# Graph Query Networks for Object Detection with Automotive Radar

arXiv: https://arxiv.org/abs/2511.15271 · HTML: https://mlsanity.com/paper/2511.15271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Vision GNN: An Image is Worth Graph of Nodes](https://mlsanity.com/paper/2206.00272/) (2022)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2310.08261/) (2023)
- [Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar](https://mlsanity.com/paper/2205.02111/) (2022)
- [GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2403.11848/) (2024)
- [RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception](https://mlsanity.com/paper/2304.06547/) (2023)
- [RadarPillars: Efficient Object Detection from 4D Radar Point Clouds](https://mlsanity.com/paper/2408.05020/) (2024)
- [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://mlsanity.com/paper/2204.01184/) (2022)
- [A recurrent CNN for online object detection on raw radar frames](https://mlsanity.com/paper/2212.11172/) (2022)
- [CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar](https://mlsanity.com/paper/2311.01423/) (2023)
- [Deep Learning on Radar Centric 3D Object Detection](https://mlsanity.com/paper/2003.00851/) (2020)
- [Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks](https://mlsanity.com/paper/2305.15836/) (2023)
- [FocalAD: Local Motion Planning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.11419/) (2025)
- [Quantification of Uncertainties in Deep Learning-based Environment Perception](https://mlsanity.com/paper/2306.03018/) (2023)
- [SIRA: Scalable Inter-frame Relation and Association for Radar Perception](https://mlsanity.com/paper/2411.02220/) (2024)
- [AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection](https://mlsanity.com/paper/2504.00559/) (2025)

## Abstract (arXiv)

Object detection with 3D radar is essential for 360-degree automotive perception, but radar's long wavelengths produce sparse and irregular reflections that challenge traditional grid and sequence-based convolutional and transformer detectors. This paper introduces Graph Query Networks (GQN), an attention-based framework that models objects sensed by radar as graphs, to extract individualized relational and contextual features. GQN employs a novel concept of graph queries to dynamically attend over the bird's-eye view (BEV) space, constructing object-specific graphs processed by two novel modules: EdgeFocus for relational reasoning and DeepContext Pooling for contextual aggregation. On the NuScenes dataset, GQN improves relative mAP by up to +53%, including a +8.2% gain over the strongest prior radar method, while reducing peak graph construction overhead by 80% with moderate FLOPs cost.