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11.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：US20250002050A1
公开(公告)日：2025-01-02
申请号：US18883299
申请日：2024-09-12
Applicant: Aurora Operations, Inc.
Inventor： Bin Yang , Ming Liang , Wenyuan Zeng , Min Bai , Raquel Urtasun
Abstract: Techniques for improving the performance of an autonomous vehicle (AV) by automatically annotating objects surrounding the AV are described herein. A system can obtain sensor data from a sensor coupled to the AV and generate an initial object trajectory for an object using the sensor data. Additionally, the system can determine a fixed value for the object size of the object based on the initial object trajectory. Moreover, the system can generate an updated initial object trajectory, wherein the object size corresponds to the fixed value. Furthermore, the system can determine, based on the sensor data and the updated initial object trajectory, a refined object trajectory. Subsequently, the system can generate a multi-dimensional label for the object based on the refined object trajectory. A motion plan for controlling the AV can be generated based on the multi-dimensional label.
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13.发明申请公开(公告)号：US20240391097A1
公开(公告)日：2024-11-28
申请号：US18670288
申请日：2024-05-21
Applicant: Aurora Operations, Inc.
Inventor： Sergio Casas , Davi Eugenio Nascimento Frossard , Shun Da Suo , Xuanyuan Tu , Raquel Urtasun
Abstract: Systems and methods for streaming sensor packets in real-time are provided. An example method includes obtaining a sensor data packet representing a first portion of a three-hundred and sixty degree view of a surrounding environment of a robotic platform. The method includes generating, using machine-learned model(s), a local feature map based at least in part on the sensor data packet. The local feature map is indicative of local feature(s) associated with the first portion of the three-hundred and sixty degree view. The method includes updating, based at least in part on the local feature map, a spatial map to include the local feature(s). The spatial map includes previously extracted local features associated with a previous sensor data packet representing a different portion of the three-hundred and sixty degree view than the first portion. The method includes determining an object within the surrounding environment based on the updated spatial map.
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公开(公告)号：US12103554B2
公开(公告)日：2024-10-01
申请号：US17150982
申请日：2021-01-15
Applicant: Aurora Operations, Inc.
Inventor： Raquel Urtasun , Kelvin Ka Wing Wong , Qiang Zhang , Bin Yang , Ming Liang , Renjie Liao
CPC classification number: B60W60/001 , B60W50/00 , G06F30/27 , G06N3/08 , B60W2050/0019 , B60W2050/0083
Abstract: Systems and methods of the present disclosure are directed to a method. The method can include obtaining simplified scenario data associated with a simulated scenario. The method can include determining, using a machine-learned perception-prediction simulation model, a simulated perception-prediction output based at least in part on the simplified scenario data. The method can include evaluating a loss function comprising a perception loss term and a prediction loss term. The method can include adjusting one or more parameters of the machine-learned perception-prediction simulation model based at least in part on the loss function.
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15.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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Patent Agency Ranking
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：US12205004B2
公开(公告)日：2025-01-21
申请号：US18495434
申请日：2023-10-26
Applicant: Aurora Operations, Inc.
Inventor： Sergio Casas , Cole Christian Gulino , Shun Da Suo , Raquel Urtasun
Abstract: The present disclosure provides systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).
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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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20.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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