Autonomous Control Systems
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The TITAN (Tactical Interfaces for Teaming with Autonomy) multiphase endeavor from Charles River Analytics intends to create visualizations that facilitate crewed-uncrewed teaming.
The advanced display tools will assist human operators in challenging communication environments to effectively team with autonomous systems, such as by providing communication and task plan insights in context.
Facilitated by a Small Business Technology Transfer (STTR) Phase II contract from the US Air Force Research Laboratory (AFRL), TITAN will develop and evaluate controls and support interfaces that help increase situation awareness during distributed tactical operations.
Charles River Analytics assert that while uncrewed systems are increasingly being deployed in tactical Air Force missions, human operators must still remain in the loop and are the final decision-makers.
The challenges are numerous and complex. Environments are constantly changing, whether that is through weather patterns or congested traffic in the airspace; mission objectives can reset on the fly, such as when target priorities shift; and clear and consistent communication between all parties is not a given.
These complexities demand team situation awareness during times of high cognitive workload due to rapidly changing situations in real time, something operators might have difficulty maintaining.
TITAN is designed to be an ally in such cases. By exploring complex communication and teaming scenarios, TITAN delivers contextual information to operators in a format that can be quickly and readily understood.
TITAN Phase I
Dr. Nicolette McGeorge, Human Factors Engineering Scientist at Charles River, is the Principal Investigator on the TITAN effort. Phase I, executed with Dr. Michael Rayo at Ohio State University, laid the groundwork using cognitive systems engineering (CSE) methods.
The philosophy of CSE is to understand complex situations and identify cognitive needs that should be considered in the design of visualizations to support those situations. This, combined with the tactical knowledge of collaborators at Phoenix Flight Test, Inc. (including Lt. Col. Leonard “Sherman” Kearl, USAF Ret., President), resulted in a series of detailed operational scenarios used to elicit requirements for visualizations that support these complex teaming environments.
Abstraction networks, a method in CSE, help create an artifact of the requirements for each scenario. The team injected complicating factors into the scenarios to further enable identification of requirements under complex teaming situations.
Examples of complicating factors within the scenarios included the following:
- Data overload – When a pilot receives a flood of information at the same time that they must sift through and interpret effectively to quickly gain situation awareness.
- Misleading indicators – When information from a source might not accurately reflect the real-world conditions.
- Distributed information – When data from a variety of systems needs to be synchronized to deliver a single source of truth.
- Ambiguous cues – When operators receive information from their team but its meaning is not immediately clear.
In cases of ambiguous cues, Dr. McGeorge states; “You have to wait and see and gather more information before you can figure out what’s really going on, or you might need to make a guess based on the time pressures involved. Because these missions are conducted at the tactical edge, it’s critical to understand not only the situation but also the necessity of who needs to communicate with whom and how.”
As automation and artificial intelligence are more widely adopted, the possibilities for more uncrewed vehicle teammates are increasing. Dr. McGeorge adds; “It’s important to understand the different types of capabilities that could exist, different teaming structures and mission scenarios, and how these all will affect future crewed and uncrewed teaming.”
Now in Phase II, the team will broaden the operational scenarios to encompass a larger range of crewed and uncrewed systems operations and enhance the TITAN prototype to support more vigorous testing and integration with representative frameworks.
