Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Lee

et al. 2022

Sci Rep

Anthropometric profiles are important indices for assessing medical conditions, including malnutrition, obesity, and growth disorders. Noncontact methods for estimating those parameters could have considerable value in many practical situations, such as the assessment of young, uncooperative infants or children and the prevention of infectious disease transmission. The purpose of this study was to investigate the feasibility of obtaining noncontact anthropometric measurements using the impulse-radio ultrawideband (IR-UWB) radar sensor technique. A total of 45 healthy adults were enrolled, and a convolutional neural network (CNN) algorithm was implemented to analyze data extracted from IR-UWB radar. The differences (root-mean-square error, RMSE) between values from the radar and bioelectrical impedance analysis (BIA) as a reference in the measurement of height, weight, and body mass index (BMI) were 2.78, 5.31, and 2.25, respectively; predicted data from the radar highly agreed with those from the BIA. The intraclass correlation coefficients (ICCs) were 0.93, 0.94, and 0.83. In conclusion, IR-UWB radar can provide accurate estimates of anthropometric parameters in a noncontact manner; this study is the first to support the radar sensor as an applicable method in clinical situations.

Section: Discussionmentioning

confidence: 99%

Preclinical trial of noncontact anthropometric measurement using IR-UWB radar

Lee

et al. 2022

Sci Rep

IEEE Robot. Autom. Lett.

“…Recently, several automotive datasets containing radar data were published for various tasks such as localization [35], [36], object classification [37], or scene understanding with a stationary radar sensor [38]. In this section, we focus on detection datasets that contain realistic recordings from a moving egovehicle.…”

Section: Radar Datasetsmentioning

confidence: 99%

Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset

Palffy

Pool

Baratam

et al. 2022

Next-generation automotive radars provide elevation data in addition to range-, azimuth-and Doppler velocity. In this experimental study, we apply a state-of-the-art object detector (PointPillars), previously used for LiDAR 3D data, to such 3+1D radar data (where 1D refers to Doppler). In ablation studies, we first explore the benefits of the additional elevation information, together with that of Doppler, radar cross section and temporal accumulation, in the context of multi-class road user detection. We subsequently compare object detection performance on the radar and LiDAR point clouds, object class-wise and as a function of distance. To facilitate our experimental study, we present the novel View-of-Delft (VoD) automotive dataset. It contains 8693 frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D radar-data acquired in complex, urban traffic. It consists of 123106 3D bounding box annotations of both moving and static objects, including 26587 pedestrian, 10800 cyclist and 26949 car labels. Our results show that object detection on 64-layer LiDAR data still outperforms that on 3+1D radar data, but the addition of elevation information and integration of successive radar scans helps close the gap. The VoD dataset is made freely available for scientific benchmarking at https://intelligent-vehicles.org/datasets/view-ofdelft/.

“…In fact, self-attention is basically a set operator, so it is well suited for sparse point clouds. Radar transformer et al [154] is a classification network constructed entirely of self-attention modules. The 4D radar point cloud is first sent to an MLP network for input embedding.…”

Section: Point Cloud Detectormentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.