Applied multivariate statistical analysis
Applied multivariate statistical analysis
Attention allocation within the abstraction hierarchy
International Journal of Human-Computer Studies - Special issue on knowledge acquisition for planning
Theory and Evaluation of Human Robot Interactions
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 5 - Volume 5
Common metrics for human-robot interaction
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Evaluation criteria for human-automation performance metrics
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Designing for Situation Awareness: An Approach to User-Centered Design, Second Edition
Designing for Situation Awareness: An Approach to User-Centered Design, Second Edition
Identifying Predictive Metrics for Supervisory Control of Multiple Robots
IEEE Transactions on Robotics
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Broad metric classes were proposed in the literature in order to facilitate metric selection for evaluating human-autonomous vehicle interaction. However, there still lacks a systematic method for selecting an efficient set of metrics from the many metrics available. We previously identified a list of evaluation criteria that can help determine the quality of a metric, and generated a list of potential metric costs and benefits. Depending on research objectives and limitations, these costs and benefits can have different weights of importance. Through an experiment with subject matter experts, we investigated which metric characteristics human factors practitioners consider to be important in evaluating human supervisory control of unmanned vehicles. We also tested two different multi-criteria decision making methods to help practitioners assign subjective weights to the cost/benefit criteria. The majority of participants rated the evaluation criteria used in both tools as very useful. However, the majority of participants' metric selections before using the methods were the same as the suggestions provided by the methods. Since determining weights of metric importance is an inherently subjective process, even with objective computational tools, the real value of using such a tool may be reminding human factors practitioners of the important experimental criteria and relationships between these criteria that should be considered when designing an experiment.