Transfer learning for adaptable autonomy

Michael Ganger; Anthony Bloch; Patrick V. Haggerty

doi:10.1117/12.3012977

7 June 2024 Transfer learning for adaptable autonomy

Michael Ganger, Anthony Bloch, Patrick V. Haggerty

Proceedings Volume 13055, Unmanned Systems Technology XXVI; 130550B (2024) https://doi.org/10.1117/12.3012977
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

We explore the use of transfer learning to reduce the data and computing resources required for training convolutional neural networks used by autonomous vehicles for predicting target behavior and improving target tracking as the scenario/environment changes. We demonstrate the ability to adapt to four different changes to the baseline scenario: a new target behavior, mission, adversary, and environment. The results from all four scenarios demonstrate positive transfer learning with reduced training datasets and show that transfer learning is a robust approach to dealing with changing environments even when the input or output dimensions of the neural network are changed.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Michael Ganger, Anthony Bloch, and Patrick V. Haggerty "Transfer learning for adaptable autonomy", Proc. SPIE 13055, Unmanned Systems Technology XXVI, 130550B (7 June 2024); https://doi.org/10.1117/12.3012977

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