发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对汽车毫米波雷达硬件故障数据稀缺问题，提出Rad-R原始ADC雷达数据集与捕获不变RadrNet模型，经基准评估，该模型在受控跨严重程度故障诊断任务中性能优于对比模型，且在其他任务中也表现领先。
AI 中文摘要
汽车毫米波雷达可能出现振动、天线失准、雷达罩遮挡以及接收通道退化等问题，这些问题会在感知开始前就破坏信号。由于每种故障都必须在物理硬件上进行诱导和测量，因此相关数据十分稀缺。我们推出Rad-R，这是一个采用4芯片77GHz TI MMWCAS-RF-EVM级联（192个虚拟通道）捕获的原始ADC数据集。与现有的原始雷达数据集不同，Rad-R的每条记录都对应一个经校准严重程度的受控硬件故障、一项独立的物理严重程度测量值，以及与帧同步的IMU、温度、GPS和相机数据流。Rad-R是单会话数据集，因此我们的泛化主张仅限于受控跨严重程度协议，其中训练和测试使用物理上不同的捕获数据。一个可复现的基准评估了7种代表性视觉骨干网络和所提出的原始IQ Mamba SSM（RadrNet）在片段内、逐线性调频随时、少样本跨捕获以及受控跨严重程度协议下的性能。片段内性能接近饱和（宏F1值>0.98），而跨严重程度泛化仍然困难：采用绝对相位的RadrNet-DS的宏F1值降至0.49。RadrNet-DS-CI用逐帧标准化幅度和相对线性调频间相位取代绝对相位，在受控基准中排名第一（3个随机种子下，宏F1值为0.663，而最强的RD-CNN为0.628）；RadrNet系列在随时和少样本预算下也处于领先地位。描述性跨模态分析进一步发现，雷达微多普勒与独立测量的IMU振动能量存在协变（跨所有条件的合并斯皮尔曼ρ=0.41）。完整数据集和代码将以宽松许可公开发布。
英文摘要
Automotive mmWave radar can develop vibration, antenna misalignment, radome blockage, and receive-channel degradation that corrupt the signal before perception begins. Data for these faults are scarce because each condition must be induced and measured on physical hardware. We introduce Rad-R, a raw-ADC dataset captured with a 4-chip 77GHz TI MMWCAS-RF-EVM cascade (192 virtual channels). Unlike existing raw-radar datasets, Rad-R pairs each recording with a controlled hardware fault at a calibrated severity, an independent physical severity measurement, and frame-synchronised IMU, temperature, GPS, and camera streams. Rad-R is a single-session dataset, so our generalisation claims are confined to a controlled cross-severity protocol in which train and test use physically distinct captures. A reproducible benchmark evaluates seven representative vision backbones and the proposed raw-IQ Mamba SSM (RadrNet) under within-clip, chirp-wise anytime, few-shot cross-capture, and controlled cross-severity protocols. Within-clip performance is near-saturated ($>0.98$ macro-F1), whereas cross-severity generalisation remains difficult: the absolute-phase RadrNet-DS falls to $0.49$ macro-F1. RadrNet-DS-CI replaces absolute phase with per-frame-standardised magnitude and relative chirp-to-chirp phase and ranks first on the controlled benchmark ($0.663$ vs. $0.628$ for the strongest RD-CNN; three seeds); the RadrNet family also leads on the anytime and few-shot budgets. A descriptive cross-modal analysis further finds that radar micro-Doppler covaries with independently measured IMU vibration energy (pooled Spearman $ρ=0.41$ across conditions). The complete dataset and code will be released publicly under permissive licences.
Commentsv2: author list updated
