Semantic Classification of Car Styling Using Machine Learning - Hung-Hsiang Wang; Yen-Ting Lu (Engineering Proceedings, 2025)
Product semantics is essential for car styling because it shapes how consumers perceive and interact with cars, influences user experiences, and allows for product differentiation. Although many AI tools are available to assist car designers, research on applying machine learning techniques to evaluate product semantics is rare. Therefore, we developed a classification model that helps designers identify and evaluate the semantics conveyed by car styling using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool. We used Python web scraping to collect isometric drawings and introductory articles of 1320 SUV cars of various brands from 2009 to 2024 via websites such as Car Body Design and Car Design News. We also summarized four semantic types of car styling, namely “aggressive”, “sporty”, “clean”, and “off-road”, to create the dataset. We used WEKA image classification to randomly select 792 (60%) images from the dataset to train a classification model of car styling semantics. The remaining 528 images (40%) were used for verification. The classification model trained with the Binary Pattern Pyramid Filter and the Random Forest classifier achieved an accuracy of 84.6%. The model was evaluated in terms of whether 10 SUVs created by 10 graduate design students using AI conveyed the anticipated product semantics. Seven of the ten SUVs were correctly classified and the rest were not. All of the participants agreed that the predictions were satisfactory. However, it is necessary to improve the accuracy of each semantic classification, especially the “clean” type. The results of this study demonstrate the capability of machine learning to identify the semantics of car styling effectively, improve the communication and evaluation of product semantics by designers in the design process, and create a car styling with a good appearance that resonates with consumers.