HyDRA: Hybrid Domain-Aware Robust Architecture for Heterogeneous Collaborative Perception

Abstract

In collaborative perception, an agent's performance can be degraded by heterogeneity arising from differences in model architecture or training data distributions. To address this challenge, we propose HyDRA (Hybrid Domain-Aware Robust Architecture), a unified pipeline that integrates intermediate and late fusion within a domain-aware framework. We introduce a lightweight domain classifier that dynamically identifies heterogeneous agents and assigns them to the late-fusion branch. Furthermore, we propose anchor-guided pose graph optimization to mitigate localization errors inherent in late fusion, leveraging reliable detections from intermediate fusion as fixed spatial anchors. Extensive experiments demonstrate that, despite requiring no additional training, HyDRA achieves performance comparable to state-of-the-art heterogeneity-aware CP methods. Importantly, this performance is maintained as the number of collaborating agents increases, enabling zero-cost scaling without retraining.

Figures (6)

• Figure 1: Heterogeneity in dynamic collaboration. (b) Architecture Heterogeneity occurs when agents employ diverse model architectures, making direct feature fusion structurally infeasible. (c) Latent Domain Heterogeneity is from an independent training setting even when agents adopt identical architectures, resulting in a more subtle threat. Our proposed framework successfully identifies not only explicit structural mismatches but also these hidden latent domain shifts to prevent feature contamination.
• Figure 2: Overview of HyDRA. Domain Classifier computes a domain similarity score $\mathcal{S}_{\text{domain}}$ by comparing the ego-decoded prediction $\mathcal{B}_{pred}$ with the received auxiliary detection result $\mathcal{B}_A$. In Hybrid Fusion, homogeneous agents ($\mathcal{N}_{int}$) participate in intermediate fusion to generate Stage 1 detections ($\mathcal{B}_{stage1}$), while heterogeneous agents ($\mathcal{N}_{late}$) participate in late fusion using their transmitted detection results. At the late-fusion stage, Anchor-Guided Pose Graph Optimization mitigates pose noise by treating the reliable Stage 1 results as fixed spatial anchors to correct the poses of heterogeneous agents.
• Figure 3: Concept of AG-PGO. Instead of global optimization, we leverage the reliable detections from homogeneous agents ($\mathcal{N}_{int}$) as fixed spatial anchors (). The module corrects the pose of heterogeneous agents ($\mathcal{N}_{late}$) by minimizing the residual errors between their predictions and the anchors, ensuring global consistency.
• Figure 4: Performance comparison with baseline methods under varying pose noise and scalability analysis
• Figure 5: Analysis of domain classifier scores comparing homogeneous vs. heterogeneous agents under ideal and noisy ($\sigma=0.4$) conditions.