Papers › Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

16 Jan 2023arXiv:2301.06262archive 2025-07-28

Yushan Han, HUI ZHANG, Huifang Li, Yi Jin, Congyan Lang, Yidong Li

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

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