公开(公告)号：US20240104897A1

公开(公告)日：2024-03-28

申请号：US17953042

申请日：2022-09-26

Applicant: Micron Technology, Inc.

Inventor： Poorna Kale ,  Saideep Tiku ,  Robert Noel Bielby

IPC: G06V10/77 ,  B60R1/12 ,  B60R1/22 ,  G01S7/48 ,  G01S17/08 ,  G01S17/89 ,  G06V10/20 ,  G06V10/60 ,  G06V20/40 ,  G06V20/58 ,  H04N7/18

CPC classification number: G06V10/77 ,  B60R1/12 ,  B60R1/22 ,  G01S7/4808 ,  G01S17/08 ,  G01S17/89 ,  G06V10/20 ,  G06V10/60 ,  G06V20/41 ,  G06V20/58 ,  H04N7/183 ,  B60R2001/1253 ,  B60R2300/105 ,  B60R2300/30 ,  G06V2201/08

Abstract: Methods, systems, and devices for video stream augmentation using a deep learning device are described. A machine learning device of a vehicle may augment a video stream received from cameras of the vehicle and may output the augmented video stream to a display component of the vehicle. For example, a camera of the vehicle may record a video stream of and a sensor of the vehicle may detect information about an environment associated with the vehicle. The camera and sensor may transmit the video stream and information, respectively, to the machine learning device, which may process and modify the video stream based on parameters of the video stream and/or the information. The machine learning device may transmit the modified video streams to the display component, and the display component may display aspects of the modified video stream on a display of the vehicle, such as a rearview mirror.

公开(公告)号：US20240087333A1

公开(公告)日：2024-03-14

申请号：US17943576

申请日：2022-09-13

Applicant: NVIDIA Corporation

Inventor： Siva Kumar Sastry Hari ,  Jason Lavar Clemons ,  Timothy Kohchih Tsai

IPC: G06V20/58 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06V20/58 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V2201/08

Abstract: In various examples, techniques for detecting occluded objects within an environment are described. For instance, systems and methods may receive training data representing images and ground truth data indicating whether the images are associated with occluded objects or whether the images are not associated with occluded objects. The systems and methods may then train a neural network to detect occluded objects using the training data and the ground truth data. After training, the systems and methods may use the neural network to detect occluded objects within an environment. For instance, while a vehicle is navigating, the vehicle may process sensor data using the neural network. The neural network may then output data indicating whether an object is located within the environment and occluded from view of the vehicle. In some examples, the neural network may further output additional information associated with the occluded object.

公开(公告)号：US20240071089A1

公开(公告)日：2024-02-29

申请号：US18495108

申请日：2023-10-26

Applicant: CellXion Ltd

Inventor： Anthony TIMSON

IPC: G06V20/54 ,  G06V20/62 ,  G06V40/16 ,  G08G1/01 ,  G08G1/017 ,  G08G1/04 ,  H04N7/18

CPC classification number: G06V20/54 ,  G06V20/625 ,  G06V40/172 ,  G08G1/0116 ,  G08G1/0175 ,  G08G1/04 ,  H04N7/188 ,  G06V2201/08

Abstract: A system for use in identifying a person or vehicle carrying a mobile communications device includes a transmitter arranged to transmit a beacon signal for reception by the mobile communications device, a timing entity communicable with the mobile communications device via a base station apparatus and arranged to determine a capture time in dependence on a measurement report received from the mobile communications device indicating a peak measurement of the beacon signal by the mobile communications device, and a camera arranged to capture an image at the determined capture time for use in identifying the person or vehicle. The camera is positioned relative to the transmitter such that the image captured at the determined capture time contains the person or vehicle.

公开(公告)号：US20240059326A1

公开(公告)日：2024-02-22

申请号：US18211237

申请日：2023-06-16

Applicant: HL KLEMOVE CORP.

Inventor： Dokyung LEE

IPC: B60W60/00 ,  B60Q1/50 ,  B60W10/04 ,  B60W10/20 ,  G08G1/0967 ,  G06V20/58 ,  G06V20/56

CPC classification number: B60W60/005 ,  B60Q1/507 ,  B60W10/04 ,  B60W10/20 ,  G08G1/096725 ,  G08G1/096791 ,  G06V20/58 ,  G06V20/588 ,  B60W2420/42 ,  B60W2420/52 ,  B60W2556/65 ,  B60W2552/53 ,  G06V2201/08

Abstract: In an assist apparatus for travel of a vehicle, the assist apparatus may include a camera installed on a vehicle, having a field outside view of the vehicle, and configured to obtain image data, a radar installed on the vehicle, having a sensing area outside the vehicle, and configured to obtain radar data, a communication part configured to transmit and receive communication data to and from a nearby vehicle or external server, and a processor configured process at least one piece of data of the image data, the radar data, and the communication data, wherein the processor recognizes an autonomous driving level of the nearby vehicle on the basis of processing the data and can monitor whether the nearby vehicle needs assistance in response to the autonomous driving level recognized as a lower level than the vehicle.

