HERO: Heterogeneous Evidential Robust Object-Level Collaborative Perception

Real-world collaborative perception agents typically deploydiverse sensor configurations and network architectures. While feature-level fusion methods address this heterogeneity by aligning feature spaces,they require collaborative training and large communication overhead. Incontrast, object-level fusion sidesteps these constraints, as transmittedbounding boxes are directly usable across varying architectures. How-ever, standard late fusion relies only on box coordinates and confidencescores, which provide insufficient information for robust association andleave the system vulnerable to localization noise and asynchronous de-lays. To address this, we propose HERO, an uncertainty-guided object-level collaborative perception method that equips proposals with evi-dential statistics and decouples the two fusion decisions, aggregating se-mantic evidence across matched proposals while selecting geometry fromthe most reliable source, to perform robust association and fusion underpose noise and asynchrony. Experiments on OPV2V-H and DAIR-V2Xshow that HERO matches the SOTA feature-level fusion methods whiletransmitting only about 2 KB per frame of sparse object proposals. Un-der localization error and latency scenarios, HERO further outperformsfeature-level SOTA models.