Real-Time Detection of Operator Workload as Input to Scalable Autonomy During Dynamic Flight Operations

Navy STTR 24.B - Topic N24B-T028
NAVAIR - Naval Air Systems Command
Pre-release 4/17/24   Opened to accept proposals 5/15/24   Closes 6/12/24 12:00pm ET    [ View Q&A ]

N24B-T028 TITLE: Real-Time Detection of Operator Workload as Input to Scalable Autonomy During Dynamic Flight Operations

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software; Human-Machine Interfaces; Trusted AI and Autonomy

OBJECTIVE: Develop real-time assessment of operator workload that can be utilized in dynamic flight operations to as input to scalable autonomy in human-autonomy teams.

DESCRIPTION: Changes in the competitive capabilities of our adversaries has brought us to a new playing field in which we need to quickly develop and successfully leverage and integrate new technologies to support of our warfighters and the mission to maintain naval superiority. New developments in Machine Learning and Artificial Intelligence (ML/AI) provide opportunities for the development of new autonomous and automated systems including autonomous platforms that can work alongside the warfighter as a teammate rather than an aid or tool.

Effective human-autonomy teaming in naval aviation will only be achieved if the human operator and autonomy system or agent are reactive to—and collaborative with—each other. A reduction in workload due to automation does not always result in superior operator and system performance; if task load is manageable, then offloading of tasks can result in underload and a loss of situational awareness [Ref 3]. Furthermore, automation does not always result in reduced workload. The paradox of automation is that monitoring the autonomous system, in addition to other mission responsibilities, can inadvertently increase workload. This increase in workload is thought to be due to the taxing nature of passive monitoring [Ref 4], which ultimately can result in complacency.

One proposed strategy to enhance human-autonomy teaming effectiveness is to build autonomous systems that adapt to the human operator in their current workload state (e.g., underloaded, overloaded, or optimal task load) in real time. The goal of such a strategy is to maintain situational awareness while moderating workload by increasing or decreasing levels of autonomy (i.e., number and types of tasks that are offloaded, type of decision aids provided, level of transparency, level and types of automated/autonomy functions, etc., [Ref 2]) based upon indicators of operator workload states [Refs 4 and 5]. Ideally, high operator workload would be addressed by increasing levels of automation or autonomous features, and offloading/modifying tasks to ultimately enhance operator performance. Lower operator workload states would require relatively less autonomy to ensure that the human remains in the loop to maintain engagement and situational awareness.

The current state of autonomous functions of a system is either: (a) to be active at all times, (b) be manually turned on/off by the user, or (c) be manually selected by the user from various predetermined levels of autonomy. Thus, innovation is still needed to develop adaptive automation in real time, so that autonomy can be scaled to match the current operator need in order to ensure mission success. For this, we first need to:

• Identify valid and consistent metric(s), method(s), and/or tool(s) to estimate the multidemonsionial nature of operator workload and state.
Use the identified metric(s), method(s), and/or tool(s) to develop:
Real-time indications of workload. These could include but are not limited to psychophysiological and operator, aircraft, and/or mission performance measures.

• The resulting tools and methods need to be unobtrusive to operator performance and comfort.

• The metric(s), method(s), and/or tool(s) need to be able to be resilient and function in naval aviation operational environments, to include in-aircraft use.

• Develop a model for operationally defining workload thresholds (i.e., overloaded or underloaded), that would require changes in system automation or autonomy.

• Propose tasks and task allocation strategies between operator and autonomy/automation that would result in increased and/or decreased levels of autonomy/automation as needed, and would be based on the real-time workload indicators.

A solution that addresses the above-mentioned needs would provide a first step in supporting future human-autonomy teams that are inherent in the ever-growing manned-unmanned missions.

Note: NAVAIR will provide Phase I performers with the appropriate guidance required for human research protocols so that they have the information to use while preparing their Phase II Initial Proposal. Institutional Review Board (IRB) determination as well as processing, submission, and review of all paperwork required for human subject use can be a lengthy process. As such, no human research will be allowed until Phase II and work will not be authorized until approval has been obtained, typically as an option to be exercised during Phase II.

PHASE I: Identify and provide a justification for the metric(s), method(s), and/or tool(s) that will be used for the real-time assessment of operator workload. Propose and provide a strategy on how these will be used and combined to produce an estimation of the operator workload. The Phase I effort will include prototype plans to be developed under Phase II. Note: Please refer to the statement included in the Description above regarding human research protocol for Phase II.

PHASE II: Develop, demonstrate, and validate an unobtrusive and affordable stand-alone kit for the dynamic assessment of operator workload, and its use and effectiveness as input for scalable automation/autonomy. An ideal kit would measure operator workload in an unobtrusive manner, so as not to interfere with operator task load, and would be viable for use in various naval aviation environments to include in-aircraft use. It will also include the development of an algorithm to operationally define overload and underload, as well as optimal workload. In addition, strategies should be proposed for manipulating the levels of automation in response to workload. Note: Please refer to the statement included in the Description above regarding human research protocol for Phase II.

