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

Source: [Raw Radar data based Object Detection and Heading estimation using Cross Attention](<https://www.sota2.com/research/paper/68fa5a151e209131ce9bce58>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T16:18:39.070Z

Authors: Ravi Kothari, Ali Kariminezhad, Christian Mayr, Haoming Zhang

Published: 2022-05-17T14:42:13.000Z

## 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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.08406>)

[PDF](<https://arxiv.org/pdf/2205.08406.pdf>)