公开(公告)号：US20240345243A1

公开(公告)日：2024-10-17

申请号：US18682148

申请日：2022-08-08

Applicant: VAYYAR IMAGING LTD.

Inventor： Michael ORLOVSKY ,  Raviv MELAMED ,  Omer GAL ,  Tanya CHERNYAKOVA ,  Shay MOSHE ,  Assaf KARTOWSKY

IPC: G01S13/88 ,  G01S7/41 ,  G01S13/00 ,  G01S13/86 ,  G01S13/89 ,  G01V3/12

CPC classification number: G01S13/887 ,  G01S7/412 ,  G01S13/003 ,  G01S13/89 ,  G01V3/12 ,  G01S13/867

Abstract: Systems and methods for scanning concealed surface and detecting concealed objects using a radar. A radar-based sensor unit has an array of transmitters and receivers which transmit a beam of electromagnetic radiations towards a subject being scanned and receive the reflected electromagnetic signals. A processing unit receives raw complex image data from the radar unit and processes the data for detecting specific concealed objects. A display unit displays images representing the concealed object. A database stores the processed data from the processing unit along with the raw complex image and the processed image data. Stored data may be used to train the processing unit for accurately detecting the specific concealed objects. A communicator may transmit notifications through a communication network.

公开(公告)号：US20240310512A1

公开(公告)日：2024-09-19

申请号：US18120531

申请日：2023-03-13

Applicant: APSTEC Systems Ltd

Inventor： Aleksei EVSENIN

IPC: G01S13/88 ,  G01S7/02 ,  G01S7/03 ,  G01S7/41 ,  G01S13/86 ,  G06T7/20 ,  G06V10/764 ,  G06V10/80

CPC classification number: G01S13/887 ,  G01S7/025 ,  G01S7/03 ,  G01S7/412 ,  G01S13/86 ,  G01S13/867 ,  G06T7/20 ,  G06V10/764 ,  G06V10/80 ,  G06T2207/10016 ,  G06V2201/05

Abstract: The disclosed invention comprises a security screening system and device for detecting and classifying items concealed on the body of a moving or stationary person, as well as in carried or wheeled luggage, in real time. The invention combines radio wave technology, magnetometer technology, and optical sensors to detect dielectric and/or metal objects on targets moving through the portal formed by the invention. The sensed data is then sent to a calculation processor of the system which forms 2-D images to determine the type of object, the location of the object, and other additional information relating to the target and/or object.

公开(公告)号：US12085636B2

公开(公告)日：2024-09-10

申请号：US17071700

申请日：2020-10-15

Applicant: Raytheon Company

Inventor： Eugene Lee ,  Steven G. Labitt ,  Charles L. Holland ,  Colin S. Whelan ,  Benjamin L. Caplan

IPC: G01S13/72 ,  G01S7/41

CPC classification number: G01S13/723 ,  G01S7/412

Abstract: Radar systems and methods detecting and tracking an unmanned aerial system (UAS) are provided. The radar system and methods can include determining whether or not a UAS is included in the plurality of electromagnetic signals received by the radar system based on one or more expected frequencies that one or more UAS devices use to transmit signals to remote controls. The radar systems and method can also involve switching the radar system into a track mode upon detecting a UAS.

公开(公告)号：US12072442B2

公开(公告)日：2024-08-27

申请号：US17456045

申请日：2021-11-22

Applicant: NVIDIA Corporation

Inventor： Tommi Koivisto ,  Pekka Janis ,  Tero Kuosmanen ,  Timo Roman ,  Sriya Sarathy ,  William Zhang ,  Nizar Assaf ,  Colin Tracey

IPC: G06V10/46 ,  B60W50/00 ,  G01S7/41 ,  G05D1/00 ,  G06F16/35 ,  G06F18/21 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2413 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06V10/20 ,  G06V10/44 ,  G06V10/762 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V20/58 ,  G01S7/48 ,  G01S13/86 ,  G01S13/931 ,  G01S17/931 ,  G06N3/047 ,  G06N3/048

