The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However, training radar models is hindered by the cost and difficulty of annotating largescale radar data. To overcome this bottleneck, we propose a self-supervised learning framework to leverage the large amount of unlabeled radar data to pre-train radar only embeddings for self-driving perception tasks. The proposed method combines radar-to-radar and radar-to-vision contrastive losses to learn a general representation from unlabeled radar heatmaps paired with their corresponding camera images. When used for downstream object detection, we demonstrate that the proposed self-supervision framework can improve the accuracy of state-of-the-art supervised baselines by 5.8% in mAP. Code is available at https://github.com/yiduohao/Radical.

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 10× 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.

This paper introduces RFVibe, a system that enables contactless material and object identification through the fusion of millimeter wave wireless signals with acoustic signals. In particular, RFVibe plays an audio sound next to the object that generates micro-vibrations in the object. These micro-vibrations can be captured by shining a millimeter wave radar signal on the object and analyzing the phase of the reflected wireless signal. RFVibe can then extract several features including resonance frequencies and vibration modes, damping time of vibrations, and wireless reflection coefficients. These features are then used to enable more accurate identification, with a step towards generalizing towards different setups and locations. We implement RFVibe using an off-the-shelf millimeter-wave radar and an acoustic speaker. We evaluate it on 23 objects of 7 material types (Metal, Wood, Ceramic, Glass, Plastic, Cardboard, and Foam), obtaining 81.3% accuracy for material classification, a 30% improvement over prior work. RFVibe is able to classify with reasonable accuracy in scenarios that it has not encountered before, including different locations, angles, boundary conditions, and objects.

This paper introduces RFVibe, a system that enables contactless material and object identification through the fusion of millimeter wave wireless signals with acoustic signals. In particular, RFVibe plays an audio sound next to the object that generates micro-vibrations in the object. These micro-vibrations can be captured by shining a millimeter wave radar signal on the object and analyzing the phase of the reflected wireless signal. RFVibe can then extract several features including resonance frequencies and vibration modes, damping time of vibrations, and wireless reflection coefficients. These features are then used to enable more accurate identification, with a step towards generalizing towards different setups and locations. We implement RFVibe using an off-the-shelf millimeter-wave radar and an acoustic speaker. We evaluate it on 23 objects of 7 material types (Metal, Wood, Ceramic, Glass, Plastic, Cardboard, and Foam), obtaining 81.3% accuracy for material classification, a 30% improvement over prior work. RFVibe is able to classify with reasonable accuracy in scenarios that it has not encountered before, including different locations, angles, boundary conditions, and objects.

This paper demonstrates high-resolution imaging using millimeter Wave (mmWave) radars that can function even in dense fog. We leverage the fact that mmWave signals have favorable propagation characteristics in low visibility conditions, unlike optical sensors like cameras and LiDARs which cannot penetrate through dense fog. Millimeter-wave radars, however, suffer from very low resolution, specularity, and noise artifacts. We introduce HawkEye, a system that leverages a cGAN architecture to recover high-frequency shapes from raw low-resolution mmWave heat-maps. We propose a novel design that addresses challenges specific to the structure and nature of the radar signals involved. We also develop a data synthesizer to aid with large-scale dataset generation for training. We implement our system on a custom-built mmWave radar platform and demonstrate performance improvement over both standard mmWave radars and other competitive baselines.