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公开(公告)号：US12116015B2
公开(公告)日：2024-10-15
申请号：US17528559
申请日：2021-11-17
Applicant: Aurora Operations, Inc.
Inventor： Bin Yang , Ming Liang , Wenyuan Zeng , Min Bai , Raquel Urtasun
CPC classification number: B60W60/0027 , G05D1/0221 , G05D1/0231 , G06N20/00 , B60W2554/4026 , B60W2554/4029 , B60W2554/4041 , B60W2554/4044 , B60W2556/45
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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公开(公告)号：US12282328B2
公开(公告)日：2025-04-22
申请号：US17150987
申请日：2021-01-15
Applicant: Aurora Operations, Inc.
Inventor： Raquel Urtasun , Bob Qingyuan Wei , Mengye Ren , Wenyuan Zeng , Ming Liang , Bin Yang
Abstract: Systems and methods for generating attention masks are provided. In particular, a computing system can access sensor data and map data for an area around an autonomous vehicle. The computing system can generate a voxel grid representation of the sensor data and map data. The computing system can generate an attention mask based on the voxel grid representation. The computing system can generate, by using the voxel grid representation and the attention mask as input to a machine-learned model, an attention weighted feature map. The computing system can determine using the attention weighted feature map, a planning cost volume for an area around the autonomous vehicle. The computing system can select a trajectory for the autonomous vehicle based, at least in part, on the planning cost volume.
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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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Patent Agency Ranking
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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公开(公告)号：US20250123620A1
公开(公告)日：2025-04-17
申请号：US18990684
申请日：2024-12-20
Applicant: Aurora Operations, Inc.
Inventor： Raquel Urtasun , Mengye Ren , Andrei Pokrovsky , Bin Yang
IPC: G05D1/00 , G01S17/86 , G01S17/89 , G01S17/931
Abstract: The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.
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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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公开(公告)号：US12210344B2
公开(公告)日：2025-01-28
申请号：US18513119
申请日：2023-11-17
Applicant: Aurora Operations, Inc.
Inventor： Raquel Urtasun , Mengye Ren , Andrei Pokrovsky , Bin Yang
IPC: G05D1/00 , G01S17/86 , G01S17/89 , G01S17/931
Abstract: The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.
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