Enabling enterprise autonomy development with a unified data infrastructure

Kyle Dotterrer; Brian A. Schramke; Abby E. V. Ponce-Pore; Eric M. Sturzinger; Henry Hargrove; Christopher J. Lowrance

doi:10.1117/12.2663718

13 June 2023 Enabling enterprise autonomy development with a unified data infrastructure

Kyle Dotterrer, Brian A. Schramke, Abby E. V. Ponce-Pore, Eric M. Sturzinger, Henry Hargrove, Christopher J. Lowrance

Proceedings Volume 12522, Big Data V: Learning, Analytics, and Applications ; 125220A (2023) https://doi.org/10.1117/12.2663718
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Data is the cornerstone of Artificial Intelligence (AI) and Machine Learning (ML) systems. As the Department of Defense (DoD) leverages AI/ML to develop, test, and deploy autonomous vehicle capabilities, management of autonomy data will become increasingly important. Modern sensors on autonomous vehicles generate an enormous amount of data, and making this data available for further research presents a significant challenge. Moving such large volumes of data from a field environment to a centralized, cloud-based data lake is not straightforward, nor necessarily efficient for data of unknown enterprise utility. As a result, much of DoD’s autonomy data remains siloed in geographically or logically separated on-premises and cloud-based data stores in mixed formats. Organizations within DoD’s modernization enterprise require a mature data infrastructure to store, discover, share, and collaborate upon datasets, models, and other artifacts efficiently. In this paper, we examine the characteristics a data infrastructure must exhibit to meet the needs of the DoD for autonomy research. These characteristics are identified through a review of existing solutions, use cases, and current industry best practices. On the basis of this review, we propose a set of requirements for DoD’s data infrastructure for autonomous systems research. Moreover, an analysis of the viability of various options, including centralized and decentralized architectures, is provided through the lens of DoD data requirements and unique organizational constraints. While data infrastructure for autonomy is our primary concern, the requirements and design we propose generalize to other AI tasks that are of interest to DoD.

Conference Presentation

Citation Download Citation

Kyle Dotterrer, Brian A. Schramke, Abby E. V. Ponce-Pore, Eric M. Sturzinger, Henry Hargrove, and Christopher J. Lowrance "Enabling enterprise autonomy development with a unified data infrastructure", Proc. SPIE 12522, Big Data V: Learning, Analytics, and Applications , 125220A (13 June 2023); https://doi.org/10.1117/12.2663718

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