Situation awareness (SA) and Trust Repair in Multi-Agent Human-Automation Teams

Project begins 2021 and was completed by 2023 Q1.

How can multiple teams composed of soldiers, soldier-operated vehicles, tele-operated vehicles, and autonomous vehicles effectively accomplish unit goals in dynamic environments? Answers to this question speak directly to what the Department of Defense (DoD) calls “revolutionary collaboration” where soldiers are expected to view machines as valuable and critical teammates collaborating with humans within hierarchical military unit structures.

The premise of this project is that the success of MUM-T (manned-umanned teaming) depends on maintaining shared situation awareness (SA) and repair trust in a Multiteam System (MTS) consisting of both manned and unmanned agents.

The research objectives are:

• identify the information needed to support SA and trust repair to avoid failures,
• assess the impact of autonomy on SA, trust repair and cognitive load.

Situation Awareness is the perception and comprehension of information that allows individuals to project future courses of action to properly respond to a dynamic environment. At the team level, SA is defined as “the degree to which every team member possesses the SA required for his or her responsibilities”. Robot teammates like human teammates make mistakes that undermine trust. Therefore, it is critical to understand how human trust in robots can be repaired.

This work produced a simulator that has been deployed internally in other projects (ARC 2.17 (SACO) and IE.01 (SASI)) and is being developed further. The system will ideally be published within the 2023-2024 academic year as an open-source software and will be made available to other researchers openly.

Publication:

• Esterwood, C., Ali, A., George, Z., Dubrow, S., Smereka, J., Riegner, K., Tilbury, D. and Robert, L.P. (2023). Promises and Trust Repair in UGVs. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Washington DC.

Publications from prior work closely related to project:

• Azevedo-Sa, H., Jayaraman, S., Esterwood, C., Yang, X. J., Robert, L. P. and Tilbury, D. (2020). Context- Adaptive Management of Drivers’ Trust in Automated Vehicles, IEEE Robotics and Automation Letters (RA- L), 5(4), 6908-6915. https://doi.org/10.1109/LRA.2020.3025736.
• Azevedo-Sa, H., Jayaraman, S., Esterwood, C., Yang, X. J., Robert, L. P. and Tilbury, D. (2020). Real-Time Estimation of Drivers’ Trust in Automated Driving Systems, International Journal of Social Robotics, https://doi.org/10.1007/s12369-020-00694-1.
• Azevedo-Sa, H., Yang, X. J., Robert, L. P. and Tilbury, D. (2021). A Unified Bi-Directional Model for Natural and Artificial Trust in Human-Robot Collaboration, IEEE Robotics and Automation Letters, 6(3), 5913-5920. https://doi.org/10.1109/LRA.2021.3088082.

#2.A94