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Radar is an inevitable part of the perception sensor set for autonomousdriving functions. It plays a gap-filling role to complement the shortcomingsof other sensors in diverse scenarios and weather conditions. In this paper, wepropose a Deep Neural Network (DNN) based end-to-end object detection andheading estimation framework using raw radar data. To this end, we approach theproblem in both a Data-centric and model-centric manner. We refine the publiclyavailable CARRADA dataset and introduce Bivariate norm annotations. Besides,the baseline model is improved by a transformer inspired cross-attention fusionand further center-offset maps are added to reduce localisation error. Ourproposed model improves the detection mean Average Precision (mAP) by 5%, whilereducing the model complexity by almost 23%. For comprehensive sceneunderstanding purposes, we extend our model for heading estimation. Theimproved ground truth and proposed model is available at Github

Raw Radar data based Object Detection and Heading estimation using Cross Attention