Level of automation effects on performance, situation awareness and workload in a dynamic control task.
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.
Department of Industrial Engineering, Mississippi State University 39762-9542, USA.
M R
MR
Endsley
D B
DB
Kaber
D000328:Adult; D000704:Analysis of Variance; D001331:Automation / Q000379:methods*; D001364:Awareness; D003071:Cognition; D004868:Equipment Failure; D005069:Evaluation Studies as Topic; D005260:Female; D006801:Humans; D007389:Internal-External Control; D008297:Male; D013647:Task Performance and Analysis; D014584:User-Computer Interface; D016526:Workload