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Sensor-based assessment of the in-situ quality of human computer interaction in the cars : final research report.
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2016-01-01
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Abstract:Human attention is a finite resource. When interrupted while performing a task, this
resource is split between two interactive tasks. People have to decide whether the benefits
from the interruptive interaction will be enough to offset the loss of attention from the
ongoing task.
The issue of dealing with self-interruptions and external interruptions is particularly critical
in driving situations. In general, interruptions result in a time lag before users resume their
primary task, increase mental workload, and thus decrease primary task performance.
Therefore, being able to identify when a driver is interruptible is critical for building
systems that can mediate these interruptions.
In order to identify situations in which drivers enter either low or high cognitive load states
during naturalistic dring (i.e., opportune moments for driver interruption – e.g., more
interruptible states vs. less interruptible states), we have examined a broad range of sensor
data streams to understand real-time driver/driving states (e.g., motion capture, peripheral
interaction monitoring, psycho-physiological responses, etc.), and presented a modelbased
driver/driving assessment by using machine learning technology.
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[PDF-3.57 MB]
