AUTHOR=Weirich Christopher , Lin Yandan , Khanh Tran Quoc TITLE=Evidence for human-centric in-vehicle lighting: Part 2—Modeling illumination based on color-opponents JOURNAL=Frontiers in Neuroscience VOLUME=Volume 16 - 2022 YEAR=2022 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.969125 DOI=10.3389/fnins.2022.969125 ISSN=1662-453X ABSTRACT=
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 (