公开(公告)号：US12214808B2

公开(公告)日：2025-02-04

申请号：US18178641

申请日：2023-03-06

Applicant: Aurora Operations, Inc.

Inventor： Abhishek Sen ,  Ashton James Fagg ,  Brian C. Becker ,  Yang Xu ,  Nathan Nicolas Pilbrough ,  Carlos Vallespi-Gonzalez

IPC: B60W60/00 ,  G01S7/48 ,  G01S17/58 ,  G01S17/66 ,  G01S17/931 ,  G05D1/00 ,  G06N20/00

Abstract: An autonomous vehicle computing system can include a primary perception system configured to receive a plurality of sensor data points as input generate primary perception data representing a plurality of classifiable objects and a plurality of paths representing tracked motion of the plurality of classifiable objects. The autonomous vehicle computing system can include a secondary perception system configured to receive the plurality of sensor data points as input, cluster a subset of the plurality of sensor data points of the sensor data to generate one or more sensor data point clusters representing one or more unclassifiable objects that are not classifiable by the primary perception system, and generate secondary path data representing tracked motion of the one or more unclassifiable objects. The autonomous vehicle computing system can generate fused perception data based on the primary perception data and the one or more unclassifiable objects.

公开(公告)号：US20240239377A1

公开(公告)日：2024-07-18

申请号：US18611175

申请日：2024-03-20

Applicant: Aurora Operations, Inc.

Inventor： Brian C. Becker ,  Michael Lee Phillips ,  Yang Xu ,  Eric Michael Perko ,  Narek Melik-Barkhudarov

IPC: B60W60/00 ,  B60W30/085 ,  G06V10/762 ,  G06V20/58 ,  G06V40/10 ,  G08G1/16

CPC classification number: B60W60/0018 ,  B60W30/085 ,  G06V10/762 ,  G06V20/58 ,  G06V40/10 ,  G08G1/166 ,  B60W2554/4029

Abstract: Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.

公开(公告)号：US20240369977A1

公开(公告)日：2024-11-07

申请号：US18656210

申请日：2024-05-06

Applicant: Aurora Operations, Inc.

Inventor： Abhishek Mohta ,  Fang-Chieh Chou ,  Carlos Vallespi-Gonzalez ,  Brian C. Becker ,  Nemanja Djuric

IPC: G05B13/02 ,  B60W50/00 ,  B60W60/00

Abstract: Systems and methods are disclosed for detecting and predicting the motion of objects within the surrounding environment of a system such as an autonomous vehicle. For example, an autonomous vehicle can obtain sensor data from a plurality of sensors comprising at least two different sensor modalities (e.g., RADAR, LIDAR, camera) and fused together to create a fused sensor sample. The fused sensor sample can then be provided as input to a machine learning model (e.g., a machine learning model for object detection and/or motion prediction). The machine learning model can have been trained by independently applying sensor dropout to the at least two different sensor modalities. Outputs received from the machine learning model in response to receipt of the fused sensor samples are characterized by improved generalization performance over multiple sensor modalities, thus yielding improved performance in detecting objects and predicting their future locations, as well as improved navigation performance.

公开(公告)号：US12131487B2

公开(公告)日：2024-10-29

申请号：US17725879

申请日：2022-04-21

Applicant: Aurora Operations, Inc.

Inventor： Shivam Gautam ,  Brian C. Becker ,  Carlos Vallespi-Gonzalez ,  Cole Christian Gulino

IPC: G06T7/70 ,  G05D1/00 ,  G06T7/20 ,  G06T7/246 ,  G06V10/764 ,  G06V10/82 ,  G06V20/58 ,  G06V40/10

CPC classification number: G06T7/246 ,  G05D1/0088 ,  G05D1/0231 ,  G06T7/20 ,  G06V10/764 ,  G06V10/82 ,  G06V20/58 ,  G06V40/10 ,  G06T2207/30261

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with object association and tracking are provided. Input data can be obtained. The input data can be indicative of a detected object within a surrounding environment of an autonomous vehicle and an initial object classification of the detected object at an initial time interval and object tracks at time intervals preceding the initial time interval. Association data can be generated based on the input data and a machine-learned model. The association data can indicate whether the detected object is associated with at least one of the object tracks. An object classification probability distribution can be determined based on the association data. The object classification probability distribution can indicate a probability that the detected object is associated with each respective object classification. The association data and the object classification probability distribution for the detected object can be outputted.

公开(公告)号：US12259694B2

公开(公告)日：2025-03-25

申请号：US18656210

申请日：2024-05-06

Applicant: Aurora Operations, Inc.

Inventor： Abhishek Mohta ,  Fang-Chieh Chou ,  Carlos Vallespi-Gonzalez ,  Brian C. Becker ,  Nemanja Djuric

IPC: G05B13/02 ,  B60W50/00 ,  B60W60/00

Abstract: Systems and methods are disclosed for detecting and predicting the motion of objects within the surrounding environment of a system such as an autonomous vehicle. For example, an autonomous vehicle can obtain sensor data from a plurality of sensors comprising at least two different sensor modalities (e.g., RADAR, LIDAR, camera) and fused together to create a fused sensor sample. The fused sensor sample can then be provided as input to a machine learning model (e.g., a machine learning model for object detection and/or motion prediction). The machine learning model can have been trained by independently applying sensor dropout to the at least two different sensor modalities. Outputs received from the machine learning model in response to receipt of the fused sensor samples are characterized by improved generalization performance over multiple sensor modalities, thus yielding improved performance in detecting objects and predicting their future locations, as well as improved navigation performance.

公开(公告)号：US20240087378A1

公开(公告)日：2024-03-14

申请号：US18467273

申请日：2023-09-14

Applicant: Aurora Operations, Inc.

Inventor： Brian C. Becker ,  Peter VanTuyl Bentley ,  Matthew Thomas Bradley ,  Nathan Falk ,  Jack Wyatt Harris ,  Marion Le Borgne ,  Timothy Patrick Wojtaszek

IPC: G07C5/08 ,  B60W60/00

CPC classification number: G07C5/0841 ,  B60W60/001 ,  B60W2540/215

Abstract: An example method includes (a) generating a request to obtain log data descriptive of operation of an autonomous vehicle control system in a subject scenario, the log data indexed by an indexing parameter; (b) submitting the request to a log repository service, the log repository service configured to receive the request and serve, responsive to the request, log metadata; (c) loading, based on the log metadata and an index value of the indexing parameter, a log data sketch associated with the index value; and (d) loading, based on the log metadata and the index value, and responsive to an inspection indicator, detailed log data associated with the index value.