The effect of fog on detection of driving hazards after dark

Abstract

The presence of fog leads to an increase in road traffic accidents. An experiment was carried out using a scale model to investigate how the detection of hazards in peripheral vision was affected by changes in luminance (0.1 cd/m 2 and 1.0 cd/m 2 road surface luminance), scotopic/photopic (S/P) ratio (0.65 and 1.40) and fog density (none, thin and thick). Two hazards were used, a road surface obstacle and lane change of another vehicle. Increasing luminance, and reducing from thick to thin fog, led to significant increase in detection rate and a reduction in reaction time, for both types of hazard. The effect of a change in S/P ratio was significant only when measuring detection of the surface obstacle using reaction times, under the thick fog, with an increase in S/P ratio leading to a shorter reaction time.

The transition between lit and unlit sections of road and detection of driving hazards after dark

“…Reported here is a summary of the apparatus: a more complete description is reported elsewhere. 8,9 Figure 1 shows the test chamber, this having dimensions approximately 5 m long, 2.5 m wide and 1.5 m high. A driver-participant sitting outside the chamber viewed the interior through an acrylic windscreen (Figure 2), placing their horizontal sightline approximately 150 mm above the chamber floor (the road surface), with the top of the windscreen being 200 mm above this surface.…”

“…Specifically, the 'No fog' condition of the fog experiment 9 was equivalent to the baseline period of the transition experiment during 'On-Off' trials, i.e. when the overhead lights were on during the baseline period.…”

Road lighting research for drivers and pedestrians: The basis of luminance and illuminance recommendations

“…The significance of SPD for the detection of peripheral targets may, however, depend on the characteristics of the target and the independent variable used to quantify detection. 98 Researchers have attempted to develop a model of lighting based on visual performance. Hills considered the visibility of tail lights, road surface obstacles and pedestrians, and characterised this using the luminance difference between the target and background and the visual size of the target.…”

Power Analysis, Sample Size, and Assessment of Statistical Assumptions—Improving the Evidential Value of Lighting Research

“…The nonnormal data are based on a positively skewed exGaussian distribution. This type of distribution is frequently found in reaction time data (Palmer et al 2011), and reaction times are commonly used as a response measure in lighting research (e.g., Cengiz et al 2015;Fotios et al 2017;He et al 1997). The simulated nonnormal data have been produced using the "retimes" package (Massidda 2013), with the same parameters as for the normal data (sample size = 100, mean = 5, standard deviation = 1.5) and with the additional tau parameter, representing the exponential decay of the distribution tail, set at 4.…”