Exploiting Virtual Array Diversity for Accurate Radar Detection

Using millimeter-wave radars as a perception sensor provides self-driving cars with robust sensing capability in adverse weather. However, mmWave radars currently lack sufficient spatial resolution for semantic scene understanding. This paper introduces Radatron++, a system leverages cascaded MIMO (Multiple-Input Multiple-Output) radar to achieve accurate vehicle detection for self-driving cars. We develop a novel hybrid radar processing and deep learning approach to leverage the 10x finer angular resolution while combating unique challenges of cascaded MIMO radars. We train and evaluate Radatron++ with a novel cascaded radar dataset. Radatron++ achieves 93.9% and 58.5% Average Precisions with 0.5 and 0.75 Intersection over Union thresholds respectively in 2D bounding box detection, outperforming prior work using low-resolution radars by 9.3% and 18.1% respectively.