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    Three-dimensional location prediction from images
    3.
    发明授权
    Three-dimensional location prediction from images 有权

    公开(公告)号：US12299916B2

    公开(公告)日：2025-05-13

    申请号：US17545987

    申请日：2021-12-08

    Applicant: Waymo LLC

    Inventor： Longlong Jing ,  Ruichi Yu ,  Jiyang Gao ,  Henrik Kretzschmar ,  Kang Li ,  Ruizhongtai Qi ,  Hang Zhao ,  Alper Ayvaci ,  Xu Chen ,  Dillon Cower ,  Congcong Li

    IPC: G06T7/50 ,  G06T7/70 ,  G06V10/40 ,  G06V10/80 ,  G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting three-dimensional object locations from images. One of the methods includes obtaining a sequence of images that comprises, at each of a plurality of time steps, a respective image that was captured by a camera at the time step; generating, for each image in the sequence, respective pseudo-lidar features of a respective pseudo-lidar representation of a region in the image that has been determined to depict a first object; generating, for a particular image at a particular time step in the sequence, image patch features of the region in the particular image that has been determined to depict the first object; and generating, from the respective pseudo-lidar features and the image patch features, a prediction that characterizes a location of the first object in a three-dimensional coordinate system at the particular time step in the sequence.

    Identification of spurious radar detections in autonomous vehicle applications
    4.
    发明授权
    Identification of spurious radar detections in autonomous vehicle applications 有权

    公开(公告)号：US12276752B2

    公开(公告)日：2025-04-15

    申请号：US17445129

    申请日：2021-08-16

    Applicant: Waymo LLC

    Inventor： Xu Chen ,  Nichola Abdo ,  Ruichi Yu ,  Chang Gao

    IPC: G01S7/41 ,  B60W60/00 ,  G01S13/86 ,  G01S13/931 ,  G06F18/24 ,  G06F18/25 ,  G06T7/20 ,  G06V20/56

    Abstract: The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.

    STATEFUL AND END-TO-END MULTI-OBJECT TRACKING
    6.
    发明公开
    STATEFUL AND END-TO-END MULTI-OBJECT TRACKING 审中-公开

    公开(公告)号：US20240303827A1

    公开(公告)日：2024-09-12

    申请号：US18600449

    申请日：2024-03-08

    Applicant: Waymo LLC

    Inventor： Longlong Jing ,  Ruichi Yu ,  Xu Chen ,  Zhengli Zhao ,  Shiwei Sheng

    IPC: G06T7/20 ,  G01S17/89 ,  G06V10/44

    CPC classification number: G06T7/20 ,  G01S17/89 ,  G06V10/44 ,  G06T2207/30241 ,  G06V2201/07

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for tracking objects in an environment across time. In one aspect, a method comprises: receiving a set of current object detections, each characterizing features of a respective detected object; maintaining data, including track query feature representations, that identifies one or more object tracks (each associated with respective earlier object detections classified as characterizing the same object; and, for each object track: (i) selecting a subset of the current object detections as candidate object detections for the object track, (ii) generating a respective association score for each candidate object detection based on an input derived from the candidate object detections and the track query feature representation for the object track using a track-detection interaction neural network, and (iii) determining whether to associate any of the current object detections with the object track based on the respective association scores.

    End-to-end object tracking using neural networks with attention
    8.
    发明授权
    End-to-end object tracking using neural networks with attention 有权

    公开(公告)号：US12175767B2

    公开(公告)日：2024-12-24

    申请号：US17715838

    申请日：2022-04-07

    Applicant: Waymo LLC

    Inventor： Ruichi Yu ,  Xu Chen ,  Shiwei Sheng ,  Luming Tang ,  Chieh-En Tsai

    IPC: G06V20/58 ,  B60W60/00 ,  G06T7/20 ,  G06V10/62 ,  G06V10/82

    Abstract: The described aspects and implementations enable efficient calibration of a sensing system of a vehicle. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to obtain a plurality of images associated with a corresponding time of a plurality of times. The system further includes a data processing system operatively coupled to the sensing system and configured to generate a plurality of sets of feature tensors (FTs) associated with one or more objects of the environment depicted in a respective image. The data processing system is further to obtain a combined FT and process the combined FT using a neural network to identify one or more tracks characterizing motion of a respective object.

    ASSOCIATION OF CAMERA IMAGES AND RADAR DATA IN AUTONOMOUS VEHICLE APPLICATIONS
    10.
    发明申请
    ASSOCIATION OF CAMERA IMAGES AND RADAR DATA IN AUTONOMOUS VEHICLE APPLICATIONS 有权

    公开(公告)号：US20230038842A1

    公开(公告)日：2023-02-09

    申请号：US17444338

    申请日：2021-08-03

    Applicant: Waymo LLC

    Inventor： Ruichi Yu ,  Shiwei Sheng ,  Kang Li ,  Xu Chen

    IPC: G06K9/00 ,  G06K9/62 ,  G06N20/20 ,  G01S13/86 ,  G01S7/41 ,  G01S13/931

    Abstract: The described aspects and implementations enable fast and accurate object identification in autonomous vehicle (AV) applications by combining radar data with camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar image of a first hypothetical object in an environment of the AV, obtaining a camera image of a second hypothetical object in the environment of the AV, and processing the radar image and the camera image using one or more machine-learning models MLMs to obtain a prediction measure representing a likelihood that the first hypothetical object and the second hypothetical object correspond to a same object in the environment of the AV.

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