  Accurate perception is essential for advancing autonomous driving and
addressing safety challenges in modern transportation systems. Despite
significant advancements in computer vision for object recognition, current
perception methods still face difficulties in complex real-world traffic
environments. Challenges such as physical occlusion and limited sensor field of
view persist for individual vehicle systems. Cooperative Perception (CP) with
Vehicle-to-Everything (V2X) technologies has emerged as a solution to overcome
these obstacles and enhance driving automation systems. While some research has
explored CP's fundamental architecture and critical components, there remains a
lack of comprehensive summaries of the latest innovations, particularly in the
context of V2X communication technologies. To address this gap, this paper
provides a comprehensive overview of the evolution of CP technologies, spanning
from early explorations to recent developments, including advancements in V2X
communication technologies. Additionally, a contemporary generic framework is
also proposed to illustrate the V2X-based CP workflow, aiding in the structured
understanding of CP system components. Furthermore, this paper categorizes
prevailing V2X-based CP methodologies based on the critical issues they
address. An extensive literature review is conducted within this taxonomy,
evaluating existing datasets and simulators. Finally, open challenges and
future directions in CP for autonomous driving are discussed by considering
both perception and V2X communication advancements.
