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

Published

Journal Article

We examined the impact of priming on operator trust and system performance when supervising a decentralized network of heterogeneous unmanned vehicles (UVs).Advances in autonomy have enabled a future vision of single-operator control of multiple heterogeneous UVs. Real-time scheduling for multiple UVs in uncertain environments requires the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Because of system and environmental uncertainty, appropriate operator trust will be instrumental to maintain high system performance and prevent cognitive overload.Three groups of operators experienced different levels of trust priming prior to conducting simulated missions in an existing, multiple-UV simulation environment.Participants who play computer and video games frequently were found to have a higher propensity to overtrust automation. By priming gamers to lower their initial trust to a more appropriate level, system performance was improved by 10% as compared to gamers who were primed to have higher trust in the automation.Priming was successful at adjusting the operator's initial and dynamic trust in the automated scheduling algorithm, which had a substantial impact on system performance.These results have important implications for personnel selection and training for futuristic multi-UV systems under human supervision. Although gamers may bring valuable skills, they may also be potentially prone to automation bias. Priming during training and regular priming throughout missions may be one potential method for overcoming this propensity to overtrust automation.

Full Text

Duke Authors

Cited Authors

  • Clare, AS; Cummings, ML; Repenning, NP

Published Date

  • November 2015

Published In

Volume / Issue

  • 57 / 7

Start / End Page

  • 1208 - 1218

PubMed ID

  • 26060238

Pubmed Central ID

  • 26060238

Electronic International Standard Serial Number (EISSN)

  • 1547-8181

International Standard Serial Number (ISSN)

  • 0018-7208

Digital Object Identifier (DOI)

  • 10.1177/0018720815587803

Language

  • eng