SBIR Phase II: A Non-invasive Screening Tool to Detect Overweight Trucks on Roads in Real Time

Award ID: 2051992

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This Small Business Innovation Research Phase II project focuses on the detection and deterrence of overweight vehicles on roads. This technology is developing a practical, economical, and reliable real-time screening tool that does not interfere with traffic flow. Overweight commercial vehicles are a significant contributor to the nation's crumbling roads and bridges because the damage to a road surface increases substantially with the relative load, e.g., the road damage caused by an 80,000-pound truck is thousands of times that of a car which is twenty times lighter than the truck. Any efforts to renovate or build new infrastructures will be hindered without addressing this issue broadly. Nearly 71% of U.S freight representing $700.3 billion in economic activity in 2017 moves via trucks. Also, overweight trucks are a significant safety risk. Being overweight reduces the maneuverability of the massive vehicle and causes the truck's mechanical components to be prone to failure, which in turn often causes crashes, with significant human and economic tolls, especially if the load is hazardous. The intellectual merit of this project is to effectively and reliably measure the weight of a vehicle and consistently detect overweight trucks without interfering with the traffic flow or the integrity of the pavement. The proposed fully-automated system is a disruptive technology that is non-intrusive and infers the weight of vehicles from a distance using a novel, ultra-high-spatial-resolution, electro-optical system. The product is a user-friendly mobile application that displays to subscribers (government agencies or firms in the private sector) the weight of the relevant vehicle in real time. The proposed system is an internet-of-things system, a combination of proprietary electro-optical system, algorithms, signal processing, artificial intelligence and deep learning, system design and integration. 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.

NSF Program Director: Benaiah Schrag