RadarMP：自动驾驶中4D毫米波雷达的运动感知
RadarMP: Motion Perception for 4D mmWave Radar in Autonomous Driving
AI总结：
针对4D毫米波雷达因点云稀疏噪声导致运动感知不精确的问题，提出RadarMP方法，通过统一架构联合建模目标检测与运动估计，利用自监督损失函数提升感知性能，增强自动驾驶系统在恶劣条件下的感知能力。
摘要：
准确的3D场景运动感知显著提升自动驾驶系统的安全性和可靠性。得益于全天候运行能力和独特的感知特性，4D毫米波雷达已成为高级自动驾驶的关键组件。然而，稀疏且带噪声的雷达点常导致运动感知不精确，使自动驾驶车辆在光学传感器因恶劣天气退化时感知能力受限。本文提出RadarMP，一种利用连续两帧低层次雷达回波信号实现精确3D场景运动感知的新方法。与现有分离雷达目标检测和运动估计的方法不同，RadarMP在统一架构中联合建模两项任务，实现一致的雷达点云生成和逐点3D场景流预测。针对雷达特性，我们设计了由多普勒频移和回波强度引导的专用自监督损失函数，无需显式标注即可有效监督空间和运动一致性。在公开数据集上的大量实验表明，RadarMP在不同天气和光照条件下实现了可靠的运动感知，优于基于雷达的解耦运动感知流水线，增强了全场景自动驾驶系统的感知能力。
英文摘要：
Accurate 3D scene motion perception significantly enhances the safety and reliability of an autonomous driving system. Benefiting from its all-weather operational capability and unique perceptual properties, 4D mmWave radar has emerged as an essential component in advanced autonomous driving. However, sparse and noisy radar points often lead to imprecise motion perception, leaving autonomous vehicles with limited sensing capabilities when optical sensors degrade under adverse weather conditions. In this paper, we propose RadarMP, a novel method for precise 3D scene motion perception using low-level radar echo signals from two consecutive frames. Unlike existing methods that separate radar target detection and motion estimation, RadarMP jointly models both tasks in a unified architecture, enabling consistent radar point cloud generation and pointwise 3D scene flow prediction. Tailored to radar characteristics, we design specialized self-supervised loss functions guided by Doppler shifts and echo intensity, effectively supervising spatial and motion consistency without explicit annotations. Extensive experiments on the public dataset demonstrate that RadarMP achieves reliable motion perception across diverse weather and illumination conditions, outperforming radar-based decoupled motion perception pipelines and enhancing perception capabilities for full-scenario autonomous driving systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
