Object Detection and Heading Estimation from Radar Raw data

IEEE Intelligent Vehicles Symposium, pp. 1–7

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 [1] dataset and introduce Bivariate norm annotations. Besides, the baseline model is improved by a transformer [2] 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

   Robert Bosch (Germany)

   Affiliation as printed

   Robert-Bosch GmbH,Hildesheim,Germany,31139

2. Christian Mayr
   Affiliation as printed

   e:fs TechHub GmbH,Gaimersheim,Germany,85080

3. Haoming Zhang Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Automatic Control,Aachen,Germany,52074