In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells ($ipRGCs$) and their function, which were first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance ($mEDI$), melanopic daylight (D65) efficacy ratio ($mDER$), and four other parameters. Due to the importance of $mEDI$ and $mDER$, this work synthesizes a simple computational model of $mDER$ as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient $R^2$ of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the $mDER$ model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the $mEDI$ calculated directly from the spectra and that obtained by processing the RGB sensor and applying the $mDER$ model reached ± 3.3% after matrix transformation and illuminance processing combined with the successful $mDER$ calculation model. This result opens the potential for low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate for the non-visual effective parameter $mEDI$ using daylight and artificial light in indoor spaces. The goal of the research on $RGB$ sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other research.

Although research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. Here we propose a deep learning-driven concept of a pupil model, which reconstructs the pupil’s time course either from photometric and colourimetric or receptor-based stimulus quantities. By merging feed-forward neural networks with a biomechanical differential equation, we predict the temporal pupil light response with a mean absolute error below 0.1 mm from polychromatic (2007 $$\pm$$± 1 K, 4983 $$\pm$$± 3 K, 10,138 $$\pm$$± 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m². This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour.

Additional signaling devices for highly automated vehicles (AVs) that can communicate their driving state to other road users can simplify the integration process in existing road traffic. This paper presents the results of an international, virtual reality-based study conducted in China, South Korea and the USA in which subjects assume the role of a pedestrian and are placed in direct encounter situations with an AV in a parking lot. A novel communication interface consisting of three displays is attached to the AV's front and used to show additional information about its driving state. In total, three encounter scenarios are investigated: the AV approaches from the left, front and right outside of the pedestrian's line of sight. The influence of different symbol types on the subject's moving behavior, recognition of intention and perceived safety is investigated. The results show that additional signals ensure a better perception of the AV's intention and increase the perceived safety. The moving behavior of subjects is significantly changed when additional signals are used during driving tasks compared to the same tasks without such signals. The change of moving behaviour is similar in encounter situations where the AV approaches from the left and front but differs in encounter situations from the right. These results could equally be proven for all nationalities, which shows that a uniform, international solution for additional signaling devices of highly automated vehicles is possible.

The unique radiative, photometric and colorimetric characteristic of a light-emitting diode is derived from its spectral power distribution. Modeling such characteristics with respect to the forward current, temperature or operating time has been subject of various studies. Deriving a simple analytical model, however, is not trivial due to the unique emission pattern varying with different types and technologies of light emitting diodes. For this purpose, curve fitting multiple superimposed Gaussian probability density functions to the spectral power distribution is a common approach. Despite excellent $R^{2}$ goodness of fit results, significant deviations within the photometric and colorimetric parameters, such as luminous flux or chromaticity coordinates, are observed. In addition, most studies were conducted on a small sample set of very few different spectral power distributions. This work provides a comprehensive comparison and evaluation of 19 different (superimposed) probability density function based models provided by the literature tested on a total of 15 different spectral power distributions of monochromatic blue, green and red light-emitting diode as well as phosphor-converted spectra of lime, purple and white samples with different correlated color temperatures. All models were evaluated by means of their coefficient of determination, radiant flux, chromaticity coordinate deviation and Bayesian Information Criterion. This study shows that a superimposed (split) Pearson VII model is able to outperform the commonly used Gaussian model approach by far. In addition, an application example in regard of forward current dependence is given to prove the proposed approach.