探索多样环境条件下基于雷达的三维目标检测中的域偏移
Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions
AI总结：
本研究首次综合分析了4D雷达三维目标检测中的域偏移问题，揭示了天气、道路类型变化引发的显著域偏移及雷达点云生成的关键作用，为环境变化下的雷达数据采集策略提供参考。
摘要：
深度学习的快速发展及其与自动驾驶系统的融合，推动了基于多模态传感器的三维感知技术取得重大进展。值得注意的是，在恶劣天气和光照变化条件下，雷达传感器相比相机和激光雷达表现出更强的鲁棒性。本研究深入探讨了基于4D雷达的目标检测中常被忽视却至关重要的域偏移问题，考察了不同天气模式、道路类型等环境条件变化如何影响三维目标检测性能。研究结果揭示了不同天气场景下存在明显的域偏移，体现出独特的数据集敏感性，凸显了雷达点云生成的关键作用。此外，研究表明不同道路类型之间的转换（尤其是从高速公路到城市场景）会引发显著的域偏移，强调了在多样道路环境中开展多元化数据采集的必要性。据我们所知，这是首次针对域偏移对基于4D雷达的目标检测的影响开展的综合分析。我们认为这项实证研究有助于理解雷达数据中域偏移的复杂特性，并为应对环境变化的数据采集策略指明了方向。
英文摘要：
The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability.
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