Abstract
This thesis explored methods for proactively predicting operators’ RTM performanceprior to automation failing using simulated ATC as a complex task environment testbed. Studies 1and 2 examined operator states (workload, fatigue, trust in automation, and task engagement) aspredictors of subsequent RTM performance following an automation failure under high or low degreeof automation (DOA). The findings provided preliminary support for the prediction of subsequent RTMperformance from dynamic changes in operator states. Study 3 furthered this predictive approach byexamining the relationship between manual skill and automation failure detection performance in highand low DOA.
Original language | English |
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Qualification | Doctor of Philosophy |
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Award date | 13 May 2024 |
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Publication status | Unpublished - 2024 |