公开(公告)号：US12091043B2

公开(公告)日：2024-09-17

申请号：US18109113

申请日：2023-02-13

Applicant: Aurora Operations, Inc.

Inventor： Jake Charland ,  Ethan Eade ,  Karthik Lakshmanan ,  Daniel Munoz ,  Samuel Sean ,  Yuchen Xie ,  Luona Yang

IPC: B60W60/00 ,  G01S7/41 ,  G01S7/48 ,  G01S13/58 ,  G01S17/66 ,  G06T15/08

CPC classification number: B60W60/001 ,  G06T15/08 ,  B60W2420/403 ,  B60W2420/408 ,  B60W2510/0638

Abstract: A method may include obtaining lidar data comprising a plurality of lidar returns from an environment of an autonomous vehicle. The lidar data may be processed with a machine learning model to generate, for the plurality of lidar returns, a plurality of first outputs that each identify a respective lidar return as belonging to an object or non-object and a plurality of second outputs that identify lidar returns belonging to objects as harmful or non-harmful to the autonomous vehicle. A subset of the lidar returns identified as belonging to objects that (i) do not correspond to any of a plurality of pre-classified objects and (ii) were identified as harmful to the autonomous vehicle may be determined. The autonomous vehicle may be controlled based at least in part on the subset of lidar returns.

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

IPC: G06K9/62 ,  G06N3/08 ,  B60W60/00 ,  G05D1/00 ,  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.

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.

公开(公告)号：US11623658B1

公开(公告)日：2023-04-11

申请号：US17840218

申请日：2022-06-14

Applicant: Aurora Operations, Inc.

Inventor： Jake Charland ,  Ethan Eade ,  Karthik Lakshmanan ,  Daniel Munoz ,  Samuel Sean ,  Yuchen Xie ,  Luona Yang

IPC: B60W60/00 ,  G06T15/08

Abstract: A method may include obtaining sensor data that include a plurality of sensor returns from an environment of an autonomous vehicle. A first set of features may be extracted from the sensor data. The first set of features may be processed with a machine learning model to generate, for at least a subset of the plurality of sensor returns, a first output that classifies a respective sensor return as corresponding to an object or non-object and a second output that indicates a property of the object. The sensor returns classified as corresponding to objects may be compared to a plurality of pre-classified objects to generate one or more generic object classifications. The autonomous vehicle may be controlled based at least in part on the one or more generic object classifications.

公开(公告)号：US20230373520A1

公开(公告)日：2023-11-23

申请号：US18109113

申请日：2023-02-13

Applicant: Aurora Operations, Inc.

Inventor： Jake Charland ,  Ethan Eade ,  Karthik Lakshmanan ,  Daniel Munoz ,  Samuel Sean ,  Yuchen Xie ,  Luona Yang

IPC: B60W60/00 ,  G06T15/08

CPC classification number: B60W60/001 ,  G06T15/08 ,  B60W2420/52 ,  B60W2510/0638 ,  B60W2420/42

Abstract: A method may include obtaining lidar data comprising a plurality of lidar returns from an environment of an autonomous vehicle. The lidar data may be processed with a machine learning model to generate, for the plurality of lidar returns, a plurality of first outputs that each identify a respective lidar return as belonging to an object or non-object and a plurality of second outputs that identify lidar returns belonging to objects as harmful or non-harmful to the autonomous vehicle. A subset of the lidar returns identified as belonging to objects that (i) do not correspond to any of a plurality of pre-classified objects and (ii) were identified as harmful to the autonomous vehicle may be determined. The autonomous vehicle may be controlled based at least in part on the subset of lidar returns.