Raw High-Definition Radar for Multi-Task Learning
- URL: http://arxiv.org/abs/2112.10646v1
- Date: Mon, 20 Dec 2021 16:15:26 GMT
- Title: Raw High-Definition Radar for Multi-Task Learning
- Authors: Julien Rebut, Arthur Ouaknine, Waqas Malik and Patrick P\'erez
- Abstract summary: We propose a novel HD radar sensing model, FFT-RadNet, that eliminates the overhead of computing the Range-Azimuth-Doppler 3D tensor.
FFT-RadNet is trained both to detect vehicles and to segment free driving space.
On both tasks, it competes with the most recent radar-based models while requiring less compute and memory.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With their robustness to adverse weather conditions and ability to measure
speeds, radar sensors have been part of the automotive landscape for more than
two decades. Recent progress toward High Definition (HD) Imaging radar has
driven the angular resolution below the degree, thus approaching laser scanning
performance. However, the amount of data a HD radar delivers and the
computational cost to estimate the angular positions remain a challenge. In
this paper, we propose a novel HD radar sensing model, FFT-RadNet, that
eliminates the overhead of computing the Range-Azimuth-Doppler 3D tensor,
learning instead to recover angles from a Range-Doppler spectrum. FFT-RadNet is
trained both to detect vehicles and to segment free driving space. On both
tasks, it competes with the most recent radar-based models while requiring less
compute and memory. Also, we collected and annotated 2-hour worth of raw data
from synchronized automotive-grade sensors (camera, laser, HD radar) in various
environments (city street, highway, countryside road). This unique dataset,
nick-named RADIal for "Radar, Lidar et al.", is available at
https://github.com/valeoai/RADIal.
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