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    Autonomous Vehicle Testing Systems and Methods
    63.
    发明申请
    Autonomous Vehicle Testing Systems and Methods 有权

    公开(公告)号：US20250053493A1

    公开(公告)日：2025-02-13

    申请号：US18932304

    申请日：2024-10-30

    Applicant: Aurora Operations, Inc.

    Inventor： Charly Mario Walther ,  Nathan Steven Appelson ,  Jeffrey Allen Howell

    IPC: G06F11/34 ,  G06F11/30

    Abstract: Systems and methods for autonomous vehicle testing are provided. In one example embodiment, a computer-implemented method includes obtaining, by a computing system, data indicative of a test of an autonomous vehicle computing system. The method can include determining, by the computing system, one or more autonomous vehicle capabilities that are tested by the test. The method includes determining, by the computing system, a testing scenario that corresponds to the test. The testing scenario can generated at least in part using real-world data. The method includes associating, by the computing system, the data indicative of the test with data indicative of the one or more autonomous vehicle capabilities that are tested by the test and data indicative of the testing scenario. The method includes storing such associated data in in an accessible memory.

    Semiconductor Laser and Optical Amplifier Photonic Package
    66.
    发明申请
    Semiconductor Laser and Optical Amplifier Photonic Package 有权

    公开(公告)号：US20250035752A1

    公开(公告)日：2025-01-30

    申请号：US18794534

    申请日：2024-08-05

    Applicant: Aurora Operations, Inc.

    Inventor： Lei Wang ,  Sen Lin ,  Andrew Steil Michaels

    IPC: G01S7/481 ,  H01S5/02253

    Abstract: A light detection and ranging (LIDAR) device includes a first wafer layer, a laser assembly disposed on the first wafer layer, a capping layer, a second wafer layer, and a photonic integrated circuit (PIC). The capping layer is coupled to the first wafer layer and configured to seal the laser assembly. The second wafer layer is at least partially coupled to the first wafer layer. The PIC is formed on the second wafer layer. The second wafer includes an exit feature configured to outcouple laser light from the laser assembly.

    Sparse convolutional neural networks
    67.
    发明授权
    Sparse convolutional neural networks 有权

    公开(公告)号：US12210344B2

    公开(公告)日：2025-01-28

    申请号：US18513119

    申请日：2023-11-17

    Applicant: Aurora Operations, Inc.

    Inventor： Raquel Urtasun ,  Mengye Ren ,  Andrei Pokrovsky ,  Bin Yang

    IPC: G05D1/00 ,  G01S17/86 ,  G01S17/89 ,  G01S17/931

    Abstract: The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.

    Perception validation for autonomous vehicles
    68.
    发明授权
    Perception validation for autonomous vehicles 有权

    公开(公告)号：US12202512B1

    公开(公告)日：2025-01-21

    申请号：US18628336

    申请日：2024-04-05

    Applicant: Aurora Operations, Inc.

    Inventor： Davis Edward King

    IPC: B60W60/00 ,  B60W50/06

    Abstract: An example method includes (a) obtaining an object detection from a perception system that describes an object in an environment of the autonomous vehicle; (b) obtaining, from a reference dataset, a label that describes a reference position of the object in the environment; (c) determining a plurality of component divergence values respectively for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the object detection and the label; (d) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the object detection and the label, wherein the machine-learned model includes a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; (e) evaluating a quality of a match between the object detection and the label based on the score.

    System and Method for Identifying Travel Way Features for Autonomous Vehicle Motion Control
    70.
    发明申请
    System and Method for Identifying Travel Way Features for Autonomous Vehicle Motion Control 有权

    公开(公告)号：US20240427022A1

    公开(公告)日：2024-12-26

    申请号：US18672986

    申请日：2024-05-23

    Applicant: Aurora Operations, Inc.

    Inventor： Raquel Urtasun ,  Min Bai ,  Shenlong Wang

    IPC: G01S17/931 ,  G06T7/10 ,  G06T7/70 ,  G06T17/00 ,  G06T17/10 ,  G06V10/26 ,  G06V10/80 ,  G06V20/56 ,  G06V20/58

    Abstract: Systems and methods for identifying travel way features in real time are provided. A method can include receiving two-dimensional and three-dimensional data associated with the surrounding environment of a vehicle. The method can include providing the two-dimensional data as one or more input into a machine-learned segmentation model to output a two-dimensional segmentation. The method can include fusing the two-dimensional segmentation with the three-dimensional data to generate a three-dimensional segmentation. The method can include storing the three-dimensional segmentation in a classification database with data indicative of one or more previously generated three-dimensional segmentations. The method can include providing one or more datapoint sets from the classification database as one or more inputs into a machine-learned enhancing model to obtain an enhanced three-dimensional segmentation. And, the method can include identifying one or more travel way features based at least in part on the enhanced three-dimensional segmentation.

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