公开(公告)号：US20250131065A1

公开(公告)日：2025-04-24

申请号：US19007214

申请日：2024-12-31

Applicant: Aurora Operations, Inc.

Inventor： Joseph Lawrence Amato ,  Nemanja Djuric ,  Shivam Gautam ,  Abhishek Mohta ,  Fang-Chieh Chou

IPC: G06F18/241 ,  G06F18/214 ,  G06F18/243 ,  G06N7/01 ,  G06N20/00 ,  G06T7/521 ,  G06T15/08 ,  G06V10/28 ,  G06V10/50 ,  G06V10/56 ,  G06V20/58 ,  G06V20/64

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

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.