Effects of Visual and Cognitive Load on User Interface of Electric Vehicle - Using Eye Tracking Predictive Technology

Abstract

Purpose - With the increasing integration of car functions, as well as the increasing operation and information on the central control screen, we explored how to improve the user interface, in order to reduce cognitive load and improve reading efficiency. Methodology - This paper applied the neural network-based eye-tracking prediction model to analyze the eye-tracking data of mainstream smart electric vehicle center control screens. Through analyzing and discussing the attention map, clarity map, regions of interest, etc., we assess the usability of user interface and propose design guidelines. Conclusion - In a landscape central control screen, dock bar is more visually significant on the left side. The layout should avoid scattering, the shape of the function card should avoid using long stripes, and the information should not be too concentrated. Important information should be designed with high contrast and distinctive colors, and filled types icons should be used. Important text should be succinct, enlarged, bolded, and not be too dense. Concentrated text is more likely to attract users’ attention, but it will also cause higher cognitive load.