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This article reports on two studies that explored the effects of severe visual challenges on steering performance in visually healthy young drivers. The experiments explored the extent to which induced blur, educed luminance, and reduced visual fields affect drivers' steering performance in a driving simulator. In the first experiment, 10 young drivers (median age 21.2 years) drove at approximately 89 km/h (55 mph) along a curvy roadway while being exposed to blur, luminance, and visual field manipulations. In the second experiment, a different group of 10 young drivers (median age 18.5 years) drove while exposed to 7 visual field sizes (1.7 to 150 degrees). Steering was shown to be sensitive to a reduced field size but not to the blur and luminance challenges. Acuity, on the other hand, was sensitive to the blur and luminance challenges but not to reduced field size. The authors conclude that in healthy young drivers, steering performance is remarkably robust to severe blur and to extremely low luminances. Thus steering performance is preserved at night even when the ability to recognize objects and hazards is not. The authors call for additional research to investigate the other hypothesis hinted at by these studies: that drivers fail to understand the extent to which their visual abilities are degraded at night.

Most young drivers are comfortable driving at night despite the increased danger. The selective degradation hypothesis predicts that drivers' overconfidence at night is a consequence of their ability to steer being preserved even though their ability to recognize details is impaired. To test this hypothesis, ten participants drove a curvy simulated roadway with varying levels of luminance, blur, and visual field size. Steering performance, but not visual acuity, was degraded when the visual field was reduced. Acuity, but not steering performance, was severely degraded by extreme blur and extremely low luminance. These findings support the selective degradation hypothesis, suggest that separate neural mechanisms support visual recognition and visual guidance skills, and emphasize the problems that occur when unexpected low contrast objects are present in the roadway environment at night.

A methodology is introduced to assist in the construction of performance-based occupational vision standards. A simple image discrimination model is first calibrated using stimuli representative of airframe and powerplant cracks. It is then used to predict simulated crack visibility for cracks of different lengths and widths. Visual acuity declines are simulated using a Gaussian blur function on the crack images. Crack width is shown to be a salient cue for crack detection. This modeling technique can generate the data necessary to construct empirically-based occupational vision standards. Future research will validate model predictions with human psychophysical data.