The illumination of registration plates poses challenges for lighting technology. To achieve the wide and steep angles required for homogeneous illumination of the license plate, optics are needed to refract and reflect light. In this work, registration plate lamps with different components are designed. After the construction, the concepts are tested for their limits in terms of position and manufacturability. The
entire work is carried out at the company CREAT GmbH in Ingolstadt. All developments in the field of registration plate lighting are subject to the UNECE and country-specific regulations, which must be considered in the development of the lights.[1] Some vehicles currently available on the automotive market are measured to classify and compare the different lamp systems. From the average values, the lamps are designed. Adjustments are repeatedly made to the lighting device to achieve the most
homogeneous illumination of the registration plate. As soon as the lights meet the legal requirements, the geometric limits of the lighting device are defined in relation to the plate. As there are different polymers to produce optics, these are simulated with their refractive indices and tested for their effects on the light image.
The objective of this study was to develop an experimental framework that allows research on whether the combined use of a virtual reality (VR) driving simulator, electroencephalography signals collected during driving tests and designed attentional paradigms could be applied to assess the performance of affective warning sounds for re-channelling driver’s attention in certain driving situations. Three VR driving scenarios with different stimuli and two driving tasks were designed and implemented. Acquisition of driver’s physiological data and behavioural metrics were integrated to be gathered synchronously. In addition, an experimental protocol was designed for the assessment of the impact of affective warning sounds. The technical feasibility of the framework was evaluated with the results showing that the proposed framework enables the data acquisition and analysis for a variety of warning stimuli. After successful technical deployment, a study with experimental subjects will follow to evaluate the effectiveness of affective warning sounds.
This study aims to compare two different implementations of speech emotion recognition models.The emphasis is directed towards evaluating their efficacy in capturing and characterizing dialogues
portrayed by actors within a film scene to create suitable musical intervals. The goal of the overarching research intends to derive indications to enhance the compositional process of film scores by recognizing the emotion in a particular scene. Based on established deep learning models, the study delves into the exploration of two distinct emotion classification metrics: The Six Emotion Prediction and the Valence/Arousal/Dominance Prediction. To facilitate a comparative analysis, a preliminary study an a following survey is deployed. The preliminary study confirms a significant difference in the generated MIDI data. For this reason, a survey is essential to detect the better fitting algorithm. Participants are tasked to rate the affective suitability of eight generated interval sequences to the corresponding film scenes. The Suitability is verified quantitatively using a bidirectional rating system. Both model assessments are conducted within a uniform sound design, thus ensuring unbiased conditions for evaluation. Upon a thorough examination of our extensive analysis, a preference for method A becomes increasingly evident.