Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 432634, 12 pages
Research Article

Driver Cognitive Distraction Detection Using Driving Performance Measures

Transportation College, Jilin University, Changchun, Jilin 130022, China

Received 17 August 2012; Revised 26 October 2012; Accepted 27 October 2012

Academic Editor: Wuhong Wang

Copyright © 2012 Lisheng Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.