ELMAR: Enhancing LiDAR Detection with 4D Radar Motion Awareness and Cross-modal Uncertainty

TL;DR

This paper introduces ELMAR, a LiDAR detection framework that integrates 4D radar motion data and cross-modal uncertainty estimation to improve perception accuracy and robustness in autonomous driving.

Contribution

The novel framework leverages 4D radar motion awareness and uncertainty modeling to enhance LiDAR detection, addressing modality misalignment issues.

Findings

Achieves state-of-the-art mAP of 74.89% on VoD dataset.

Maintains real-time inference at 30.02 FPS.

Effectively mitigates cross-modal misalignment.

Abstract

LiDAR and 4D radar are widely used in autonomous driving and robotics. While LiDAR provides rich spatial information, 4D radar offers velocity measurement and remains robust under adverse conditions. As a result, increasing studies have focused on the 4D radar-LiDAR fusion method to enhance the perception. However, the misalignment between different modalities is often overlooked. To address this challenge and leverage the strengths of both modalities, we propose a LiDAR detection framework enhanced by 4D radar motion status and cross-modal uncertainty. The object movement information from 4D radar is first captured using a Dynamic Motion-Aware Encoding module during feature extraction to enhance 4D radar predictions. Subsequently, the instance-wise uncertainties of bounding boxes are estimated to mitigate the cross-modal misalignment and refine the final LiDAR predictions. Extensive experiments on the View-of-Delft (VoD) dataset highlight the effectiveness of our method, achieving state-of-the-art performance with the mAP of 74.89% in the entire area and 88.70% within the driving corridor while maintaining a real-time inference speed of 30.02 FPS.