Chromatic adaptation is an extensively studied concept. However, less is known about the time course of chromatic adaptation under gradually-changing lighting. Two experiments were carried out to quantify the time course of chromatic adaptation under dynamic lighting. In the first experiment, a step change in lighting chromaticity was used. The time course of adaptation was well described by the Rinner and Gegenfurtner slow adaptation exponential model [Vision Research, 40(14), 2000], and the adaptation state after saturation differed between observers. In the second experiment, chromatic adaptation was measured in response to two different speeds of lighting chromaticity transitions. An adjusted exponential model was able to fit the observed time course of adaptation for both lighting transition speeds.

Sufficient contrast between road surface and road markings is key for a safe and comfortable driving experience. This calls for a comprehensive and well established contrast (threshold) model, which ideally results in a single contrast threshold value independent of object angular size or road luminance. The contrast threshold model introduced by Adrian is still commonly used in road lighting. More recently, new contrast metrics that also predict supra-threshold contrast visibility have been proposed, but the corresponding visibility thresholds are not yet known. In the present study, participants are presented a rendering of a highway, including road marking arrows of various size and luminance and were asked to indicate the direction of the arrow. The luminance of the road surface, acting as background for the markings, was varied too. Due to the very low luminance values and the very small differences in luminance, measurement accuracy and calibration issues require special attention. The results show good agreement with Adrian's visibility model (R 2 = 0.75) in terms of luminance contrast, background luminance and size. In addition, we used our experimental data to define contrast thresholds for several other existing image based contrast models. Unfortunately, it seems to be impossible to state one unique threshold contrast value independent of object angular size and road luminance.

The Unified Glare Rating (UGR) and the modified version (UGR’) have been developed and widely accepted in multiple standards for measuring the discomfort glare of a luminaire in typical indoor environments; however, a standardized glare metric for non-uniform outdoor luminaires is still missing. In this paper, the possibility to apply UGR and UGR’ to an outdoor residential luminaire with a non-uniform spatial luminance distribution is explored. The luminaire was characterized in a large near-field goniophotometer (NFG) and luminance images were captured at four angles specified in the CIE 232:2019 document. Some practical issues of applying the UGR’ for a non-uniform residential luminaire are discussed, such as selecting the luminous area, the blurring parameter, the viewing angles, and the background luminance. In addition to these practical issues, possible solutions and suggestions are explored, such as a different blurring parameter, viewing angle, and background luminance. In the end, employing a human visual system to evaluate the amount of discomfort glare for both indoor and outdoor applications might be preferred.

To drive safely and comfortably, an adequate contrast between the road surface and road markings is needed. This contrast can be improved by using optimized road illumination designs and luminaires with dedicated luminous intensity distributions, taking advantage of the (retro)reflective characteristics of the road surface and road markings. Since little is known about road markings’ (retro)reflective characteristics for the incident and viewing angles relevant for street luminaires, bidirectional reflectance distribution function (BRDF)-values of some retroreflective materials are measured for a wide range of illumination and viewing angles using a luminance camera in a commercial near-field goniophotometer setup. The experimental data are fitted to a new and optimized “RetroPhong” model, which shows good agreement with the data [root mean squared error (RMSE)<0.13, normalized root mean squared error (NRMSE)<0.04, and the normalized cross correlation ratio (NCC)>0.8]. The RetroPhong model is benchmarked with other relevant (retro)reflective BRDF models, and the results suggest that the RetroPhong model is most suitable for the current set of samples and measurement conditions.