The Unmanned Aircraft System (UAS) Traffic Management (UTM) concept was designed to support autonomous small UAS operations at a large-scale and without direct human intervention. However, human-autonomy interactions will be impacted by situation awareness, workload, and trust in the autonomy. Method: Nine participants monitored live small UAS operations in a representative UTM system during a series of traffic conflict scenarios and then provided subjective responses regarding situation awareness, workload, and trust in the autonomous separation method. The study employed a 3 (Separation Method: Autonomous Sense and Avoid, Geofence, Manual) × 2 (Incursion: High, Medium) within subjects design. Results: Situation awareness ratings for both autonomous separation methods were significantly lower than the manual condition. An interaction indicated differential workload ratings for the Autonomous Sense and Avoid separation ratings. Trust ratings significantly dropped when the Geofencing separation method failed. Conclusion: Subjective responses of remote operators in the UTM system are affected by the vehicle separation methods. Operators’ understanding of decisions made by the autonomous systems onboard the vehicle likely influence this effect
Effects of Autonomous sUAS Separation Methods on Subjective Workload, Situation Awareness, and Trust
AIAA SciTech ; 2021 ; Virtual , US
Aufsatz (Konferenz)
Keine Angabe
Englisch
sUAS , UAS , Human Factors , UTM , Behavioral Sciences