MX2021012911A - Sistema recomendador que selecciona un conductor de múltiples candidatos. - Google Patents

Sistema recomendador que selecciona un conductor de múltiples candidatos.

Info

Publication number
MX2021012911A
MX2021012911A MX2021012911A MX2021012911A MX2021012911A MX 2021012911 A MX2021012911 A MX 2021012911A MX 2021012911 A MX2021012911 A MX 2021012911A MX 2021012911 A MX2021012911 A MX 2021012911A MX 2021012911 A MX2021012911 A MX 2021012911A
Authority
MX
Mexico
Prior art keywords
driver
vehicle
data
historical data
passenger
Prior art date
Application number
MX2021012911A
Other languages
English (en)
Inventor
Di Zhang
Wei Huang
Lei Yang
Hai Yu
Original Assignee
Huawei Tech Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huawei Tech Co Ltd filed Critical Huawei Tech Co Ltd
Publication of MX2021012911A publication Critical patent/MX2021012911A/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
    • G06F18/2178—Validation; Performance evaluation; Active pattern learning techniques based on feedback of a supervisor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
    • G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation

Abstract

Se divulgan sistemas y métodos para mejorar la seguridad de conductor. Este método puede incluir obtener de uno o más sensores datos actuales para un conductor de un vehículo y para cada uno de los pasajeros del vehículo que son conductores potenciales. Además, los datos históricos para el conductor del vehículo y los datos históricos para cada uno de los pasajeros que son conductores potenciales se obtienen de uno o más almacenes de datos. Los datos actuales y los datos históricos para el conductor del vehículo, y los datos actuales y los datos históricos para cada uno de los pasajeros que son conductores potenciales se analizan para identificar de este modo un candidato preferido para conducir el vehículo. Este análisis se realiza usando un modelo implementado por computadora, tal como una red neuronal implementada por computadora. Luego se emite una recomendación de que el candidato preferido conduzca el vehículo. La retroalimentación se puede obtener y usar para actualizar el modelo.
MX2021012911A 2019-04-25 2019-04-25 Sistema recomendador que selecciona un conductor de múltiples candidatos. MX2021012911A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2019/029168 WO2020219054A1 (en) 2019-04-25 2019-04-25 Recommender system selecting a driver out of multiple candidates

Publications (1)

Publication Number Publication Date
MX2021012911A true MX2021012911A (es) 2022-01-18

Family

ID=66821361

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2021012911A MX2021012911A (es) 2019-04-25 2019-04-25 Sistema recomendador que selecciona un conductor de múltiples candidatos.

Country Status (5)

Country Link
EP (1) EP3953856A1 (es)
JP (1) JP7303901B2 (es)
CN (1) CN113728323A (es)
MX (1) MX2021012911A (es)
WO (1) WO2020219054A1 (es)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220176978A1 (en) * 2020-12-09 2022-06-09 International Business Machines Corporation Vehicular environment management for sudden events
US11881036B2 (en) * 2021-01-01 2024-01-23 Nauto, Inc. Devices and methods for detecting drowsiness of drivers of vehicles
CN113825118B (zh) * 2021-09-24 2024-02-27 中车大连电力牵引研发中心有限公司 一种基于人体骨声纹的列车操纵系统
CN115144013A (zh) * 2022-09-02 2022-10-04 荣耀终端有限公司 驾驶员检测方法、电子设备和存储介质

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10897650B2 (en) * 2010-06-07 2021-01-19 Affectiva, Inc. Vehicle content recommendation using cognitive states
JP6329915B2 (ja) 2015-02-27 2018-05-23 株式会社日立アドバンストシステムズ 測位システム
JP6237725B2 (ja) 2015-07-27 2017-11-29 トヨタ自動車株式会社 乗員情報取得装置及び車両制御システム
US20180089605A1 (en) * 2016-09-23 2018-03-29 Intel Corporation Enhanced ride sharing user experience
CN110023168B (zh) 2016-11-29 2022-03-08 本田技研工业株式会社 车辆控制系统、车辆控制方法及车辆控制程序
EP3559895A4 (en) * 2016-12-22 2020-09-09 Xevo Inc. METHOD AND SYSTEM FOR PROVIDING INTERACTIVE PARKING MANAGEMENT THROUGH ARTIFICIAL INTELLIGENCE (AIA) ANALYTICAL SERVICES USING A CLOUD NETWORK

Also Published As

Publication number Publication date
EP3953856A1 (en) 2022-02-16
JP2022530481A (ja) 2022-06-29
CN113728323A (zh) 2021-11-30
JP7303901B2 (ja) 2023-07-05
WO2020219054A1 (en) 2020-10-29

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