I-Corps: Risk Analysis System for Collision Avoidance with Wildlife

The broader impact/commercial potential of this I-Corps project is the development of a traffic safety data analytics platform that will help reduce the injuries, property damage, trauma, and environmental impact caused by wildlife-vehicle collisions (WVCs). According to public insurance and US DOT sources, over 1.3 million WVCs occur every year in the US, causing over $20B in losses for insurers, DOTs, and freight haulers.

Preliminary statistical analysis indicated the effectiveness of a novel statistical model in predicting the likelihood of encountering a large animal on a roadway, and the likelihood of a collision. Extensive customer discovery with public safety managers, auto insurance claims managers, truckers, and passenger drivers, indicated widespread and financial and safety needs to reduce WVCs.

Commercialization of the proposed technology as a service will enable public safety managers to understand risk factors in their districts, and preemptively and selectively deploy safety countermeasures for drivers.

This I-Corps project is based on the development of a suite of statistical analytics algorithms that predict the likelihood of a driver witnessing an animal on the road, the likelihood of hitting the animal, and the potential severity of the collision (property damage, light injury, serious injury, or fatality). These proposed risk models fuse data feeds from historical crashes, past and predictive weather data, and road and surface data.

The proposed technology also includes internet of things (IoT) devices, called RADs: Roadside Animal Deterrents. The proposed algorithms may determine where to install the RADs, and also evaluate changes in crash statistics over time. The technology may enable public roadway managers to keep their roads safe, maintained, and properly operating.

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.