Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities

Engineering notes

Key topics: autonomous driving system, autonomous driving, autonomous vehicle, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original 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.

Links and sources

• Official / arXiv page
• PDF from original source

Comments