中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

44 records (took 0.044 seconds)

    System and method for detecting a perceived level of driver discomfort in an automated vehicle
    2.
    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.

    System and method for learning naturalistic driving behavior based on vehicle dynamic data
    4.
    发明授权
    System and method for learning naturalistic driving behavior based on vehicle dynamic data 有权

    公开(公告)号：US11584379B2

    公开(公告)日：2023-02-21

    申请号：US16055798

    申请日：2018-08-06

    Applicant: Honda Motor Co., Ltd.

    Inventor： Teruhisa Misu ,  Yi-Ting Chen

    IPC: B60W40/09 ,  G05D1/00 ,  B60W40/04 ,  B60W50/00

    Abstract: A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

    System and method for learning and predicting naturalistic driving behavior
    5.
    发明授权
    System and method for learning and predicting naturalistic driving behavior 有权

    公开(公告)号：US11370446B2

    公开(公告)日：2022-06-28

    申请号：US16185514

    申请日：2018-11-09

    Applicant: Honda Motor Co., Ltd.

    Inventor： Teruhisa Misu ,  Yi-Ting Chen

    IPC: B60W40/09 ,  G05D1/00 ,  G06N20/00 ,  G06V20/56 ,  G06V20/59

    Abstract: A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and predicting at least one behavioral event as a goal-oriented action based on the stimulus-driven action. The system and method additionally include building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

    SYSTEM AND METHOD FOR OUTPUTTING VEHICLE DYNAMIC CONTROLS USING DEEP NEURAL NETWORKS
    7.
    发明申请
    SYSTEM AND METHOD FOR OUTPUTTING VEHICLE DYNAMIC CONTROLS USING DEEP NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200301437A1

    公开(公告)日：2020-09-24

    申请号：US16359271

    申请日：2019-03-20

    Applicant: Honda Motor Co., Ltd.

    Inventor： Teruhisa Misu ,  Ashish Tawari ,  Sujitha Catherine Martin

    IPC: G05D1/02 ,  G05D1/00 ,  B60W10/18 ,  B60W10/20 ,  G06N3/08 ,  G06N3/04

    Abstract: A system and method for outputting vehicle dynamic controls using deep neural networks that include receiving environmental sensor data from at least one sensor of a vehicle of a surrounding environment of the vehicle. The system and method also include inputting the environmental sensor data to a primary deep neural network structure and inputting intermediate representation, at least one applicable traffic rule, and at least one applicable vehicle maneuver to a secondary deep neural network structure. The system and method further include outputting vehicle dynamic controls to autonomously control the vehicle to navigate within the surrounding environment of the vehicle based on the at least one applicable traffic rule and the at least one applicable vehicle maneuver.

    Method and system for the correction-centric detection of critical speech recognition errors in spoken short messages
    8.
    发明授权
    Method and system for the correction-centric detection of critical speech recognition errors in spoken short messages 有权

    公开(公告)号：US09653071B2

    公开(公告)日：2017-05-16

    申请号：US14465890

    申请日：2014-08-22

    Applicant: Honda Motor Co., Ltd.

    Inventor： Rakesh Gupta ,  Teruhisa Misu ,  Aasish Pappu

    IPC: G10L15/22 ,  G10L15/08 ,  G10L15/14 ,  G10L15/32

    CPC classification number: G10L15/14 ,  G10L15/32

    Abstract: A method and system are disclosed for recognizing speech errors, such as in a spoken short messages, using an audio input device to receive an utterance of a short message, using an automated speech recognition module to generate a text sentence corresponding to the utterance, generating an N-best list of predicted error sequences for the text sentence using a linear-chain conditional random field (CRF) module, where each word of the text sentence is assigned a label in each of the predicted error sequences, and each label is assigned a probability score. The predicted error sequence labels are rescored using a metacost matrix module, the best rescored error sequence from the N-best list of predicted error sequences is selected using a Recognition Output Voting Error Reduction (ROVER) module, and a dialog action is executed by a dialog action module based on the best rescored error sequence and the dialog action policy.

Patent Agency Ranking