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1.发明授权公开(公告)号：US12248075B2
公开(公告)日：2025-03-11
申请号：US18672986
申请日：2024-05-23
Applicant: Aurora Operations, Inc.
Inventor： Raquel Urtasun , Min Bai , Shenlong Wang
IPC: G01S17/931 , G06T7/10 , G06T7/70 , G06T17/00 , G06T17/10 , G06V10/26 , G06V10/80 , G06V20/56 , G06V20/58
Abstract: Systems and methods for identifying travel way features in real time are provided. A method can include receiving two-dimensional and three-dimensional data associated with the surrounding environment of a vehicle. The method can include providing the two-dimensional data as one or more input into a machine-learned segmentation model to output a two-dimensional segmentation. The method can include fusing the two-dimensional segmentation with the three-dimensional data to generate a three-dimensional segmentation. The method can include storing the three-dimensional segmentation in a classification database with data indicative of one or more previously generated three-dimensional segmentations. The method can include providing one or more datapoint sets from the classification database as one or more inputs into a machine-learned enhancing model to obtain an enhanced three-dimensional segmentation. And, the method can include identifying one or more travel way features based at least in part on the enhanced three-dimensional segmentation.
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公开(公告)号：US12222832B2
公开(公告)日：2025-02-11
申请号：US18466286
申请日：2023-09-13
Applicant: Aurora Operations, Inc.
Inventor： Sivabalan Manivasagam , Shenlong Wang , Wei-Chiu Ma , Kelvin Ka Wing Wong , Wenyuan Zeng , Raquel Urtasun
Abstract: The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.
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3.发明授权公开(公告)号：US12106435B2
公开(公告)日：2024-10-01
申请号：US18345431
申请日：2023-06-30
Applicant: Aurora Operations, Inc.
Inventor： Sivabalan Manivasagam , Shenlong Wang , Wei-Chiu Ma , Raquel Urtasun
CPC classification number: G06T17/05 , G01S17/89 , G01S17/931 , G05D1/0231 , G06N20/00 , G06T15/06 , G07C5/02
Abstract: The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned geometry model can predict one or more adjusted depths for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.
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Patent Agency Ranking
4.发明授权公开(公告)号：US12124269B2
公开(公告)日：2024-10-22
申请号：US17515923
申请日：2021-11-01
Applicant: Aurora Operations, Inc.
Inventor： Anqi Joyce Yang , Can Cui , Ioan Andrei Bârsan , Shenlong Wang , Raquel Urtasun
IPC: G05D1/00 , B60R1/27 , G01S17/894
CPC classification number: G05D1/0251 , B60R1/27 , G01S17/894 , G05D1/0212 , G05D1/0248 , B60R2300/303 , B60R2300/304
Abstract: Systems and methods for the simultaneous localization and mapping of autonomous vehicle systems are provided. A method includes receiving a plurality of input image frames from the plurality of asynchronous image devices triggered at different times to capture the plurality of input image frames. The method includes identifying reference image frame(s) corresponding to a respective input image frame by matching the field of view of the respective input image frame to the fields of view of the reference image frame(s). The method includes determining association(s) between the respective input image frame and three-dimensional map point(s) based on a comparison of the respective input image frame to the one or more reference image frames. The method includes generating an estimated pose for the autonomous vehicle the one or more three-dimensional map points. The method includes updating a continuous-time motion model of the autonomous vehicle based on the estimated pose.
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公开(公告)号：US20250130909A1
公开(公告)日：2025-04-24
申请号：US19007149
申请日：2024-12-31
Applicant: Aurora Operations, Inc.
Inventor： Sivabalan Manivasagam , Shenlong Wang , Wei-Chiu Ma , Kelvin Ka Wing Wong , Wenyuan Zeng , Raquel Urtasun
Abstract: The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.
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公开(公告)号：US12198358B2
公开(公告)日：2025-01-14
申请号：US17962624
申请日：2022-10-10
Applicant: Aurora Operations, Inc.
Inventor： Raquel Urtasun , Wei-Chiu Ma , Shenlong Wang , Yuwen Xiong , Rui Hu
Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with motion flow estimation are provided. For example, scene data including representations of an environment over a first set of time intervals can be accessed. Extracted visual cues can be generated based on the representations and machine-learned feature extraction models. At least one of the machine-learned feature extraction models can be configured to generate a portion of the extracted visual cues based on a first set of the representations of the environment from a first perspective and a second set of the representations of the environment from a second perspective. The extracted visual cues can be encoded using energy functions. Three-dimensional motion estimates of object instances at time intervals subsequent to the first set of time intervals can be determined based on the energy functions and machine-learned inference models.
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7.发明申请公开(公告)号：US20250013235A1
公开(公告)日：2025-01-09
申请号：US18890881
申请日：2024-09-20
Applicant: Aurora Operations, Inc.
Inventor： Anqi Joyce Yang , Can Cui , Ioan Andrei Bârsan , Shenlong Wang , Raquel Urtasun
IPC: G05D1/00 , B60R1/27 , G01S17/894
Abstract: Systems and methods for the simultaneous localization and mapping of autonomous vehicle systems are provided. A method includes receiving a plurality of input image frames from the plurality of asynchronous image devices triggered at different times to capture the plurality of input image frames. The method includes identifying reference image frame(s) corresponding to a respective input image frame by matching the field of view of the respective input image frame to the fields of view of the reference image frame(s). The method includes determining association(s) between the respective input image frame and three-dimensional map point(s) based on a comparison of the respective input image frame to the one or more reference image frames. The method includes generating an estimated pose for the autonomous vehicle the one or more three-dimensional map points. The method includes updating a continuous-time motion model of the autonomous vehicle based on the estimated pose.
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8.发明申请公开(公告)号：US20240427022A1
公开(公告)日：2024-12-26
申请号：US18672986
申请日：2024-05-23
Applicant: Aurora Operations, Inc.
Inventor： Raquel Urtasun , Min Bai , Shenlong Wang
IPC: G01S17/931 , G06T7/10 , G06T7/70 , G06T17/00 , G06T17/10 , G06V10/26 , G06V10/80 , G06V20/56 , G06V20/58
Abstract: Systems and methods for identifying travel way features in real time are provided. A method can include receiving two-dimensional and three-dimensional data associated with the surrounding environment of a vehicle. The method can include providing the two-dimensional data as one or more input into a machine-learned segmentation model to output a two-dimensional segmentation. The method can include fusing the two-dimensional segmentation with the three-dimensional data to generate a three-dimensional segmentation. The method can include storing the three-dimensional segmentation in a classification database with data indicative of one or more previously generated three-dimensional segmentations. The method can include providing one or more datapoint sets from the classification database as one or more inputs into a machine-learned enhancing model to obtain an enhanced three-dimensional segmentation. And, the method can include identifying one or more travel way features based at least in part on the enhanced three-dimensional segmentation.
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