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    System and method for generating diagnostic procedure for vehicle fault based on most probable causes
    51.
    发明授权
    System and method for generating diagnostic procedure for vehicle fault based on most probable causes 有权

    公开(公告)号：US11640732B2

    公开(公告)日：2023-05-02

    申请号：US17333329

    申请日：2021-05-28

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Shengbing Jiang ,  Zifeng Peng ,  Michael B. Gastmeier ,  Brian Buck

    IPC: G07C5/08

    Abstract: A system includes a diagnostic step module and an optimal diagnostic procedure module. The diagnostic step module is configured to identify diagnostic steps of a service procedure to be performed to diagnose a root cause of a fault on a vehicle based on a diagnostic trouble code identifying the fault, and identify a cost associated with performing each of the diagnostic steps. The optimal diagnostic procedure module is configured to determine an order in which to perform the diagnostic steps that minimizes the cost of diagnosing the fault, and output an optimal diagnostic procedure that indicates the diagnostic steps and the order in which to perform the diagnostic steps.

    Hidden hazard situational awareness
    54.
    发明授权
    Hidden hazard situational awareness 有权

    公开(公告)号：US11195063B2

    公开(公告)日：2021-12-07

    申请号：US16790799

    申请日：2020-02-14

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Prakash Mohan Peranandam ,  Erik B. Golm ,  Meng Jiang ,  Shengbing Jiang ,  Jiyu Zhang

    IPC: G06K9/62 ,  G06K9/00 ,  G01V1/00 ,  G06N3/02

    Abstract: A system and method for determining the presence of a hidden hazard may include identification of an operational scene for a host vehicle, and identification of an operational situation for the host vehicle. Information from a plurality of proximity sensors is collected and classified. A plurality of hidden hazard presence probabilities corresponding to the information from each of the plurality of proximity sensors, the operational scene, the operational situation, and at least one of a comparative process and a dynamic neural network process are estimated. A fusion process may be performed upon the plurality of hidden hazard presence probabilities to determine the presence of a hidden hazard.

    Data-driven approach for effective system change identification
    55.
    发明授权
    Data-driven approach for effective system change identification 有权

    公开(公告)号：US11150623B2

    公开(公告)日：2021-10-19

    申请号：US16456776

    申请日：2019-06-28

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Shengbing Jiang ,  Chaitanya Sankavaram ,  Yilu Zhang ,  Paul A. Krajewski

    IPC: G05B19/048 ,  G06N3/08 ,  G06N20/10 ,  G06N20/00 ,  B60W50/00 ,  G07C5/08

    Abstract: A control system for identifying and responding to effective system changes in a monitored system includes data collection, change identification, classifier construction, effective system change and parameter adjustment modules. The data collection module tracks parameters of the monitored system. The change identification module, based on the parameters, identifies: during training, performance changes and first system changes; and post training, a set of performance changes and second system changes. The classifier construction module, based on the performance changes and the first system changes construct a performance-system change classifier module. The performance-system change classifier module, based on the set of performance changes, determines possible system changes and respective probability values. The effective system change module, based on the second and possible system changes, determines effective system changes and respective probability values. The parameter adjustment module controls an actuator of the monitored system based on the effective system changes and the probability values.

    SENSOR FUSION BASED ON INTERSECTION SCENE TO DETERMINE VEHICLE COLLISION POTENTIAL
    58.
    发明申请
    SENSOR FUSION BASED ON INTERSECTION SCENE TO DETERMINE VEHICLE COLLISION POTENTIAL 有权

    公开(公告)号：US20210245743A1

    公开(公告)日：2021-08-12

    申请号：US16785771

    申请日：2020-02-10

    Applicant: GM Global Technology Operations LLC

    Inventor： Shengbing Jiang ,  Shuqing Zeng ,  Prakash Mohan Peranandam ,  Meng Jiang ,  Jiyu Zhang ,  Mohamed A. Layouni

    IPC: B60W30/095 ,  B60W30/09 ,  G01S13/931 ,  G01S17/931 ,  B60W50/16

    Abstract: Methods and systems to implement sensor fusion to determine collision potential for a vehicle include identifying a specific intersection that the vehicle is approaching, and identifying collision potential scenarios associated with one or more paths through the specific intersection. Each collision potential scenario defines a risk of a collision between the vehicle and an object in a specified area. A weight with which one or more information sources of the vehicle are considered is adjusted for each collision potential scenario such that a highest weight is given to one or more of the one or more information sources that provide most relevant and reliable information about the specified area. Sensor fusion is implemented based on the adjusting the weight of the one or more information sources and performing detection based on the sensor fusion, and an alert is provided or actions are implemented according to the detection.

    VEHICLE FAULT ROOT CAUSE DIAGNOSIS
    60.
    发明申请
    VEHICLE FAULT ROOT CAUSE DIAGNOSIS 审中-公开

    公开(公告)号：US20200184742A1

    公开(公告)日：2020-06-11

    申请号：US16212120

    申请日：2018-12-06

    Applicant: GM Global Technology Operations LLC

    Inventor： Shengbing Jiang ,  Chaitanya Sankavaram ,  David A. Frank ,  Brian L. Buck ,  Janelle Kane Rolando

    IPC: G07C5/08 ,  G07C5/00 ,  G06N20/00 ,  G06N7/00

    Abstract: A method of root cause diagnosis of fault data from a vehicle includes identifying a first vehicle fault and selecting from field repair data a vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying from the field repair data an effective repair of the identified first vehicle fault. The method additionally includes training and testing via a machine learning algorithm, a labor code classifier using the identified effective repair of the first vehicle fault and the selected vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying and classifying, using the trained classifier, indistinguishable labor codes. Furthermore, the method includes communicating the identified and classified indistinguishable labor codes for diagnosing a root cause of real time first vehicle fault data. A computer-readable medium storing an executable computer algorithm for performing the root cause diagnosis of vehicle fault data is also envisioned.

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