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Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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Horizontal curves are essential in highway and railroad design, ensuring smooth and safe transitions between straight path segments, or tangents. These curves allow vehicles to maintain speed without abrupt changes, minimizing accidents and improving travel efficiency.A horizontal curve is typically defined by its geometric relationship to two tangents that meet at an intersection point (P.I.), where a simple curve is introduced to connect them. The back tangent refers to the initial tangent...
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Vertical curves are parabolic transitions that connect different grades on highways and railroads, ensuring a smooth alignment between back and forward tangents. The back tangent represents the initial grade, while the forward tangent defines the subsequent grade. These curves can be symmetrical, with equal tangent lengths, or nonsymmetrical, with varying lengths. The key points defining a vertical curve include the Point of Vertical Intersection (P.V.I.), where the tangents meet; the Point of...
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Vertical curves are essential in roadway design because they provide smooth transitions between varying roadway grades. Designing vertical curves involves calculating intermediate elevations and identifying the curve's highest or lowest point, which is essential for optimal roadway performance.Intermediate elevations on a vertical curve are determined using the tangent offset method. This method considers the initial elevation at the start of the curve, the grades, and the curve's geometry. The...
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Lane heading difference: An innovative model for drowsy driving detection using retrospective analysis around curves.

Drew M Morris1, June J Pilcher1, Fred S Switzer1

  • 1Department of Psychology, Clemson University, Clemson, SC, USA.

Accident; Analysis and Prevention
|April 23, 2015
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Sleepy drivers cause accidents. A new lane heading difference metric effectively detects drowsy driving, outperforming other methods by analyzing vehicle behavior variability.

Keywords:
Driver assistance systemDriving performanceDrowsy driver detectionLane deviationTransportation safetyVehicle heading

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Area of Science:

  • Road safety
  • Human factors engineering
  • Automotive technology

Background:

  • Drowsy driving is a significant cause of road accidents.
  • Existing vehicle monitoring technologies can detect variability in drowsy drivers' behavior.
  • Previous methods for detecting drowsy driving have limitations.

Purpose of the Study:

  • To develop and validate a novel methodological approach for detecting drowsy driving.
  • To improve the effectiveness of drowsy driving detection using a vehicle heading difference metric.
  • To compare the new metric against commonly used methods for identifying driving impairment.

Main Methods:

  • Twenty participants underwent simulated highway driving sessions after sleep deprivation.
  • Vehicle behavior was analyzed using lateral lane position and vehicle heading difference variability.
  • Two statistical methods were employed to analyze driving data.
  • Fatigue was assessed through measures of reaction time, attention, and oculomotor movement.

Main Results:

  • The vehicle heading difference metric, using the absolute value of raw data, demonstrated superior detection of driving variability compared to other statistical models.
  • Fatigue measures confirmed increased reaction time, attention lapses, and decreased oculomotor reactivity over the night.
  • The new metric accurately identified driving impairments at lower levels of fatigue.

Conclusions:

  • The absolute value of lane heading difference is a promising metric for enhanced drowsy driving detection.
  • This method offers improved accuracy in identifying driving impairments caused by fatigue.
  • The findings suggest potential for real-world application in vehicle safety systems.