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Patent Agency Ranking
公开(公告)号：US20220107641A1
公开(公告)日：2022-04-07
申请号：US17553209
申请日：2021-12-16
Applicant: Aurora Operations, Inc.
Inventor： Jean-Sebastien Valois , Ethan Eade
Abstract: Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.
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公开(公告)号：US12169256B2
公开(公告)日：2024-12-17
申请号：US17321045
申请日：2021-05-14
Applicant: Aurora Operations, Inc.
Inventor： Jean-Sebastien Valois , David McAllister Bradley , Adam Charles Watson , Peter Anthony Melick , Andrew Gilbert Miller
IPC: G01S7/00 , G01S7/497 , G01S17/08 , G01S17/931 , G01S7/40
Abstract: A vehicle sensor calibration system can detect an SDV on a turntable surrounded by a plurality of fiducial targets, and rotate the turntable using a control mechanism to provide the sensor system of the SDV with a sensor view of the plurality of fiducial targets. The vehicle sensor calibration system can receive, over a communication link with the SDV, a data log corresponding to the sensor view from the sensor system of the SDV recorded as the SDV rotates on the turntable. Thereafter, the vehicle sensor calibration system can analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system of the SDV.
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公开(公告)号：US20220230026A1
公开(公告)日：2022-07-21
申请号：US17713782
申请日：2022-04-05
Applicant: Aurora Operations, Inc.
Inventor： Jean-Sebastien Valois , Thomas Pilarski , Daniel Munoz
Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.
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公开(公告)号：US20250165790A1
公开(公告)日：2025-05-22
申请号：US19031860
申请日：2025-01-18
Applicant: Aurora Operations, Inc.
Inventor： Jean-Sebastien Valois , Thomas Pilarski , Daniel Munoz
IPC: G06N3/084 , B60W60/00 , G06F18/214 , G06V10/764 , G06V10/82 , G06V20/56
Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.
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公开(公告)号：US11630458B2
公开(公告)日：2023-04-18
申请号：US17553209
申请日：2021-12-16
Applicant: Aurora Operations, Inc.
Inventor： Jean-Sebastien Valois , Ethan Eade
Abstract: Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.
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