Multi-channel LED luminaires offer a powerful tool to vary retinal receptor signals while keeping visual parameters such as color or brightness perception constant. This technology could provide new fields of application in indoor lighting since the spectrum can be enhanced individually to the users' favor or task. One possible application would be to optimize a light spectrum by using the pupil diameter as a parameter to increase the visual acuity. A spectral-and time-dependent pupil model is the key requirement for this aim. We benchmarked in our work selected Land M-cone based pupil models to find the estimation error in predicting the pupil diameter for chromatic and polychromatic spectra at 100 cd/m 2. We report an increased estimation error up to 1.21 mm for 450 nm at 60-300 s exposure time. At short exposure times, the pupil diameter was approximately independent of the used spectrum, allowing to use the luminance for a pupil model. Polychromatic spectra along the Planckian locus showed at 60-300 s exposure time, a prediction error within a tolerance range of ± 0.5 mm. The time dependency seems to be more essential than the spectral dependency when using polychromatic spectra. The pupil aperture is an essential factor in photometric and visual investigations because of its direct influence on both retinal illumination and the retinal image quality 1. A smaller pupil diameter can ensure a larger depth of field 2 and achieve a decrease of optical aberrations 3,4 , which has positive effects on the visual acuity of the eye 4,5. Visual acuity is relevant in the interior lighting of workplaces or production facilities since an enhanced visual performance leads to fewer accidents or human injuries 6. Various studies have shown that the optimal pupil diameter is approximately between two and three millimeters for visual tasks in the photopic luminance range 1,4,7-11. With today's technology of multi-channel LED luminaires, it is possible to optimize artificial light spectra to influence the pupil aperture, color perception, brightness perception or other lighting metrics 12,13. The number of narrow-band light-emitting diodes in such a system determines the degree of freedom, which allows keeping specific parameters constant while changing others. The first step to actively optimize the pupil aperture through illumination without influencing other image-forming vision parameters such as brightness or color perception is the construction of an accurate model which predicts the spectral and time-dependent pupil diameter. Such a model can be used in a heuristic or gradient-based optimization procedure as an objective or constraint function to design the desired light spectrum for visual tasks. Eight empirical models are proposed in the literature with different dependent parameters and test conditions. The most famous models are from Holladay 14 , Crawford 15 , Moon and Spencer 16 , De Groot and Gebhard 17 , Stanley and Davies 18 , Barten 19 and Blackie and Howland 20. In 2012, Watson and Yellot 21 reviewed these pup...

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In 1931, the CIE published and standardised the photopic luminous efficiency function. Based on these standardized curves,luminous flux in lumens, luminance in cd/m2, and illuminance in lux are determined by an integral of these curves and theincident light spectra in photometers and are considered as a physical brightness. However, human brightness perception isnot only weighted by this simple determination, but is a more complicated combination of all L-cones, M-cones, S-cones, rods,and later ipRGCs, which was partly illustrated by the equivalent brightness of Fotios et al. with the correction factor (S/V)0.24.Recently, new researches have mentioned the role of ipRGCs in the human brightness perception. However, it is still unclearhow these signal components of the human visual system are involved in overall human brightness perception. In this work, thehuman brightness perception under photopic conditions was studied by experimenting with 28 subjects under 25 different lightspectra. These spectra were varied not only in brightness but also in spectral geometry. In this way, the contributions of thesignal components can be investigated. Subsequently, an optimization process was performed with the obtained database. Theresults show that not only the photopic component, but also the S-cones and ipRGC play their role, although it is smaller. Thus,the visually scaled brightness model based on the database optimization was constructed with not only illuminance but alsoS-cones and ipRGC with R2 of 0.9554 and RMSE of 4.7802. These results are much better than the Fotios-based brightnessmodel with only S-cones (R2 = 0.8161, RMSE = 9.7123) and the traditional model without S-cones and ipRGC (R2 = 0.8121,RMSE = 9.8171). It also suggests that a "blue-sensitive" signal (S or G=ipRGC or their combination) should be given seriousenough attention in the human brightness perception, and a more comprehensive study is needed to investigate it more deeply.

Modern indoor lighting faces the challenge of finding an appropriate balance between energy consumption, legal requirements, visual performance, and the circadian effectiveness of a spectrum. Multi-channel LED luminaires have the option of keeping image-forming metrics steady while varying the melanopic radiance through metamer spectra for non-visual purposes. Here, we propose the theoretical concept of an automated smart lighting system that is designed to satisfy the user’s visual preference through neural networks while triggering the non-visual pathway via metamers. To quantify the melanopic limits of metamers at a steady chromaticity point, we have used 561 chromaticity coordinates along the Planckian locus (2700 K to 7443 K, ±Duv 0 to 0.048) as optimisation targets and generated the spectra by using a 6-channel, 8-channel, and 11-channel LED combination at three different luminance levels. We have found that in a best-case scenario, the melanopic radiance can be varied up to 65% while keeping the chromaticity coordinates constant (Δu′v′≤7.05×10−5) by using metamer spectra. The highest melanopic metamer contrast can be reached near the Planckian locus between 3292 and 4717 K within a Duv range of −0.009 to 0.006. Additionally, we publish over 1.2 million optimised spectra generated by multichannel LED luminaires as an open-source dataset along with this work.

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 human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10–2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT.

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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.