自动驾驶中的协同感知：方法、数据集与挑战
Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
AI总结：
本文系统综述了自动驾驶中协同感知的协作方案、理想与现实场景方法及大规模数据集基准，并指出了当前学术研究与实际应用间的差距与挑战。
摘要：
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，对协同感知新工作的理论和实验研究大幅增加。然而，迄今为止很少有综述关注系统性的协作模块和大规模协同感知数据集。本工作回顾了该领域的最新成果以弥合这一差距并推动未来研究。我们首先简要概述协作方案。之后，我们系统总结了针对理想场景和现实问题的协同感知方法。前者关注协作模块和效率，后者致力于解决实际应用中的问题。此外，我们展示了大规模公开数据集并总结了这些基准上的定量结果。最后，我们强调了当前学术研究与现实应用之间的差距和被忽视的挑战。项目页面为 https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Driving
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
