A Vehicle Dashboard Dataset Towards Visual Complexity Design

With the expansion of the in-vehicle information system features, there are more and more new elements integrated into modern dashboards, which may lead to an increase in their visual complexity and additionally threatens drivers’ safety. To establish the cognitively efficient dashboards, protect driving safety and performance, it is essential for researchers and designers to identify what objective features increase the visual complexity of dashboard. However, due to various reasons, useful experiment materials of modern dashboards are rare for researchers and designers. To fill the gap, present study collected 1400 images of vehicle dashboards from 170 different brands online, then filtered, cropped the poor-quality images, and used the super-resolution technique to improve the images’ resolution with a self-made Python program. After pre-processing and evaluating objective visual complexity (OVC), present study recruited 160 participants to rate image’s subjective visual complexity (SVC), and finally form a vehicle dashboard dataset of 100 high-quality images with both SVC and OVC scores. Present study also conducted eye-track experiments to examine the validity of dataset. The result showed that 1) dashboards with high SVC would increase participants’ information searching time, deteriorate their searching accuracy; 2) In terms of gaze duration, top three influential objective features are: maps or vehicle state models, warning icons, chunks of information. In short, present study provide a useful vehicle dashboard dataset towards visual complexity design for researchers and designers, which may also be helpful for user-experience, ergonomics, or Human vehicle interaction research.