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公开(公告)号：US12266148B2
公开(公告)日：2025-04-01
申请号：US18309882
申请日：2023-05-01
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chih Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
IPC: G06V10/44 , G05D1/00 , G06F18/2413 , G06N3/084 , G06T7/10 , G06V10/46 , G06V10/764 , G06V10/82 , G06V20/40 , G06V20/56
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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公开(公告)号：US20240062657A1
公开(公告)日：2024-02-22
申请号：US18491492
申请日：2023-10-20
Applicant: NVIDIA Corporation
Inventor： Yue Wu , Pekka Janis , Xin Tong , Cheng-Chieh Yang , Minwoo Park , David Nister
IPC: G08G1/16 , G06V10/82 , G06V20/58 , G06V20/10 , G06F18/214 , G05D1/00 , G05D1/02 , G06N3/04 , G06T7/20
CPC classification number: G08G1/166 , G06V10/82 , G06V20/58 , G06V20/10 , G06F18/214 , G05D1/0088 , G05D1/0289 , G06N3/0418 , G06T7/20 , G05D2201/0213
Abstract: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.
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公开(公告)号：US20200210726A1
公开(公告)日：2020-07-02
申请号：US16728595
申请日：2019-12-27
Applicant: NVIDIA Corporation
Inventor： Yilin Yang , Bala Siva Sashank Jujjavarapu , Pekka Janis , Zhaoting Ye , Sangmin Oh , Minwoo Park , Daniel Herrera Castro , Tommi Koivisto , David Nister
Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.
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公开(公告)号：US20240232616A9
公开(公告)日：2024-07-11
申请号：US18343291
申请日：2023-06-28
Applicant: NVIDIA Corporation
Inventor： Yilin Yang , Bala Siva Sashank Jujjavarapu , Pekka Janis , Zhaoting Ye , Sangmin Oh , Minwoo Park , Daniel Herrera Castro , Tommi Koivisto , David Nister
IPC: G06N3/08 , B60W30/14 , B60W60/00 , G06F18/214 , G06V10/762 , G06V20/56
CPC classification number: G06N3/08 , B60W30/14 , B60W60/0011 , G06F18/2155 , G06V10/763 , G06V20/56
Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.
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公开(公告)号：US11704890B2
公开(公告)日：2023-07-18
申请号：US17522624
申请日：2021-11-09
Applicant: NVIDIA Corporation
Inventor： Yilin Yang , Bala Siva Jujjavarapu , Pekka Janis , Zhaoting Ye , Sangmin Oh , Minwoo Park , Daniel Herrera Castro , Tommi Koivisto , David Nister
CPC classification number: G06V10/25 , G06T7/536 , G06V10/454 , G06V10/70 , G06V10/82 , G06V20/58 , G06T2207/20084 , G06T2207/30261
Abstract: In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.
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Patent Agency Ranking
公开(公告)号：US11308338B2
公开(公告)日：2022-04-19
申请号：US16728595
申请日：2019-12-27
Applicant: NVIDIA Corporation
Inventor： Yilin Yang , Bala Siva Sashank Jujjavarapu , Pekka Janis , Zhaoting Ye , Sangmin Oh , Minwoo Park , Daniel Herrera Castro , Tommi Koivisto , David Nister
Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.
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公开(公告)号：US20220114702A1
公开(公告)日：2022-04-14
申请号：US17406902
申请日：2021-08-19
Applicant: Nvidia Corporation
Inventor： Shiqiu Liu , Robert Pottorff , Guilin Liu , Karan Sapra , Jon Barker , David Tarjan , Pekka Janis , Edvard Fagerholm , Lei Yang , Kevin Jonathan Shih , Marco Salvi , Timo Roman , Andrew Tao , Bryan Catanzaro
Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights.
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公开(公告)号：US20220114701A1
公开(公告)日：2022-04-14
申请号：US17172330
申请日：2021-02-10
Applicant: Nvidia Corporation
Inventor： Shiqiu Liu , Robert Pottorff , Guilin Liu , Karan Sapra , Jon Barker , David Tarjan , Pekka Janis , Edvard Fagerholm , Lei Yang , Kevin Shih , Marco Salvi , Timo Roman , Andrew Tao , Bryan Catanzaro
Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.
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公开(公告)号：US20220101635A1
公开(公告)日：2022-03-31
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: G06V20/58 , G06V10/25 , G06V10/774 , G06V10/77 , G06N20/00
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.
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公开(公告)号：US11210537B2
公开(公告)日：2021-12-28
申请号：US16277895
申请日：2019-02-15
Applicant: NVIDIA Corporation
Inventor： Tommi Koivisto , Pekka Janis , Tero Kuosmanen , Timo Roman , Sriya Sarathy , William Zhang , Nizar Assaf , Colin Tracey
IPC: G06K9/00 , G06K9/46 , G06K9/62 , B60W50/00 , G06N3/04 , G01S7/41 , G05D1/02 , G06N3/08 , G06K9/32 , G06K9/48 , G01S13/86 , G01S7/48 , G01S17/931 , G01S13/931
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.
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