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      Driver Cognitive Distraction Detection Using Driving Performance Measures

      , , , , ,
      Discrete Dynamics in Nature and Society
      Hindawi Limited

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          Abstract

          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.

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          Most cited references12

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          Driver Inattention Monitoring System for Intelligent Vehicles: A Review

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            Driver distraction and driver inattention: definition, relationship and taxonomy.

            There is accumulating evidence that driver distraction and driver inattention are leading causes of vehicle crashes and incidents. However, as applied psychological constructs, they have been inconsistently defined and the relationship between them remains unclear. In this paper, driver distraction and driver inattention are defined and a taxonomy is presented in which driver distraction is distinguished from other forms of driver inattention. The taxonomy and the definitions provided are intended (a) to provide a common framework for coding different forms of driver inattention as contributing factors in crashes and incidents, so that comparable estimates of their role as contributing factors can be made across different studies, and (b) to make it possible to more accurately interpret and compare, across studies, the research findings for a given form of driver inattention. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines

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                Author and article information

                Journal
                Discrete Dynamics in Nature and Society
                Discrete Dynamics in Nature and Society
                Hindawi Limited
                1026-0226
                1607-887X
                2012
                2012
                : 2012
                :
                : 1-12
                Article
                10.1155/2012/432634
                f26e6aa6-c440-46e6-8572-c2883b26cb22
                © 2012

                http://creativecommons.org/licenses/by/3.0/

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