Traclabs Inc. — National Aeronautics and Space Administration SBIR Phase I: A3

Traclabs Inc. — SBIR Phase I award from National Aeronautics and Space Administration.

Amount
$124,898
Agency
National Aeronautics and Space Administration
Program / Phase
SBIR · Phase I
Topic
A3
Solicitation
SBIR_20_P1
NAICS
Place of performance
TX
Period
2020-08-05 → 2021-03-01

Description

Addressing the challenges associated withnbsp;dramatic increase in the complexity of the National Airspace System (NAS)nbsp;has required the introduction of autonomous capabilities to maintain efficiency and safety. However, as increasingly autonomous (IA) capabilities and systems are introduced into existing human-centric environments, the roles and responsibilities of humans change, especially when working in collaborative environments, such as the Airport Operations Area (AOA) and Urban Air Mobility (UAM). The integration of autonomous capabilities into traditionally human-centric environments with the goal of Human-Automation Interaction and Teaming (HAIT) makes it difficult for IA systems to not be brittle (i.e., working well in the lab under nominal circumstance, but perform poorly in unexpected situations) and accident-prone (i.e. account for emergent behavior due to unexpected decision by people or environment changes) as they attempt to work collaboratively with people. Therefore, we propose the Virtual EnviRonment for InFormative analYsis (VERIFY) framework, which links physical spaces to a virtual environment (i.e. mixed-reality). VERIFY will be used as a research tool for proactively understanding how humans and IA systems will need to work collaboratively to address and mitigate system hazards and unexpected events as a HAIT. By using a mixed-reality environment, researchers can explore multiple environmental variables simultaneously, to understand their impact on individual tasks across multiple HAIT arrangements. The objective is to leverage use cases to define key characteristics for probabilistic scenario generation to define HAIT test cases for evaluation within a mixed-reality environment. This approach also enables engineers to safely employ both physical and virtual hazards for training adaptive and non-deterministic systems and human operators to work alongside each other under nominal and off-nominal conditions.