CHORD - Collaborative Human Autonomy Operational Review - SBIR Topic DAF26BZ01-DV007

Disclaimer:
This topic was temporarily posted by the Department of War SBIR Program on March 2nd 2026 and removed the following day.
We believe this topic is planned to be released once the SBIR program is reauthorized; however, this topic may ultimately be modified or withdrawn.

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Funding Amount:

Est. $140,000

Deadline to Apply:

Est. April 29th, 2026.

Objective:

Future Autonomous Collaborative Platforms (ACPs) will introduce AI-enabled uncrewed aircraft into the fleet. These platforms will assume significant tactical decision-making responsibilities and operate alongside traditional crewed aircraft. This paradigm shift complicates knowledge elicitation for post-mission debriefing, as it necessitates understanding both human and autonomous aircraft decision-making processes. This introduces a new research challenge: effectively logging the necessary information from human and ACP decision-actions for debriefing and presenting it to warfighters through innovative human-machine interfaces (HMIs). The primary objective of this topic is to prototype and develop debriefing approaches that effectively fuse the decision-making chains of both human operators and multiple autonomous ACPs, presenting that information clearly and concisely.

Description:

Mission debriefing for manned and remotely piloted aircraft (RPA) and crewed aircraft in military operations is currently conducted manually by warfighters. This typically involves verbal communication and classroom-style discussions, with little to no AI or software assistance for reflecting on missions, identifying lessons learned, or pinpointing areas for improvement. As pilots are the primary tactical decision-makers, verbal communication sessions are essential for eliciting and understanding their decision-making processes. As teams of ACPs begin making tactical decisions with a high level of autonomy, it is unknown what information needs to be logged during mission and how that information should be displayed so that the warfighter can audit and understand after mission debriefing, what decisions, tactics, techniques, and procedures (TTPs) the autonomous systems acted on. This topic looks to advance existing debriefing tools for replaying mission execution and enhance them with additional functionality targeting debriefing of autonomous ACPs.

A secondary focus of this topic is to identify data input requirements from autonomy that would be necessary for support debriefing of autonomy. Modern methods for autonomous decision-making tend to employ black-box deep learning algorithms with limited transparency, leading to lack of trust and assurance that autonomous agent decisions comply with the Law of Armed Conflict (LOAC). XAI (Explainable Artificial Intelligence) is actively researching techniques to make black box models more understandable while other areas are using more transparent symbolic methods that are rooted in explicit rules to perform reasoning and problem-solving. An ACP will likely include a combination of inherently explainable and low transparency algorithms for different decision-making processes. Information needs for debriefing that will be identified in this topic should guide autonomy development with regards to autonomy logging/reporting for debriefing and algorithm practicality.

A consideration for DP2 participation is the demonstration of an existing debriefing tool that the proposer has developed that is used in military operations or that it is being developed under a recognized US DoD program. This will allow for a solid foundation for which CHORD can build upon that focuses specifically on debriefing of human machine teaming for ACPs. An expectation of common debriefing functionality such as data playback, a digital map display, timeline, event logs, and data visualization of vehicle fuel, health, and status will be necessary for DP2 consideration. It is not essential that the existing debriefing tool has been applied to unmanned systems, and debriefing tools in non-air or crewed vehicle domains will be considered.

While logging and video playback of ACP mission execution are critical components of debriefing functionality, they will likely be inadequate for truly understanding ACP decision-making. Software analytics, AI tools, and novel HMI designs will be necessary to answer key questions, such as: What tactical decisions did the ACP make? When were these decisions made? What was the rationale or considerations behind the ACP's decisions? As ACPs assume greater responsibility in tactical decision-making, it is crucial to conduct research and develop software tools that enable warfighters to understand and trust these autonomous systems.

Core Research Questions:

 What types of information must be logged and exchanged between ACPs and the warfighter during post-mission debriefing to support transparency and trust in autonomous operations?

 Do current government reference architectures and standards adequately support the information exchange requirements for debriefing ACP teams?

 How should information from ACPs be structured and visualized within the HMI to align with warfighter cognitive models and situational awareness needs?

 What HMI features for debriefing best support comprehension of ACP autonomy decision chains, contextual reasoning, and deviations from expected behavior?

 What types of software or AI-enabled analytics tools would be most useful to summarize, explain, and visualize autonomous decision-making by ACPs?

Who will win?

If you can achieve the objective above better than any other company on the market, you have a very high-likelihood of success and should apply.

Who is eligible to apply?

Any company that meets the following criteria:

• For-profit company

• U.S.-owned and controlled.

• 500 or fewer employees (including affiliates)

How Can BW&CO Help?

1) End-to-end support including, strategy, writing of the full proposal, and administrative & compliance support.

2) Proposal strategy and review.

3) Administrative & compliance support.

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