The relationships between automobile head-up display presentation images and drivers’ Kansei

Abstract

This study explored the relationships between automobile head-up display (HUD) presentation image designs and drivers’ Kansei, using quantitative and qualitative analysis. There were two major stages in this study. The objective of the first stage was to find representative Kansei factors from a large semantic space, using factor analysis and cluster analysis. In the second stage, a prediction model for the relationships between the representative Kansei factors and HUD physical image design properties were created, using Quantification Theory Type 1. Results were discussed based on the whole subject population, age differences, and gender differences, respectively. Finally, two existing HUD presentation images on the market were used to test the validity and feasibility of the prediction model, using a one-sample t-test. The results show that our model can successfully predict drivers’ Kansei for a given HUD presentation image. The results can also be used to customize a HUD presentation image which caters to the drivers’ feelings and emotions.