Accurate detection for self driving cars using multi-resolution MIMO radar

Ahmed, Waleed

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https://hdl.handle.net/2142/116267 Copy

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

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.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Waleed Ahmed

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