Inspired by AI? A Novel Generative AI System To Assist Conceptual Automotive Design
Ye Wang, Nicole B. Damen, Thomas Gale, Voho Seo, Hooman Shayani
TL;DR
The paper addresses the challenge of integrating generative AI into automotive conceptual design by studying how designers currently use text and image inspiration and what interaction modes they prefer for AI assistance. It develops a diffusion-based system, grounded in Versatile Diffusion, that fuses keyword-driven text direction with image inspiration, applying radial symmetry constraints to generate concept-ready wheel designs. Through surveys, a data-augmentation task, and a three-day workshop with designers, the work reveals a preference for hierarchical inspiration, continuous generation, and feedback-driven refinement, while highlighting the need for explainability and contextual alignment with design vocabularies. The resulting prototype demonstrates intuitive UI, scalable architecture, and real-world feasibility, suggesting that AI can meaningfully accelerate ideation in automotive design when tightly integrated with human workflows and domain-specific vocabularies.
Abstract
Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather image ideas, followed by sketching on paper or using digital tools to develop concepts. Emerging generative AI techniques, such as diffusion models, offer a promising avenue to streamline these processes by swiftly generating design concepts based on text and image inspiration inputs, subsequently using the AI generated design concepts as fresh sources of inspiration for further concept development. However, applying these generative AI techniques directly within a design context has challenges. Firstly, generative AI tools may exhibit a bias towards particular styles, resulting in a lack of diversity of design outputs. Secondly, these tools may struggle to grasp the nuanced meanings of texts or images in a design context. Lastly, the lack of integration with established design processes within design teams can result in fragmented use scenarios. Focusing on these challenges, we conducted workshops, surveys, and data augmentation involving teams of experienced automotive designers to investigate their current practices in generating concepts inspired by texts and images, as well as their preferred interaction modes for generative AI systems to support the concept generation workflow. Finally, we developed a novel generative AI system based on diffusion models to assist conceptual automotive design.