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    Multi-Task Multi-Sensor Fusion for Three-Dimensional Object Detection
    3.
    发明申请
    Multi-Task Multi-Sensor Fusion for Three-Dimensional Object Detection 有权

    公开(公告)号：US20230043931A1

    公开(公告)日：2023-02-09

    申请号：US17972249

    申请日：2022-10-24

    Applicant: UATC, LLC

    Inventor： Raquel Urtasun ,  Bin Yang ,  Ming Liang

    IPC: G06T7/73 ,  G06T11/60 ,  G06T7/55 ,  G01S17/89 ,  G05D1/00 ,  G05D1/02 ,  G06N20/00

    Abstract: Provided are systems and methods that perform multi-task and/or multi-sensor fusion for three-dimensional object detection in furtherance of, for example, autonomous vehicle perception and control. In particular, according to one aspect of the present disclosure, example systems and methods described herein exploit simultaneous training of a machine-learned model ensemble relative to multiple related tasks to learn to perform more accurate multi-sensor 3D object detection. For example, the present disclosure provides an end-to-end learnable architecture with multiple machine-learned models that interoperate to reason about 2D and/or 3D object detection as well as one or more auxiliary tasks. According to another aspect of the present disclosure, example systems and methods described herein can perform multi-sensor fusion (e.g., fusing features derived from image data, light detection and ranging (LIDAR) data, and/or other sensor modalities) at both the point-wise and region of interest (ROI)-wise level, resulting in fully fused feature representations.

    Systems and methods for object detection, tracking, and motion prediction
    4.
    发明授权
    Systems and methods for object detection, tracking, and motion prediction 有权

    公开(公告)号：US11475351B2

    公开(公告)日：2022-10-18

    申请号：US16124966

    申请日：2018-09-07

    Applicant: UATC, LLC

    Inventor： Wenjie Luo ,  Bin Yang ,  Raquel Urtasun

    IPC: G06N3/08 ,  G06N20/00

    Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for object detection, tracking, and motion prediction are provided. For example, the disclosed technology can include receiving sensor data including information based on sensor outputs associated with detection of objects in an environment over one or more time intervals by one or more sensors. The operations can include generating, based on the sensor data, an input representation of the objects. The input representation can include a temporal dimension and spatial dimensions. The operations can include determining, based on the input representation and a machine-learned model, detected object classes of the objects, locations of the objects over the one or more time intervals, or predicted paths of the objects. Furthermore, the operations can include generating, based on the input representation and the machine-learned model, an output including bounding shapes corresponding to the objects.

    Three-Dimensional Object Detection
    5.
    发明申请
    Three-Dimensional Object Detection 有权

    公开(公告)号：US20220214457A1

    公开(公告)日：2022-07-07

    申请号：US17571845

    申请日：2022-01-10

    Applicant: UATC, LLC

    Inventor： Ming Liang ,  Bin Yang ,  Shenlong Wang ,  Wei-Chiu Ma ,  Raquel Urtasun

    IPC: G01S17/89 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G01S17/931 ,  G01S7/481 ,  G05D1/02 ,  G06N3/02 ,  G06V20/64

    Abstract: Generally, the disclosed systems and methods implement improved detection of objects in three-dimensional (3D) space. More particularly, an improved 3D object detection system can exploit continuous fusion of multiple sensors and/or integrated geographic prior map data to enhance effectiveness and robustness of object detection in applications such as autonomous driving. In some implementations, geographic prior data (e.g., geometric ground and/or semantic road features) can be exploited to enhance three-dimensional object detection for autonomous vehicle applications. In some implementations, object detection systems and methods can be improved based on dynamic utilization of multiple sensor modalities. More particularly, an improved 3D object detection system can exploit both LIDAR systems and cameras to perform very accurate localization of objects within three-dimensional space relative to an autonomous vehicle. For example, multi-sensor fusion can be implemented via continuous convolutions to fuse image data samples and LIDAR feature maps at different levels of resolution.

