UECP: Uncertainty-Enhanced Collaborative Perception

Collaborative perception serves as a pivotal solution to en-hance the perception capability of individual agents in autonomous driv-ing, where a core challenge lies in seeking reliable evidence to quantifyand weight the contribution of each participating agent. Existing meth-ods typically rely on a confidence map (co-trained with the detectionhead), which is, however, inherently correlated with the detection re-sults and thus fails to provide unbiased physical evidence. Furthermore,how to deeply integrate evidence into the cooperative fusion process re-mains an open question. To address these issues, this paper first pro-poses uncertainty map, a physically grounded and unambiguous met-ric for evaluating perception quality. This map is directly supervisedby real-time sensor signals (i.e., LiDAR point density), ensuring de-coupling from detection noise and thereby providing physical scenario-aware evidence for weighting agent contribution. Based on this map,we develop the Uncertainty-Enhanced Collaborative Perception (UECP)framework, centered on the Uncertainty-Aware Pyramid Fusion (UAPF)module. UAPF uses a coarse-to-fine strategy, with two key components:Uncertainty-Weighted Downsampling (UWD) for high-fidelity featurepreservation, and Uncertainty-Guided Residual Fusion (UGRF) to rein-force ego features, suppressing noise and ensuring robust fusion. Exten-sive experiments on real-world datasets show UECP outperforms SOTAmethods in effectiveness and robustness by embedding the uncertaintymap into fusion. Code will be publicly available.