| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1095877 | International Journal of Industrial Ergonomics | 2016 | 7 Pages |
•Effects of time pressure and target uncertainty on autonomous unmanned aerial vehicle operator task performance and workload were examined.•Time pressure and uncertainty interact on human system performance, they must be considered together.•Practical implication for safe integration of unmanned aerial vehicles into the airspace systems in the near future was given.
Autonomous unmanned aircraft systems (UAS) are being utilized at an increasing rate for a number of military applications. The role of a human operator differs from that of a pilot in a manned aircraft, and this new role creates a need for a shift in interface and task design in order to take advantage of the full potential of these systems. This study examined the effects of time pressure and target uncertainty on autonomous unmanned aerial vehicle operator task performance and workload. A 2 × 2 within subjects experiment design was conducted using Multi-Modal Immersive Intelligent Interface for Remote Operation (MIIIRO) software. The primary task was image identification, and secondary tasks consisted of responding to events encountered in typical UAS operations. Time pressure was found to produce a significant difference in subjective workload ratings as well as secondary task performance scores, while target uncertainty was found to produce a significant difference in the primary task performance scores. Interaction effects were also found for primary tasks and two of the secondary tasks. This study has contributed to the knowledge of UAS operation, and the factors which may influence performance and workload within the UAS operator. Performance and workload effects were shown to be elicited by time pressure. Relevance to industry: The research findings from this study will help the UAS community in designing human computer interface and enable appropriate business decisions for staffing and training, to improve system performance and reduce the workload.
