Ninan, StephenRathinam, Sivakumar2023-09-082023-09-082023-08http://hdl.handle.net/10919/116252A significant majority of state-of-the-art autonomous sensing and navigation technologies rely on good lane markings or detailed 3D maps of the environment, making them more suited for urban communities. Conversely, many rural roads in the U.S. do not have lane markings and have irregular boundaries. These challenges are common to many small and rural communities (SRCs), which are sparsely connected and cover huge areas. The objective of this project was to develop an efficient sensing and navigation system for SRCs that uses crowdsourced topological maps, such as OpenStreetMap, and provides high-level road network information in concert with onboard sensing systems that include lidar and cameras to localize and navigate an autonomous vehicle. The system was tested and validated on rural roads in an SRC around Bryan, TX.application/pdfenCC0 1.0 Universalautonomous vehiclesrural roadsobject detectionsemantic segmentationdatasetlocalizationparticle filterReport