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arXiv:2209.14499 (cs)
[Submitted on 29 Sep 2022 (v1), last revised 1 Mar 2023 (this version, v2)]

Title:NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving

Authors:Alexander Popov, Patrik Gebhardt, Ke Chen, Ryan Oldja, Heeseok Lee, Shane Murray, Ruchi Bhargava, Nikolai Smolyanskiy
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Abstract:Detecting obstacles is crucial for safe and efficient autonomous driving. To this end, we present NVRadarNet, a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors. The network utilizes temporally accumulated data from multiple RADAR sensors to detect dynamic obstacles and compute their orientation in a top-down bird's-eye view (BEV). The network also regresses drivable free space to detect unclassified obstacles. Our DNN is the first of its kind to utilize sparse RADAR signals in order to perform obstacle and free space detection in real time from RADAR data only. The network has been successfully used for perception on our autonomous vehicles in real self-driving scenarios. The network runs faster than real time on an embedded GPU and shows good generalization across geographic regions.
Comments: 7 pages, 6 figures, ICRA 2023 conference, for associated video file, see this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
MSC classes: 68T07 (Primary), 68T45 (Secondary)
ACM classes: I.2.10; I.2.6; I.2.9
Cite as: arXiv:2209.14499 [cs.CV]
  (or arXiv:2209.14499v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2209.14499

Submission history

From: Nikolai Smolyanskiy [view email]
[v1] Thu, 29 Sep 2022 01:30:34 UTC (1,221 KB)
[v2] Wed, 1 Mar 2023 05:45:02 UTC (2,459 KB)
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