Abstract
In large-scale, remotely operated systems of autonomous vehicles, human operators’ situation awareness will depend on their use of multiple information sources which may include target classification estimates provided by the system. This experiment assessed to what degree participants relied on a likelihood estimate to assist in the classification of novel stimuli in varying levels of uncertainty. Participants were trained to classify two sets of novel visual stimuli, then classified variations of the stimuli with the aid of an estimate displaying the likelihood of belonging to either group. The results showed that participants were able to integrate the automated estimate into their classification responses, and as the level of uncertainty increased, the average reliance on the automated estimate also increased. The findings show that training participants to identify new stimuli, then presenting participants with a likelihood estimate in conjunction with the visual stimuli may facilitate situation awareness in conditions of uncertainty.
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Acknowledgments
The authors would like to acknowledge the support from Air Force Research Laboratory and OSD for sponsoring this research under agreement number FA8750-15-2-0116. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory, OSD, or the U.S. Government.
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Hoenig, A., Stephens, J.D. (2020). Decision Making Using Automated Estimates in the Classification of Novel Stimuli. In: Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2019. Advances in Intelligent Systems and Computing, vol 962. Springer, Cham. https://doi.org/10.1007/978-3-030-20467-9_3
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DOI: https://doi.org/10.1007/978-3-030-20467-9_3
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