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    Sparse convolutional neural networks
    1.
    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.

    System and Method for Identifying Travel Way Features for Autonomous Vehicle Motion Control
    2.
    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.

    Systems and Methods for Motion Forecasting and Planning for Autonomous Vehicles
    3.
    发明申请
    Systems and Methods for Motion Forecasting and Planning for Autonomous Vehicles 有权

    公开(公告)号：US20240367688A1

    公开(公告)日：2024-11-07

    申请号：US18658674

    申请日：2024-05-08

    Applicant: Aurora Operations, Inc.

    Inventor： Alexander Yuhao Cui ,  Abbas Sadat ,  Sergio Casas ,  Renjie Liao ,  Raquel Urtasun

    IPC: B60W60/00 ,  B60W40/04 ,  G05B13/02 ,  G06N3/045

    Abstract: Systems and methods are disclosed for motion forecasting and planning for autonomous vehicles. For example, a plurality of future traffic scenarios are determined by modeling a joint distribution of actor trajectories for a plurality of actors, as opposed to an approach that models actors individually. As another example, a diversity objective is evaluated that rewards sampling of the future traffic scenarios that require distinct reactions from the autonomous vehicle. An estimated probability for the plurality of future traffic scenarios can be determined and used to generate a contingency plan for motion of the autonomous vehicle. The contingency plan can include at least one initial short-term trajectory intended for immediate action of the AV and a plurality of subsequent long-term trajectories associated with the plurality of future traffic scenarios.

    Sparse Convolutional Neural Networks
    4.
    发明申请
    Sparse Convolutional Neural Networks 有权

    公开(公告)号：US20250123620A1

    公开(公告)日：2025-04-17

    申请号：US18990684

    申请日：2024-12-20

    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.

    Semantic segmentation of three-dimensional data
    5.
    发明授权
    Semantic segmentation of three-dimensional data 有权

    公开(公告)号：US12223705B2

    公开(公告)日：2025-02-11

    申请号：US18299970

    申请日：2023-04-13

    Applicant: Aurora Operations, Inc.

    Inventor： Chris Jia-Han Zhang ,  Wenjie Luo ,  Raquel Urtasun

    IPC: G06V10/82 ,  G01S7/48 ,  G06T3/06 ,  G06V10/764 ,  G06V10/776 ,  G06V20/10 ,  G06V20/40 ,  G06V20/56 ,  G06V20/64 ,  G01S17/89

    Abstract: Systems and methods for performing semantic segmentation of three-dimensional data are provided. In one example embodiment, a computing system can be configured to obtain sensor data including three-dimensional data associated with an environment. The three-dimensional data can include a plurality of points and can be associated with one or more times. The computing system can be configured to determine data indicative of a two-dimensional voxel representation associated with the environment based at least in part on the three-dimensional data. The computing system can be configured to determine a classification for each point of the plurality of points within the three-dimensional data based at least in part on the two-dimensional voxel representation associated with the environment and a machine-learned semantic segmentation model. The computing system can be configured to initiate one or more actions based at least in part on the per-point classifications.

    Generating autonomous vehicle testing data through perturbations and adversarial loss functions
    6.
    发明授权
    Generating autonomous vehicle testing data through perturbations and adversarial loss functions 有权

    公开(公告)号：US12214801B2

    公开(公告)日：2025-02-04

    申请号：US17528549

    申请日：2021-11-17

    Applicant: Aurora Operations, Inc.

    Inventor： Jingkang Wang ,  Ava Alison Pun ,  Xuanyuan Tu ,  Mengye Ren ,  Abbas Sadat ,  Sergio Casas ,  Sivabalan Manivasagam ,  Raquel Urtasun

    IPC: B60W60/00 ,  G06N3/086

    Abstract: Techniques for generating testing data for an autonomous vehicle (AV) are described herein. A system can obtain sensor data descriptive of a traffic scenario. The traffic scenario can include a subject vehicle and actors in an environment. Additionally, the system can generate a perturbed trajectory for a first actor in the environment based on perturbation values. Moreover, the system can generate simulated sensor data. The simulated sensor data can include data descriptive of the perturbed trajectory for the first actor in the environment. Furthermore, the system can provide the simulated sensor data as input to an AV control system. The AV control system can be configured to process the simulated sensor data to generate an updated trajectory for the subject vehicle in the environment. Subsequently, the system can evaluate an adversarial loss function based on the updated trajectory for the subject vehicle to generate an adversarial loss value.

    Systems and Methods for Generating Synthetic Motion Predictions
    7.
    发明申请
    Systems and Methods for Generating Synthetic Motion Predictions 有权

    公开(公告)号：US20240391504A1

    公开(公告)日：2024-11-28

    申请号：US18676029

    申请日：2024-05-28

    Applicant: Aurora Operations, Inc.

    Inventor： Shun Da Suo ,  Sebastián David Regalado Lozano ,  Sergio Casas ,  Raquel Urtasun

    IPC: B60W60/00 ,  G06F18/214 ,  G06N3/045 ,  G06V20/58

    Abstract: Systems and methods for generating synthetic testing data for autonomous vehicles are provided. A computing system can obtain map data descriptive of an environment and object data descriptive of a plurality of objects within the environment. The computing system can generate context data including deep or latent features extracted from the map and object data by one or more machine-learned models. The computing system can process the context data with a machine-learned model to generate synthetic motion prediction for the plurality of objects. The synthetic motion predictions for the objects can include one or more synthesized states for the objects at future times. The computing system can provide, as an output, synthetic testing data that includes the plurality of synthetic motion predictions for the objects. The synthetic testing data can be used to test an autonomous vehicle control system in a simulation.

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