What2comm: Towards Communication-efficient Collaborative Perception via Feature Decoupling
Multi-agent collaborative perception has received increasing attention recently as an emerging application in driving scenarios. Despite advancements in previous approaches, challenges remain due to redundant communication patterns and vulnerable collaboration processes. To address these issues, we propose What2comm, an end-to-end collaborative perception framework to achieve a trade-off between perception performance and communication bandwidth. Our novelties lie in three aspects. First, we design an efficient communication mechanism based on feature decoupling to transmit exclusive and common feature maps among heterogeneous agents to provide perceptually holistic messages. Secondly, a spatio-temporal collaboration module is introduced to integrate complementary information from collaborators and temporal ego cues, leading to a robust collaboration procedure against transmission delay and localization errors. Ultimately, we propose a common-aware fusion strategy to refine final representations with informative common features. Comprehensive experiments in real-world and simulated scenarios demonstrate the effectiveness of What2comm.
