Detectability of Nonuniformities in Automotive Exterior Displays: A Study for Model Validation
Detectability of Nonuniformities in Automotive Exterior Displays: A Study for Model Validation
The automotive industry is experiencing significant growth in the use of exterior displays, driven by advances in technology and the increasing demand for innovative ways to enhance vehicle aesthetics and functionality. This trend is benefiting from topics such as digitalization, safety and customization. In particular, the use of autonomous vehicles creates a wide range of potential applications for this technology. For observers the uniformity of the display is an important indicator for the perceived quality. Therefore, nonuniformities can disturb the observer's perception, which makes reliable detection important. To evaluate these nonuniformities objectively and reproducibly, the model of Schier et al. offers a promising approach to detect nonuniformities by predicting the visibility of contrasts in luminance distributions 1. The model's general approach is to simulate human contrast vision perception by taking the environment into account. However, the model has not yet been compared with measurement data from automotive applications. This paper describes a study to validate the previously mentioned model for the use case of exterior displays. For this purpose, an extensive test person study was conducted to determine the perception threshold of different contour shapes. For the study setup the adjustment method was chosen to determine the contrast threshold. The study was conducted with 32 test subjects and was carried out with an automotive Mini-LED display, on which specific nonuniformities were displayed. Seven different contour shapes were analyzed during the study. The results show that the shape of the contour plays a crucial role and significantly changes the perception threshold. The study design presented in the paper is suitable for determining the contrast threshold on automotive exterior displays. The results of the study can be used to verify the model and to evaluate the model performance.

