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

Source: [Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar](<https://www.sota2.com/research/paper/68fa6d1b1e209131cea37c8c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:26:36.450Z

Authors: Michael Ulrich, Sascha Braun, Daniel K\\"ohler, Daniel Niederl\\"ohner, Florian Faion, Claudius Gl\\"aser, Holger Blume

Published: 2022-05-03T06:29:03.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [3D Object Detection on nuScenes v1.0 (test)](<https://www.sota2.com/research/sota/3d-object-detection-on-nuscenes-v1-0-test.md>): 3D Object Detection; NuScenes v1.0 (test); mAP: 4.9.

- [3D Object Detection on nuScenes (test)](<https://www.sota2.com/research/sota/3d-object-detection-on-nuscenes-test.md>): 3D Object Detection; nuScenes (test); mAP: 4.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.02111>)

[PDF](<https://arxiv.org/pdf/2205.02111.pdf>)