SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
This addresses the data scarcity problem for autonomous driving researchers and developers, representing an incremental advance in generative data methods for this domain.
The paper tackles the need for large-scale annotated datasets in autonomous driving by developing SubjectDrive, a generative model with subject control that produces varied training data, showing it can effectively scale data production to improve downstream perception models.
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.