.png){kind=icon}
.png){kind=icon}
Return
Dynamic Operator Overload: A Model for Predicting Workload During Supervisory Control
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2013.2293317.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Crandall et al. and Cummings & Mitchell introduced fan-out as a measure of the maximum number of robots a single human operator can supervise in a given single-human-multiple-obot system. Fan-out is based on the time constraints imposed by limitations of the robots and of the supervisor, e. g., limitations in attention. Adapting their work, we introduced a dynamic model of operator overload that predicts failures in supervisory control in real time, based on fluctuations in time constraints and in the supervisor's allocation of attention, as assessed by eye fixations. Operator overload was assessed by damage incurred by unmanned aerial vehicles when they traversed hazard areas. The model generalized well to variants of the baseline task. We then incorporated the model into the system where it predicted in real time, when an operatorwould fail to prevent vehicle damage and alerted the operator to the threat at those times. These model-based adaptive cues reduced the damage rate by one-half relative to a control condition with no cues.
Keywords:
Cognition
human-robot interaction
multi-robot systems
predictive models
unmanned aerial vehicles
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.4
Papers:
1.1K
Citations:
3.5K
Organization
View more organizations