METHOD OF UPDATING ROAD INFORMATION, ELECTRONIC DEVICE, AND STORAGE MEDIUM

    公开(公告)号:US20230213353A1

    公开(公告)日:2023-07-06

    申请号:US18183003

    申请日:2023-03-13

    CPC classification number: G01C21/3815 G06T7/10 G06T5/002

    Abstract: A method of updating a road information, an electronic device, and a storage medium, which relate to an artificial intelligence technology field, in particular to fields of computer vision, deep learning, big data, high-definition map, intelligent transportation, automatic driving and autonomous parking, cloud service, Internet of Vehicles and intelligent cabin technologies. The method includes: processing image data corresponding to a target road region to obtain a set of first road lines; obtaining a set of second road lines according to a trajectory map corresponding to the target road region; calibrating the set of first road lines by using the set of second road lines to obtain a set of third road lines; combining the set of third road lines and a set of historical road lines corresponding to the target road region to obtain a combination result; and updating the set of historical road lines according to the combination result.

    METHOD OF RECTIFYING TEXT IMAGE, TRAINING METHOD, ELECTRONIC DEVICE, AND MEDIUM

    公开(公告)号:US20230102804A1

    公开(公告)日:2023-03-30

    申请号:US18077026

    申请日:2022-12-07

    Abstract: A method of rectifying a text image, a training method, an electronic device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, deep learning technology, intelligent transportation and high-precision maps. An exemplary implementation includes: performing, based on a gating strategy, a plurality of first layer-wise processing on a text image to be rectified, so as to obtain respective feature maps of a plurality of layer levels, wherein each of the feature maps includes a text structural feature related to the text image to be rectified, and the gating strategy is configured to increase an attention to the text structural feature; and performing a plurality of second layer-wise processing on the respective feature maps of the plurality of layer levels, so as to obtain a rectified text image corresponding to the text image to be rectified.

    TRAINING METHOD FOR MAP-GENERATION LARGE MODEL AND MAP GENERATION METHOD

    公开(公告)号:US20240344832A1

    公开(公告)日:2024-10-17

    申请号:US18747669

    申请日:2024-06-19

    CPC classification number: G01C21/32 G01C21/3804 G06F16/29

    Abstract: A training method for a map-generation large model is provided, including: obtaining a training sample set, each training sample in the training sample set including a road top-view sample, a first vectorized point set and a first category of a first road element, and a first mask of the road top-view sample; inputting the road top-view sample into an initial map-generation large model, and correspondingly outputting a second vectorized point set and a second category of the second road element, and a second mask of the road top-view sample; determining a model loss according to a matching result between the second and first road element, the first vectorized point set, the first category, the first mask, the second vectorized point set, the second category and the second mask, and adjusting a parameter of the initial map-generation large model according to the model loss to obtain a map-generation large model.

    METHOD AND APPARATUS FOR PROCESSING IMAGE

    公开(公告)号:US20230052842A1

    公开(公告)日:2023-02-16

    申请号:US17976367

    申请日:2022-10-28

    Abstract: The present disclosure provides a method and apparatus for processing an image. A specific implementation includes: acquiring a top view of a road; identifying a position of a lane line from the top view; cutting the top view into at least two areas, and determining, according to the position of the lane line in each area, a width of a lane in the each area and an average width of the lane in the top view; calculating a first perspective correction matrix by optimizing a first loss function, the first loss function being used to represent a difference between the width of the lane in the each area and the average width of the lane in the top view; and performing a lateral correction on the top view through the first perspective correction matrix to obtain a first corrected image.

    METHOD FOR GENERATING HIGH DEFINITION MAP, DEVICE AND COMPUTER STORAGE MEDIUM

    公开(公告)号:US20240185379A1

    公开(公告)日:2024-06-06

    申请号:US17758692

    申请日:2021-11-17

    CPC classification number: G06T3/14 G06T5/80 G06T7/38 G06T7/70

    Abstract: A method and an apparatus for generation a high definition map, a device and a computer storage medium, which relate to automatic driving and deep learning technologies in the field of artificial intelligence technologies, are disclosed. An implementation includes: acquiring point cloud data and front-view image data which are collected respectively by a plurality of collecting devices at a plurality of location points to obtain a sequence of point clouds and a sequence of front-view images; performing registration of the front-view images and the point clouds on the sequence of point clouds and the sequence of front-view images; transforming the sequence of front-view images into a top-view image based on the result of the registration and determining coordinate information of each pixel in the top-view image; and identifying map elements of the top-view image to obtain the high definition map.

    METHOD FOR AUTOMATICALLY PRODUCING MAP DATA, AND RELATED APPARATUS

    公开(公告)号:US20230041943A1

    公开(公告)日:2023-02-09

    申请号:US17961930

    申请日:2022-10-07

    Abstract: The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.

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