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The largest open-source dataset of car designs, including their aerodynamics

DrivAerNet++, the largest open-source dataset for car aerodynamics developed to date, can be used to quickly train an AI model to generate novel car designs. This process could potentially lead to more fuel-efficient cars and electric vehicles with longer range.

These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today. The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014.

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