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Authors: Farzan Erlik Nowruzi 1 ; 2 ; Dhanvin Kolhatkar 2 ; Prince Kapoor 2 ; Elnaz Jahani Heravi 2 ; Fahed Al Hassanat 1 ; 2 ; Robert Laganiere 1 ; 2 ; Julien Rebut 3 and Waqas Malik 3

Affiliations: 1 School of Electrical Engineering and Computer Sciences, University of Ottawa, Canada ; 2 Sensorcortek Inc., Canada ; 3 Valeo, France

Keyword(s): Deep Learning, Radar, Open Space Segmentation, Parking, Autonomous Driving, Environment Perception.

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.

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Paper citation in several formats:
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 - VEHITS; ISBN 978-989-758-513-5; ISSN 2184-495X, SciTePress, pages 413-420. DOI: 10.5220/0010434604130420

@conference{vehits21,
author={Farzan Erlik Nowruzi. and Dhanvin Kolhatkar. and Prince Kapoor. and Elnaz Jahani 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 - VEHITS},
year={2021},
pages={413-420},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010434604130420},
isbn={978-989-758-513-5},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain
SN - 978-989-758-513-5
IS - 2184-495X
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
PB - SciTePress

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