Automatic driving-based riding method, apparatus and device, and storage medium

    公开(公告)号:US12233914B2

    公开(公告)日:2025-02-25

    申请号:US17796074

    申请日:2022-01-30

    Inventor: Xin Zhang

    Abstract: Provided are an automatic driving-based riding method, apparatus and device, and a storage medium. The automatic driving-based riding method includes: selecting a target pick-up point for a target passenger from candidate pick-up points according to vehicle auxiliary information of the candidate pick-up points, where the vehicle auxiliary information of the candidate pick-up points includes coordinate information and lane information of the candidate pick-up points; and controlling an automatic driving vehicle to drive to the target pick-up point according to the vehicle auxiliary information of the target pick-up point.

    Testing method for automated driving system, electronic device and storage medium

    公开(公告)号:US12183128B2

    公开(公告)日:2024-12-31

    申请号:US17342207

    申请日:2021-06-08

    Inventor: Xi Feng

    Abstract: A testing method for an automated driving system, an electronic device and a storage medium are provided, and relate to the technical field of artificial intelligence, such as intelligent transportation and automated driving. The method includes: acquiring record data of a sensor device, the record data including a plurality of frames of data collected by the sensor device of a vehicle, each frame of data including a timestamp, and a first frame and a last frame of the record data corresponding to a same vehicle position; and writing the record data repeatedly to generate test data for testing the automated driving system; and updating the timestamp of each frame of data in the record data in the process of repeated writing, according to a number of times of a current repeated writing and a time duration corresponding to the record data.

    Method for generating user interactive information processing model and method for processing user interactive information

    公开(公告)号:US12141674B2

    公开(公告)日:2024-11-12

    申请号:US17210034

    申请日:2021-03-23

    Abstract: The present application discloses a method for generating a user interactive information processing model and a method for processing user interactive information, relates to the technical field of graph neural networks, and particularly relates to a user interactive information processing technology. The method comprises the following steps: determining node representations of network nodes of each layer in a graph neural network in an iterative manner, wherein the node representation of a single-layer node is obtained by representations of neighbor nodes of the single-layer node; adding the attribute feature of each node in each layer of network nodes to the node representation of each layer of network nodes to obtain a graph neural network to be trained; and training the graph neural network to be trained based on existing user interactive information, to obtain a user interactive information processing model, wherein the user interactive information processing model comprises adjusted node representation.

    Deep learning processing apparatus and method, device and storage medium

    公开(公告)号:US12141228B2

    公开(公告)日:2024-11-12

    申请号:US17017600

    申请日:2020-09-10

    Abstract: Embodiments of the present disclosure propose a deep learning processing apparatus and method, device and storage medium, relating to the field of artificial intelligence. A deep learning processing apparatus includes: at least one matrix multiply-add module, configured to perform a matrix multiply-add operation of a convolution kernel parameter value matrix of a convolutional layer in a convolutional neural network and a first error gradient value matrix to obtain a plurality of intermediate matrices; a storage apparatus, configured to store the plurality of intermediate matrices without reshaping elements in the plurality of intermediate matrices; and a plurality of matrix accumulation modules, configured to read the plurality of intermediate matrices from the storage apparatus and perform a matrix accumulation operation based on the plurality of intermediate matrices according to a convolution scheme of the convolutional layer in parallel, to obtain a second error gradient value matrix for the convolutional layer.

    Speed determination method, electronic device and computer storage medium

    公开(公告)号:US12104350B2

    公开(公告)日:2024-10-01

    申请号:US17594519

    申请日:2020-11-26

    CPC classification number: E02F3/435 G05D1/0223

    Abstract: A speed determination method, an electronic device and a computer storage medium are provided, relates to the field of computer technology, and may be applied to the field of artificial intelligence, especially the field of automated driving. The method includes: determining an expected speed direction of a controlled point of a first controlled target according to an actual location of the controlled point of the first controlled target and a preset trajectory of the controlled point of the first controlled target, wherein the first controlled target is one of a plurality of controlled targets having a kinematic relationship; and determining a target speed of at least one controlled target of the plurality of controlled targets according to the expected speed direction of the controlled point of the first controlled target and the kinematic relationship.

    Method and apparatus for acquiring POI state information

    公开(公告)号:US11977574B2

    公开(公告)日:2024-05-07

    申请号:US17754464

    申请日:2021-07-20

    CPC classification number: G06F16/387 G06F40/295

    Abstract: A method and apparatus for acquiring point of interest (POI) state information are suggested, which relate to a big data technology in the technical field of artificial intelligence. A specific implementation scheme involves: acquiring a text including POI information within a preset period from the Internet; and recognizing the text by using a pre-trained POI state recognition model, to obtain a two-tuple in the text, the two-tuple including a POI name and POI state information corresponding to the POI name. The POI state recognition model acquires a vector representation of each first semantic unit in the text, and acquires a vector representation of each second semantic unit in the text based on semantic dependency information of the text; fuses the vector representation of each first semantic unit and the vector representation of each second semantic unit to obtain a fusion vector representation of each first semantic unit; and predicts labels of the POI name and a POI state based on the fusion vector representation of each first semantic unit.

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