  Safely interacting with humans is a significant challenge for autonomous
driving. The performance of this interaction depends on machine learning-based
modules of an autopilot, such as perception, behavior prediction, and planning.
These modules require training datasets with high-quality labels and a diverse
range of realistic dynamic behaviors. Consequently, training such modules to
handle rare scenarios is difficult because they are, by definition, rarely
represented in real-world datasets. Hence, there is a practical need to augment
datasets with synthetic data covering these rare scenarios. In this paper, we
present a platform to model dynamic and interactive scenarios, generate the
scenarios in simulation with different modalities of labeled sensor data, and
collect this information for data augmentation. To our knowledge, this is the
first integrated platform for these tasks specialized to the autonomous driving
domain.
