A scalable framework for adding crisp knowledge to pilot/vehicle interfaces

Many types of operation are heavily dependent on automated use of data generated both on-board and off-board. Historically, the volume of data and the required response rates keep growing because of such factors as task and system complexity. This reality pushes human operators to their limits of cognition and memory, causing systems to be difficult to operate for a single human without automated assistance. Thus, humans become supervisors rather than operators. Tragedies such as that which occurred at the Three-Mile Island nuclear power plant and during the V-22 Osprey Tiltrotor crash are examples that point out the volume and complexity of the data and automation involved. These incidents also point out that it is essential to have a scalable framework for the prioritization and organization of raw data. Thus, the operator gets pertinent information necessary for reaching critical decisions. This framework also should support decision aids that offload operational details so that human resources can be applied more appropriately to goal achievement and other management issues. This paper outlines such a framework. It shows that raw data can be reduced to system condition information and then to decisions on automation tasks, such as system control and information display. This framework can be used to manage the pilot/vehicle interface, giving the operator information and assistance to prevent catastrophic failure and ensure the desired operational capability