KeyCoop: Communication-Efficient Raw-Level Cooperative Perception for Connected Autonomous Vehicles via Keypoints Extraction
Qi Xie, Xiaobo Zhou, Chuanan Wang, Tie Qiu, Wenyu Qu · 2024
Cooperative perception is an emerging paradigm that expects to conquer the sensory limitations of individual vehicles by sharing sensor information with each other and significantly improve driving safety. However, achieving highly precise data sharing and low communication overhead remains a challenge for cooperative perception, especially when real-time communication is necessary in autonomous driving. As a result, it is essential to decrease the transmitted sensor data while maintaining the perception performance. For this purpose, we propose a communication-efficient raw-level cooperative perception system for connected autonomous vehicles (CAVs), which is able to significantly compress the raw sensor data each CAV shares with each other by only transmitting the most informative keypoints. Specifically, at the local level, a voxel-based instance-aware keypoints selection strategy is proposed to select the points that belong to regions of interest. To further supervise the local keypoints selection, we present a collaborative global-local learning strategy, enabling each vehicle to consider both the local scenario and the global context when selecting the transmitted data. Comprehensive evaluations indicate the superiority of the proposed system, which achieves more than 300× lower communication volume compared to the raw data, with a performance degradation of less than 1%.