Mitigation of Human Supervisory Control Wait Times through Automation Strategies

摘要

The application of network centric operations principles to human supervisory control(HSC) domains means that humans are increasingly being asked to manage multiplesimultaneous HSC processes. However, increases in the number of available informationsources, volume of information and operational tempo, all which place highercognitive demands on operators, could become constraints limiting the success of networkcentric processes. In time-pressured scenarios typical of networked commandand control scenarios, efficiently allocating attention between a set of dynamic tasksis crucial for mission success. Inefficient attention allocation leads to system waittimes, which could eventually lead to critical events such as missed times on targetsand degraded overall mission success. One potential solution to mitigating wait timesis the introduction of automated decision support in order to relieve operator workload.However, it is not obvious what automated decision support is appropriate, ashigher levels of automation may result in a situation awareness decrement and otherproblems typically associated with excessive automation such as automation bias.To assess the impact of increasing levels of automation on human and system performancein a time-critical HSC multiple task management context, an experimentwas run in which an operator simultaneously managed four highly autonomous unmannedaerial vehicles (UAVs) executing an air tasking order, with the overall goalof destroying a pre-determined set of targets within a limited time period. Four increasinglevels automated decision support were investigated as well as high and lowoperational replanning tempos. The highest level of automation, management-byexception,had the best performance across several metrics but had a greater numberof catastrophic events during which a UAV erroneously destroyed a friendly target.Contrary to expectations, the collaborative level of decision support, which providedpredictions for possible periods of task overload as well as possible courses of actionto relieve the high workload, produced the worst performance. This is attributableto an unintended consequence of the automation where the graphical visualization ofthe computer’s predictions caused users to try to globally optimize the schedules forall UAVs instead of locally optimizing schedules in the immediate future, resulting inthem being overwhelmed. Total system wait time across both experimental factorswas dominated by wait time caused by lack of situation awareness, which is difficultto eliminate, implying that there will be a clear upper limit on the number of vehiclesthat any one person can supervise because of the need to stay cognitively aware ofunfolding events.