Cognitive Task Analysis for Data-Driven Requirements
We have expertise in the use of Cognitive Task Analysis (CTA) to generate data-driven system requirements for a variety of projects. Through the statistical analysis of data provided by Subject Matter Experts (SMEs) via interviews, surveys, questionnaires, and task observation, CSAOB researchers have developed a replicable CTA protocol which can be tailored to identify the strengths and weaknesses for both existing and proposed systems.

Human Operator and Autonomous System Integration: Advanced Air Mobility
Work performed at Langley under the Aeronautics Research Mission Directorate’s Advanced Air Mobility concept has enabled capabilities we’ve developed through decades of experience in human factors and increasingly autonomous systems research, development, and thought leadership. Our researchers in CSAOB, together with fellow members of our Intelligent Flight Systems product line at Langley, have performed much of their work in an aviation context, which includes highly trained expert operators performing safety-critical tasks – safe-keeping hundreds of lives at a time – in a high-stakes, and at times high-stress, environment.

Human Operator and Autonomous System Integration: Cyber-Physical-Human Teaming
Cyber-Physical-Human teaming enables crew autonomy via interfaces with trusted and trustworthy autonomous agents and decision support systems. Both automated and autonomous systems will be needed to achieve Earth-independent operations.

Human Operator and Autonomous System Integration: Human-Autonomy Teaming Task Battery
The Human-Autonomy Teaming Task Battery (HATTB) is a research software application that provides a simplified version of real-world tasks that are performed by remote operators (or “pilots”) monitoring and controlling multiple vehicles (e.g., drones) with varying levels of automation. Specifically, the HATTB is used to conduct human-in-the-loop (HITL) experiments that evaluate the performance of research participants as they manage or monitor increasingly autonomous vehicles and simultaneously perform various other predefined tasks.

Human Performance and Lunar Lander Simulations
We have broad experience and expertise in human-in-the-loop simulation (both low and high fidelity) and study tasking, relevant to studying the Acute CNS impacts of Space Radiation and multiple stressors, e.g., with precision landing site identification and execution, under time pressure.

Physiological Features + Machine Learning to Predict Crew State
We have machine learning and crew state experience and expertise. We pioneered the study and use of real-time-compatible methods ten years ago. The best thus far used autoencoding to deal with biological noise, and two 6-layer dense neural networks. Non-nominal attentional states are identified at a rates > 0.8.

Previously Featured Research Spotlight

Medical Care and Operations in Space