title: Data fusion to develop a driver drowsiness detection system with robustness to signal loss.
abstract: This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs.
authors.affiliation: Khaje Nasir Toosi University of Technology, Faculty of Mechanical Engineering, Tehran 19991-43344, Iran. S.Samiee@tugraz.at.
authors.forename: Sajjad
authors.initials: S
authors.lastname: Samiee
authors.affiliation: Khaje Nasir Toosi University of Technology, Faculty of Mechanical Engineering, Tehran 19991-43344, Iran. Azadi@kntu.ac.ir.
authors.forename: Shahram
authors.initials: S
authors.lastname: Azadi
authors.affiliation: Khaje Nasir Toosi University of Technology, Faculty of Mechanical Engineering, Tehran 19991-43344, Iran. Kazemi@kntu.ac.ir.
authors.forename: Reza
authors.initials: R
authors.lastname: Kazemi
authors.affiliation: Khaje Nasir Toosi University of Technology, Faculty of Mechanical Engineering, Tehran 19991-43344, Iran. Nahvi@kntu.ac.ir.
authors.forename: Ali
authors.initials: A
authors.lastname: Nahvi
authors.affiliation: Graz University of Technology, Automotive Engineering Faculty, Inffeldgasse 11, Graz 8010, Austria. Arno.Eichberger@tugraz.at.
authors.forename: Arno
authors.initials: A
authors.lastname: Eichberger
mesh_terms: D001334:Automobile Driving; D004569:Electroencephalography; D006801:Humans; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D012890:Sleep; D012894:Sleep Stages / Q000502:physiology*