Towards an explainable AI adjunct to deep network obstacle detection for multisensor vehicle maneuverability assessment

Jeffrey Dale; Trevor Bajkowski; J. Alex Hurt; David Huangal; Nelson Earle; James Keller; Grant Scott; Stanton Price

doi:10.1117/12.2585906

12 April 2021 Towards an explainable AI adjunct to deep network obstacle detection for multisensor vehicle maneuverability assessment

Jeffrey Dale, Trevor Bajkowski, J. Alex Hurt, David Huangal, Nelson Earle, James Keller, Grant Scott, Stanton Price

Author Affiliations +

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462H (2021) https://doi.org/10.1117/12.2585906
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

Advancement in remote sensing capabilities have led to unprecedented quantity and quality of data across a number of sensing modalities. It is now possible to outfit nearly any mobile platform not only with high resolution cameras, but also with inexpensive infrared and LIDAR sensors. For the specific goal of providing a comprehensive assessment of vehicle maneuverability, we address the problem of co-registering multiple sensor phenomenologies, such as visual, infrared, and LIDAR imagery collected from vehicle-mounted sensors. We show that a data fusion across these sensors provides invaluable information in hazard detection, localization, and classification. In addition, the co-registered measurements lead to the feasibility of enhanced heterogeneous data machine learning methods. This approach is verified on a dataset collected by the U.S. Army ERDC.

Conference Presentation

Citation Download Citation

Jeffrey Dale, Trevor Bajkowski, J. Alex Hurt, David Huangal, Nelson Earle, James Keller, Grant Scott, and Stanton Price "Towards an explainable AI adjunct to deep network obstacle detection for multisensor vehicle maneuverability assessment", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462H (12 April 2021); https://doi.org/10.1117/12.2585906

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