  Autonomous driving techniques have been flourishing in recent years while
thirsting for huge amounts of high-quality data. However, it is difficult for
real-world datasets to keep up with the pace of changing requirements due to
their expensive and time-consuming experimental and labeling costs. Therefore,
more and more researchers are turning to synthetic datasets to easily generate
rich and changeable data as an effective complement to the real world and to
improve the performance of algorithms. In this paper, we summarize the
evolution of synthetic dataset generation methods and review the work to date
in synthetic datasets related to single and multi-task categories for to
autonomous driving study. We also discuss the role that synthetic dataset plays
the evaluation, gap test, and positive effect in autonomous driving related
algorithm testing, especially on trustworthiness and safety aspects. Finally,
we discuss general trends and possible development directions. To the best of
our knowledge, this is the first survey focusing on the application of
synthetic datasets in autonomous driving. This survey also raises awareness of
the problems of real-world deployment of autonomous driving technology and
provides researchers with a possible solution.