公开(公告)号：US20240054795A1

公开(公告)日：2024-02-15

申请号：US17819311

申请日：2022-08-12

Applicant: Reby Inc.

Inventor： Elizabet Bayo Puxan ,  Eugeni Llagostera Saltor ,  Xiaolei Song ,  Akash Kadechkar ,  Ricard Comas Xanco ,  Julio Gonzalez Lopez

IPC: G06V20/62 ,  G06V30/19 ,  G06V30/12 ,  G06V30/146 ,  G06V30/14 ,  G06K7/14 ,  G06K7/10

CPC classification number: G06V20/625 ,  G06V30/19013 ,  G06V30/12 ,  G06V30/1473 ,  G06V30/1444 ,  G06K7/1417 ,  G06K7/10712 ,  G06V2201/08

Abstract: The present disclosure relates to a system and method for automatic vehicle recognition, based on a smart device. The system mainly includes an image capturing device integrated in the smart device, a data storage to store the images captured by the image capturing device and known identity aspects related to a vehicle allotted to a user, and a License plate processing and matching (LPPM) component to perform recognition process. LPPM component includes an identity aspect detector to detect a portion representing the identity aspect, an image processor to enhance the portion and perform Optical Characters Recognition (OCR) to extract character strings from the portion. The character string is compared against a character string of the known identity aspect to verify the vehicle.

公开(公告)号：US20240051519A1

公开(公告)日：2024-02-15

申请号：US17818471

申请日：2022-08-09

Applicant: Aptiv Technologies Limited

Inventor： Christopher A. Hedges ,  Jeremy S. Greene

IPC: B60W30/06 ,  H04W4/40 ,  G06V20/54 ,  G06T7/73

CPC classification number: B60W30/06 ,  H04W4/40 ,  G06V20/54 ,  G06T7/73 ,  B60W2556/40 ,  B60W2556/45 ,  G06V2201/08 ,  G06T2207/30204 ,  G06T2207/30252

Abstract: This document describes techniques and systems for vehicle localization based on pose corrections from remote cameras in parking garages and other GNSS denial environments. A system can include a processor and computer-readable storage media comprising instructions that, when executed by the processor, cause the system to determine an estimated pose of the host vehicle within a GNSS denial environment after the host vehicle has been parked at a drop-off area. The system can also receive a corrected pose of the host vehicle from one or more remote cameras in the GNSS denial environment. The instructions further cause the processor to use the corrected pose to determine an updated pose for the host vehicle. In this way, the system can provide highly accurate vehicle localization in GNSS denial environments in a cost-effective manner to support automated valet parking and other autonomous driving functionalities.

公开(公告)号：US20240046689A1

公开(公告)日：2024-02-08

申请号：US18482557

申请日：2023-10-06

Applicant: INVISION AI, INC.

Inventor： Karim ALI ,  Zhijie WANG ,  Seydi ZORLU ,  Arash MOHTAT ,  Carlos BECKER

IPC: G06V40/10 ,  G06V10/25 ,  G06V10/34 ,  G06V20/59 ,  G06F18/25 ,  G06V20/54 ,  H04N23/66 ,  H04N23/56 ,  H04N7/18

CPC classification number: G06V40/10 ,  G06V10/25 ,  G06V10/34 ,  G06V20/59 ,  G06F18/251 ,  G06V20/54 ,  G06V40/103 ,  H04N23/66 ,  H04N23/56 ,  H04N7/181 ,  H04N7/188 ,  G06V2201/08

Abstract: A system for detecting occupancy of a vehicle travelling in a direction of travel along a road. The system includes a roadside imaging device positioned on a roadside, and a first roadside light emitter, and a roadside vehicle detector. A processor is configured to receive a signal from the roadside vehicle detector, command the first roadside light emitter to emit light according to a first pattern for a first duration, command the roadside imaging device to capture images of the side of the vehicle, and compute a vehicle occupancy, in each of the captured images by determining one or more regions of interest in each of the captured images, and determining a number of visible occupants in the one or more regions of interest. The processor determines a most likely number of occupants based on each determined vehicle occupancy.

