Collaborative Perception for Connected and Autonomous Driving:
  Challenges, Possible Solutions and Opportunities
Senkang Hu Zhengru Fang Yiqin Deng Xianhao Chen Yuguang Fang
Autonomous driving has attracted significant attention from both academia and
industries, which is expected to offer a safer and more efficient driving
system. However, current autonomous driving systems are mostly based on a
single vehicle, which has significant limitations which still poses threats to
driving safety. Collaborative perception with connected and autonomous vehicles
(CAVs) shows a promising solution to overcoming these limitations. In this
article, we first identify the challenges of collaborative perception, such as
data sharing asynchrony, data volume, and pose errors. Then, we discuss the
possible solutions to address these challenges with various technologies, where
the research opportunities are also elaborated. Furthermore, we propose a
scheme to deal with communication efficiency and latency problems, which is a
channel-aware collaborative perception framework to dynamically adjust the
communication graph and minimize latency, thereby improving perception
performance while increasing communication efficiency. Finally, we conduct
experiments to demonstrate the effectiveness of our proposed scheme.