Our FortiFly.Train product is a Learning Management System (LMS) and flight simulator designed to help aviators use Multi-Domain Data Fusion (MDDF) and Artificial Intelligence (AI) for advanced teaming with unmanned assets. FortiFly.Train integrates cognitive burden insights with AI and autonomy systems in simulated missions to educate and inform aviators how to best use Multi-Domain Data Fusion (MDDF) to control and leverage Artificial Intelligence (AI)-powered unmanned assets; increasing mission success while reducing instances of high workload.
FortiFly.Train was developed to assist the mission – and pilot – of the future where together they must manage vastly larger data volumes while leveraging unmanned assets as an integral part of every mission. Our solution first teaches pilots how AI and MDDF can help them master these challenges, then puts these pilots into simulated missions where they gain mastery of – and confidence in – these systems. The incorporation of AI systems for control and visualization of UAS systems through AI-powered MDDF is essential to maximize Situational Awareness (SA) while keeping workload at manageable and mission sustainable levels.
By leveraging open source and Commercial Off-The-Shelf (COTS) software and hardware products we have made a low cost, high value LMS with an adaptive and flexible architecture to support a variety of end users, platforms, and technologies.
Our current LMS was built to support the Navy and Army Future Vertical Lift (FVL) Vertical Take-Off and Landing (VTOL) Family of Systems (FoS) engaging in envisioned world battles. However, our LMS and flight simulator can easily be revised to support current aviators on existing, and emerging systems and technologies.
From our decades' worth of human performance modeling experience we have constructed a dynamic mental workload solution, built mission models, and evaluated the mental workload of two-person and single-person rotorcraft crews conducting attack and reconnaissance missions. We assessed current-world and envisioned-world settings in which FVL VTOL operators commanded a range of unmanned aircraft. We explored the use of advanced automation, artificial intelligence, and decision aiding technologies to support operators in managing periods of excessive workload. We then created a LMS and flight simulator to support training and educating aviators to understand how to best implement MDDF and AI in their missions. This enables us to identify and reduce periods of high workload while enhancing mission performance.
Our team is comprised of AI experts, human performance modelers, human factors engineers, aviation training subject matter experts (SMEs), and LMS developers. The team has applied a complementary set of cognitive systems engineering methods (functional analysis, cognitive task analysis, and discrete event modeling) to identify areas of highest workload, and then determine through technologies such as MDDF and AI areas of workload that could be effectively reduced as well as areas of the mission where effectiveness could be improved. Our in-house built LMS and flight simulator work together to improve aviator mission effectiveness.
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