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Influencing Trust for Human-Automation Collaborative Scheduling of Multiple Unmanned Vehicles.

Andrew S Clare1, Mary L Cummings2, Nelson P Repenning3

  • 1Massachusetts Institute of Technology, Cambridge aclare@alum.mit.edu.

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Priming adjusted operator trust in automated systems, improving performance by 10% in simulated missions with multiple unmanned vehicles (UVs). This is crucial for future human-supervised UV operations.

Keywords:
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Area of Science:

  • Human-computer interaction
  • Automation and control systems
  • Robotics and autonomous systems

Background:

  • Autonomy enables single-operator control of multiple heterogeneous unmanned vehicles (UVs).
  • Supervising UVs in uncertain environments requires balancing computational optimization with human judgment.
  • Appropriate operator trust is key to maintaining system performance and preventing cognitive overload.

Purpose of the Study:

  • To examine the impact of trust priming on operator trust and system performance.
  • To investigate how to manage operator trust in decentralized, heterogeneous unmanned vehicle (UV) systems.

Main Methods:

  • Three operator groups received different trust priming levels.
  • Simulated missions were conducted in a multiple-unmanned vehicle (UV) simulation environment.
  • Operator trust and system performance were measured.

Main Results:

  • Frequent gamers showed a higher tendency to overtrust automation.
  • Priming gamers to reduce initial trust improved system performance by 10%.
  • Priming effectively adjusted both initial and dynamic operator trust.

Conclusions:

  • Trust priming significantly impacts system performance in supervising unmanned vehicles (UVs).
  • Personnel selection and training for multi-UV systems should consider automation bias, especially in gamers.
  • Regular priming during training and missions can mitigate overtrust in automation.