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Operator Trust Function for Predicted Drone Arrival

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 784)

Abstract

To realize the full benefit from autonomy, systems will have to react to unknown events and uncertain dynamic environments. The resulting number of behaviors is essentially infinite; thus, the system is effectively non-deterministic but an operator needs to understand and trust the actions of the autonomous vehicles. This research began to tackle non-deterministic systems and trust by beginning to develop a user trust function based on intent information displayed and the prescribed bounds on allowable behaviors/actions of the non-deterministic system. Linear regression shows promise on being able to predict a person’s confidence of the machine’s prediction. Linear regression techniques indicated that subject characteristics, scenario difficulty, the experience with the system, and confidence earlier in the scenario account for approximately 60% of the variation in confidence ratings. This paper details the specifics of the liner regression model – essentially a trust function – for predicting a person’s confidence.

Keywords

Trust function Non-deterministic system Linear regression Autonomy Confidence rating 

Notes

Acknowledgments

This research was supported by NASA Langley Research Center IRAD funding in 2016.

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Copyright information

© Springer International Publishing AG, part of Springer Nature (outside the USA) 2019

Authors and Affiliations

  1. 1.NASA Langley Research CenterHamptonUSA

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