CPC classification number: G01S7/417 ,  B60W50/00 ,  G05D1/0246 ,  G06F16/35 ,  G06F18/214 ,  G06F18/217 ,  G06F18/23 ,  G06F18/2414 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06V10/255 ,  G06V10/454 ,  G06V10/46 ,  G06V10/762 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/774 ,  G06V20/58 ,  G06V20/584 ,  G01S7/412 ,  G01S7/4802 ,  G01S13/867 ,  G01S2013/9318 ,  G01S2013/9323 ,  G01S17/931 ,  G06N3/047 ,  G06N3/048

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

公开(公告)号：US12072439B2

公开(公告)日：2024-08-27

申请号：US17077947

申请日：2020-10-22

Applicant: Robert Bosch GmbH

Inventor： Thomas Binzer ,  Anna Khoreva ,  Juergen Hasch

IPC: G01S7/40 ,  G01S7/41

CPC classification number: G01S7/4052 ,  G01S7/412 ,  G01S7/417

Abstract: A method for generating synthetic measurement data indistinguishable from actual measurement data captured by a first physical measurement modality. The first physical measurement modality is based on emitting an interrogating wave towards an object and recording a reflected wave coming from the object in a manner that allows for a determination of the time-of-flight between the emission of the interrogating beam and the arrival of the reflected wave. The method includes: obtaining a first compressed representation of the synthetic measurement data in a first latent space, wherein this first latent space is associated with a first decoder that is trained to map each element of the first latent space to a record of synthetic measurement data that is indistinguishable from records of actual measurement data of the first physical measurement modality, and applying the first decoder to the first compressed representation, so as to obtain the sought synthetic measurement data.

公开(公告)号：US12038499B1

公开(公告)日：2024-07-16

申请号：US18398309

申请日：2023-12-28

Applicant: SUN YAT-SEN UNIVERSITY

Inventor： Qingsong Wang ,  Cuilin Yu ,  Haifeng Huang ,  Tao Lai

IPC: G01S13/88 ,  G01S7/41 ,  G01S13/86 ,  G06F18/214 ,  G06F18/25

CPC classification number: G01S13/882 ,  G01S7/412 ,  G01S13/86 ,  G06F18/214 ,  G06F18/25

Abstract: A multi-source elevation data fusion method including: inputting acquired optical elevation data into a trained optical elevation error prediction model to output an optical elevation error value and an optical elevation error weight map; inputting acquired radar elevation data into a trained radar elevation error prediction model to output a radar elevation error value and a radar elevation error weight map; correcting the optical elevation data based on the optical elevation error value to obtain optical elevation data to be fused; correcting the radar elevation data based on the radar elevation error value to obtain radar elevation data to be fused; performing weighted fusion on the optical elevation data to be fused and the radar elevation data to be fused based on the optical elevation error weight map and the radar elevation error weight map to obtain fused elevation data. This method efficiently enhances the accuracy of fused elevation data.

公开(公告)号：US20240203107A1

公开(公告)日：2024-06-20

申请号：US18119625

申请日：2023-03-09

Applicant: HON HAI PRECISION INDUSTRY CO., LTD.

Inventor： YU-HSUAN CHIEN ,  CHIN-PIN KUO

IPC: G06V10/80 ,  G01S7/41 ,  G01S13/86 ,  G01S13/931 ,  G06T7/10 ,  G06T7/70 ,  G06V10/82 ,  G06V20/58

CPC classification number: G06V10/803 ,  G01S7/412 ,  G01S13/867 ,  G01S13/931 ,  G06T7/10 ,  G06T7/70 ,  G06V10/82 ,  G06V20/58 ,  G06T2207/10028 ,  G06T2207/20021 ,  G06T2207/30261

Abstract: An obstacle identification method applied to a vehicle-mounted device is provided. The method includes collecting radar information and original image information. Fused image information is obtained by fusing the radar information with the original image information. Obstacle point cloud and non-obstacle point cloud are obtained from the fused image information, and the non-obstacle point cloud is filtered out from the fused image information. Once at least one radar point cloud group is obtained by grouping the obstacle point cloud, an obstacle corresponding to each group of the at least one radar point cloud group can be identified.

