CRII: CPS: Modeling Subsurface Features and Connected Autonomous Vehicles as Cyber-Physical Systems for Reciprocal Mapping and Localization

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Underneath the ground of modern cities exists a labyrinth of subsurface infrastructure consisting of water pipes, utility cables, and gas lines. This project proposes to study the use of sensor-equipped autonomous vehicles to accurately map this complicated network and explore how to leverage the subsurface infrastructure as invariant landmarks to provide reliable and secure vehicle navigation. If successful, this research will result in automated tools that make better maps of urban subsurface to improve buried infrastructure and prevent accidents when digging is required; as well as create a new means to navigate autonomous vehicle in cluttered and distressed urban areas during and after natural or man-made disasters. This project will also offer career development opportunities for participating students including those from underrepresented groups.To achieve these objectives, multiple connected and autonomous vehicles will be equipped with ground penetrating radar to collect radargrams of subsurface infrastructure along coordinated trajectories. A novel algorithm will be created to detect and identify the signatures in radargram that correspond to elements of the subsurface infrastructure. The unwanted features such as signal interferences and signature occlusions will be turned into useful clues to aggregate multiple radargrams to produce complete subsurface maps. From these maps, a new radargram-based odometry method will be devised to estimate the global poses of connected vehicles based on partially observed vehicle-feature networks. The methods will be tested and evaluated to determine how key parameters such as vehicle speed and network topology affect the accuracy of mapping, localization, and navigation.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.