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arXiv · 2205.08406

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

Ravi Kothari·Ali Kariminezhad·Christian Mayr·Haoming Zhang
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Abstract

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

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Ravi Kothari, Ali Kariminezhad, Christian Mayr, Haoming Zhang. 2022-05-17. Raw Radar data based Object Detection and Heading estimation using Cross Attention. https://arxiv.org/abs/2205.08406

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