Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities
This work addresses safety and efficiency issues in autonomous driving for the automotive industry, though it appears incremental as it builds on existing collaborative perception concepts.
The paper tackles the limitations of single-vehicle autonomous driving by proposing collaborative perception for connected and autonomous vehicles, addressing challenges like data asynchrony and latency with a channel-aware framework that improves perception performance and communication efficiency.
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.