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

arXiv (Cornell University)

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

Authors 3

1. Ali Kariminezhad
2. Christian Mayr

   OTH Regensburg

   Affiliation as printed

   AVL GmbH , 93059 Regensburg , Germany

3. Haoming Zhang Aachen author is with Institute of Automatic Control

   RWTH Aachen University

   Affiliation as printed

   The author is with Institute of Automatic Control , RWTH Aachen Univer- sity , 52074 Aachen , Germany