RadarMP: Motion Perception for 4D mmWave Radar in Autonomous Driving

TL;DR

RadarMP introduces a unified, self-supervised approach for precise 3D scene motion perception using 4D mmWave radar, improving autonomous driving safety under various weather conditions.

Contribution

It presents a novel joint modeling method for radar target detection and motion estimation, tailored to radar data, with self-supervised learning to enhance robustness and accuracy.

Findings

Outperforms existing radar-based motion perception methods.

Reliable across diverse weather and illumination conditions.

Enhances autonomous driving perception capabilities.

Abstract

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.