RaUF: Learning the Spatial Uncertainty Field of Radar

TL;DR

RaUF introduces a novel framework for radar perception that models spatial uncertainty using anisotropic probabilistic techniques and a bidirectional attention mechanism, significantly improving detection reliability and robustness in adverse conditions.

Contribution

The paper presents a physically grounded anisotropic probabilistic model and a Bidirectional Domain Attention mechanism to better handle radar ambiguities and spurious reflections.

Findings

RaUF achieves highly reliable spatial detections with well-calibrated uncertainty.

The framework improves robustness against multipath and clutter effects.

Downstream tasks benefit from enhanced reliability and scalability.

Abstract

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.