论文标题
基于雷达数据的原始对象检测和标题估计，使用交叉注意
Raw Radar data based Object Detection and Heading estimation using Cross Attention
论文作者
论文摘要
雷达是用于自动驾驶功能的感知传感器设置的必然部分。它在各种情况和天气条件下还可以补充其他传感器的缺点。在本文中，我们建议使用原始雷达数据基于深度神经网络（DNN）基于端到端对象检测和标题估计框架。为此，我们以以数据为中心和以模型为中心的方式解决了问题。我们完善了公开可用的Carrada数据集并引入双变量标准注释。此外，由变压器启发的跨注意融合改善了基线模型，并添加了进一步的中心偏移图以减少本地化误差。我们提出的模型将检测平均平均精度（MAP）提高了5％，同时将模型复杂性降低了几乎23％。为了进行全面的场景理解目的，我们扩展了模型以进行标题估算。改进的地面真相和建议的模型可在GitHub上获得
Radar is an inevitable part of the perception sensor set for autonomous driving functions. It plays a gap-filling role to complement the shortcomings of other sensors in diverse scenarios and weather conditions. In this paper, we propose a Deep Neural Network (DNN) based end-to-end object detection and heading estimation framework using raw radar data. To this end, we approach the problem in both a Data-centric and model-centric manner. We refine the publicly available CARRADA dataset and introduce Bivariate norm annotations. Besides, the baseline model is improved by a transformer inspired cross-attention fusion and further center-offset maps are added to reduce localisation error. Our proposed model improves the detection mean Average Precision (mAP) by 5%, while reducing the model complexity by almost 23%. For comprehensive scene understanding purposes, we extend our model for heading estimation. The improved ground truth and proposed model is available at Github
