  Autonomous driving progress relies on large-scale annotated datasets. In this
work, we explore the potential of generative models to produce vast quantities
of freely-labeled data for autonomous driving applications and present
SubjectDrive, the first model proven to scale generative data production in a
way that could continuously improve autonomous driving applications. We
investigate the impact of scaling up the quantity of generative data on the
performance of downstream perception models and find that enhancing data
diversity plays a crucial role in effectively scaling generative data
production. Therefore, we have developed a novel model equipped with a subject
control mechanism, which allows the generative model to leverage diverse
external data sources for producing varied and useful data. Extensive
evaluations confirm SubjectDrive's efficacy in generating scalable autonomous
driving training data, marking a significant step toward revolutionizing data
production methods in this field.
