Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities
Senkang Hu, Zhengru Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang
Abstract
Autonomous driving has attracted significant attention from both academia and industries, and it is expected to offer a safer and more efficient driving system. However, current autonomous driving systems are mostly based on a single-agent perception, which has significant limitations, causing serious safety concerns. Collaborative perception with connected and autonomous vehicles (CAV) shows a promising solution to overcoming these limitations. In this article, we first identify the challenges of collaborative perception, such as data sharing asynchrony, large 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.