Managing Cockpit Crew Excess Task Load in Military Manned-Unmanned Teaming Missions by Dual-Mode Cognitive Automation Approaches

This work addresses approaches to enable the cockpit crew of a helicopter to manage multiple Uninhabited Aerial Vehicles (UAVs) while at the same time performing a military flight mission with the own air vehicle. Aiming for more flexibility and safety in combat, such Manned-Unmanned Teaming (MUM-T) missions require a high level of interoperability between manned and unmanned assets. The resulting additional responsibilities pose extreme work demands to the cockpit crew. The key elements for achieving the workload reduction necessary to facilitate such missions are threefold: first, high-level human-UAV interaction based on tasks (supervisory control), second, the delegation of helicopter commander tasks to the pilot flying (modified crew coordination concept), and third, the introduction of knowledge-based assistant systems, one for each crewmember to support their individual task performance (cooperative control). We implemented the mentioned functionalities along the design guidelines of the so-called DualMode Cognitive Automation approach, in which the functions that the human operator interacts with in supervisory control (mode one) and cooperative control (mode two) are realized as artificial cognitive systems. These systems make use of cognitive task understanding, human-machine mixed-initiative interaction, human operator observation, and human mental resource prediction approaches. In addition to the concepts and methods behind the functionalities mentioned, this article describes comprehensive evaluation experiments conducted in our research helicopter mission simulator and discusses the corresponding results.