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.
Collaborative Perception Datasets for Autonomous Driving: A Review
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing)（中国矿业大学（北京）机械与电子工程学院）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与移动系统国家重点实验室）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Mechatronics Engineering, Harbin Institute of Technology（哈尔滨工业大学机械电子工程学院）；Department of Electronic & Electrical Engineering, University of Bath（巴斯大学电子与电气工程系）
CERF: Communication-Efficient and Retraining-Free Collaborative Perception
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
CoST: Efficient Collaborative Perception From Unified Spatiotemporal Perspective
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；University of California, Los Angeles（美国加州大学洛杉矶分校）
CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；City University of Hong Kong（香港城市大学）
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统上海研究院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Mechatronic Engineering and Automation, Shanghai University（上海大学机械电子工程与自动化学院）
V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University（上海交通大学合作 medianet 创新中心）；Shanghai AI Laboratory（上海人工智能实验室）；Sangfor Technologies Inc.（深信服科技股份有限公司）
CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion
Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education（交通运输大数据与人工智能重点实验室，教育部）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Xiamen University（厦门大学）
Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective
XITASO GmbH（XITASO公司）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄大学）；Technische Hochschule Ingolstadt (THI)（因戈尔施塔特技术大学）；CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)（CARISSMA电动、连接与安全移动研究所）；Federal University of Santa Catarina (UFSC)（圣卡塔琳娜联邦大学）
School of Information, Renmin University of China（中国人民大学信息学院）；Sony Research and Development Center China（索尼（中国）研发有限公司）；National University of Defense Technology（国防科技大学）
INSTINCT: Instance-Level Interaction Architecture for Query-Based Collaborative Perception
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）
INTACT: Ego-Guided Typed Sparse Evidence Retrieval for Heterogeneous Collaborative Perception
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（多谱信息智能处理国家级重点实验室，人工智能与自动化学院，华中科技大学）；Zhejiang Normal University（浙江师范大学）
CoDS: Enhancing Collaborative Perception in Heterogeneous Scenarios via Domain Separation
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education and School of Computer Science and Technology, Beijing Jiaotong University（大数据与人工智能交通实验室（北京交通大学）及计算机科学与技术学院，北京交通大学）
DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions
Academy for Engineering and Technology, Fudan University（复旦大学工程技术研究院）；Innovation Platform for Academicians of Hainan Province（海南省院士创新平台）；Duke Kunshan University（昆山杜克大学）；Chongqing Changan Automobile Co., Ltd.（重庆长安汽车股份有限公司）
