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.

自动驾驶引起学术界和工业界的广泛关注，但目前的自动驾驶系统大多基于单一车辆，存在较大限制，可能威胁驾驶安全。基于连接和自动驾驶车辆的协作感知被认为是解决这些限制的一种有前景的方法。本文首先确定了协作感知的挑战，如数据共享异步性、数据量和姿态误差等。然后，我们讨论了使用各种技术解决这些挑战的可能方法，并阐述了相关的研究机会。此外，我们提出了一种应对通信效率和延迟问题的方案，即一种通道感知的协作感知框架，通过动态调整通信图并减小延迟，提高感知性能和通信效率。最后，我们进行实验证明了我们提出方案的有效性。