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公开(公告)号：US11928822B2
公开(公告)日：2024-03-12
申请号：US17864026
申请日：2022-07-13
申请人： NVIDIA Corporation
发明人： Trung Pham , Berta Rodriguez Hervas , Minwoo Park , David Nister , Neda Cvijetic
IPC分类号： G06T7/11 , G05B13/02 , G06F18/21 , G06F18/24 , G06N3/04 , G06N3/08 , G06T5/00 , G06T11/20 , G06V10/26 , G06V10/34 , G06V10/44 , G06V10/82 , G06V20/56 , G06V30/19 , G06V30/262
CPC分类号： G06T7/11 , G05B13/027 , G06F18/21 , G06F18/24 , G06N3/04 , G06N3/08 , G06T3/4046 , G06T5/002 , G06T11/20 , G06V10/267 , G06V10/34 , G06V10/454 , G06V10/82 , G06V20/56 , G06V30/19173 , G06V30/274 , G06T2207/20081 , G06T2207/20084 , G06T2207/30252 , G06T2210/12
摘要： In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.
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公开(公告)号：US20240127454A1
公开(公告)日：2024-04-18
申请号：US18391276
申请日：2023-12-20
申请人： NVIDIA Corporation
发明人： Trung Pham , Berta Rodriguez Hervas , Minwoo Park , David Nister , Neda Cvijetic
IPC分类号： G06T7/11 , G05B13/02 , G06F18/21 , G06F18/24 , G06N3/04 , G06N3/08 , G06T3/4046 , G06T5/70 , G06T11/20 , G06V10/26 , G06V10/34 , G06V10/44 , G06V10/82 , G06V20/56 , G06V30/19 , G06V30/262
CPC分类号： G06T7/11 , G05B13/027 , G06F18/21 , G06F18/24 , G06N3/04 , G06N3/08 , G06T3/4046 , G06T5/70 , G06T11/20 , G06V10/267 , G06V10/34 , G06V10/454 , G06V10/82 , G06V20/56 , G06V30/19173 , G06V30/274 , G06T2207/20081 , G06T2207/20084 , G06T2207/30252 , G06T2210/12
摘要： In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.
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公开(公告)号：US20240101118A1
公开(公告)日：2024-03-28
申请号：US18537527
申请日：2023-12-12
申请人： NVIDIA Corporation
发明人： Sayed Mehdi Sajjadi Mohammadabadi , Berta Rodriguez Hervas , Hang Dou , Igor Tryndin , David Nister , Minwoo Park , Neda Cvijetic , Junghyun Kwon , Trung Pham
IPC分类号： B60W30/18 , B60W30/09 , B60W30/095 , B60W60/00 , G06N3/08 , G06V10/25 , G06V10/75 , G06V10/764 , G06V10/80 , G06V10/82 , G06V20/56 , G06V20/70 , G08G1/01
CPC分类号： B60W30/18154 , B60W30/09 , B60W30/095 , B60W60/0011 , G06N3/08 , G06V10/25 , G06V10/751 , G06V10/764 , G06V10/803 , G06V10/82 , G06V20/56 , G06V20/588 , G06V20/70 , G08G1/0125
摘要： In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.
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4.发明申请公开(公告)号：US20220379913A1
公开(公告)日：2022-12-01
申请号：US17827280
申请日：2022-05-27
申请人： NVIDIA Corporation
发明人： Berta Rodriguez Hervas , Hang Dou , Hsin-I Chen , Kexuan Zou , Nizar Gandy Assaf , Minwoo Park
摘要： In various examples, lanes may be grouped and a sign may be assigned to a lane in a group, then propagated to another lane in the group to associate semantic meaning corresponding to the sign with the lanes. The sign may be assigned to the most similar lane as quantified by a matching score subject to the lane meeting any hard constraints. Propagation of an assignment of the sign to a different lane may be based on lane attributes and/or sign attributes. Lane attributes may be evaluated and assignments of signs may occur for a lane as a whole, and/or for particular segments of a lane (e.g., of multiple segments perceived by the system). A sign may be a compound sign that is identified as individual signs, which are associated with one another. Attributes of the compound sign may provide semantic meaning used to operate a machine.
