Driver cognitive distraction detection

Feature estimation and implementation

Matti Kutila, Maria Jokela, Tapani Mäkinen, Jouko Viitanen, Gustav Markkula, Trent Victor

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)

Abstract

Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver’s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver’s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80% success in detecting visual distraction and a 68–86 % success in detecting cognitive distraction, which are satisfactory results.
Original languageEnglish
Pages (from-to)1027-1040
JournalProceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering
Volume221
Issue number9
DOIs
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed

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Stereo vision
Passenger cars
Trucks
Support vector machines
Electronic equipment
Monitoring
Industry

Keywords

  • Cognitive distraction
  • vehicle
  • machine vision
  • driver monitoring
  • SVM

Cite this

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title = "Driver cognitive distraction detection: Feature estimation and implementation",
abstract = "Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver’s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver’s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80{\%} success in detecting visual distraction and a 68–86 {\%} success in detecting cognitive distraction, which are satisfactory results.",
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author = "Matti Kutila and Maria Jokela and Tapani M{\"a}kinen and Jouko Viitanen and Gustav Markkula and Trent Victor",
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Driver cognitive distraction detection : Feature estimation and implementation. / Kutila, Matti; Jokela, Maria; Mäkinen, Tapani; Viitanen, Jouko; Markkula, Gustav; Victor, Trent.

In: Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, Vol. 221, No. 9, 2007, p. 1027-1040.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Kutila, Matti

AU - Jokela, Maria

AU - Mäkinen, Tapani

AU - Viitanen, Jouko

AU - Markkula, Gustav

AU - Victor, Trent

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AB - Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver’s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver’s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80% success in detecting visual distraction and a 68–86 % success in detecting cognitive distraction, which are satisfactory results.

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