雷达空间不确定性建模新方法，提升恶劣天气下感知可靠性。
RaUF: Learning the Spatial Uncertainty Field of Radar
- 基于物理特性的各向异性概率模型，学习雷达测量的细粒度不确定性。
- 在公开数据集和真实场景中实现高可靠空间检测，不确定性校准良好。
- 适合自动驾驶等对感知可靠性要求高的实际应用。
毫米波雷达在恶劣天气下具有独特优势，但存在空间分辨率低、方位模糊和杂波引起的虚假回波问题。现有方法主要通过粗到精的跨模态监督提升空间感知效果，却常忽略特征与标签映射的模糊性，导致几何推理病态，给下游感知任务带来根本挑战。本文提出RaUF，一种空间不确定性场学习框架，通过物理上合理的各向异性特性建模雷达测量。为解决冲突的特征-标签映射，设计各向异性概率模型以学习细粒度不确定性；为进一步提升可靠性，提出双向域注意力机制，利用空间结构与多普勒一致性之间的互补性，有效抑制虚假或多径反射。在公开基准和真实世界数据集上的大量实验表明，RaUF实现了高度可靠的时空检测，并具备良好的不确定性校准能力。下游案例研究进一步验证了其在复杂真实驾驶场景下的增强可靠性和可扩展性。
原文摘要 · Abstract (English)
Millimeter-wave radar offers unique advantages in adverse weather but suffers from low spatial fidelity, severe azimuth ambiguity, and clutter-induced spurious returns. Existing methods mainly focus on improving spatial perception effectiveness via coarse-to-fine cross-modal supervision, yet often overlook the ambiguous feature-to-label mapping, which may lead to ill-posed geometric inference and pose fundamental challenges to downstream perception tasks. In this work, we propose RaUF, a spatial uncertainty field learning framework that models radar measurements through their physically grounded anisotropic properties. To resolve conflicting feature-to-label mapping, we design an anisotropic probabilistic model that learns fine-grained uncertainty. To further enhance reliability, we propose a Bidirectional Domain Attention mechanism that exploits the mutual complementarity between spatial structure and Doppler consistency, effectively suppressing spurious or multipath-induced reflections. Extensive experiments on public benchmarks and real-world datasets demonstrate that RaUF delivers highly reliable spatial detections with well-calibrated uncertainty. Moreover, downstream case studies further validate the enhanced reliability and scalability of RaUF under challenging real-world driving scenarios.
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