Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This work reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focus on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlooked challenges between current academic research and real-world applications.

翻译：协同感知对于解决自动驾驶中的遮挡和传感器故障问题至关重要。近年来，关于协同感知的新兴工作的理论与实验研究大幅增加。然而，目前鲜有综述聚焦于系统化的协同模块及大规模协同感知数据集。为填补这一空白并推动未来研究，本文回顾了该领域的最新成果。我们首先简要概述了协同方案。随后，系统总结了理想场景下的协同感知方法及面向现实问题的相关研究，前者侧重于协同模块与效率，后者致力于解决实际应用中的难题。此外，我们介绍了大规模公开数据集，并总结了这些基准测试上的量化结果。最后，我们指出了当前学术研究与实际应用之间的差距及被忽视的挑战。