A Software Tool for Evaluating Unmanned Autonomous Systems
This provides the DoD T&E community with greater insight into autonomous systems, but it is incremental as it builds on existing simulation methodologies.
The researchers tackled the problem of evaluating autonomous systems by developing a simulation-based tool, DIPT, which uses external observations to infer perceptions and behavioral states of UAVs, enabling testers to view operating states and predict target-detection status without internal knowledge.
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.