A Software Tool for Evaluating Unmanned Autonomous Systems

TL;DR

This paper introduces DIPT, a simulation-based tool that infers autonomous UAV system states and behaviors using external observations, aiding DoD testing without internal system modifications.

Contribution

The paper presents DIPT, a novel external observation-based simulation tool for evaluating autonomous systems, specifically UAVs, enhancing understanding of their internal states during testing.

Findings

DIPT accurately predicts UAV target detection status.

DIPT provides reasoning for UAV behaviors.

The tool improves insight into autonomous system operations.

Abstract

The North Carolina Agriculture and Technical State University (NC A&T) in collaboration with Georgia Tech Research Institute (GTRI) has developed methodologies for creating simulation-based technology tools that are capable of inferring the perceptions and behavioral states of autonomous systems. These methodologies have the potential to provide the Test and Evaluation (T&E) community at the Department of Defense (DoD) with a greater insight into the internal processes of these systems. The methodologies use only external observations and do not require complete knowledge of the internal processing of and/or any modifications to the system under test. This paper presents an example of one such simulation-based technology tool, named as the Data-Driven Intelligent Prediction Tool (DIPT). DIPT was developed for testing a multi-platform Unmanned Aerial Vehicle (UAV) system capable of conducting collaborative search missions. DIPT's Graphical User Interface (GUI) enables the testers to view the aircraft's current operating state, predicts its current target-detection status, and provides reasoning for exhibiting a particular behavior along with an explanation of assigning a particular task to it.