies plan repairs sooner and more efficiently. By enabling more timely maintenance decisions, the project can reduce vehicle damage costs while improving safety. This project will investigate an automation framework that enables complete, lane-level road coverage using low-cost mobile sensors while reducing two major operational bottlenecks: manual route planning and manual validation of uncertain distress detections. The project will investigate a scalable route optimization architecture that combines offline graph partitioning with online deep reinforcement learning to generate traffic-aware, constraint-aware inspection routes that reflect real-world road networks. This project will develop methods to convert municipal geographic information system road files into directed, traffic-weighted graphs and partition them into feasible daily subgraphs. A deep reinforcement learning agent, augmented with graph neural network embeddings, will then learn efficient within-partition traversal strategies and adapt to changing conditions during deployment. In parallel, the project will improve distress detection reliability by developing a Bayesian multi-frame data fusion framework that integrates redundant observations across consecutive frames. Rather than treating each image independently, the fusion approach will combine evidence over time to suppress false positives and strengthen consistent detections, improving accuracy and reducing reliance on human review. Phase I success targets include measurable reductions in route planning overhead and human validation effort, while improving detection performance under real-world variability. The resulting methods will advance artificial intelligence for infrastructure monitoring. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.