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1.发明授权公开(公告)号：US12223734B2
公开(公告)日：2025-02-11
申请号：US17151001
申请日：2021-01-15
Applicant: Aurora Operations, Inc.
Inventor： Xuanyuan Tu , Raquel Urtasun , Tsun-Hsuan Wang , Sivabalan Manivasagam , Jingkang Wang , Mengye Ren
IPC: G06V20/56 , G01S17/931 , G05D1/00 , G06F18/21 , G06F18/24 , G06N20/00 , G06V10/764 , G06V10/82
Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An adverse system can obtain sensor data representative of an environment proximate to a targeted system. The adverse system can generate an intermediate representation of the environment and a representation deviation for the intermediate representation. The representation deviation can be designed to disrupt a machine-learned model associated with the target system. The adverse system can communicate the intermediate representation modified by the representation deviation to the target system. The target system can train the machine-learned model associated with the target system to detect the modified intermediate representation. Detected modified intermediate representations can be discarded before disrupting the machine-learned model.
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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
3.发明授权公开(公告)号：US12214801B2
公开(公告)日：2025-02-04
申请号：US17528549
申请日：2021-11-17
Applicant: Aurora Operations, Inc.
Inventor： Jingkang Wang , Ava Alison Pun , Xuanyuan Tu , Mengye Ren , Abbas Sadat , Sergio Casas , Sivabalan Manivasagam , Raquel Urtasun
Abstract: Techniques for generating testing data for an autonomous vehicle (AV) are described herein. A system can obtain sensor data descriptive of a traffic scenario. The traffic scenario can include a subject vehicle and actors in an environment. Additionally, the system can generate a perturbed trajectory for a first actor in the environment based on perturbation values. Moreover, the system can generate simulated sensor data. The simulated sensor data can include data descriptive of the perturbed trajectory for the first actor in the environment. Furthermore, the system can provide the simulated sensor data as input to an AV control system. The AV control system can be configured to process the simulated sensor data to generate an updated trajectory for the subject vehicle in the environment. Subsequently, the system can evaluate an adversarial loss function based on the updated trajectory for the subject vehicle to generate an adversarial loss value.
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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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7.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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