Vehicle control method and apparatus, electronic device and storage medium

    公开(公告)号:US11667285B2

    公开(公告)日:2023-06-06

    申请号:US17234644

    申请日:2021-04-19

    Abstract: The present disclosure relates to adaptive cruise control in the field of automatic driving, and discloses a vehicle control method, an apparatus, an electronic device and a storage medium. A specific implementation is: firstly, determining a target travelling scenario according to real-time monitoring data upon fulfilment of a preset update condition; then, determining a target time headway according to the target travelling scenario, where the target time headway is used to dynamically adjust a relative motion state between an host vehicle and a surrounding vehicle; and finally, controlling a vehicle according to the target time headway. It solves the problem of the prior art in overemphasizing the state of the vehicle ahead for automatic driving control while overlooking the perception of the driver or passenger of the host vehicle in the travelling scenario can prompt the driver to manually intervene, compromising the experience of the automatic driving.

    TRAINING METHOD FOR CHARACTER GENERATION MODEL, CHARACTER GENERATION METHOD, APPARATUS AND STORAGE MEDIUM

    公开(公告)号:US20230154077A1

    公开(公告)日:2023-05-18

    申请号:US17682295

    申请日:2022-02-28

    CPC classification number: G06T11/203 G06F40/109

    Abstract: Provided is a training method for a character generation model. The training method for a character generation model includes: a first training sample is input into a target model to calculate a first loss, where the first training sample includes a first source domain sample word and a first target domain sample word, and content of the first source domain sample word is different from content of the first target domain sample word; a second training sample is input into the target model to calculate a second loss, where the second training sample includes a second source domain sample word and a second target domain sample word, content of the second source domain sample word is the same as content of the second target domain sample word; and a parameter of the character generation model is adjusted according to the first loss and the second loss.

    ESTIMATION OF CLASSICAL CAPACITY OF QUANTUM CHANNEL

    公开(公告)号:US20230153674A1

    公开(公告)日:2023-05-18

    申请号:US18094304

    申请日:2023-01-06

    CPC classification number: G06N10/20 G06N10/60

    Abstract: A method is provided. The method includes: determining m first parameterized quantum circuits and a second parameterized quantum circuit of an m-dimensional quantum system; obtaining m first quantum states obtained after the first parameterized quantum circuits act on an initial quantum state and m second quantum states obtained after the quantum channel acts on the m first quantum states; obtaining a quantum state matrix obtained after the second parameterized quantum circuit acts on the initial quantum state, where diagonal elements of the matrix correspond to the first quantum states to constitute an ensemble; optimizing parameters of the parameterized quantum circuits by minimizing a loss function, where the loss function is determined based on Holevo information of the quantum channel at the current ensemble; and determining the Holevo information, obtained after the optimization, of the quantum channel as an estimated value of the classical capacity of the quantum channel.

    TRANSLATION METHOD, ELECTRONIC DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20230153548A1

    公开(公告)日:2023-05-18

    申请号:US17885152

    申请日:2022-08-10

    CPC classification number: G06F40/58

    Abstract: A translation method, an electronic device and a storage medium, which relate to the field of artificial intelligence technologies, such as machine learning technologies, information processing technologies, are disclosed. An implementation includes: acquiring an intermediate translation result generated by each of multiple pre-trained translation models for a to-be-translated specified sentence in a same iteration of a translation process, so as to obtain multiple intermediate translation results; acquiring a co-occurrence word based on the multiple intermediate translation results; and acquiring a target translation result of the specified sentence based on the co-occurrence word.

    REGRESSION TEST METHOD, ELECTRONIC DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20230153511A1

    公开(公告)日:2023-05-18

    申请号:US17884899

    申请日:2022-08-10

    Inventor: Xin JIN

    CPC classification number: G06F30/398

    Abstract: There is provided a regression test method, an electronic device and a storage medium, and relates to the field of artificial intelligence, such as artificial intelligence chips, cloud computing, intelligent voices, or the like. The method includes: when execution of any regression test is completed, determining a to-be-adjusted test case from test cases according to a current test result; and adjusting a randomization weight corresponding to a data range randomized by the to-be-adjusted test case in a current test.

    METHOD OF GENERATING PRE-TRAINING MODEL, ELECTRONIC DEVICE, AND STORAGE MEDIUM

    公开(公告)号:US20230145853A1

    公开(公告)日:2023-05-11

    申请号:US17980095

    申请日:2022-11-03

    Inventor: Teng XI Gang ZHANG

    CPC classification number: G06N3/08 G06K9/6262

    Abstract: A method of generating a pre-training model, an electronic device, and a storage medium, which relate to a field of an artificial intelligence technology, in particular to a field of a computer vision and deep learning technology. The method includes: determining, for each of a plurality of tasks, a performance index set corresponding to a candidate model structure set, the candidate model structure set is determined from a plurality of model structures included in a search space, and the search space is a super-network-based search space; determining, from the candidate model structure set, a target model structure according to a plurality of performance index sets, the target model structure is a model structure meeting a performance index condition, and the plurality of performance index sets correspond to the plurality of tasks respectively; and determining the target model structure as the pre-training model.

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