PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain

Farzan Nowruzi, Farzan Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Elnaz Heravi, Fahed Al Hassanat, Fahed Al Hassanat, Robert Laganiere, Robert Laganiere, Julien Rebut, Waqas Malik

2021

Abstract

Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems. Radar still relies on traditional signal processing techniques, unlike camera and Lidar based methods. We believe this is the missing link to achieve the most robust perception system. Identifying drivable space and occupied space is the first step in any autonomous decision making task. Occupancy grid map representation of the environment is often used for this purpose. In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation. We explore various input-output representations. Our experiments show that PolarNet is a effective way to process radar data that achieves state-of-the-art performance and processing speeds while maintaining a compact size.

Paper Citation

in Harvard Style

Nowruzi F., Kolhatkar D., Kapoor P., Heravi E., Al Hassanat F., Laganiere R., Rebut J. and Malik W. (2021). PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 413-420. DOI: 10.5220/0010434604130420

in Bibtex Style

@conference{vehits21,
author={Farzan Nowruzi and Dhanvin Kolhatkar and Prince Kapoor and Elnaz Heravi and Fahed Al Hassanat and Robert Laganiere and Julien Rebut and Waqas Malik},
title={PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={413-420},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010434604130420},
isbn={978-989-758-513-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain
SN - 978-989-758-513-5
AU - Nowruzi F.
AU - Kolhatkar D.
AU - Kapoor P.
AU - Heravi E.
AU - Al Hassanat F.
AU - Laganiere R.
AU - Rebut J.
AU - Malik W.
PY - 2021
SP - 413
EP - 420
DO - 10.5220/0010434604130420