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.
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Notes
- 1.
The use of trademarks or names of manufacturers in this report is for accurate reporting and does not constitute an official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics and Space Administration.
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This research was supported by NASA Langley Research Center IRAD funding in 2016.
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Trujillo, A.C. (2019). Operator Trust Function for Predicted Drone Arrival. In: Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2018. Advances in Intelligent Systems and Computing, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-319-94346-6_1
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