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：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：US12205030B2

公开(公告)日：2025-01-21

申请号：US18497025

申请日：2023-10-30

Applicant: Aurora Operations, Inc.

Inventor： Eric Randall Kee ,  Carlos Vallespi-Gonzalez ,  Gregory P. Meyer ,  Ankit Laddha

IPC: G06V10/00 ,  G05D1/00 ,  G06F18/21 ,  G06F18/2321 ,  G06N3/02 ,  G06N3/08 ,  G06V10/762 ,  G06V10/776 ,  G06V20/58

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for detecting objects are provided. For example, the disclosed technology can obtain a representation of sensor data associated with an environment surrounding a vehicle. Further, the sensor data can include sensor data points. A point classification and point property estimation can be determined for each of the sensor data points and a portion of the sensor data points can be clustered into an object instance based on the point classification and point property estimation for each of the sensor data points. A collection of point classifications and point property estimations can be determined for the portion of the sensor data points clustered into the object instance. Furthermore, object instance property estimations for the object instance can be determined based on the collection of point classifications and point property estimations for the portion of the sensor data points clustered into the object instance.

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：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.