Affiliation:
1. Beijing Institute of Technology
2. Dalian Minzu University, School of Science
3. Beijing Institute of Technology, School of Information and
4. Tsinghua University, Shool of Vehicle and Mobility
Abstract
<div class="section abstract"><div class="htmlview paragraph">The fusion of multi-modal perception in autonomous driving plays a pivotal role
in vehicle behavior decision-making. However, much of the previous research has
predominantly focused on the fusion of Lidar and cameras. Although Lidar offers
an ample supply of point cloud data, its high cost and the substantial volume of
point cloud data can lead to computational delays. Consequently, investigating
perception fusion under the context of 4D millimeter-wave radar is of paramount
importance for cost reduction and enhanced safety. Nevertheless, 4D
millimeter-wave radar faces challenges including sparse point clouds, limited
information content, and a lack of fusion strategies. In this paper, we
introduce, for the first time, an approach that leverages Graph Neural Networks
to assist in expressing features from 4D millimeter-wave radar point clouds.
This approach effectively extracts unstructured point cloud features, addressing
the loss of object detection due to sparsity. Additionally, we propose the
Multi-Modal Fusion Module (MMFM), which aligns and fuses features from graphs,
radar pseudo-images generated from Pillars, and camera images within a geometric
space. We validate our model using the View-of-Delft (VoD) dataset. Experimental
results demonstrate that the proposed method efficiently fuses camera and 4D
radar features, resulting in enhanced 3D detection performance.</div></div>
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献