Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/40219
Title: Automated terrain classification for vehicle mobility in off-road conditions
Authors: Hodgdon, Taylor S.
Fuentes, Anthony J.
Olivier, Jason L.
Quinn, Brian G.
Shoop, Sally A. (Sally Annette)
Keywords: Automated vehicles--Off-road operation
Computer algorithms
Machine learning
Military geography
Remote sensing
Trafficability
Publisher: Cold Regions Research and Engineering Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/CRREL TR-21-4
Abstract: 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.
Description: Technical Report
Gov't Doc #: ERDC/CRREL TR-21-4
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/40219
http://dx.doi.org/10.21079/11681/40219
Appears in Collections:Technical Report

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