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arXiv · 2601.13386

Leveraging Transformer Decoder for Automotive Radar Object Detection

Changxu Zhang·Zhaoze Wang·Tai Fei·Christopher Grimm·Yi Jin·Claas Tebruegge·Ernst Warsitz·Markus Gardill
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Abstract

In this paper, we present a Transformer-based architecture for 3D radar object detection that uses a novel Transformer Decoder as the prediction head to directly regress 3D bounding boxes and class scores from radar feature representations. To bridge multi-scale radar features and the decoder, we propose Pyramid Token Fusion (PTF), a lightweight module that converts a feature pyramid into a unified, scale-aware token sequence. By formulating detection as a set prediction problem with learnable object queries and positional encodings, our design models long-range spatial-temporal correlations and cross-feature interactions. This approach eliminates dense proposal generation and heuristic post-processing such as extensive non-maximum suppression (NMS) tuning. We evaluate the proposed framework on the RADDet, where it achieves significant improvements over state-of-the-art radar-only baselines.

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Changxu Zhang, Zhaoze Wang, Tai Fei, Christopher Grimm, Yi Jin, Claas Tebruegge, Ernst Warsitz, Markus Gardill. 2026-01-19. Leveraging Transformer Decoder for Automotive Radar Object Detection. https://arxiv.org/abs/2601.13386

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