  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.
