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This research aims to assess how multi-operator supervision affects performance and subjective workload in autonomous systems.

Hidden Cost of Re-Engagement: Workload in Dynamic Multi-Operator Supervision of Autonomous Systems

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This research aims to assess how multi-operator supervision affects performance and subjective workload in autonomous systems.
Conducted a 2 × 2 × 2 between-subjects online experiment with participants assigned to different supervision paradigms.
Participants completed tasks involving landing, flying, supervisory flying, and monitoring under varying workload schedules.
Performance was measured using baseline-normalized accuracy and task duration; subjective workload was assessed using NASA-TLX.
Multi-operator supervision maintained normalized accuracy but resulted in lower perceived performance and greater effort from operators.
Operators reported higher subjective workload when engaged in multi-operator tasks compared to single-operator tasks.

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Randomized trial examines workload and performance in multi-operator supervision of autonomous systems, suggesting hidden workload costs.

Marupudi et al. (2026) studied this question.

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