公开(公告)号：US20240013546A1

公开(公告)日：2024-01-11

申请号：US18474311

申请日：2023-09-26

Applicant: Panasonic Holdings Corporation

Inventor： YURI NISHIKAWA ,  JUN OZAWA

IPC: G06V20/52 ,  G06V40/10 ,  G06T7/50 ,  H04N7/18 ,  G06F40/42 ,  G06K7/10

CPC classification number: G06V20/52 ,  G06V40/10 ,  G06T7/50 ,  H04N7/181 ,  G06F40/42 ,  G06K7/10366 ,  G06V2201/08

Abstract: This information providing method includes causing a computer to acquire first information concerning a user (person) present in a first area of an escalator, acquire second information concerning the user present in a second area of the escalator, acquire third information concerning a vehicle present on the escalator that is relevant to at least one of the first information or the second information, determine a change in state of the vehicle on the basis of the third information, and output notification information indicative of notification contents decided on the basis of the determined change in state of the vehicle.

公开(公告)号：US11863900B2

公开(公告)日：2024-01-02

申请号：US17357745

申请日：2021-06-24

Applicant: DENSO CORPORATION

Inventor： Shingo Imura ,  Hirohiko Yanagawa ,  Woocheol Shin

IPC: H04N5/262 ,  G06T7/70 ,  B60R1/00 ,  G06T7/20 ,  G06T15/60 ,  H04N5/272 ,  H04N7/18 ,  G06V20/56 ,  G06V20/64 ,  G06V40/10

CPC classification number: H04N5/2628 ,  B60R1/00 ,  G06T7/20 ,  G06T7/70 ,  G06T15/60 ,  G06V20/56 ,  G06V20/64 ,  G06V40/103 ,  H04N5/272 ,  H04N7/181 ,  B60R2300/304 ,  B60R2300/607 ,  G06T2207/30252 ,  G06V2201/08

Abstract: In an image generation apparatus, an image acquisition unit is configured to acquire, from at least one camera operable to capture an image of surroundings of a vehicle, a captured image. A bird's-eye view image generation unit is configured to generate a bird's-eye view image from the captured image. A three-dimensional object recognition unit is configured to recognize a three-dimensional object in the captured image. A superimposition-image acquisition unit is configured to acquire a superimposition image that represents the three-dimensional object recognized by the three-dimensional object recognition unit, by performing a process depending on a type of the three-dimensional object recognized by the three-dimensional object recognition unit. A superimposition unit is configured to superimpose, onto the bird's-eye view image, the superimposition image acquired by the superimposition-image acquisition unit, at a position where the three-dimensional object is present in the bird's-eye view image.

公开(公告)号：US20230419839A1

公开(公告)日：2023-12-28

申请号：US18463265

申请日：2023-09-07

Applicant: Humanising Autonomy Limited

Inventor： Raunaq Bose ,  Leslie Cees Nooteboom ,  Maya Audrey Lara Pindeus

IPC: G08G1/16 ,  G06N20/00 ,  G06V20/52 ,  B60W40/09 ,  B60W50/14 ,  G08G1/017 ,  G06V10/80 ,  G06V20/40 ,  G06V20/58 ,  G06V40/20

CPC classification number: G08G1/166 ,  G06N20/00 ,  G06V20/52 ,  B60W40/09 ,  B60W50/14 ,  G08G1/0175 ,  G06V10/809 ,  G06V20/46 ,  G06V20/58 ,  G06V40/20 ,  G06V2201/07 ,  G06V2201/08 ,  B60W2040/0863

Abstract: The systems and methods disclosed herein provide a risk prediction system that uses trained machine learning models to make predictions that a VRU will take a particular action. The system first receives, in a video stream, an image depicting a VRU operating a micro-mobility vehicle and extract the depictions from the image. The extraction process may be determined by bounding box classifiers trained to identify various VRUs and micro-mobility vehicles. The system feeds the extracted depictions to machine learning models and receives, as an output, risk profiles for the VRU and the micro-mobility vehicle. The risk profile may include data associated with the VRU/micro-mobility vehicle determined based on classifications of the VRU and the micro-mobility vehicles. The system may then generate a prediction that the VRU operating the micro-mobility vehicle will take a particular action based on the risk profile.