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公开(公告)号：US20210114587A1
公开(公告)日：2021-04-22
申请号：US17134858
申请日：2020-12-28
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yuzheng Zhuang , Qiang Gu , Wulong Liu
Abstract: This application provides a method for determining an automatic parking strategy. The method includes: determining, a target parking action corresponding to a current parking stage performing the target parking action; obtaining feedback information, where the feedback information is used to indicate whether a result of performing the target parking action reaches a predetermined objective, and the predetermined objective is a predetermined position of the vehicle relative to a target parking spot, and/or the predetermined objective is a status of the vehicle in the parking process; and updating the automatic parking strategy based on the feedback information. In the foregoing method, the entire parking process is divided into several parking stages, and a control strategy is obtained by using a different method at each stage. This can increase a success rate of automatic parking in a complex parking scenario.
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公开(公告)号：US11861499B2
公开(公告)日：2024-01-02
申请号：US16452290
申请日：2019-06-25
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Fenglong Song , Wulong Liu , Xijun Xue , Huimin Zhang
CPC classification number: G06N3/082 , G06F9/5011 , G06F9/5027 , G06N3/063
Abstract: This application provides a method, a terminal-side device, and a cloud-side device for data processing and a terminal-cloud collaboration system. The method includes: sending, by the terminal-side device, a request message to the cloud-side device; receiving, by the terminal-side device, a second neural network model that is obtained by compressing a first neural network model and that is sent by the cloud-side device, where the first neural network model is a neural network model on the cloud-side device that is used to process the cognitive computing task, and a hardware resource required when the second neural network model runs on the terminal-side device is within an available hardware resource capability range of the terminal-side device; and processing, by the terminal-side device, the cognitive computing task based on the second neural network model.
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公开(公告)号：US11409438B2
公开(公告)日：2022-08-09
申请号：US16545932
申请日：2019-08-20
Applicant: Huawei Technologies Co., Ltd.
Inventor： Wulong Liu , Jun Yao , Yu Wang , Ming Cheng
Abstract: A peripheral circuit includes a data preparation circuit, configured to selectively import, to a row or column of the resistive random access memory (RRAM) crossbar array based on a first control signal, preprocessed data obtained by first preprocessing on first data that is input into the data preparation circuit, a data selection circuit, configured to selectively export second data from the row or column of the RRAM crossbar array based on a second control signal, and perform second preprocessing on the second data to obtain third data, a data reading circuit, configured to: perform a weight update control operation, and perform a max pooling operation on fourth data that is input into the data reading circuit, to obtain fifth data, and a reverse training computation circuit, configured to calculate an error and a derivative of sixth data that is input into the reverse training computation circuit.
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Patent Agency Ranking
公开(公告)号：US12168455B2
公开(公告)日：2024-12-17
申请号：US18048679
申请日：2022-10-21
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Bin Wang , Yuzheng Zhuang , Wulong Liu
Abstract: In the method for optimizing decision-making regulation and control, a first traveling sequence is obtained, where the first traveling sequence includes a first trajectory sequence of the vehicle in information about a first environment and first target driving behavior output by a behavior decision-making layer of a decision-making and control system based on the information about the first environment. A second traveling sequence is obtained, where the second traveling sequence includes a second trajectory sequence output by a motion planning layer of the decision-making and control system based on preset second target driving behavior and the second target driving behavior. The behavior decision-making layer is optimized based on a difference between the first traveling sequence and a preset traveling sequence, and the motion planning layer is optimized based on a difference between the second traveling sequence and the preset traveling sequence.
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公开(公告)号：US20240092385A1
公开(公告)日：2024-03-21
申请号：US18521128
申请日：2023-11-28
Applicant: Huawei Technologies Co., Ltd.
Inventor： Shixiong Kai , Bin Wang , Wulong Liu
IPC: B60W60/00 , B60W30/09 , B60W30/095 , B60W30/18 , B60W40/04 , B60W40/06 , B60W40/105
CPC classification number: B60W60/0011 , B60W30/09 , B60W30/0956 , B60W30/18159 , B60W40/04 , B60W40/06 , B60W40/105 , B60W60/00274 , B60W2554/4041 , B60W2554/4045 , B60W2554/406 , B60W2556/10
Abstract: In a driving policy determining method, for each object, a first target motion trajectory of the object is calculated on a premise that the object does not collide with another object that moves based on an initial motion trajectory. Then, for the ego vehicle, a second target motion trajectory of the ego vehicle is calculated on a premise that the ego vehicle does not collide with another object that moves based on a first target motion trajectory. Then, a driving policy is determined based on the second target motion trajectory of the ego vehicle and a first target motion trajectory of at least one game object. The foregoing operations are repeated until the determined driving policy matches an initial driving policy.
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公开(公告)号：US11897454B2
公开(公告)日：2024-02-13
申请号：US17134858
申请日：2020-12-28
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yuzheng Zhuang , Qiang Gu , Wulong Liu
CPC classification number: B60W30/06 , B60W60/001 , B60W2510/20
Abstract: This application provides a method for determining an automatic parking strategy. The method includes: determining, a target parking action corresponding to a current parking stage performing the target parking action; obtaining feedback information, where the feedback information is used to indicate whether a result of performing the target parking action reaches a predetermined objective, and the predetermined objective is a predetermined position of the vehicle relative to a target parking spot, and/or the predetermined objective is a status of the vehicle in the parking process; and updating the automatic parking strategy based on the feedback information. In the foregoing method, the entire parking process is divided into several parking stages, and a control strategy is obtained by using a different method at each stage. This can increase a success rate of automatic parking in a complex parking scenario.
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公开(公告)号：US11475300B2
公开(公告)日：2022-10-18
申请号：US16714011
申请日：2019-12-13
Applicant: Huawei Technologies Co., Ltd.
Inventor： Jun Yao , Wulong Liu , Yu Wang , Lixue Xia
Abstract: A neural network training method includes inputting neuron input values of a neural network to the RRAM, and performing calculation for the neuron input values based on filters in the RRAM, to obtain neuron output values of the neural network, performing calculation based on kernel values of the RRAM, the neuron input values, the neuron output values, and backpropagation error values of the neural network, to obtain backpropagation update values of the neural network, comparing the backpropagation update values with a preset threshold, and when the backpropagation update values are greater than the preset threshold, updating the filters in the RRAM based on the backpropagation update values.
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公开(公告)号：US20190340508A1
公开(公告)日：2019-11-07
申请号：US16511560
申请日：2019-07-15
Applicant: Huawei Technologies Co., Ltd.
Inventor： Wulong Liu , Jun Yao , Yu Wang
Abstract: A computing device includes: a first computing unit configured to perform a first operation on an input first matrix M times, to obtain a second matrix, a second computing unit, configured to perform a second operation on the input second matrix, and a control unit, configured to: control the first computing unit to perform an ith first operation of the M first operations on the first matrix, to obtain an ith data element of the second matrix, store the ith data element of the second matrix into a first storage unit, and control, if data elements currently stored in the first storage unit are sufficient for performing one second operation, the second computing unit to perform a one second operation.
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