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The rise in artificial intelligence capabilities in autonomy-enabled systems and robotics has pushed research to address the unique nature of human-autonomy team collaboration. The goal of these advanced technologies is to enable rapid decision making, enhance situation awareness, promote shared understanding, and improve team dynamics. Simultaneously, use of these technologies is expected to reduce risk to those who collaborate with these systems. Yet, for appropriate human- autonomy teaming to take place, especially as we move beyond dyadic partnerships, proper calibration of team trust is needed to effectively coordinate interactions during high-risk operations. But to meet this end, critical measures of team trust for this new dynamic of human-autonomy teams are needed. This paper seeks to expand on trust measurement principles and the foundation of human-autonomy teaming to propose a “toolkit” of novel methods that support the development, maintenance and calibration of trust in human-autonomy teams operating within uncertain, risky, and dynamic environments.

Evaluation of team communication can provide critical insights into team dynamics, cohesion, trust, and performance on joint tasks. Although many communication-based measures have been tested and validated for human teams, this review article extends this research by identifying key approaches specific to human-autonomy teams. It is not possible to identify all approaches for all situations, though the following seem to generalize and support multi-size teams and a variety of military operations. Therefore, this article will outline several key approaches to assessing communication, associated data requirements, example applications, verification of methods through HAT use cases, and lessons learned, where applicable. Some approaches are based on the structure of team communication; others draw from dynamical systems theory to consider perspectives across different timescales; other approaches leverage features of team members’ voices or facial expressions to detect emotional states that can provide windows into other workings of the team; still others consider the content of communication to produce insights. Taken together, these approaches comprise a varied toolkit for deriving critical information about how team interactions affect, and are affected by, coordination, trust, cohesion, and performance outcomes. Future research directions describe four critical areas for further study of communication in human-autonomy teams.

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