    Sparse convolutional neural networks
    6.
    发明授权
    Sparse convolutional neural networks 有权

    公开(公告)号：US11061402B2

    公开(公告)日：2021-07-13

    申请号：US15890886

    申请日：2018-02-07

    Applicant: UATC, LLC

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

    IPC: G05D1/00 ,  G01S17/89 ,  G01S17/86 ,  G01S17/931 ,  G05D1/02

    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.

    Systems and Methods for Vehicle-to-Vehicle Communications for Improved Autonomous Vehicle Operations
    7.
    发明申请
    Systems and Methods for Vehicle-to-Vehicle Communications for Improved Autonomous Vehicle Operations 有权

    公开(公告)号：US20210152997A1

    公开(公告)日：2021-05-20

    申请号：US17066104

    申请日：2020-10-08

    Applicant: UATC, LLC

    Inventor： Sivabalan Manivasagam ,  Ming Liang ,  Bin Yang ,  Wenyuan Zeng ,  Raquel Urtasun ,  Tsu-shuan Wang

    IPC: H04W4/46 ,  G08G1/01 ,  G06N3/04 ,  H04W4/38

    Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining from a first autonomous vehicle, by a second autonomous vehicle, a first compressed intermediate environmental representation. The first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle. The method includes generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation. The method includes determining, using one or more machine-learned models, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and a second intermediate environmental representation generated by the second autonomous vehicle. The method includes generating an autonomy output for the second autonomous vehicle based at least in part on the updated intermediate environmental representation.

    Systems and Methods for Generating Motion Forecast Data for a Plurality of Actors with Respect to an Autonomous Vehicle
    8.
    发明申请
    Systems and Methods for Generating Motion Forecast Data for a Plurality of Actors with Respect to an Autonomous Vehicle 有权

    公开(公告)号：US20210146963A1

    公开(公告)日：2021-05-20

    申请号：US17010416

    申请日：2020-09-02

    Applicant: UATC, LLC

    Inventor： Lingyun Li ,  Bin Yang ,  Wenyuan Zeng ,  Ming Liang ,  Mengye Ren ,  Sean Segal ,  Raquel Urtasun

    IPC: B60W60/00 ,  G06N20/00

    Abstract: A computing system can input first relative location embedding data into an interaction transformer model and receive, as an output of the interaction transformer model, motion forecast data for actors relative to a vehicle. The computing system can input the motion forecast data into a prediction model to receive respective trajectories for the actors for a current time step and respective projected trajectories for the actors for a subsequent time step. The computing system can generate second relative location embedding data based on the respective projected trajectories from the second time step. The computing system can produce second motion forecast data using the interaction transformer model based on the second relative location embedding. The computing system can determine second respective trajectories for the actors using the prediction model based on the second forecast data.

    Perception and Motion Prediction for Autonomous Devices
    9.
    发明申请
    Perception and Motion Prediction for Autonomous Devices 审中-公开

    公开(公告)号：US20200298891A1

    公开(公告)日：2020-09-24

    申请号：US16826895

    申请日：2020-03-23

    Applicant: UATC, LLC

    Inventor： Ming Liang ,  Bin Yang ,  Yun Chen ,  Raquel Urtasun

    IPC: B60W60/00 ,  G06N20/00 ,  G06N5/04 ,  G01S17/931 ,  G01S17/89 ,  G05D1/00 ,  G05D1/02

    Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with object perception and prediction of object motion are provided. For example, a plurality of temporal instance representations can be generated. Each temporal instance representation can be associated with differences in the appearance and motion of objects over past time intervals. Past paths and candidate paths of a set of objects can be determined based on the temporal instance representations and current detections of objects. Predicted paths of the set of objects using a machine-learned model trained that uses the past paths and candidate paths to determine the predicted paths. Past path data that includes information associated with the predicted paths can be generated for each object of the set of objects respectively.

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