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

翻译：雷达是自动驾驶功能中不可或缺的感知传感器之一，它在多样化的场景和天气条件下扮演着填补其他传感器短板的角色。本文提出一种基于深度神经网络（DNN）的端到端目标检测与航向估计框架，该框架直接使用原始雷达数据。为此，我们同时从数据驱动和模型驱动两个角度切入问题：首先对公开的CARRADA数据集进行优化，并引入双变量范数标注；同时，通过受Transformer启发的交叉注意力融合机制改进基线模型，并额外引入中心偏移图以减少定位误差。所提模型在将检测平均精度（mAP）提升5%的同时，将模型复杂度降低近23%。为实现全面的场景理解，我们进一步扩展模型以支持航向估计。改进后的标注数据与模型已开源至Github。