  Collaborative perception is essential to address occlusion and sensor failure
issues in autonomous driving. In recent years, theoretical and experimental
investigations of novel works for collaborative perception have increased
tremendously. So far, however, few reviews have focused on systematical
collaboration modules and large-scale collaborative perception datasets. This
work reviews recent achievements in this field to bridge this gap and motivate
future research. We start with a brief overview of collaboration schemes. After
that, we systematically summarize the collaborative perception methods for
ideal scenarios and real-world issues. The former focuses on collaboration
modules and efficiency, and the latter is devoted to addressing the problems in
actual application. Furthermore, we present large-scale public datasets and
summarize quantitative results on these benchmarks. Finally, we highlight gaps
and overlook challenges between current academic research and real-world
applications. The project page is
https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Driving
