  Realistic and diverse traffic scenarios in large quantities are crucial for
the development and validation of autonomous driving systems. However, owing to
numerous difficulties in the data collection process and the reliance on
intensive annotations, real-world datasets lack sufficient quantity and
diversity to support the increasing demand for data. This work introduces
DriveSceneGen, a data-driven driving scenario generation method that learns
from the real-world driving dataset and generates entire dynamic driving
scenarios from scratch. DriveSceneGen is able to generate novel driving
scenarios that align with real-world data distributions with high fidelity and
diversity. Experimental results on 5k generated scenarios highlight the
generation quality, diversity, and scalability compared to real-world datasets.
To the best of our knowledge, DriveSceneGen is the first method that generates
novel driving scenarios involving both static map elements and dynamic traffic
participants from scratch.
