  Perceiving the environment is one of the most fundamental keys to enabling
Cooperative Driving Automation (CDA), which is regarded as the revolutionary
solution to addressing the safety, mobility, and sustainability issues of
contemporary transportation systems. Although an unprecedented evolution is now
happening in the area of computer vision for object perception,
state-of-the-art perception methods are still struggling with sophisticated
real-world traffic environments due to the inevitably physical occlusion and
limited receptive field of single-vehicle systems. Based on multiple spatially
separated perception nodes, Cooperative Perception (CP) is born to unlock the
bottleneck of perception for driving automation. In this paper, we
comprehensively review and analyze the research progress on CP and, to the best
of our knowledge, this is the first time to propose a unified CP framework.
Architectures and taxonomy of CP systems based on different types of sensors
are reviewed to show a high-level description of the workflow and different
structures for CP systems. Node structure, sensor modality, and fusion schemes
are reviewed and analyzed with comprehensive literature to provide detailed
explanations of specific methods. A Hierarchical CP framework is proposed,
followed by a review of existing Datasets and Simulators to sketch an overall
landscape of CP. Discussion highlights the current opportunities, open
challenges, and anticipated future trends.
