title: Using external cortical EEG characteristics, visual perception and cognitive emotional feedbacks to investigate human-centric interior lighting for autonomous vehicles
Creators: name: Weirich, Christopher
Publisher: name: UNSPECIFIED
Year: 2023

description: Following the prediction published by the United Nations, until 2050, 68% of the world population prefers to live in cities. Therefore, more large- and megacities will be globally established to carry the growing flow of people. In addition, the current transformation within the automotive industry from manual driving to full autonomous driving with robocars plays a major role in this sociological context. If fully developed by the latest technology, driving will become a transportation process based on self-driving vehicles and therefore the importance of the vehicle interior will increase as a new third living space. Studies were performed to identify photometric limits for driving applications or combined in-vehicle lighting with driving assistant systems. In the field of non-visual lighting, blue-enriched white light was applied aiming to reduce the level of driving fatigue that was only successful under monotonous driving conditions. Furthermore, psychologically evaluated, ambient lighting was able to improve attraction, orientation and vehicle safety. However, the application of light inside this new space is until now primarily driven by vehicle designers and was less defined from vehicle occupant's point of view. In this context, evidence from human vision and neuroscience is necessary to develop understandings of in-vehicle lighting preferences and connections between the exterior and interior illumination since these relationships were less researched. Therefore, to close this research gap four systematical research studies were conducted investigating lighting in the context of vehicle-signaling and illumination by combining approaches of subjective psychophysical and objective neuroscientific study designs to create development guidelines and expert knowledge for modern human-centric vehicles. At first, in the context of signaling, behavioral dependencies were found in the fields of light color preferences, light-mood correlations and light-position preferences. There, ten investigated lighting colors were classified in three categories with a high ability either to polarize, to achieve a high level of acceptance or merge the preferences of study participants from China and Europe. Furthermore, only in the investigated European group, a strong color-attention association was established which was missing for the Chinese group. Second, in the context of white light illumination combined with psychological attributes, strong relationships between external driving scenes and in-vehicle lighting settings were discovered. By varying the surrounding between different time and location settings, characterized as darker, brighter, monotonous and interesting, and transforming presented 360° sRGB renderings into the CIE~CAM16 perceptional color space, three development guidelines were discovered. First, no lightness differences should exist between external and internal areas. Next, the level of chroma should be enhanced for darker interesting scenes by following the law of Hunt and the averaged hue angle of the inner vehicle scene and the outer vehicle scene should be equally perceived. Thus far, it can be concluded that a mixed ratio of cooler and warmer white tones connected with a mixed level of focused and spatial light distributions performed best compared to other lighting settings in several psychological dimensions rated as semantic differentials. However, a deeper understanding about this correlation was missing, which was investigated in the third and fourth study of this dissertation. Cortical activities were recorded by electroencephalography and fundamental cortical correlations between photoreceptor activities and contrast changes in hue angle, chroma and lightness were established including a cortical color space, significantly related to the CIE LMS color space, by classifying 20 cortical features with support vector machines and optimizing their correlations with genetic algorithms. Furthermore, relationships were created between strong positive and negative emotions with single cortical signal features based on strong emotional images. Finally, the identified cortical emotional space was extended with human eye gaze data to model preferences based on in-vehicle lighting variations. To conclude, a better understanding was discovered that vehicle internal and external lighting must be integrated with each other based on the guidelines and expert knowledge discovered in this thesis. Therefore, in a modern human-centric in-vehicle lighting system, lighting and sensing are strongly connected with each other to provide people with an increased level of perceptual quality. Interdisciplinary, a fundamental understanding was established to bridge classical color science with neuroaesthetics for the next level of visual perception.