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PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain
[article]
2021
arXiv
pre-print
Preserved Fulltext
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. ...
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. ...
A deep model, PolarNet, that takes radar frames in polar coordinate and generates an open-space segmentation mask in a parking-lot scenario 2. ...
arXiv:2103.03387v1
fatcat:mwdttkalanbwxeypnp6x2iseva
Deep learning for radar data exploitation of autonomous vehicle
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
A deep learning architecture is also proposed to estimate the RADAR signal processing pipeline while performing multitask learning for object detection and free driving space segmentation. ...
This thesis then present a proposed set of deep learning architectures with their associated loss functions for RADAR semantic segmentation. ...
For these reasons, there was no open source RADAR dataset for automotive application before 2019, which has hampered research on deep learning applied to RADAR data. ...
arXiv:2203.08038v1
fatcat:zjupxkpaffgavm45oqpwnhkczq