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    SECURE STORAGE ENHANCEMENTS FOR AUTHENTICATION SYSTEMS
    4.
    发明申请
    SECURE STORAGE ENHANCEMENTS FOR AUTHENTICATION SYSTEMS 有权

    公开(公告)号：US20210273800A1

    公开(公告)日：2021-09-02

    申请号：US16803462

    申请日：2020-02-27

    Applicant: FORD GLOBAL TECHNOLOGIES, LLC

    Inventor： Ali HASSANI ,  Ryan Edwin HANSON ,  Daniel M. KING ,  Hamid M. GOLGIRI ,  Cameron SMYTH

    IPC: H04L9/08 ,  H04L9/06 ,  H04L9/32

    Abstract: A secured device has a secure storage area and is configured to communicate with an authentication manager of a key server. A salt and a key identifier of a key are received to the secured device from the key server. Information corresponding to the key identifier is embedded into the salt to create a combined identifier-salt value. The combined identifier-salt value is stored in the secure storage area. The combined identifier-salt value is utilized as additional input to a hash function along with a password. The key is identified using the information corresponding to the key identifier embedded into the salt.

    Imaging System for Locating a Moving Object in Relation to Another Object
    7.
    发明申请
    Imaging System for Locating a Moving Object in Relation to Another Object 审中-公开
    用于根据另一个对象定位移动对象的成像系统

    公开(公告)号：US20160332572A1

    公开(公告)日：2016-11-17

    申请号：US14713537

    申请日：2015-05-15

    Applicant: Ford Global Technologies, LLC

    Inventor： John Paul GIBEAU ,  Christopher W. BELL ,  Ali HASSANI ,  Brad Alan IGNACZAK ,  Adil Nizam SIDDIQUI

    IPC: B60R1/00 ,  G06T11/60 ,  G06K9/00 ,  H04N7/18

    CPC classification number: B60R1/00 ,  B60L53/12 ,  B60L53/37 ,  B60L2250/16 ,  B60R2300/10 ,  B60R2300/307 ,  B60R2300/80 ,  B60R2300/806 ,  B62D15/0275 ,  G06T11/00 ,  Y02T10/7005 ,  Y02T10/7072 ,  Y02T90/12 ,  Y02T90/121 ,  Y02T90/122 ,  Y02T90/125 ,  Y02T90/14

    Abstract: Electric and plug-in hybrid electric vehicles include a rechargeable traction battery. An automated vehicle charging system is configured to charge the traction battery with minimal operator intervention. The vehicle charging system includes at least one tire pad including a plurality of pressure-sensitive sensors arranged at known locations of the tire pad and outputting signals having a magnitude indicative of a pressure applied by a tire at the known locations. The vehicle charging system also includes at least one controller programmed to receive the signals and control movement of a transmit coil according to a position of the tire on the tire pad that is derived from the signals. The known locations may be generally equally spaced or unevenly spaced across the tire pad. The tire pad may include a tire stop to limit motion of the tire in one direction.

    Abstract translation: 电动和插电式混合动力电动车辆包括可充电牵引电池。 自动车辆充电系统被配置为以最小的操作员干预对牵引电池充电。 车辆充电系统包括至少一个轮胎垫，其包括布置在轮胎胎垫的已知位置处的多个压敏传感器，并输出具有指示在已知位置处由轮胎施加的压力的量值的信号。 车辆充电系统还包括至少一个控制器，其被编程为接收信号并且根据从该信号导出的轮胎胎垫上的轮胎的位置来控制发射线圈的移动。 已知的位置可以大致等间隔或者跨越轮胎垫不均匀地间隔开。 轮胎衬垫可以包括轮胎止动件以限制轮胎在一个方向上的运动。

    MACHINE LEARNING MOBILE DEVICE LOCALIZATION
    9.
    发明申请
    MACHINE LEARNING MOBILE DEVICE LOCALIZATION 有权

    公开(公告)号：US20220292388A1

    公开(公告)日：2022-09-15

    申请号：US17196544

    申请日：2021-03-09

    Applicant: FORD GLOBAL TECHNOLOGIES, LLC

    Inventor： Vivekanandh ELANGOVAN ,  Ali HASSANI ,  Hemanth Yadav ARADHYULA ,  Hamid M. GOLGIRI ,  John Robert VAN WIEMEERSCH

    IPC: G06N20/00 ,  H04W4/029 ,  G07C5/00

    Abstract: A machine-learning localization scheme is provided. Calibration data is received from a plurality of vehicles, the calibration data including wireless data indicative of locations of mobile devices within the plurality of vehicles, ground truth data with respect to the locations of the mobile devices, and contextual information with respect to one or more of operating system versions of the mobile devices or battery levels of the mobile devices. A machine-learning model is trained using the wireless data and the contextual information as inputs and the ground truth data as output. Responsive to an error rate for the machine-learning model being within an error target, the machine-learning model is provided to the plurality of vehicles.

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