Accurate Detection Using Multi-Resolution Cascaded MIMO Radar
University of Illinois Urbana-Champaign
* indicates equal contribution
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 paper, 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 Multiple Input Multiple Output (MIMO) radar.
Our radar achieves 5cm range resolution and 1.2 degree 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.
Project Overview Video