公开(公告)号：US20210341886A1

公开(公告)日：2021-11-04

申请号：US17319442

申请日：2021-05-13

Applicant: Huawei Technologies Co., Ltd.

Inventor： Lifeng Liu ,  Yingxuan Zhu ,  Jun Zhang ,  Xiaofian Yin ,  Jian Li ,  Yongxiang Tao ,  Dayao Liang

IPC: G05B13/02 ,  G06F16/2457 ,  B60W50/06

Abstract: A computer implemented method for self-learning of a control system. The method includes creating an initial knowledge base. The method learns first principles using the knowledge base. The method creates initial control commands derived from the knowledge base. The method generates constraints for the control commands. The method performs constrained reinforcement learning by executing the control commands with the constraints and observing feedback to improve the control commands. The method enriches the knowledge base based on the feedback.

公开(公告)号：US12271828B2

公开(公告)日：2025-04-08

申请号：US17158969

申请日：2021-01-26

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Xiaotian Yin ,  Jian Li ,  Yongxiang Tao

IPC: G06N5/022 ,  B60W30/08 ,  B62D15/02 ,  G06N5/025

Abstract: An embodiment of the application provides a method for representing internet of vehicles (IOV) knowledge based on a simplicial complex (SC). The method includes: representing k′-dimensional knowledge in a k-dimensional continuous space by using a k-dimensional SC with k′-dimensional function values; and representing a safe boundary of the IOV knowledge by using a boundary of the SC, where the IOV knowledge includes a steering wheel angle of an ego-vehicle, a road curvature, a speed of the ego-vehicle, and an inter-parameter relationship compliant with an objective law of vehicle dynamics, where the steering wheel angle of the ego-vehicle, the road curvature, and the speed of the ego-vehicle are obtained by using a sensor on the ego-vehicle.

公开(公告)号：US20210276572A1

公开(公告)日：2021-09-09

申请号：US17322388

申请日：2021-05-17

Applicant: Huawei Technologies Co., Ltd.

Inventor： Mingbo Du ,  Yongxiang Tao

IPC: B60W40/105 ,  B60W40/04 ,  B60W30/14

Abstract: A method and an apparatus for determining a vehicle speed computing a probability distribution of action intentions based on observation information of a surrounding object. Then, a probability redistribution of the different action intentions is computed based on travel times for the vehicle to travel from a current position to risk areas corresponding to the different action intentions, motion status variations of the surrounding object with the different action intentions are predicted based on the travel times for the vehicle to travel to the risk areas corresponding to the different action intentions. Finally, the travelling speed of the vehicle is determined based on the probability redistribution of the different action intentions, the motion status variations of the surrounding object with the different action intentions, and motion status variations of the vehicle under different travelling speed control actions.

公开(公告)号：US12189349B2

公开(公告)日：2025-01-07

申请号：US17319442

申请日：2021-05-13

Applicant: Huawei Technologies Co., Ltd.

Inventor： Lifeng Liu ,  Yingxuan Zhu ,  Jun Zhang ,  Xiaotian Yin ,  Jian Li ,  Yongxiang Tao ,  Dayao Liang

IPC: G05B13/02 ,  B60W50/06 ,  B60W60/00 ,  G06F16/2457 ,  B60W50/00

Abstract: A computer implemented method for self-learning of a control system. The method includes creating an initial knowledge base. The method learns first principles using the knowledge base. The method creates initial control commands derived from the knowledge base. The method generates constraints for the control commands. The method performs constrained reinforcement learning by executing the control commands with the constraints and observing feedback to improve the control commands. The method enriches the knowledge base based on the feedback.

公开(公告)号：US20220155732A9

公开(公告)日：2022-05-19

申请号：US17319442

申请日：2021-05-13

Applicant: Huawei Technologies Co., Ltd.

Inventor： Lifeng Liu ,  Yingxuan Zhu ,  Jun Zhang ,  Xiaotian Yin ,  Jian Li ,  Yongxiang Tao ,  Dayao Liang

IPC: G05B13/02 ,  G06F16/2457 ,  B60W50/06

Abstract: A computer implemented method for self-learning of a control system. The method includes creating an initial knowledge base. The method learns first principles using the knowledge base. The method creates initial control commands derived from the knowledge base. The method generates constraints for the control commands. The method performs constrained reinforcement learning by executing the control commands with the constraints and observing feedback to improve the control commands. The method enriches the knowledge base based on the feedback.

公开(公告)号：US11273838B2

公开(公告)日：2022-03-15

申请号：US17322388

申请日：2021-05-17

Applicant: Huawei Technologies Co., Ltd.

Inventor： Mingbo Du ,  Yongxiang Tao

IPC: B60W40/105 ,  B60W30/14 ,  B60W40/04

Abstract: A method and an apparatus for determining a vehicle speed computing a probability distribution of action intentions based on observation information of a surrounding object. Then, a probability redistribution of the different action intentions is computed based on travel times for the vehicle to travel from a current position to risk areas corresponding to the different action intentions, motion status variations of the surrounding object with the different action intentions are predicted based on the travel times for the vehicle to travel to the risk areas corresponding to the different action intentions. Finally, the travelling speed of the vehicle is determined based on the probability redistribution of the different action intentions, the motion status variations of the surrounding object with the different action intentions, and motion status variations of the vehicle under different travelling speed control actions.