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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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Patent Agency Ranking
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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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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公开(公告)号：US20250037298A1
公开(公告)日：2025-01-30
申请号：US18910738
申请日：2024-10-09
Applicant: Aurora Operations, Inc.
Inventor： Ming Liang , Wei-Chiu Ma , Sivabalan Manivasagam , Raquel Urtasun , Bin Yang , Ze Yang
Abstract: Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.
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公开(公告)号：US12141995B2
公开(公告)日：2024-11-12
申请号：US17388372
申请日：2021-07-29
Applicant: Aurora Operations, Inc.
Inventor： Ming Liang , Wei-Chiu Ma , Sivabalan Manivasagam , Raquel Urtasun , Bin Yang , Ze Yang
Abstract: Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.
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6.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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