Accurate detection for self driving cars using multi-resolution MIMO radar
Ahmed, Waleed
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https://hdl.handle.net/2142/116267
Description
Title
Accurate detection for self driving cars using multi-resolution MIMO radar
Author(s)
Ahmed, Waleed
Issue Date
2022-07-19
Director of Research (if dissertation) or Advisor (if thesis)
Al-Hassanieh, Haitham
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Radar Perception
Self-driving Cars
Object Detection
MIMO Radar
mmWave Sensing
Automotive Radar Dataset
Language
eng
Abstract
Millimeter wave (mmWave) radars are becoming a more popular sensing modality in self-driving cars due to their favorable characteristics in adverse weather. Yet, they currently lack sufficient spatial resolution for semantic scene understanding. In this thesis, we present Radatron, a system capable of accurate object detection using mmWave radar as a stand-alone sensor. To enable Radatron, we introduce a first-of-its-kind, high resolution automotive radar dataset collected with a cascaded MIMO (Multiple Input Multiple Output) radar. Our radar achieves 5cm range resolution and 1.2 degrees angular resolution, 10x finer than other publicly available datasets. We also develop a novel hybrid radar processing and deep learning approach to achieve high vehicle detection accuracy. We train and extensively evaluate Radatron to show it achieves 92.6% AP50 and 56.3% AP75 accuracy in 2D bounding box detection, an 8% and 15.9% improvement over prior art respectively.
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