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.
| Original language | English |
|---|---|
| Pages (from-to) | 228-234 |
| Number of pages | 7 |
| Journal | IEEE Wireless Communications |
| Volume | 32 |
| Issue number | 5 |
| Early online date | 2 Jun 2025 |
| DOIs | |
| Publication status | Published - Oct 2025 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2002-2012 IEEE.
Funding
The research work described in this article was conducted in the JC STEM Lab of Smart City, funded by The Hong Kong Jockey Club Charities Trust under Contract 2023-0108. The work was supported in part by the Hong Kong SAR Government under the Global STEM Professorship and Research Talent Hub. The work of Senkang Hu was supported in part by the Hong Kong Innovation and Technology Commission under InnoHK Project CIMDA. The work of Yiqin Deng was supported in part by the National Natural Science Foundation of China under Grant No. 62301300. The work of Xianhao Chen was supported in part by the Research Grants Council of Hong Kong under Grant 27213824.