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Accurate perception is essential for advancing autonomous driving andaddressing safety challenges in modern transportation systems. Despitesignificant advancements in computer vision for object recognition, currentperception methods still face difficulties in complex real-world trafficenvironments. Challenges such as physical occlusion and limited sensor field ofview persist for individual vehicle systems. Cooperative Perception (CP) withVehicle-to-Everything (V2X) technologies has emerged as a solution to overcomethese obstacles and enhance driving automation systems. While some research hasexplored CP's fundamental architecture and critical components, there remains alack of comprehensive summaries of the latest innovations, particularly in thecontext of V2X communication technologies. To address this gap, this paperprovides a comprehensive overview of the evolution of CP technologies, spanningfrom early explorations to recent developments, including advancements in V2Xcommunication technologies. Additionally, a contemporary generic framework isalso proposed to illustrate the V2X-based CP workflow, aiding in the structuredunderstanding of CP system components. Furthermore, this paper categorizesprevailing V2X-based CP methodologies based on the critical issues theyaddress. An extensive literature review is conducted within this taxonomy,evaluating existing datasets and simulators. Finally, open challenges andfuture directions in CP for autonomous driving are discussed by consideringboth perception and V2X communication advancements.

V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges