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公开(公告)号：US20250029393A1
公开(公告)日：2025-01-23
申请号：US18356504
申请日：2023-07-21
Applicant: QUALCOMM Incorporated
Inventor： Venkatraman Narayanan , Varun Ravi Kumar , Senthil Kumar Yogamani
IPC: G06V20/58
Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving a plurality of image frames representative of a scene; receiving point cloud data representative of the scene; determining, using a NeRF model, a three-dimensional reconstruction of the scene based on the plurality of image frames; and outputting fused data that combines first BEV features of the three-dimensional reconstruction of the scene and second BEV features of the point cloud data. Other aspects and features are also claimed and described.
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公开(公告)号：US20250029355A1
公开(公告)日：2025-01-23
申请号：US18354074
申请日：2023-07-18
Applicant: QUALCOMM Incorporated
Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method includes receiving an image frame representing a scene; receiving point cloud data representing the scene; determining first sets of image frame features; determining second sets of point cloud data features based on a plurality of voxels representing the point cloud data; determining a third set of features of the image frame based on a first set of features of the plurality of first sets of features of the image frame and a second set of features of the plurality of second sets of features of the point cloud data; and outputting fused data that combines the third set of features of the image frame and a fourth set of features of the point cloud data. Other aspects and features are also claimed and described.
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Patent Agency Ranking
公开(公告)号：US20240412494A1
公开(公告)日：2024-12-12
申请号：US18332394
申请日：2023-06-09
Applicant: QUALCOMM Incorporated
Abstract: This disclosure provides systems, methods, and devices that support image processing. In a first aspect, a method for multi-sensor fusion includes receiving first information indicative of a first set of BEV features of image data captured by an image sensor; receiving second information indicative of a second set of BEV features of non-image sensor data captured by a non-image sensor; and determining fused data that combines the image data and the non-image sensor data based on the first information, the second information, and third information indicative of differences between BEV features of training data and the first set of BEV features and the second set of BEV features. The BEV features of the training data include a third set of BEV features associated with the image sensor and a fourth set of BEV features associated with the non-image sensor. Other aspects and features are also claimed and described.
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公开(公告)号：US20240412486A1
公开(公告)日：2024-12-12
申请号：US18330113
申请日：2023-06-06
Applicant: QUALCOMM Incorporated
Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving an image frame from an image sensor of a camera; receiving an indicator associated with a type of lens of the camera; determining a first tensor grid associated with the indicator, the first tensor grid including a plurality of image framework positions associated with the type of lens; and determining, using a machine learning model, a BEV feature map corresponding to the image frame based on features of the image frame and the first tensor grid. Other aspects and features are also claimed and described.
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公开(公告)号：US20240400079A1
公开(公告)日：2024-12-05
申请号：US18329416
申请日：2023-06-05
Applicant: QUALCOMM Incorporated
Inventor： Sweta Priyadarshi , Shivansh Rao , Varun Ravi Kumar , Senthil Kumar Yogamani
Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving, by a processor, image data from a camera image sensor; receiving, by the processor, point cloud data from a light detection and ranging (LiDAR) sensor; generating, by the processor and using a first machine learning model, fused image data that combines the image data and the point cloud data; and determining, by the processor and using a second machine learning model, whether the fused image data satisfies a criteria based on whether a population risk function of the first machine learning model exceeds a threshold. Other aspects and features are also claimed and described.
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公开(公告)号：US20240378743A1
公开(公告)日：2024-11-14
申请号：US18314592
申请日：2023-05-09
Applicant: QUALCOMM Incorporated
Inventor： Varun Ravi Kumar , Senthil Kumar Yogamani
Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method is provided that includes determining a first set of feature vectors for received images for a top view representation of an area surrounding a vehicle and a second set of feature vectors for a cylindrical representation of the area. The method may further include determining a first set of locations based on the first set of feature vectors and determining a second set of locations based on the second set of feature vectors. A third set of locations may be determined based on the first and second sets of locations, such as combining the first and second sets using a transformer attention process. Vehicle control instructions may then be determined based on the third set of locations. Other aspects and features are also claimed and described.
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公开(公告)号：US20240371023A1
公开(公告)日：2024-11-07
申请号：US18311950
申请日：2023-05-04
Applicant: QUALCOMM Incorporated
Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method is provided for determining the locations and bounding surfaces of objects depicted in image frames captured by fisheye image sensors attached to a vehicle. The method includes receiving raw fisheye image data from the sensor and using machine learning models to determine the locations and three-dimensional bounding surfaces of objects in the image frame. The bounding surfaces may be defined by three-dimensional polar coordinates representing portions of the viewing area of the fisheye image sensor. Control instructions for the vehicle may then be determined based on the bounding surfaces. In certain implementations, the bounding surfaces may be determined in three-dimensional polar coordinates. Other aspects and features are also claimed and described.
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