Based on Kansei Engineering, this study explored the relationships between HUD (head-up display) presentation image physical properties and drivers’ Kansei images by quantitative and qualitative analysis. Finally, three existing commercial HUD presentation images on the market were used to verify the accuracy and feasibility of the results. The questionnaire suvery in study was divided into two steps. First, existing HUD presentation images were collected and analyzed. Eighteen new samples, using Taguchi L-18 table, were created, and six out of them were chosen as representative samples. Meanwhile, 32 pairs of Kansei words describing HUD image designs were collected and developed. These Kansei words were then arranged with the six representative samples to create the first-step questionnaire. The results were analyzed using factor analysis and cluster analysis, and the original 32 pairs of Kansei words were classified and reduced into five pairs of final representative Kansei words. The second step was to modify the HUD image samples in the first questionnaire survery and reconstruct them into 18 new samples. Then, the second-step questionnaire was developed by combining the new 18 samples with the five pairs of final Kansei words. The results were analyzed using Quantification Theory Type 1 (QT1) method based on subjects’ age and gender. According to the results, a predict model based on QT1 was generated. To test the validity of the results, three existing commerical HUD presentation images were chosen and validity questionnaire survey was conducted. The predicting Kansei values were calculated using QT1 predicting model built in the second-step. Finally, the validity and feasibility of the results were tested using one-sample t-test.