Human Adaptive Calibration for Machine Operation Without Awareness

摘要

Optimum human-machine operation is defined as when a operated machine with "human calculated dynamics model" coincides with its "machine dynamics." Based on this definition, we propose calibration that brings the machine dynamics closer to the human-calculated machine model. Operators tend to learn dynamics without awareness during machine operation, so changes in machine dynamics appear to pose problems that both make operators uncomfortable and hinder their learning. We propose calibration for changes in machine dynamics without operator awareness, by quantifying perception based on cognitive scientific knowledge. We applied this calibration to a task involving the maneuvering of a mobile vehicle.