Research on the Intelligent Modeling Design of a Truck Front Face Driven by User Imagery

Abstract

The design of the front face of a truck can directly affect the user’s sensory evaluation of the vehicle. Therefore, based on Kansei Engineering theory and deep learning technology, this paper proposes an intelligent design method for the rapid generation of truck front face modeling solutions driven by user images. First, through Kansei Engineering’s relevant experimental methods and scientific data analysis process, the emotional image of the truck’s front face is deeply excavated and positioned, and the corresponding relationship between the characteristics of the truck’s front face and the user’s emotional image cognition is explored. Then, we used the generative confrontation network to integrate the user’s emotional image of the front face of the truck into the intelligent and rapid generation process of the new design scheme of the front face of the truck. Finally, the physiological data of the Electroencephalogram (EEG) experiment are used to evaluate the degree of objective matching between the generated modeling design scheme and the expected image. The purpose of this research is to improve the efficiency, reliability, and intelligence level of truck front face design, and to achieve a more personalized, precise, and high-quality design. This helps to improve the conformity of the modeling design scheme under specific image semantics.