13 Jun 2025

Enhanced Route Reconnaissance—Generation 1

Abstract: The movement of soldiers and materiel across battlespace is critical to a successful military operation. Knowledge of the road network condition ensures safe and successful vehicle maneuver. This research focused on remote assessment of poor-quality paved road networks for vehicle maneuver using data products derived from three-dimensional point clouds. Point clouds were generated from lidar sensors deployed from ground and airborne platforms to enable engineering analysis of the pavement surface. A series of algorithms developed to extract roughness, grade, radius of curvature, and width along the road network ensured storage of information for graphical display. A vehicle speed lookup table was calculated by conducting computer simulations using the NATO Reference Mobility Model over a range of road parameters. The lookup table enabled determination of the maximum allowable speed for a given vehicle type associated with the extracted road parameters. A graphical interface, developed for displaying the percentage speed reduction as either red, amber, or green squares along the road network, provided visual assessments of road condition. This report summarizes developing a software suite to calculate and visualize speed reduction over a road network as a function of route geometry, condition, and vehicle type. The interface developed can aid in critical logistical decisions that influence the success of military maneuver operations.