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公开(公告)号：US20200293796A1
公开(公告)日：2020-09-17
申请号：US16814351
申请日：2020-03-10
申请人： NVIDIA Corporation
发明人： Sayed Mehdi Sajjadi Mohammadabadi , Berta Rodriguez Hervas , Hang Dou , Igor Tryndin , David Nister , Minwoo Park , Neda Cvijetic , Junghyun Kwon , Trung Pham
摘要： In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.
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公开(公告)号：US20230282005A1
公开(公告)日：2023-09-07
申请号：US18309878
申请日：2023-05-01
申请人： NVIDIA Corporation
发明人： Minwoo Park , Junghyun Kwon , Mehmet K. Kocamaz , Hae-Jong Seo , Berta Rodriguez Hervas , Tae Eun Choe
CPC分类号： G06V20/588 , B60W60/00272 , G06T7/292 , G06V20/58 , B60W2554/4029 , B60W2554/4044 , B60W2556/35 , G06T2207/20081 , G06T2207/20084
摘要： In various examples, a multi-sensor fusion machine learning model – such as a deep neural network (DNN) – may be deployed to fuse data from a plurality of individual machine learning models. As such, the multi-sensor fusion network may use outputs from a plurality of machine learning models as input to generate a fused output that represents data from fields of view or sensory fields of each of the sensors supplying the machine learning models, while accounting for learned associations between boundary or overlap regions of the various fields of view of the source sensors. In this way, the fused output may be less likely to include duplicate, inaccurate, or noisy data with respect to objects or features in the environment, as the fusion network may be trained to account for multiple instances of a same object appearing in different input representations.
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公开(公告)号：US11688181B2
公开(公告)日：2023-06-27
申请号：US17353231
申请日：2021-06-21
申请人： NVIDIA Corporation
发明人： Minwoo Park , Junghyun Kwon , Mehmet K. Kocamaz , Hae-Jong Seo , Berta Rodriguez Hervas , Tae Eun Choe
CPC分类号： G06V20/588 , B60W60/00272 , G06T7/292 , G06V20/58 , B60W2554/4029 , B60W2554/4044 , B60W2556/35 , G06T2207/20081 , G06T2207/20084
摘要： In various examples, a multi-sensor fusion machine learning model—such as a deep neural network (DNN)—may be deployed to fuse data from a plurality of individual machine learning models. As such, the multi-sensor fusion network may use outputs from a plurality of machine learning models as input to generate a fused output that represents data from fields of view or sensory fields of each of the sensors supplying the machine learning models, while accounting for learned associations between boundary or overlap regions of the various fields of view of the source sensors. In this way, the fused output may be less likely to include duplicate, inaccurate, or noisy data with respect to objects or features in the environment, as the fusion network may be trained to account for multiple instances of a same object appearing in different input representations.
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公开(公告)号：US11195331B2
公开(公告)日：2021-12-07
申请号：US16820164
申请日：2020-03-16
申请人： NVIDIA Corporation
发明人： Dongwoo Lee , Junghyun Kwon , Sangmin Oh , Wenchao Zheng , Hae-Jong Seo , David Nister , Berta Rodriguez Hervas
摘要： A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.
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公开(公告)号：US20210201145A1
公开(公告)日：2021-07-01
申请号：US17116138
申请日：2020-12-09
申请人： NVIDIA Corporation
发明人： Trung Pham , Berta Rodriguez Hervas , Minwoo Park , David Nister , Neda Cvijetic
摘要： In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.
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公开(公告)号：US20240169549A1
公开(公告)日：2024-05-23
申请号：US18424219
申请日：2024-01-26
申请人： NVIDIA Corporation
发明人： Dongwoo Lee , Junghyun Kwon , Sangmin Oh , Wenchao Zheng , Hae-Jong Seo , David Nister , Berta Rodriguez Hervas
IPC分类号： G06T7/13 , G06T7/40 , G06T17/30 , G06V10/44 , G06V10/75 , G06V10/772 , G06V10/82 , G06V20/58
CPC分类号： G06T7/13 , G06T7/40 , G06T17/30 , G06V10/454 , G06V10/751 , G06V10/772 , G06V10/82 , G06V20/586 , G06T2207/10021 , G06T2207/20084 , G06T2207/30264
摘要： A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.
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