Illumination preference models are usually defined in a static scenery, rating common-colored objects by a single scale or semantic differentials. Recently, it was reported that two to three illumination characteristics are necessary to define a high correlation in a bright office-like environment. However, white-light illumination preferences for vehicle-occupants in a dynamic semi- to full automated modern driving context are missing. Here we conducted a global free access online survey using VR engines to create 360° sRGB static in-vehicle sceneries. A total of 164 participants from China and Europe answered three levels in our self-hosted questionnaire by using mobile access devices. First, the absolute perceptional difference should be defined by a variation of CCT for 3,000, 4,500, and 6,000 K or combinations, and light distribution, either in a spot- or spatial way. Second, psychological light attributes should be associated with the same illumination and scenery settings. Finally, we created four driving environments with varying external levels of interest and time of the day. We identified three key results: (1) Four illumination groups could be classified by applying nMDS. (2) Combinations of mixed CCTs and spatial light distributions outperformed compared single light settings (p < 0.05), suggesting that also during daylight conditions artificial light supplements are necessary. (3) By an image transformation in the IPT and CAM16 color appearance space, comparing external and in-vehicle scenery, individual illumination working areas for each driving scenery could be identified, especially in the dimension of chroma-, partially following the Hunt-Effect, and lightness contrast, which synchronizes the internal and external brightness level. We classified our results as a starting point, which we intend to prove in a follow-up-controlled laboratory study with real object arrangements. Also, by applying novel methods to display high fidelity 360° rendered images on mobile access devices, our approach can be used in the future interdisciplinary research since high computational mobile devices with advanced equipped sensory systems are the new standard of our daily life.

Today, up to hundreds of RGB and W-LEDs are positioned in a vehicle’s interior context and are able to be individually controlled in intensity, color and sequence. However, which kind of illumination distracts or supports car occupants and how to define such a modern illumination system is still under discussion and unknown. For that, first a definition for an in-vehicle lighting system is introduced. Second, a globally distributed study was performed based on a free-access online survey to investigate in-vehicle lighting for visual signaling within 10 colors, eight positions and six dynamic patterns. In total, 238 participants from China and Europe rated color preferences, color moods, light-position preferences, differences between manual and autonomous driving and also different meanings for dynamic lighting patterns. Out of these, three strong significant (p < 0.05) color preference groups were identified with a polarized, accepted or merged character. For the important driving-signaling mood attention, we found a significant hue dependency for Europeans which was missing within the Chinese participants. In addition, we identified that light positioned at the door and foot area was globally favored. Furthermore, we evaluated qualitative results: men are primarily focusing on fast-forward, whereas women paid more attention on practical light usage. These findings conclude the need for a higher lighting-car-occupant adaptation in the future grounded by deeper in-vehicle human factors research to achieve a higher satisfaction level. In interdisciplinary terms, our findings might also be helpful for interior building or general modern cockpit designs for trains or airplanes.

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This paper investigated colour discrimination based on current available indexes, and predictors were proposed for global and targeted colour scenarios. Thirty participants conducted the Farnsworth–Munsell 100 hue test under 21 lighting conditions. The experiment results revealed that the distribution of total error score (TES) and adjusted total error score (TESadj) showed arc shapes centred on the optimal point (100, 100) in both Rf –Rg and colour rendering index–gamut area index coordinate systems. On this basis, global colour discrimination scores, CDS1 and CDS2, based on the colour fidelity and colour gamut characteristics, were proposed. The results demonstrated that both CDS1 ( r = 0.82, p < 0.001) and CDS2 ( r = 0.81, p < 0.001) provided good linear correlations with TES, and CDS1 ( r = 0.75, p < 0.001) and CDS2 ( r = 0.77, p < 0.001) also exhibited a good linear correlation with TESadj. Furthermore, the global colour gamut was divided into four local colour spaces (red–yellow, yellow–green, green–blue and blue–red), and the CDSs in the local gamut (CDSlocal and CDSadj,local) were constructed using the local colour properties, including Rcs,local, Rhs,local and Rf,local. The linear regression results demonstrated that CDSlocal ([Formula: see text]) and CDSadj,local ([Formula: see text]) can be effective colour discrimination predictors for the targeted colour scenarios.