Fully aligned junctions
    1.
    发明授权

    公开(公告)号:US12264936B2

    公开(公告)日:2025-04-01

    申请号:US17824305

    申请日:2022-05-25

    Abstract: Systems and methods for creating maps used in navigating autonomous vehicles are disclosed. In one implementation at least one processor is programmed to receive drive information from each of a plurality of vehicles that traverse different entrance-exit combinations of a road junction; for each of the entrance-exit combinations, align three-dimensional feature points in the drive information to generate a plurality of aligned three-dimensional feature point groups, one for each entrance-exit combination of the road junction; correlate one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included in every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups; and generate a sparse map based on the correlation, the sparse map including a target trajectory associated with each of the entrance-exit combinations.

    BATCH ALIGNMENT FOR NAVIGATION
    4.
    发明申请

    公开(公告)号:US20200089232A1

    公开(公告)日:2020-03-19

    申请号:US16694596

    申请日:2019-11-25

    Abstract: Systems and methods are provided for aligning navigation information from a plurality of vehicles. A server may be configured to receive navigation information from a plurality of vehicles, wherein the navigation information is associated with a common road segment. The server may align the navigation information within a coordinate system local to the common road segment, wherein the local coordinate system comprises a coordinate system based on a plurality of images captured by image sensors included on the plurality of vehicles; store the aligned navigational information in association with the common road segment; and distribute the aligned navigational information to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

    STEREO-ASSIST NETWORK FOR DETERMINING AN OBJECT'S LOCATION

    公开(公告)号:US20240273753A1

    公开(公告)日:2024-08-15

    申请号:US18645962

    申请日:2024-04-25

    CPC classification number: G06T7/70 G06T7/80 G06V20/56 H04W4/46 G06T2207/20084

    Abstract: Systems and methods for navigating a host vehicle are disclosed. In one implementation at least one processor is programmed to receive a first signature encoding generated by a first trained model implemented by a first processor associated with a first camera; receive a second signature encoding generated by a second trained model implemented by a second processor associated with a second camera; input the first signature encoding and the second signature encoding into a third trained model, wherein the third trained model is configured to determine a location of an object represented in the first image and the second image; and receive an indicator of the location of the object determined by the third trained model.

    Stereo-assist network for determining an object's location

    公开(公告)号:US11858504B2

    公开(公告)日:2024-01-02

    申请号:US17974789

    申请日:2022-10-27

    Abstract: Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive a first image acquired by a first camera and a second image acquired by a second camera onboard the host vehicle; identify a first representation of an object in the first image and a second representation of the object in the second image; input to a first trained model at least a portion of the first image; input to a second trained model at least a portion of the second image; receive the first signature encoding determined by the first trained model and the second signature encoding determined by the second trained model; input to a third trained model the first signature encoding and the second signature encoding; and receive an indicator of a location of the object determined by the third trained model.

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