Endsley M R, Kaber D B
Department of Industrial Engineering, Mississippi State University 39762-9542, USA.
Ergonomics. 1999 Mar;42(3):462-92. doi: 10.1080/001401399185595.
Various levels of automation (LOA) designating the degree of human operator and computer control were explored within the context of a dynamic control task as a means of improving overall human/machine performance. Automated systems have traditionally been explored as binary function allocations; either the human or the machine is assigned to a given task. More recently, intermediary levels of automation have been discussed as a means of maintaining operator involvement in system performance, leading to improvements in situation awareness and reductions in out-of-the-loop performance problems. A LOA taxonomy applicable to a wide range of psychomotor and cognitive tasks is presented here. The taxonomy comprises various schemes of generic control system function allocations. The functions allocated to a human operator and/or computer included monitoring displays, generating processing options, selecting an 'optimal' option and implementing that option. The impact of the LOA taxonomy was assessed within a dynamic and complex cognitive control task by measuring its effect on human/system performance, situation awareness and workload. Thirty subjects performed simulation trials involving various levels of automation. Several automation failures occurred and out-of-the-loop performance decrements were assessed. Results suggest that, in terms of performance, human operators benefit most from automation of the implementation portion of the task, but only under normal operating conditions; in contrast, removal of the operator from task implementation is detrimental to performance recovery if the automated system fails. Joint human/system option generation significantly degraded performance in comparison to human or automated option generation alone. Lower operator workload and higher situation awareness were observed under automation of the decision making portion of the task (i.e. selection of options), although human/system performance was only slightly improved. The implications of these findings for the design of automated systems are discussed.
在动态控制任务的背景下，探讨了不同自动化水平（LOA），以确定人类操作员和计算机控制的程度，作为提高整体人机性能的一种手段。传统上，自动化系统被视为二元功能分配；即要么将人类要么将机器分配到给定任务。最近，中间自动化水平被作为一种让操作员参与系统性能维护的手段进行讨论，这会提高态势感知并减少脱离回路性能问题。本文提出了一种适用于广泛的心理运动和认知任务的LOA分类法。该分类法包括通用控制系统功能分配的各种方案。分配给人类操作员和/或计算机的功能包括监控显示、生成处理选项、选择“最佳”选项并实施该选项。通过测量其对人类/系统性能、态势感知和工作量的影响，在动态复杂的认知控制任务中评估了LOA分类法的影响。30名受试者进行了涉及不同自动化水平的模拟试验。发生了几次自动化故障，并评估了脱离回路性能下降情况。结果表明，在性能方面，人类操作员在任务实施部分自动化时受益最大，但仅在正常操作条件下如此；相比之下，如果自动化系统出现故障，将操作员从任务实施中移除不利于性能恢复。与单独由人类或自动化生成选项相比，人类/系统联合生成选项显著降低了性能。在任务决策部分（即选项选择）自动化的情况下，观察到操作员工作量较低且态势感知较高，尽管人类/系统性能仅略有提高。讨论了这些发现对自动化系统设计的影响。