公开(公告)号：US20240192355A1

公开(公告)日：2024-06-13

申请号：US18286340

申请日：2022-06-07

Applicant: QUALCOMM Incorporated

Inventor： Dan ZHANG ,  Kapil GULATI ,  Junyi LI ,  Ahmed BEDEWY ,  Stelios STEFANATOS

IPC: G01S13/72 ,  G01S7/288 ,  G01S7/292 ,  G01S7/41 ,  G01S13/931

CPC classification number: G01S13/726 ,  G01S7/2883 ,  G01S7/2922 ,  G01S7/2927 ,  G01S7/412 ,  G01S13/931

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for radar target tracking based on return signal strength. A wireless device, such as a radar device, may measure a received signal strength of a detected signal. The received signal strength may be associated with at least one bin of a radar image. The wireless device may compare the received signal strength to a filtered signal strength. The filtered signal strength may be associated with a threshold difference in signal strength from the filtered signal strength. The wireless device may track the at least one bin of the radar image in response to the received signal strength being less than or equal to the threshold difference in signal strength from the filtered signal strength.

公开(公告)号：US11966673B2

公开(公告)日：2024-04-23

申请号：US16818551

申请日：2020-03-13

Applicant: NVIDIA Corporation

Inventor： Steen Kristensen ,  Alessandro Ferrari ,  Ayman Elsaeid

IPC: G06F30/27 ,  G01S7/40 ,  G01S7/41 ,  G06N3/088 ,  G01S13/931

CPC classification number: G06F30/27 ,  G01S7/4052 ,  G01S7/412 ,  G06N3/088 ,  G01S13/931 ,  G01S2013/9323 ,  G01S2013/9324

Abstract: In various examples, a sensor model may be learned to predict virtual sensor data for a given scene configuration. For example, a sensor model may include a deep neural network that supports generative learning—such as a generative adversarial network (GAN). The sensor model may accept an encoded representation of a scene configuration as an input using any number of data structures and/or channels (e.g., concatenated vectors, matrices, tensors, images, etc.), and may output virtual sensor data. Real-world data and/or virtual data may be collected and used to derive training data, which may be used to train the sensor model to predict virtual sensor data for a given scene configuration. As such, one or more sensor models may be used as virtual sensors in any of a variety of applications, such as in a simulated environment to test features and/or functionality of one or more autonomous or semi-autonomous driving software stacks.

公开(公告)号：US20240118410A1

公开(公告)日：2024-04-11

申请号：US17963319

申请日：2022-10-11

Applicant: GM Cruise Holdings LLC

Inventor： Kotung Lin ,  Daniel Flores Tapia

IPC: G01S13/86 ,  G01S7/41 ,  G01S13/58 ,  G01S13/89 ,  G01S13/931 ,  G06V10/44 ,  G06V10/80 ,  G06V10/82 ,  G06V20/58

CPC classification number: G01S13/867 ,  G01S7/412 ,  G01S13/584 ,  G01S13/89 ,  G01S13/931 ,  G06V10/44 ,  G06V10/803 ,  G06V10/82 ,  G06V20/58

Abstract: Curvelet-based low level fusion of camera and RADAR sensor information is disclosed and includes processing RADAR data corresponding to a scene to generate a RADAR point cloud and processing camera image data corresponding to the scene using a curvelet transform to identify a target of interest in the scene and generate for the target of interest a target type, (x,y) coordinate values, and a curvelet magnitude per decomposition level. If discrepancies exist between (x,y) coordinate values of the RADAR point cloud and the target type, (x,y) coordinate values, and curvelet magnitude per composition level of the target of interest, a portion of the RADAR data processing is repeated to regenerate the RADAR point cloud; otherwise, the RADAR point cloud to a perception stack of a vehicle.