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    Learned deconvolutional upsampling decoding layer
    1.
    发明授权
    Learned deconvolutional upsampling decoding layer 有权

    公开(公告)号：US12175764B1

    公开(公告)日：2024-12-24

    申请号：US17537843

    申请日：2021-11-30

    Applicant: Zoox, Inc.

    Inventor： Qian Song ,  Benjamin Isaac Zwiebel

    IPC: G06V20/00 ,  B60W60/00 ,  G06N3/04 ,  G06V20/58 ,  G06V20/64

    Abstract: Techniques for performing deconvolution operations on data structures representing condensed sensor data are disclosed herein. Autonomous vehicle sensors can capture data in an environment that may include one or more objects. The sensor data may be processed by a convolutional neural network to generate condensed sensor data. The condensed sensor data may be processed by one or more deconvolution layers using a machine-learned upsampling transformation to generate an output data structure for improved object detection, classification, and/or other processing operations.

    OBJECT DETECTION AND TRACKING
    3.
    发明申请
    OBJECT DETECTION AND TRACKING 有权

    公开(公告)号：US20210181758A1

    公开(公告)日：2021-06-17

    申请号：US16779576

    申请日：2020-01-31

    Applicant: Zoox, Inc.

    Inventor： Subhasis Das ,  Benjamin Isaac Zwiebel ,  Kai Yu ,  James William Vaisey Philbin

    IPC: G05D1/02 ,  G01S17/89 ,  G06T7/215 ,  G06T7/246 ,  G06T7/292 ,  G01S17/931 ,  G01S13/89 ,  G01S13/931

    Abstract: Tracking a current and/or previous position, velocity, acceleration, and/or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. However, multiple types of sensor data may be used to detect objects and some objects may not be detected by different sensor types or may be detected differently, which may confound attempts to track an object. An ML model may be trained to receive outputs associated with different sensor types and/or a track associated with an object, and determine a data structure comprising a region of interest, object classification, and/or a pose associated with the object.

    END-TO-END VEHICLE PERCEPTION SYSTEM TRAINING
    4.
    发明公开
    END-TO-END VEHICLE PERCEPTION SYSTEM TRAINING 审中-公开

    公开(公告)号：US20230177804A1

    公开(公告)日：2023-06-08

    申请号：US17542354

    申请日：2021-12-03

    Applicant: Zoox, Inc.

    Inventor： Cheng-Hsin Wuu ,  Subhasis Das ,  Po-Jen Lai ,  Qian Song ,  Benjamin Isaac Zwiebel

    IPC: G06V10/70 ,  G06V20/58 ,  G06V20/56 ,  G06V20/40

    CPC classification number: G06V10/70 ,  G06V20/582 ,  G06V20/584 ,  G06V20/588 ,  G06V20/41

    Abstract: Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

    OCCULSION AWARE PLANNING AND CONTROL
    6.
    发明申请
    OCCULSION AWARE PLANNING AND CONTROL 审中-公开

    公开(公告)号：US20200225672A1

    公开(公告)日：2020-07-16

    申请号：US16831581

    申请日：2020-03-26

    Applicant: Zoox, Inc.

    Inventor： William Anthony Silva ,  Dragomir Dimitrov Anguelov ,  Benjamin Isaac Zwiebel ,  Juhana Kangaspunta

    IPC: G05D1/02 ,  G05D1/00 ,  G08G1/16 ,  G06T7/10 ,  G06K9/00 ,  G06F16/29 ,  G01S17/931

    Abstract: Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.

    OCCULSION AWARE PLANNING AND CONTROL
    7.
    发明申请
    OCCULSION AWARE PLANNING AND CONTROL 审中-公开

    公开(公告)号：US20190384302A1

    公开(公告)日：2019-12-19

    申请号：US16011468

    申请日：2018-06-18

    Applicant: Zoox, Inc.

    Inventor： William Anthony Silva ,  Dragomir Dimitrov Anguelov ,  Benjamin Isaac Zwiebel ,  Juhana Kangaspunta

    IPC: G05D1/02 ,  G06F17/30 ,  G05D1/00 ,  G01S17/93 ,  G08G1/16 ,  G06T7/10 ,  G06K9/00

    Abstract: Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.

    CENTER-BASED DETECTION AND TRACKING
    8.
    发明申请
    CENTER-BASED DETECTION AND TRACKING 有权

    公开(公告)号：US20250005935A1

    公开(公告)日：2025-01-02

    申请号：US18821385

    申请日：2024-08-30

    Applicant: Zoox, Inc.

    Inventor： Qian Song ,  Benjamin Isaac Zwiebel

    IPC: G06V20/58 ,  B60W60/00 ,  G06N20/00 ,  G06T7/20 ,  G06T7/70 ,  G06V10/25 ,  G06V10/75

    Abstract: Techniques for detecting and tracking objects in an environment are discussed herein. For example, techniques can include detecting a center point of a block of pixels associated with an object. Unimodal (e.g., Gaussian) confidence values may be determined for a group of pixels associated with an object. Proposed detection box center points may be determined based on the Gaussian confidence values of the pixels and an output detection box may be determined using filtering and/or suppression techniques. Further, a machine-learned model can be trained by determining parameters of a center pixel of the detection box and a focal loss based on the unimodal confidence value which can then be backpropagated to the other pixels of the detection.

    Vehicle perception system with temporal tracker
    9.
    发明授权
    Vehicle perception system with temporal tracker 有权

    公开(公告)号：US12030528B2

    公开(公告)日：2024-07-09

    申请号：US17542352

    申请日：2021-12-03

    Applicant: Zoox, Inc.

    Inventor： Cheng-Hsin Wuu ,  Subhasis Das ,  Po-Jen Lai ,  Qian Song ,  Benjamin Isaac Zwiebel

    IPC: B60W60/00 ,  G06N20/00 ,  G06V10/764 ,  G06V20/58

    CPC classification number: B60W60/0027 ,  G06N20/00 ,  G06V10/764 ,  G06V20/58 ,  B60W2554/404 ,  B60W2554/80

    Abstract: Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

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