What is it about?

In this study, we investigated the robustness between the time of training data collection and the time of the detection of cognitive distraction while driving. We identify that while our system could identify cognitive distraction with high accuracy, there was a large temporal impact. This lead to a reduction of classification accuracy, with an increase in time between the training of the system and the use of the system for distraction detection.

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Why is it important?

While identifying "eyes-on-the-road" has been investigated quite extensively, mental distractions can occur even while looking at the road. Therefore, we believe it is crucial to determine when the drivers mental focuses switches from the road to something non-driving related.

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This page is a summary of: Temporal Impact on Cognitive Distraction Detection for Car Drivers using EEG, December 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3441000.3441013.
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