Conference Proceeding

Using a Layered Algorithm to Detect Driver Cognitive Distraction

Authors
  • Yulan Liang (Liberty Mutual Research Institute for Safety, Hopkinton, MA)
  • Ben D Lee (University of Wisconsin, Madison, WI)

Abstract

Detection of cognitive distraction presents an indispensable function for driver distraction mitigation systems. In this study, we developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering method—to identify cognitive distraction from drivers’ eye movements and driving performance measures. We used the data collected in a simulator study to compare the layered algorithm with the original DBN and found that the layered algorithm obtained comparable prediction performance as the original DBN. Meanwhile, the layered algorithm shortened training and prediction time and revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that data mining methods are suitable to identify human cognitive state from performance.

How to Cite:

Liang, Y. & Lee, B., (2013) “Using a Layered Algorithm to Detect Driver Cognitive Distraction”, Driving Assessment Conference 7(2013), 327-333. doi: https://doi.org/10.17077/drivingassessment.1508

Rights: Copyright © 2013 the author(s)

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Published on
19 Jun 2013
Peer Reviewed