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

arXiv: https://arxiv.org/abs/2603.23975 · HTML: https://mlsanity.com/paper/2603.23975/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 24 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [An Extensible Framework for Open Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2401.13964/) (2024)
- [Communication-Efficient Collaborative Perception via Information Filling with Codebook](https://mlsanity.com/paper/2405.04966/) (2024)
- [V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2403.16034/) (2024)
- [Bridging the Domain Gap for Multi-Agent Perception](https://mlsanity.com/paper/2210.08451/) (2022)
- [Model-Agnostic Multi-Agent Perception Framework](https://mlsanity.com/paper/2203.13168/) (2022)
- [Adversarial Attacks On Multi-Agent Communication](https://mlsanity.com/paper/2101.06560/) (2021)
- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [Among Us: Adversarially Robust Collaborative Perception by Consensus](https://mlsanity.com/paper/2303.09495/) (2023)
- [One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2411.16799/) (2024)
- [CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception](https://mlsanity.com/paper/2412.12000/) (2024)
- [Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism](https://mlsanity.com/paper/2510.19618/) (2025)
- [Malicious Agent Detection for Robust Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2310.11901/) (2023)
- [CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception](https://mlsanity.com/paper/2502.07807/) (2025)
- [Rethinking the Role of Infrastructure in Collaborative Perception](https://mlsanity.com/paper/2410.11259/) (2024)
- [A Late Collaborative Perception Framework for 3D Multi-Object and Multi-Source Association and Fusion](https://mlsanity.com/paper/2507.02430/) (2025)

## Abstract (arXiv)

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.