Managing Multiple UAVs through a Timeline Display

Network-centric operations, in which both automated and human entities are linked in order to leverage information superiority, will bring increases in available information sources, volume of information and operational tempo, placing higher cognitive demands on operators. In the future vision of allowing a single operator to control multiple unmanned vehicles (which could be on land, in the air, or under water), it is not well understood how operators will manage multiple vehicles, what kind of decision support can compliment operators, and how human cognitive limitations will impact overall system effectiveness. To this end, this paper presents the results of an experiment in which an operator simultaneously managed four highly autonomous UAVs executing an air tasking order, with the overall goal of destroying a predetermined set of targets within a limited time period. The primary factors under investigation were different levels of automation from manual to management-by-exception represented in a timeline, as well as different levels of replanning which are needed in time-sensitive targeting scenarios. Increasing levels of automation can reduce workload but they can also result in situation awareness degradation as well as automation bias. Results demonstrate that operators became fixated on the need to globally optimize their schedules and did not adequately weigh uncertainty in their decisions. This fixation significantly degraded operator performance to the point that operators without any decision support performed better than those with probabilistic prediction information and the ability to negotiate potential outcomes.