Collaborative Perception Datasets for Autonomous Driving: A Review

Collaborative perception (CP) has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving (AD) through multiagent information fusion. With the advancement of vehicle-to-everything (V2X) communication, numerous CP datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on CP datasets, this work reviews and compares existing resources from a multidimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of CP datasets and related literature: https://github.com/frankwnb/Collaborative-Perception-Datasets-for-Autonomous-Driving.