恶劣天气条件下基于激光雷达-4D雷达融合的混合鲁棒协同感知
Hybrid Robust Collaborative Perception with LiDAR-4D Radar Fusion under Adverse Weather Conditions
在车路/车车协同感知场景下，用4D毫米波雷达补足退化激光雷达的稀疏点云，其双向跨模态门控（BCMG）做特征可靠性互校验、自适应特征增强（AFE）修复被抑制区域，可直接迁移到多智能体多模态融合的鲁棒检测框架中。
原文依据 we propose a bidirectional cross-modal gating (BCMG) module that enables LiDAR and 4D radar to mutually validate feature reliability
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论当前的协同感知系统已显著提升了3D目标检测性能。然而，广泛使用的激光雷达与相机系统在恶劣天气条件下往往出现性能退化。具备天气鲁棒性的4D雷达为解决这一挑战提供了有前景的方案。尽管如此，由于跨模态损坏与信息丢失，将稀疏的4D雷达测量与退化的激光雷达数据有效融合仍是一项重大挑战。本文提出一种新颖的混合鲁棒协同感知框架（HRCP），旨在通过激光雷达-4D雷达融合提升恶劣天气条件下的协同感知性能。具体而言，我们引入一种混合协作策略，该策略考虑二者不同的物理特性，并在信息传输过程中对其分别处理。此外，我们提出双向跨模态门控（BCMG）模块，使激光雷达与4D雷达能够相互校验特征可靠性，从而保证跨模态表征的一致性；并提出自适应特征增强（AFE）模块，对退化与被抑制区域进行全面的细化，以缓解信息丢失。大量实验结果表明，在恶劣天气条件下，本方法优于此前的最优方法。
英文原文摘要 1357 字符
Current collaborative perception systems have significantly improved 3D object detection performance. However, widely used LiDAR and camera systems often suffer performance degradation under adverse weather conditions. The weather-robust 4D radar provides a promising solution to address this challenge. Nevertheless, the effective fusion of sparse 4D radar measurements with degraded LiDAR data remains a significant challenge due to cross-modal corruption and information loss. In this work, we propose a novel hybrid robust collaborative perception framework (HRCP), designed to improve the collaborative perception performance under adverse weather conditions through LiDAR-4D radar fusion. Specifically, we introduce a hybrid collaboration strategy that considers their distinct physical properties and differently processes them during information transmission. Additionally, we propose a bidirectional cross-modal gating (BCMG) module that enables LiDAR and 4D radar to mutually validate feature reliability, ensuring consistent cross-modal representation, and an adaptive feature enhancement (AFE) module that enables comprehensive refinement of degraded and suppressed regions to mitigate information loss. Extensive experimental results demonstrate that our method outperforms previous state-of-the-art approaches under adverse weather conditions.