DRIFT: Dual-Representation Inter-Fusion Transformer for Automated Driving Perception with 4D Radar Point Clouds

TL;DR

DRIFT introduces a dual-path transformer architecture that fuses local and global features from 4D radar point clouds, significantly improving perception tasks in automated driving under challenging conditions.

Contribution

The paper presents a novel dual-representation inter-fusion transformer that effectively combines local and global features from 4D radar data for enhanced perception.

Findings

Outperforms baseline models on object detection and road estimation

Achieves 52.6% mAP on VoD dataset, surpassing existing methods

Effectively fuses local and global features through feature-sharing layers

Abstract

4D radars, which provide 3D point cloud data along with Doppler velocity, are attractive components of modern automated driving systems due to their low cost and robustness under adverse weather conditions. However, they provide a significantly lower point cloud density than LiDAR sensors. This makes it important to exploit not only local but also global contextual scene information. This paper proposes DRIFT, a model that effectively captures and fuses both local and global contexts through a dual-path architecture. The model incorporates a point path to aggregate fine-grained local features and a pillar path to encode coarse-grained global features. These two parallel paths are intertwined via novel feature-sharing layers at multiple stages, enabling full utilization of both representations. DRIFT is evaluated on the widely used View-of-Delft (VoD) dataset and a proprietary internal dataset. It outperforms the baselines on the tasks of object detection and/or free road estimation. For example, DRIFT achieves a mean average precision (mAP) of 52.6% (compared to, say, 45.4% of CenterPoint) on the VoD dataset.