V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions

Document Type

Conference Paper

Publication Date

2025

Publication Title

2025 International Conference on Robotics and Automation-ICRA-Annual

Abstract

Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection. While these models perform well in the trained clean weather, they struggle in unseen adverse weather conditions with the domain gap. In this paper, we propose a Domain Generalization based approach, named V2X-DGW, for LiDAR-based 3D object detection on multi-agent perception system under adverse weather conditions. Our research aims to not only maintain favorable multi-agent performance in the clean weather but also promote the performance in the unseen adverse weather conditions by learning only on the clean weather data. To realize the Domain Generalization, we first introduce the Adaptive Weather Augmentation (AWA) to mimic the unseen adverse weather conditions, and then propose two alignments for generalizable representation learning: Trust-region Weather-invariant Alignment (TWA) and Agent-aware Contrastive Alignment (ACA). To evaluate this research, we add Fog, Rain, Snow conditions on two publicized multi-agent datasets based on physics-based models, resulting in two new datasets: OPV2V-w and V2XSet-w. Extensive experiments demonstrate that our V2X-DGW achieved significant improvements in the unseen adverse weathers. The code is available at https://github.com/Baolu1998/V2X-DGW.

Comments

Meeting: 2025 International Conference on Robotics and Automation-ICRA-Annual Location: Atlanta, GA
Date: MAY 19-23, 2025 
Sponsor: Institute of Electrical and Electronics Engineers Inc

This research is partially supported by NSF 2215388 and 2343167.

Original Citation

B. Li et al., "V2X-DGW: Domain Generalization for Multi-Agent Perception Under Adverse Weather Conditions," 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 2025, pp. 974-980, doi: 10.1109/ICRA55743.2025.11127945.

DOI

10.1109/ICRA55743.2025.11127945

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