用注意力机制提升雷达点云的时空建模，显著提高车辆检测精度。
AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection
- 引入基于注意力的循环网络，动态融合时序关联结构
- 在nuScenes数据集上，车辆检测mAP提升21%
- 无需外接车辆运动信息，适合真实驾驶场景
鸟瞰图（BEV）目标检测在高级汽车3D雷达感知系统中日益重要。然而，雷达数据固有的稀疏性和非确定性限制了传统单帧BEV范式的有效性。本文提出AttentiveGRU，一种针对雷达特性设计的新型注意力增强循环方法，通过动态识别并融合当前与记忆状态间的时序相关结构，为物体提取个性化的时空上下文。利用物体在时间上潜在表示的一致性，该方法挖掘时序关系以丰富静态与移动物体的特征表示，从而提升检测性能，并消除对外部提供或估计本车运动信息的需求。在公开的nuScenes数据集上的实验结果表明，车辆类别mAP较最佳雷达仅提交方案提升21%。在额外数据集上的评估也显示检测能力显著增强，验证了方法的适用性与有效性。
原文摘要 · Abstract (English)
Bird's-eye view (BEV) object detection has become important for advanced automotive 3D radar-based perception systems. However, the inherently sparse and non-deterministic nature of radar data limits the effectiveness of traditional single-frame BEV paradigms. In this paper, we addresses this limitation by introducing AttentiveGRU, a novel attention-based recurrent approach tailored for radar constraints, which extracts individualized spatio-temporal context for objects by dynamically identifying and fusing temporally correlated structures across present and memory states. By leveraging the consistency of object's latent representation over time, our approach exploits temporal relations to enrich feature representations for both stationary and moving objects, thereby enhancing detection performance and eliminating the need for externally providing or estimating any information about ego vehicle motion. Our experimental results on the public nuScenes dataset show a significant increase in mAP for the car category by 21% over the best radar-only submission. Further evaluations on an additional dataset demonstrate notable improvements in object detection capabilities, underscoring the applicability and effectiveness of our method.
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