Confidence Metrics Improve Human-Autonomy Integration

Controls frameworks for human-autonomy integration (HAI) often treat human sources of information as highly reliable [1]. However, data from human sensing are quite variable, both because of human nature and limitations on sensor technologies. This paper focuses on estimating the degree of uncertainty in human sensed data, i.e., developing confidence metrics, and implementing those metrics in a HAI for target recognition. Here we demonstrate that applying such confidence estimates to sensed human data can mitigate effects of variability in human sensing and improve HAI performance.