Towards Communication-Efficient Cooperative Perception via Planning-Oriented Feature Sharing

Abstract

Autonomous driving systems are fundamentally composed of sequential modular tasks, i.e., perception, prediction, and planning. For connected autonomous vehicles (CAVs), cooperative perception offers a promising solution to surpass their perception limitations, such as occlusion, by sharing sensing data with each other through wireless communication. Existing works typically prioritize sharing data from potential object-containing areas to maximize object detection accuracy under constrained communication resources. However, such detection-oriented approaches ignore a crucial fact that more accurate detection does not equal safer planning. Sharing large amounts of sensing data for detection accuracy can lead to communication resource wastage and performance degradation of subsequent driving tasks. To address this, we introduce Plan2comm, a communication-efficient cooperative perception framework via planning-oriented feature sharing, which shares only sensing data around planned trajectories to enable safer planning rather than mere detection accuracy. Specifically, a planning-oriented communication mechanism is designed to select and transmit the most valuable features from the perspective of the planning task. Moreover, an uncertainty-aware spatial-temporal feature fusion strategy is proposed to enhance high-quality information aggregation. Comprehensive experiments demonstrate that Plan2comm outperforms all other cooperative perception methods on motion prediction performance, and is more communication-efficient.