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 focuses 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 overlook challenges between current academic research and real-world applications. The project page is https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Driving

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，针对协同感知的新方法的理论和实验研究大幅增加。然而，迄今为止，鲜有综述聚焦于系统化的协同模块和大规模协同感知数据集。本文回顾了该领域的最新成果，以填补这一空白并促进未来研究。我们首先简要概述协同方案。随后，系统总结了理想场景和实际挑战下的协同感知方法：前者聚焦协同模块与效率，后者致力于解决实际应用中的问题。此外，我们介绍了大规模公开数据集，并总结了这些基准上的量化结果。最后，我们指出了当前学术研究与实际应用之间的空白和被忽视的挑战。项目页面为https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Driving