Cooperative Perception for Automated Driving: A Survey of Algorithms, Applications, and Future Directions

Cooperative perception (CP) has emerged as an important research topic in connected and automated transportation systems, significantly enhancing situational awareness in challenging conditions such as limited communication bandwidth, harsh weather, and poor lighting. By sharing and aggregating data from multiple sources—including vehicles, roadside units (RSUs), and other infrastructure—CP addresses critical issues like occlusion and long-range perception in complex environments. This article provides a comprehensive review of existing CP methodologies, evaluating their effectiveness across diverse scenarios and fusion layers. We investigate approaches ranging from early to late collaboration strategies, highlighting how multimodal sensing at the network level can improve detection accuracy, system robustness, and adaptability. Additionally, we review publicly available datasets, identifying key gaps in their coverage of heterogeneous sensors, adverse conditions, and large-scale spatial scenarios. Finally, we propose actionable future research directions, including advanced algorithms for adverse environments, adaptive fusion strategies for heterogeneous sensors, and the integration of emerging technologies such as high-definition (HD) maps, large language models (LLMs), and end-to-end frameworks. These advancements aim to ensure reliability, interoperability, and scalability for real-world connected and automated vehicle (CAV) applications.