Development of a Nighttime Visual Performance Model by Examining Distributions of Detection Distances


Report (964.37 KB)
Downloads: 55

TR Number



Journal Title

Journal ISSN

Volume Title



Modeling the visual performance of drivers at night is complex. In addition to factors like luminance, contrast, observer age, and object size, research has shown that the motion of the object and the expectancy of the observer play an important role in the observer’s ability to detect an object on the roadway at night. Thus, it is important for a visual performance model to account for these factors. However, accounting for these factors could result in highly complex models, as accurately measuring driver expectancy and attention is difficult. A probabilistic approach to modeling nighttime driver visual performance could offer promise. In a probabilistic modeling approach, the variable of interest is treated as a random variable and the probability distribution of this variable is studied as a response to different conditions. In the case of night driving, we propose to use the detection distance of an object (such as a pedestrian) as the variable of interest. Detection distance is a measure of the reaction time of the driver. By studying the distribution of detection distances of objects under different lighting conditions, we can accurately understand the change in the detection probability of an object as a driver approaches an object. The current report had two goals. The first goal was to test if the detection distance distributions are accurately defined by the Weibull distribution. The second goal was to understand how different light levels affect the detection distance distributions of a child-sized mannequin. This was accomplished by performing a distribution analysis involving fitting a Weibull distribution to the detection distance data. The distribution fit will indicate how parameters like shape and scale vary across different conditions and their practical impacts on driver visual performance. The results of the study showed that the Weibull distribution could be used to fit the detection distance data, and that changing the light level definitely influenced the parameters of the distribution. An increase in light level increased the scale parameter and caused the detection distance distribution to stretch out from the pedestrian’s location. The results of the study also showed that both the scale and shape parameters could be used to compare the effectiveness of different lighting systems or interventions. The survivor functions of the detection distance data from the fitted Weibull distribution could be used to compare the effectiveness of a lighting system or a countermeasure by calculating the percentage of the population that detected the pedestrian from a distance greater than the stopping sight distance.



transportation, safety, probabilistic modeling, Weibull distribution, road design and lighting, illumination