The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.
Automated Terrain Classification for Vehicle Mobility in Off-Road Conditions
2021
35 pages
Report
No indication
English
Transportation , Logistics, Military Facilities, & Supplies , Navigation Systems , Supervised machine learning , Machine learning , Unsupervised machine learning , Artificial intelligence , Information science , Data mining , Remote sensing , Engineering , Geography , Artificial satellites , Autonomous vehicles , Synthetic aperture radar , Artificial intelligence software , Computer vision , Engineers , Navigation , Vehicles , Army corps of engineers , Coordinate systems , Detectors
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