Location

Bolton Landing, New York

Date

19-6-2013

Session

Session 7 – Poster Session B

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.

Rights

Copyright © 2013 the author(s)

DC Citation

Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, June 17-20, 2013, Bolton Landing, New York. Iowa City, IA: Public Policy Center, University of Iowa, 2013: 327-333.

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Jun 19th, 12:00 AM

Using a Layered Algorithm to Detect Driver Cognitive Distraction

Bolton Landing, New York

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.