Toyota’s New GenAI Tool Is Transforming Vehicle Design

“The hope is that, by using this tool, they can expand the power of design ideas while at the same time drastically improving the speed of design development,” says Balachandran. “Generative AI is a powerful new tool. We’re exploring, across our many research areas, how to leverage it responsibly so it can amplify our people.”

“To overcome these limitations, we built an AI model that can incorporate precise engineering constraints—like minimizing aerodynamic drag—to maximize the performance of these potential cars,” says Balachandran. “This will cut down on the number of iterations considerably and allow designers and engineers to work more closely and quickly.”

TRI recently shared a GenAI process that could overcome those limitations to assist vehicle designers. These designers can already use publicly available, text-to-image generative AI tools as an early step in their creative process. But TRI’s new technique combines early design sketches and engineering constraints in the process. This reconciles design ideas with engineering constraints early in the process and results in fewer iterations to reach the final design.

Published: Monday, October 23, 2023 – 12:02

The new generative AI technique optimizes aerodynamic drag in successive iterations based on parameter inputs from the designer. Image: Toyota

The tool is currently being used for vehicle handling characteristics such as drag, ride height, chassis position, and structural integrity. Balachandran’s team is working with its partners across Toyota’s network to enable designers to incorporate the technique into their own workflows.

For example, Toyota’s designers introduce constraints such as drag, which affects fuel efficiency, into the generative AI process. Subsequent iterations would optimize drag within the parameters defined by the designer.

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Balachandran’s team had to tackle the difficult task of reconciling a sleek and elegant design with the realities of engineering performance and safety requirements. Designers and engineers often have very different backgrounds and ways of thinking about how a vehicle looks and performs, and this requires a significant amount of back-and-forth between them to achieve a feasible solution, which can slow down the design process.

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GenAI is a type of artificial intelligence that doesn’t just focus on processing data. It uses advanced machine learning techniques—particularly deep learning—to generate new content. The technology could help carmakers optimize designs and structures, producing lighter, more aerodynamic, and more fuel-efficient vehicles. However, GenAI is still in its infancy and has encountered challenges when evaluating complex variables such as manufacturing limitations and detailed safety regulations.

Toyota’s generative AI tool also creates digital prototypes of vehicles, which are put through simulated real-world tests, enabling engineers to identify potential flaws early in the development process and avoid potentially costly flaws during production.

It’s no secret the automotive sector is racing to find ways of tapping the potential of generative artificial intelligence (GenAI) to design and build the next generation of vehicles. This technology has promise, from redefining manufacturing processes to helping carmakers design smarter, safer, and more efficient vehicles.

The technology can factor in any measure that can be inferred from the image itself—including drag. In fact, drag can be inferred because shapes have particular drag coefficients that the AI can measure. Other factors that affect ride handling, such as wheelbase and ride height, can also be optimized by the AI.

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For example, a designer can request that the tool design a vehicle based on an initial prototype sketch with qualitative parameters such as “sleek” or “like an SUV.” The tool would interpret the request and create a few designs as requested—while still optimizing quantitative performance metrics such as aerodynamic drag.

Adding those engineering constraints to the generative AI model allows the user to set limitations on the AI’s generative designs, requiring it to apply those constraints to the design. As a result, the generated design will account for factors that improve performance, safety, and reliability while satisfying the designers’ specific needs.

Published Sept. 26, 2023, on