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    SYSTEMS AND METHODS FOR MAINTAINING A SERVICE SESSION IN AN AUTOMOTIVE EDGE COMPUTING ENVIRONMENT
    7.
    发明申请
    SYSTEMS AND METHODS FOR MAINTAINING A SERVICE SESSION IN AN AUTOMOTIVE EDGE COMPUTING ENVIRONMENT 有权

    公开(公告)号：US20230060164A1

    公开(公告)日：2023-03-02

    申请号：US17412777

    申请日：2021-08-26

    申请人： Toyota Motor Engineering & Manufacturing North America, Inc.

    发明人： Haoxin Wang ,  BaekGyu Kim ,  Prashant Tiwari

    IPC分类号： H04W36/00 ,  H04W36/08

    摘要： Systems and methods for maintaining a service session in an automotive edge computing environment are disclosed herein. One embodiment computes a first estimation result pertaining to computation resources of a current edge server with which a vehicle is in communication; computes a second estimation result pertaining to link quality; computes a third estimation result pertaining to service quality; compares the first, second, and third estimation results with first, second, and third predetermined thresholds, respectively; selects a new edge server with which to maintain the service session and initiates a service migration request, when the first predetermined threshold is triggered; initiates a wireless access handover request, when only the second predetermined threshold is triggered; and selects a new edge server with which to maintain the service session and initiates a service migration request, when only the third predetermined threshold is triggered and one of two predetermined conditions is met.

    SYSTEMS AND METHODS FOR IMPROVING TASK OFFLOAD SCHEDULING IN AN EDGE-COMPUTING ENVIRONMENT
    8.
    发明申请
    SYSTEMS AND METHODS FOR IMPROVING TASK OFFLOAD SCHEDULING IN AN EDGE-COMPUTING ENVIRONMENT 有权

    公开(公告)号：US20220028185A1

    公开(公告)日：2022-01-27

    申请号：US16939409

    申请日：2020-07-27

    申请人： Toyota Motor Engineering & Manufacturing North America, Inc.

    发明人： Haoxin Wang ,  BaekGyu Kim

    IPC分类号： G07C5/00 ,  G07C5/08 ,  B60W40/09 ,  G06F9/48

    摘要： System, methods, and other embodiments described herein relate to improving scheduling tasks within an edge-computing environment. In one embodiment, a method includes, upon establishing a communication connection with a vehicle by an edge device of the edge-computing environment, collecting offloading information about the vehicle including task information describing at least a vehicle task that is to be offloaded to the edge device and context information about aspects relating to operation of the vehicle. The method includes triggering offloading of the vehicle task to the edge device in response to determining that at least the context information satisfies a scheduling threshold. The method includes providing, by the edge device, a result of executing the vehicle task to the vehicle.

    SYSTEMS AND METHODS FOR COMMUNICATION-AWARE FEDERATED LEARNING
    10.
    发明公开
    SYSTEMS AND METHODS FOR COMMUNICATION-AWARE FEDERATED LEARNING 审中-公开

    公开(公告)号：US20240174254A1

    公开(公告)日：2024-05-30

    申请号：US17994850

    申请日：2022-11-28

    申请人： Toyota Motor Engineering & Manufacturing North America, Inc.

    发明人： Yitao Chen ,  Dawei Chen ,  Haoxin Wang ,  Kyungtae Han

    IPC分类号： B60W60/00 ,  G06F18/214

    CPC分类号： B60W60/001 ,  G06F18/214 ,  B60W2420/42

    摘要： A system for communication-aware federated learning includes a server and edge nodes. Each of the edge nodes trains a machine learning model using first local data obtained by sensors of corresponding edge node. Each of the edge nodes obtains network bandwidth for a channel between corresponding edge node and the server. One or more of the edge nodes determines a level of compression based on the bandwidth for the channel, compresses the trained machine leaning model based on the determined level of compression, and transmits the compressed trained machine learning model to the server. The server decompresses the compressed trained machine learning models and aggregates the decompressed trained machine learning models to obtain the aggregated machine learning model, and transmits the aggregated machine learning model to each of the edge nodes. Each of the edge nodes receives the aggregated machine learning model from the server.