Collaborative perception is a promising approach to enhance the robustness and accuracy of 3D object detection for autonomous driving. However, typical methods relying on early fusion of raw data demand prohibitive communication bandwidth, while those using intermediate fusion of compressed features often suffer from significant information loss and detection performance degradation. In this regard, this paper proposes a communication-efficient collaborative perception framework based on a knowledge distillation paradigm. The key idea is to leverage a powerful early-fusion model as a teacher to guide the training of a communication-efficient intermediate-fusion student model. Specifically, the teacher model incorporates a spatial-channel attention module to generate enriched and discriminative feature representations. The student model employs a lightweight yet effective feature compression scheme, combining learnable quantization with entropy coding, to efficiently reduce the volume of transmitted data. Crucially, we propose an adaptive knowledge distillation loss function that selectively transfers knowledge related to potential objects, thereby suppressing background noise and aligning the student’s focus with critical detection regions. Extensive experiments on the OPV2V, V2X-Sim 2.0, and DAIR-V2X datasets demonstrate that our method achieves competitive detection accuracy. Additional on-road tests in campus environments validate the system’s practicality and robustness.
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