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MX2016012108A - Banco de pruebas de la percepcion de la superficie de una carretera virtual. - Google Patents

Banco de pruebas de la percepcion de la superficie de una carretera virtual.

Info

Publication number
MX2016012108A
MX2016012108A MX2016012108A MX2016012108A MX2016012108A MX 2016012108 A MX2016012108 A MX 2016012108A MX 2016012108 A MX2016012108 A MX 2016012108A MX 2016012108 A MX2016012108 A MX 2016012108A MX 2016012108 A MX2016012108 A MX 2016012108A
Authority
MX
Mexico
Prior art keywords
virtual
anomaly
sensor
road
test bed
Prior art date
Application number
MX2016012108A
Other languages
English (en)
Inventor
Raju Nallapa Venkatapathi
Saeger Martin
Elizabeth Micks Ashley
Blue Douglas
Original Assignee
Ford Global Tech Llc
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 Ford Global Tech Llc filed Critical Ford Global Tech Llc
Publication of MX2016012108A publication Critical patent/MX2016012108A/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/445—Program loading or initiating
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/04—Monitoring the functioning of the control system
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/776—Validation; Performance evaluation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Multimedia (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)

Abstract

Un método para probar el desempeño de uno o más algoritmos de detección de anomalías. El método puede incluir obtener datos del sensor emitidos por un sensor virtual que modela el comportamiento de un sensor de imagen. Los datos del sensor pueden corresponder a un momento en el que el sensor virtual detectó una anomalía virtual definida dentro de una superficie de la carretera virtual. Uno o más algoritmos pueden aplicarse a los datos del sensor para producir al menos una dimensión percibida de la anomalía virtual. Posteriormente, el desempeño del uno o más algoritmos puede cuantificarse mediante la comparación de la al menos una dimensión percibida con al menos una dimensión real de la anomalía virtual como se define en la superficie de la carretera virtual.
MX2016012108A 2015-09-18 2016-09-19 Banco de pruebas de la percepcion de la superficie de una carretera virtual. MX2016012108A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US14/858,671 US20170083794A1 (en) 2015-09-18 2015-09-18 Virtual, road-surface-perception test bed

Publications (1)

Publication Number Publication Date
MX2016012108A true MX2016012108A (es) 2017-03-17

Family

ID=57288607

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2016012108A MX2016012108A (es) 2015-09-18 2016-09-19 Banco de pruebas de la percepcion de la superficie de una carretera virtual.

Country Status (5)

Country Link
US (1) US20170083794A1 (es)
CN (1) CN106547588A (es)
GB (1) GB2544391A (es)
MX (1) MX2016012108A (es)
RU (1) RU2016136970A (es)

Families Citing this family (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11210436B2 (en) * 2016-07-07 2021-12-28 Ford Global Technologies, Llc Virtual sensor-data-generation system and method supporting development of algorithms facilitating navigation of railway crossings in varying weather conditions
US10521677B2 (en) * 2016-07-14 2019-12-31 Ford Global Technologies, Llc Virtual sensor-data-generation system and method supporting development of vision-based rain-detection algorithms
CN107527074B (zh) * 2017-09-05 2020-04-07 百度在线网络技术（北京）有限公司 用于车辆的图像处理方法和装置
EP3486766A1 (en) * 2017-11-17 2019-05-22 Steinbeis Interagierende Systeme GmbH Computer-implemented method of augmenting a simulation model of a physical environment of a vehicle
US11620419B2 (en) * 2018-01-24 2023-04-04 Toyota Research Institute, Inc. Systems and methods for identifying human-based perception techniques
US12243319B2 (en) * 2018-03-01 2025-03-04 Jaguar Land Rover Limited Methods and apparatus for acquisition and tracking, object classification and terrain inference
US11087176B2 (en) * 2018-05-08 2021-08-10 Microsoft Technology Licensing, Llc Spatial localization design service
US11354459B2 (en) 2018-05-08 2022-06-07 Microsoft Technology Licensing, Llc Computer vision and speech algorithm design service
US10754344B2 (en) 2018-07-19 2020-08-25 Toyota Research Institute, Inc. Method and apparatus for road hazard detection
EP3611068B1 (en) * 2018-08-16 2022-12-21 Continental Autonomous Mobility Germany GmbH Driving assistance method and device, and vehicle
RU191374U1 (ru) * 2018-11-16 2019-08-02 Автономная некоммерческая образовательная организация высшего образования "Сколковский институт науки и технологий" Устройство на основе ансамбля гетерогенных нейронных сетей для уточнения прогнозов модели metro в задаче прогнозирования параметров и оценки состояния дорожного покрытия
KR20200133920A (ko) * 2019-05-21 2020-12-01 현대자동차주식회사 인공신경망 기반의 투사정보 인식 장치 및 그 방법
US11284284B2 (en) * 2019-08-13 2022-03-22 T-Mobile Usa, Inc. Analysis of anomalies using ranking algorithm
US12228419B2 (en) 2021-07-29 2025-02-18 Zoox, Inc. Systematic fault detection in vehicle control systems

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102842250A (zh) * 2011-06-22 2012-12-26 上海日浦信息技术有限公司 基于快速控制原型的开发型汽车驾驶模拟方法
CN102999050B (zh) * 2012-12-13 2015-04-08 哈尔滨工程大学 一种智能水下机器人的自主避障方法
CN103335658B (zh) * 2013-06-19 2016-09-14 华南农业大学 一种基于弧线路径生成的自主车辆避障方法
CN104290745B (zh) * 2014-10-28 2017-02-01 奇瑞汽车股份有限公司 车辆用半自动驾驶系统的驾驶方法

Also Published As

Publication number Publication date
CN106547588A (zh) 2017-03-29
US20170083794A1 (en) 2017-03-23
RU2016136970A (ru) 2018-03-20
GB201615831D0 (en) 2016-11-02
GB2544391A (en) 2017-05-17

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