July 1, 2020
The use of autonomy requires novel approaches for teaming human operators and machine partners. Decades of research have shown that the introduction of automation can drastically change the demands of human operators morphing humans into monitors of the technology (Parasuraman & Riley, 1997). In doing so, humans are faced with novel challenges of increased workload, mode confusion, and miscalibrated trust which can hinder performance (Hoff & Bashir, 2015). Contemporary approaches seek to blend humans and machines into seamless human-autonomy teams (HATs), which raises new challenges for both the approach toward autonomy and the human-machine interfaces required to facilitate shared awareness and shared intent between the humans and machines. Given the dearth of science on what approaches are most effective for HATs, the development of interfaces to support manned-unmanned teaming (MUMT) is as much art as science. Thus, research is needed to understand the various approaches to MUMT, compare them based on the extant HAT literature, and establish guidelines for effective MUMT for future DoD systems.