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    Systems and methods for clustering human trust dynamics
    1.
    发明授权
    Systems and methods for clustering human trust dynamics 有权

    公开(公告)号：US12145599B2

    公开(公告)日：2024-11-19

    申请号：US17687521

    申请日：2022-03-04

    Applicant: Honda Motor Co., Ltd.

    Inventor： Kumar Akash ,  Teruhisa Misu ,  Xingwei Wu ,  Jundi Liu

    IPC: B60W40/09 ,  B60W60/00

    Abstract: Systems and methods for clustering human trust dynamics are provided. In one embodiment, a computer implemented method for clustering human trust dynamics is provided. The computer implemented method includes receiving trust data for a plurality of participants interacting with one or more agents in an interaction. The computer implemented method also includes identifying a plurality of phases for the interaction. The computer implemented method further includes extracting features characterizing trust dynamics from the trust data for at least one interaction for each participant of the plurality participants. The at least one interaction is between the participant and an agent of the one or more agents. The computer implemented yet further includes assigning the features characterizing trust dynamics to a phase of the plurality of phases. The computer implemented method includes grouping a subset of the participant of the plurality of participants based on the on features characterizing trust dynamics.

    System and method for detecting a perceived level of driver discomfort in an automated vehicle
    4.
    发明授权
    System and method for detecting a perceived level of driver discomfort in an automated vehicle 有权

    公开(公告)号：US12134404B2

    公开(公告)日：2024-11-05

    申请号：US17829664

    申请日：2022-06-01

    Applicant: Honda Motor Co., Ltd.

    Inventor： Zhaobo K. Zheng ,  Kumar Akash ,  Teruhisa Misu

    IPC: B60W60/00 ,  B60W40/08 ,  B60W50/10 ,  G06V20/58

    Abstract: A system and method for detecting a perceived level of driver discomfort in an automated vehicle that include receiving image data associated with a driving scene of an ego vehicle, dynamic data associated with an operation of the ego vehicle, and driver data associated with a driver of the ego vehicle during autonomous operation of the ego vehicle. The system and method also include analyzing the image data, the dynamic data, and the driver data and extracting features associated with a plurality of modalities. The system and method additionally include analyzing the extracted features and detecting the perceived level of driver discomfort. The system and method further include analyzing the perceived level of driver discomfort and detecting a probable driver takeover intent of the driver of the ego vehicle to takeover manual operation of the ego vehicle.

    HUMAN TRUST CALIBRATION FOR AUTONOMOUS DRIVING AGENT OF VEHICLE
    8.
    发明申请
    HUMAN TRUST CALIBRATION FOR AUTONOMOUS DRIVING AGENT OF VEHICLE 有权

    公开(公告)号：US20210229707A1

    公开(公告)日：2021-07-29

    申请号：US16773673

    申请日：2020-01-27

    Applicant: Honda Motor Co., Ltd.

    Inventor： Kumar Akash ,  Teruhisa Misu

    IPC: B60W60/00 ,  G06K9/00 ,  B60W40/09 ,  B60W50/14 ,  G06F3/01

    Abstract: An autonomous driving agent is provided. The autonomous driving agent determines a set of observations from sensor information of a sensor system of a vehicle. The set of observations includes human attention information for a scene of surrounding environment and a level of human reliance as indicated by human inputs to the autonomous driving agent. The autonomous driving agent estimates, based on the set of observations, belief states for a first state of human trust on the autonomous driving agent and a second state of human's cognitive workload during journey. The autonomous driving agent selects, based on the estimated belief states, a first value for a first action associated with a level of automation transparency between a human user and the autonomous driving agent and controls a display system based on the selected first value to display a cue for calibration of the human trust on the autonomous driving agent.

    PREDICTING TAKEOVERS ACROSS MOBILITIES FOR FUTURE PERSONALIZED MOBILITY SERVICES
    9.
    发明申请
    PREDICTING TAKEOVERS ACROSS MOBILITIES FOR FUTURE PERSONALIZED MOBILITY SERVICES 有权

    公开(公告)号：US20250111459A1

    公开(公告)日：2025-04-03

    申请号：US18581829

    申请日：2024-02-20

    Applicant: Honda Motor Co., Ltd.

    Inventor： Zhaobo Zheng ,  Kumar Akash ,  Teruhisa Misu

    IPC: G06Q50/26 ,  G06N3/042 ,  G06Q10/0637

    Abstract: A method for predicting takeover events across mobilities may monitor responses during takeover events in a car simulation from a plurality of participants. The method may monitor responses during takeover events in a micro-mobility simulation from the plurality of participants. The method may perform statistical analysis to find patterns and deviations between characteristics extracted from the responses during the takeover events in the car simulations and characteristics extracted from responses during the takeover events in the micro-mobility simulations. The method may form takeover predictions on a different type of micro-mobility vehicle using predictive modeling from the characteristics extracted from the responses during the takeover events in the car simulations and characteristics extracted from responses during the takeover events in the micro-mobility simulations. The method may integrate transfer learning in the predictive modeling.

    Adaptive driving style
    10.
    发明授权
    Adaptive driving style 有权

    公开(公告)号：US12246739B2

    公开(公告)日：2025-03-11

    申请号：US17883540

    申请日：2022-08-08

    Applicant: Honda Motor Co., Ltd.

    Inventor： Zhaobo K. Zheng ,  Teruhisa Misu ,  Kumar Akash

    IPC: B60W50/10 ,  B60W50/00 ,  G05B13/02 ,  G05B13/04

    Abstract: According to one aspect, an adaptive driving style system may include a set of two or more sensors, a memory, and a processor. The set of two or more sensors may receive two or more sensor signals. The memory may store one or more instructions. The processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, including training a trust model using two or more of the sensor signals as input, training a preference model using the trust model and two or more of the sensor signals as input, and generating a driving style preference based on an adaptive driving style model including the trust model and the preference model.

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