PHASE III DUAL USE APPLICATIONS: Final testing would involve validation of the technology in a naval aviation relevant use case that involves dynamic automation level modifications based on the workload assessment and demonstration that the intervention results in the intended changes in operator workload and enhanced system performance.

The real-time assessment of workload as input to scaling automation levels or autonomy behavior, could be used in a variety of Naval aviation applications that involve the interaction of a human operator with an automated system for extended periods and in dynamic environments. Some of these could be autonomous-car or transit vehicle operation, search and rescue mission systems, reconnaissance and surveillance mission systems, and monitoring systems and applications (e.g., scientific, medical, and nuclear).


  1. Hooey, B. L.; Kaber, D. B.; Adams, J. A.; Fong, T. W. and Gore, B. F. "The underpinnings of workload in unmanned vehicle systems." IEEE Transactions on Human-Machine Systems, 48(5), 2017, pp. 452-467.
  2. Parasuraman, R.; Sheridan, T. B. and Wickens, C. D. "A model for types and levels of human interaction with automation." IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, 30(3), 2000, pp. 286-297.
  3. Young, M. S. and Stanton, N. A. "Attention and automation: New perspectives on mental underload and performance." Theoretical issues in ergonomics science, 3(2), 2002, pp. 178-194.
  4. Parasuraman, R.; Bahri, T.; Deaton, J. E.; Morrison, J. G. and Barnes, M. "Theory and design of adaptive automation in aviation systems." Naval Air Warfare Center, Warminster, PA, Tech. Rep. NAWCADWAR-92, 17 July 1992, pp. 033-60.
  5. Kaber, D. B.; Riley, J. M.; Tan, K. W. and Endsley, M. R. "On the design of adaptive automation for complex systems." International Journal of Cognitive Ergonomics, 5(1), 2001, pp. 37-57.

KEYWORDS: Human-autonomy teaming; adaptive automation; operator workload; real-time monitoring; neuroergonomics; psychophysiology


The Navy Topic above is an "unofficial" copy from the Navy Topics in the DoD 24.B STTR BAA. Please see the official DoD Topic website at for any updates.

The DoD issued its Navy 24.B STTR Topics pre-release on April 17, 2024 which opens to receive proposals on May 15, 2024, and closes June 12, 2024 (12:00pm ET).

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Topic Q & A

05/15/24  Q. Does the solution have to incorporate psychophysiological sensors? Can the solution incorporate metrics from the aircraft, speech, etc.?
   A. All of the mentioned metrics are within scope. The chosen metrics do not need to be limited to psychophysiological sensors.
05/15/24  Q. Is there a specific aviation platform or mission that you plan to apply this solution to?
   A. While there are many potential applications, we are currently looking for platform-agnostic solutions for Phase I. As we progress through Phases II and III, there may be guidance towards a particular platform or mission to serve as a relevant use case.
05/15/24  Q. Who is the user?
   A. We define the user as aircrew, including Naval Aviators, Naval Flight Officers, and Naval Aircrewmen.
05/15/24  Q. Are you leaning towards any particular metric(s), method(s), or tool(s) to indicate operator workload?
   A. We are open to any innovative solutions within the scope of this topic and are not leaning towards any particular metric, method, or tool.
05/15/24  Q. Is there a minimum TRL that is required for this proposal?
   A. There is no minimum TRL required coming into this project from the perspective of the STTR program or the SBIR Program Management Office. We value innovation over technology readiness at this point in time. With that said, if there is little-to-no potential to transition to real-world aviation environment, then the proposal is not within scope.
05/15/24  Q. If firms already have a related and approved IRB protocol, can they extend it in order to collect human subject’s data on Phase I?
   A. No human testing will be allowed during the Phase I, and this includes any ongoing data collection.
05/15/24  Q. If firms already have their own archival data (i.e., data collection is complete), can they use that data during Phase I?
   A. Yes, a firm can use their own data for Phase I, as long as you find it suitable. If there are unique data rights associated with the archival data, the firm will need to mark it properly (if delivering to Navy under the Phase I) and assert data rights on that dataset coming into a Phase I contract. There are instructions on how to do that in the Volume 5 proposal instructions on the Navy SBIR site.
05/15/24  Q. Will you be providing data?
   A. We do not have data available and will not be providing data at this time. We are open to exploring the possibility after awards have been selected.
5/15/24  Q. Is this solution going to be used in assessment/training and operations, or just operations?
   A. Although the solution would be applicable to various areas and use cases, our focus is for its use during operations and any development should feasible during the dynamic and harsh environments of in-aircraft operations.

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