AccScience Publishing / IJAMD / Volume 3 / Issue 2 / DOI: 10.36922/IJAMD026190015
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ORIGINAL RESEARCH ARTICLE

AutoGen-Insight: Translating consumer reviews into probabilistic visual design parameters for generative electric vehicle concept generation

Luyao Wang1,2 ,  Chun-Hsien Chen2 ,  Danni Chang1*
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1 Department of Design, School of Design, ShanghDepartment of Design, School of Design, Shanghai Jiao Tong University, Shanghai, China ai Jiao Tong University, Shanghai, China
2 Design and Human Factors Lab, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
IJAMD 2026, 3(2), 026190015 https://doi.org/10.36922/IJAMD026190015
Received: 15 May 2026 | Revised: 15 June 2026 | Accepted: 22 June 2026 | Published online: 30 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Large-scale consumer reviews provide a valuable but weakly structured source of market insight for early-stage automotive design. However, existing generative design workflows often rely on direct prompting or manual interpretation, making it difficult to translate heterogeneous consumer preferences into inspectable visual design conditions. This paper presents AutoGen-Insight, a probabilistic semantic-to-visual parameter mapping framework for consumer-driven electric vehicle concept generation. Building on a retained corpus of 14,153 high-confidence electric vehicle reviews, the proposed framework reorganizes consumer feedback into four representative persona-level semantic preference profiles. These profiles are further transformed into probabilistic design vectors that encode both central tendencies and uncertainty across visually observable exterior concept descriptors, including vehicle proportion, styling, color, lighting, wheel design, and surface-finish parameters. A conflict-aware reasoning module integrates knowledge graph traversal with parameter-level trade-off aggregation to handle competing perceptual requirements, such as sportiness, comfort, compactness, and spatial utility. The resulting probabilistic design vectors are used to construct parameterized prompts for diffusion-based multi-view concept image generation, while the intermediate reasoning chains and parameter vectors remain available for inspection. The framework is evaluated through quantitative metrics, focused ablation analysis, qualitative conflict-resolution analysis, and expert/user assessment. Compared with direct prompting, keyword-based prompting, and large language model-generated prompting baselines, AutoGen-Insight achieves higher semantic alignment, plausibility of visual proportions, and cross-view consistency. The results suggest that probabilistic intermediate representations can improve the controllability and interpretability of early-stage image-based design exploration. This study contributes a structured and extensible approach for transforming large-scale consumer feedback into inspectable visual design knowledge for early-stage electric vehicle concept development.

Graphical abstract
Keywords
Generative design
Electric vehicle design
Consumer insights
Knowledge graph
Diffusion models
Funding
This work was supported by the Shanghai Planning Office of Philosophy and Social Science under Grant 2022BSH001, and the Ministry of Education in China Humanities and Social Sciences Project under Grant 23YJA760006.
Conflict of interest
Danni Chang is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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