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Free Space Segmentation using Automotive Radar

Conference Paper (2025)
Author(s)

S.M. Hassan (NXP Semiconductors, TU Delft - Microwave Sensing, Signals & Systems)

A. Palffy (TU Delft - Intelligent Vehicles)

F. Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)

Alexander Yarovoy (TU Delft - Microwave Sensing, Signals & Systems)

Suraj Ravindran (NXP Semiconductors)

D. Gavrila (TU Delft - Intelligent Vehicles)

Microwave Sensing, Signals & Systems
DOI related publication
https://doi.org/10.23919/EuRAD65285.2025.11233925
More Info
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Publication Year
2025
Language
English
Microwave Sensing, Signals & Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
3-6
ISBN (print)
979-8-3315-3649-7
ISBN (electronic)
978-2-87487-083-5
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

A data driven method is proposed to obtain free space segmentation using automotive radar point clouds. It aggregates automotive radar detection points from multiple timestamps, projects them into a Birds-Eye-View grid-based representation, and applies a semantic segmentation Neural Network (NN) to classify each grid for free space segmentation. A lidar based supervision is used to generate the ground truth for training. Moreover, debris objects are manually annotated to enable the NN model to learn to detect these uncommon objects. Experimental results on a proprietary 4D Imaging Radar dataset demonstrate that the proposed method gives improved free space segmentation as compared to other baseline methods.

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File under embargo until 17-05-2026