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    SYSTEM AND METHOD FOR ADJUSTING TRACK USING SENSOR DATA
    1.
    发明申请
    SYSTEM AND METHOD FOR ADJUSTING TRACK USING SENSOR DATA 有权

    公开(公告)号：US20250165869A1

    公开(公告)日：2025-05-22

    申请号：US19028221

    申请日：2025-01-17

    Applicant: Aurora Operations, Inc.

    Inventor： Davis Edward KING ,  Yan LI

    IPC: G06N20/00 ,  G01S13/58 ,  G01S17/58

    Abstract: A method includes obtaining a first track associated with an object. A first set of parameters is generated based on the first track. Measurement data are obtained from one or more sensors. A first set of features are extracted from the measurement data. Based on the first set of parameters and the first set of features, a second set of parameters are generated by a machine learning model. The second set of parameters represent an adjustment to the first set of parameters. Based on the second set of parameters, the first track is adjusted to generate a second track associated with the object. The second track is provided to an autonomous vehicle control system for autonomous control of a vehicle.

    SYSTEMS AND METHODS FOR TESTING LIDAR SENSOR SYSTEMS
    2.
    发明申请
    SYSTEMS AND METHODS FOR TESTING LIDAR SENSOR SYSTEMS 有权

    公开(公告)号：US20250138170A1

    公开(公告)日：2025-05-01

    申请号：US18883809

    申请日：2024-09-12

    Applicant: Aurora Operations, Inc.

    Inventor： Andrew Steil Michaels ,  Tsz Chun Wong

    IPC: G01S7/497 ,  G01S7/481 ,  G01S17/931

    Abstract: A device testing circuit for a LIDAR sensor system of a vehicle includes a first splitter optically coupled to a first connection and to a first device, the first device configured to generate a first output signal in response to receiving a first optical signal from the first connection through the first splitter, and a second splitter optically coupled to a second connection and to a second device, the second device configured to generate a second output signal in response to receiving a second optical signal from the second connection through the second splitter. The first splitter and the second splitter are optically coupled to each other.

    Systems and Methods for Generating Synthetic Sensor Data via Machine Learning
    3.
    发明申请
    Systems and Methods for Generating Synthetic Sensor Data via Machine Learning 有权

    公开(公告)号：US20250130909A1

    公开(公告)日：2025-04-24

    申请号：US19007149

    申请日：2024-12-31

    Applicant: Aurora Operations, Inc.

    Inventor： Sivabalan Manivasagam ,  Shenlong Wang ,  Wei-Chiu Ma ,  Kelvin Ka Wing Wong ,  Wenyuan Zeng ,  Raquel Urtasun

    IPC: G06F11/263 ,  G06F17/18 ,  G06N3/047 ,  G06N20/00 ,  G06T15/06 ,  G06T17/20

    Abstract: The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.

    Systems and Methods for Training Probabilistic Object Motion Prediction Models Using Non-Differentiable Prior Knowledge
    4.
    发明申请
    Systems and Methods for Training Probabilistic Object Motion Prediction Models Using Non-Differentiable Prior Knowledge 有权

    公开(公告)号：US20250117709A1

    公开(公告)日：2025-04-10

    申请号：US18982461

    申请日：2024-12-16

    Applicant: Aurora Operations, Inc.

    Inventor： Sergio Casas ,  Cole Christian Gulino ,  Shun Da Suo ,  Raquel Urtasun

    IPC: G06N20/00 ,  G05B13/02 ,  G06N7/01

    Abstract: The present disclosure provides systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).

    Systems and methods for sensor data processing and object detection and motion prediction for robotic platforms
    6.
    发明授权
    Systems and methods for sensor data processing and object detection and motion prediction for robotic platforms 有权

    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.

    Autonomous Vehicle Interface System with Multiple Interface Devices Featuring Redundant Vehicle Commands
    7.
    发明申请
    Autonomous Vehicle Interface System with Multiple Interface Devices Featuring Redundant Vehicle Commands 有权

    公开(公告)号：US20250083519A1

    公开(公告)日：2025-03-13

    申请号：US18932239

    申请日：2024-10-30

    Applicant: Aurora Operations, Inc.

    Inventor： Molly Castle Nix ,  Sean Chin ,  Dennis Zhao ,  Eric James Hanson

    IPC: B60K35/00 ,  B60K35/28 ,  B60K35/29 ,  B60K35/80 ,  B60K35/85

    Abstract: The present disclosure provides an autonomous vehicle and associated interface system that includes multiple vehicle interface computing devices that provide redundant vehicle commands. As one example, an autonomous vehicle interface system can include a first vehicle interface computing device located within the autonomous vehicle and physically coupled to the autonomous vehicle. The first vehicle interface computing device can provide a first plurality of selectable vehicle commands to a human passenger of the autonomous vehicle. The autonomous vehicle interface system can further include a second vehicle interface computing device that provides a second plurality of selectable vehicle commands to the human passenger. For example, the second vehicle interface computing device can be the passenger's own device (e.g., smartphone). The second plurality of selectable vehicle commands can include at least some of the same vehicle commands as the first plurality of selectable vehicle commands.

    LIDAR system design to mitigate LIDAR cross-talk
    9.
    发明授权
    LIDAR system design to mitigate LIDAR cross-talk 有权

    公开(公告)号：US12222457B2

    公开(公告)日：2025-02-11

    申请号：US18179019

    申请日：2023-03-06

    Applicant: Aurora Operations, Inc.

    Inventor： Soren Juelsgaard

    IPC: G01S7/497 ,  G01S7/48 ,  G01S7/4863 ,  G01S7/4865 ,  G01S7/495 ,  G01S17/931

    Abstract: Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

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