Graph Query Networks for Object Detection with Automotive Radar

• URL: http://arxiv.org/abs/2511.15271v1
• Date: Wed, 19 Nov 2025 09:36:49 GMT
• Title: Graph Query Networks for Object Detection with Automotive Radar
• Authors: Loveneet Saini, Hasan Tercan, Tobias Meisen,
• Abstract summary: This paper introduces Graph Query Networks (GQN), an attention-based framework that models objects sensed by radar as graphs.<n>On the NuScenes dataset, GQN improves relative mAP by up to +53%, including a +8.2% gain over the strongest prior radar method.
• Score: 15.25428401059991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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