Smart integrative lighting systems aim to support human health and wellbeing by capitalising on the light-induced effects on circadian rhythms, sleep, and cognitive functions, while optimising the light’s visual aspects like colour fidelity, visual comfort, visual preference, and visibility. Metameric spectral tuning could be an instrument to solve potential conflicts between the visual preferences of users with respect to illuminance and chromaticity and the circadian consequences of the light exposure, as metamers can selectively modulate melanopsin-based photoreception without affecting visual properties such as chromaticity or illuminance. This work uses a 6-, 8- and 11-channel LED luminaire with fixed illuminance of 250 lx to systematically investigate the metameric tuning range in melanopic equivalent daylight illuminance (EDI) and melanopic daylight efficacy ratio (melanopic DER) for 561 chromaticity coordinates as optimisation targets (2700 K to 7443 K ± Duv 0 to 0.048), while applying colour fidelity index R_f criteria from the TM-30-20 Annex E recommendations (i.e. R_f $$\ge$$≥ 85, R_f,h1 $$\ge$$≥ 85). Our results reveal that the melanopic tuning range increases with rising CCT to a maximum tuning range in melanopic DER of 0.24 (CCT: 6702 K, Duv: 0.003), 0.29 (CCT: 7443 K, Duv: 0) and 0.30 (CCT: 6702, Duv: 0.006), depending on the luminaire’s channel number of 6, 8 or 11, respectively. This allows to vary the melanopic EDI from 212.5–227.5 lx up to 275–300 lx without changes in the photopic illuminance (250 lx) or chromaticity ( $$\Delta u'v'$$Δ u ′ v ′ $$\le$$≤ 0.0014). The highest metameric melanopic Michelson contrast for the 6-, 8- and 11-channel luminaire is 0.16, 0.18 and 0.18, which is accomplished at a CCT of 3017 K (Duv: − 0.018), 3456 K (Duv: 0.009) and 3456 K (Duv: 0.009), respectively. By optimising ~ 490,000 multi-channel LED spectra, we identified chromaticity regions in the CIExy colour space that are of particular interest to control the melanopic efficacy with metameric spectral tuning.

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.

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.

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.