KEYWORDS: Data storage, Data modeling, Engineering, Data processing, Sensors, Organization management, Artificial intelligence, Army, Reflection, Standards development
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.
Commanders must remain agile and adaptive in the future Artificial Intelligence (AI)-enabled multi-domain battlespace, where critical decisions are made at the tactical edge. Over-reliance on static, cloud-centric approaches to Machine Learning Operations (MLOps) compromises such agility and adaptability. These systems must operate in a dynamic threat environment, and learn to detect novel threats during operation. They must be able to perform this learning through the execution of tactical MLOps under austere and degraded conditions, especially limited wireless network bandwidth. In response to these requirements, this paper describes Hawk, a system that leverages edge proximity for rapid and iterative execution of the Observe stage of the Observe-Orient-Decide-Act loop. Central to this architecture is the use of tactical cloudlets. These mini data centers provide cloud-like computing resources without the communication latency to exascale data centers. Hawk enables a human to guide MLOps at low cognitive load, thus enabling an operational objective to be achieved at speed and scale while remaining usable and explainable.
As the Department of Defense (DoD) looks to exploit and scale Artificial Intelligence (AI) capabilities across the warfighting domains, the Army plans to integrate advanced features into many of its combat systems. The benefits of cloud technologies offer promising solutions to these needs. While cloud-based AI-enabled capabilities leverage flexibility, common interfaces, and virtually infinite scale of resources, they suffer from their lack of proximity to the tactical edge. Tactical AI-enabled systems cannot reliably leverage advantages provided by cloud resources due to limited standardized practices for integration of on-premise/edge systems required by the deployed military. Future high-intensity conflict will be fought in a degraded, denied, intermittent, and lowbandwidth (DDIL) digital environment. As a result, tactical AI-enabled systems will be required to operate in a scenario where high speed, reliable cloud access is unavailable. This paper proposes a hybrid-cloud architecture that leverages resources of the cloud, when available, while also maintaining the capability to retrain tactical AI models in the field environment, using on-site computation and storage. The hybrid cloud construct consists of tactical cloud nodes that reside in closer proximity to AI-enabled systems at the edge. They may retain connectivity to the enterprise cloud yet have the ability to provide the common AI development platform and tool sets to support continuous integration, delivery, and deployment. Thus, its ultimate objective is to enable the seamless and expeditious operation of a distributed AI development environment for the Army and DoD that bridges the tactical edge and enterprise cloud.
This paper compares the effectiveness of two different skeletal pose models for a near real-time, multi-stage classifier. A cascaded neural-network (NN) classifier was previously developed to identify the level of threat posed by an armed person based on detected weapons and body posture. On an updated database of images containing armed individuals and groups, AlphaPose was used to calculate both MPII and COCO skeletons while OpenPose was used to calculate the COCO only. For comparison, we evaluated the importance of individual skeletal joints by systematically removing specific joints from the feature vector and retraining a reduced order network. On the database of images, the AlphaPose-COCO network was best able to correctly classify the threat presented by individuals, 83.7% on average, while AlphaPose-MPII registered 82.2% and 77.6% for OpenPose-COCO. As expected, the most important single joint in both skeleton models is the location of the pistol. As a guide for others deciding which skeleton to use for further studies, we conclude that neither skeleton significantly outperforms the other.
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