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Drowsy and Fatigued Driving Problem Significance and Detection Based on Driver Control Functions

  • Azim Eskandarian
  • Ali Mortazavi
  • Riaz Akbar Sayed

Abstract

Drowsy and fatigue driving is a major transportation safety concern and is responsible for thousands of accidents and numerous fatalities every year. The resulting harms of drowsy/fatigue driving could be even higher among commercial vehicles. Drowsy driving crashes are usually of high severity due to the drivers’ significant loss of control, often leading to unpredicted vehicle trajectory and no braking response. Reliable safety systems are needed to mitigate these crashes. The most important challenge is to detect the driver’s condition sufficiently early, prior to the onset of sleep, to avoid collisions.

Various detection methods have been proposed by researchers and a few systems are available in the commercial market. In general, drowsiness detection methods fall into two major categories of monitoring physiological and physical conditions of the drivers and monitoring vehicle-related variables based on driver control functions that correlate with the driver’s level of drowsiness. Each method has its advantages and shortcomings. A reliable detection method needs to be integrated with a safety system which may include advisory warning, semi-control, or full control of vehicle, i.e., braking and steering to achieve safe conditions. The type and intensity of warning or control should also be carefully selected and are discussed in another chapter.

This chapter first reviews the statistical significance of the crash data due to drowsiness and fatigue conditions. Then, the issues concerning various detection methods are discussed. Detection systems based on driver control functions are mainly discussed in this chapter. The concepts and approaches presented in this section are from a comprehensive literature review including the author’s past research; they can guide the development of safety systems for a passenger or commercial vehicles.

Keywords

Empirical Mode Decomposition Truck Driver Intrinsic Mode Function Steering Wheel Steer Wheel Angle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  • Azim Eskandarian
    • 1
  • Ali Mortazavi
    • 2
  • Riaz Akbar Sayed
    • 3
  1. 1.Center for Intelligent Systems ResearchThe George Washington UniversityWashingtonUSA
  2. 2.Partners for Advanced Transportation Technology (PATH)University of CaliforniaBerkeleyUSA
  3. 3.Mechanical DepartmentNWFP University of Engineering and TechnologyPeshawarPakistan

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