22 Nov 2021

Imagery Classification for Autonomous Ground Vehicle Mobility in Cold Weather Environments

Abstract: Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.

22 Nov 2021

Methodology for the Analysis of Geospatial and Vehicle Datasets in the R Language

Abstract: The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.