Towards Vehicle-to-everything Autonomous Driving: A Survey on
  Collaborative Perception
Si Liu Chen Gao Yuan Chen Xingyu Peng Xianghao Kong Kun Wang Runsheng Xu Wentao Jiang Hao Xiang Jiaqi Ma Miao Wang
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction
for developing a new generation of intelligent transportation systems.
Collaborative perception (CP) as an essential component to achieve V2X can
overcome the inherent limitations of individual perception, including occlusion
and long-range perception. In this survey, we provide a comprehensive review of
CP methods for V2X scenarios, bringing a profound and in-depth understanding to
the community. Specifically, we first introduce the architecture and workflow
of typical V2X systems, which affords a broader perspective to understand the
entire V2X system and the role of CP within it. Then, we thoroughly summarize
and analyze existing V2X perception datasets and CP methods. Particularly, we
introduce numerous CP methods from various crucial perspectives, including
collaboration stages, roadside sensors placement, latency compensation,
performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover,
we conduct extensive experimental analyses to compare and examine current CP
methods, revealing some essential and unexplored insights. Specifically, we
analyze the performance changes of different methods under different
bandwidths, providing a deep insight into the performance-bandwidth trade-off
issue. Also, we examine methods under different LiDAR ranges. To study the
model robustness, we further investigate the effects of various simulated
real-world noises on the performance of different CP methods, covering
communication latency, lossy communication, localization errors, and mixed
noises. In addition, we look into the sim-to-real generalization ability of
existing CP methods. At last, we thoroughly discuss issues and challenges,
highlighting promising directions for future efforts. Our codes for
experimental analysis will be public at
https://github.com/memberRE/Collaborative-Perception.