V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception

• URL: http://arxiv.org/abs/2503.15435v1
• Date: Wed, 19 Mar 2025 17:17:44 GMT
• Title: V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception
• Authors: Baolu Li, Zongzhe Xu, Jinlong Li, Xinyu Liu, Jianwu Fang, Xiaopeng Li, Hongkai Yu,
• Abstract summary: This paper is the first work to study the Domain Generalization problem of LiDAR-based V2X cooperative perception.<n>Our research seeks to sustain high performance not only within the source domain but also across other unseen domains.
• Score: 34.97091536254836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR-based Vehicle-to-Everything (V2X) cooperative perception has demonstrated its impact on the safety and effectiveness of autonomous driving. Since current cooperative perception algorithms are trained and tested on the same dataset, the generalization ability of cooperative perception systems remains underexplored. This paper is the first work to study the Domain Generalization problem of LiDAR-based V2X cooperative perception (V2X-DG) for 3D detection based on four widely-used open source datasets: OPV2V, V2XSet, V2V4Real and DAIR-V2X. Our research seeks to sustain high performance not only within the source domain but also across other unseen domains, achieved solely through training on source domain. To this end, we propose Cooperative Mixup Augmentation based Generalization (CMAG) to improve the model generalization capability by simulating the unseen cooperation, which is designed compactly for the domain gaps in cooperative perception. Furthermore, we propose a constraint for the regularization of the robust generalized feature representation learning: Cooperation Feature Consistency (CFC), which aligns the intermediately fused features of the generalized cooperation by CMAG and the early fused features of the original cooperation in source domain. Extensive experiments demonstrate that our approach achieves significant performance gains when generalizing to other unseen datasets while it also maintains strong performance on the source dataset.

Related papers

• V2X-ReaLO: An Open Online Framework and Dataset for Cooperative Perception in Reality [13.68645389910716]
  We introduce V2X-ReaLO, an open online cooperative perception framework deployed on real vehicles and smart infrastructure.<n>We present an open benchmark dataset designed to assess the performance of online cooperative perception systems.
  arXiv  Detail & Related papers  (2025-03-13T04:31:20Z)
• CooPre: Cooperative Pretraining for V2X Cooperative Perception [47.00472259100765]
  We present a self-supervised learning method for V2X cooperative perception. We utilize the vast amount of unlabeled 3D V2X data to enhance the perception performance.
  arXiv  Detail & Related papers  (2024-08-20T23:39:26Z)
• UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection [11.60579201022641]
  We propose a framework specifically designed for aerial-ground collaboration. We develop a virtual dataset named V2U-COO for our research. Second, we design a Cross-Domain Cross-Adaptation (CDCA) module to align the target information. Third, we introduce a Collaborative Depth Optimization (CDO) module to obtain more precise depth estimation results.
  arXiv  Detail & Related papers  (2024-06-07T05:25:45Z)
• End-to-End Autonomous Driving through V2X Cooperation [23.44597411612664]
  We introduce UniV2X, a pioneering cooperative autonomous driving framework.<n>UniV2X seamlessly integrates all key driving modules across diverse views into a unified network.
  arXiv  Detail & Related papers  (2024-03-31T15:22:11Z)
• CMP: Cooperative Motion Prediction with Multi-Agent Communication [21.60646440715162]
  This paper explores the feasibility and effectiveness of cooperative motion prediction. Our method, CMP, takes LiDAR signals as model input to enhance tracking and prediction capabilities. In particular, CMP reduces the average prediction error by 12.3% compared with the strongest baseline.
  arXiv  Detail & Related papers  (2024-03-26T17:53:27Z)
• What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception [52.41695608928129]
  Multi-agent perception (MAP) allows autonomous systems to understand complex environments by interpreting data from multiple sources. This paper investigates intermediate collaboration for MAP with a specific focus on exploring "good" properties of collaborative view. We propose a novel framework named CMiMC for intermediate collaboration.
  arXiv  Detail & Related papers  (2024-03-15T07:18:55Z)
• Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving [49.03947018718156]
  We propose a unified domain generalization framework to be utilized during the training and inference stages of collaborative perception. We also introduce an intra-system domain alignment mechanism to reduce or potentially eliminate the domain discrepancy among connected and autonomous vehicles.
  arXiv  Detail & Related papers  (2023-11-28T12:52:49Z)
• Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving [49.42873226593071]
  We propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP) for V2X communication-aided autonomous driving. We use historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.
  arXiv  Detail & Related papers  (2023-04-24T04:59:13Z)
• V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer [58.71845618090022]
  We build a holistic attention model, namely V2X-ViT, to fuse information across on-road agents. V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention. To validate our approach, we create a large-scale V2X perception dataset.
  arXiv  Detail & Related papers  (2022-03-20T20:18:25Z)
• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning [85.6386289476598]
  We develop a novel adversarial graph representation adaptation (AGRA) framework for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T15:00:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.