Determining When an Object Enters the Headlight Beam Pattern of a Vehicle

Paper #:
  • 2013-01-0787

Published:
  • 2013-04-08
DOI:
  • 10.4271/2013-01-0787
Citation:
Muttart, J., Bartlett, W., Kauderer, C., Johnston, G. et al., "Determining When an Object Enters the Headlight Beam Pattern of a Vehicle," SAE Technical Paper 2013-01-0787, 2013, https://doi.org/10.4271/2013-01-0787.
Pages:
29
Abstract:
A method for evaluating a driver's response in a nighttime crash scenario is offered. A pedestrian can be said to be within the headlight beam when the line representing the shape of a headlight beam equals the pedestrian approach vector. This method is based upon headlight beam mapping and the illumination necessary for drivers to recognize non-illuminated objects on an unlit road at night. The most notable information gained through this research is to be able to correlate headlight illumination with driver response distances. From 25 nighttime driver response distance experiments, information was gathered from many of the original authors. This information includes position left or right, headlight type, lighting, movement of the object or pedestrian, and the position (standing, slumped or laying). Also recorded were methodology variable such as experiment type, experiment type (closed course or road), whether the observer was driving and if the observer was asked to be certain of the target, or to respond as soon as possible. The headlight mapping and the experimental results allowed for the calculation of the beam size necessary before participants responded to pedestrians and objects of various shapes (black to white to retroreflective). Equations are given for various applicable beam patterns. The distance at which the headlight beam equation equals the pedestrian or object vehicle-fixed approach vector will be the distance at which the pedestrian or object entered the headlight beam. Secondary analyses are suggested as a means of addressing other variables that influence nighttime driver responses. The factors that influence a driver's ability to recognize an object at night and the limitations of these models will be addressed.
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