Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar

TL;DR

This paper introduces hybrid radar object detection architectures combining grid- and point-based methods, significantly enhancing detection accuracy and orientation estimation in automotive radar applications.

Contribution

It proposes a novel hybrid approach that leverages point-based neighborhood features before grid rendering, improving detection and orientation estimation over existing methods.

Findings

19.7% higher mAP for car detection on nuScenes

11.5% relative improvement in orientation estimation

Outperforms previous radar-only detection networks

Abstract

This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks. Purely grid-based detection models operate on a bird's-eye-view (BEV) projection of the input point cloud. These approaches suffer from a loss of detailed information through the discrete grid resolution. This applies in particular to radar object detection, where relatively coarse grid resolutions are commonly used to account for the sparsity of radar point clouds. In contrast, point-based models are not affected by this problem as they process point clouds without discretization. However, they generally exhibit worse detection performances than grid-based methods. We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering. This has significant benefits for a subsequent grid-based convolutional detection backbone. In experiments on the public nuScenes dataset our hybrid architecture achieves improvements in terms of detection performance (19.7% higher mAP for car class than next-best radar-only submission) and orientation estimates (11.5% relative orientation improvement) over networks from previous literature.