MX2017001202A - Algoritmo de entrenamiento para evitar colisiones. - Google Patents

Algoritmo de entrenamiento para evitar colisiones.

Info

Publication number
MX2017001202A
MX2017001202A MX2017001202A MX2017001202A MX2017001202A MX 2017001202 A MX2017001202 A MX 2017001202A MX 2017001202 A MX2017001202 A MX 2017001202A MX 2017001202 A MX2017001202 A MX 2017001202A MX 2017001202 A MX2017001202 A MX 2017001202A
Authority
MX
Mexico
Prior art keywords
scenario
model
lane
vehicle
machine learning
Prior art date
Application number
MX2017001202A
Other languages
English (en)
Inventor
Elizabeth Micks Ashley
Jeong Han Kyu
Banvait Harpreetsingh
J Jain Jinesh
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 MX2017001202A publication Critical patent/MX2017001202A/es

Links

Classifications

    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
    • 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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • 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
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • 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
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/19—Recognition using electronic means
    • G06V30/191—Design or setup of recognition systems or techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06V30/1916—Validation; Performance evaluation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R29/00—Monitoring arrangements; Testing arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2499/00—Aspects covered by H04R or H04S not otherwise provided for in their subgroups
    • H04R2499/10—General applications
    • H04R2499/13—Acoustic transducers and sound field adaptation in vehicles

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Automation & Control Theory (AREA)
  • Multimedia (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Otolaryngology (AREA)
  • Acoustics & Sound (AREA)
  • Signal Processing (AREA)
  • Human Computer Interaction (AREA)
  • Geometry (AREA)
  • Computer Hardware Design (AREA)
  • Traffic Control Systems (AREA)
  • Feedback Control In General (AREA)

Abstract

Un modelo de aprendizaje de máquina es entrenado definiendo un escenario que incluye modelos de vehículos y un ambiente de conducción típico. Se añade un modelo de un vehículo sujeto al escenario y se definen las ubicaciones del sensor en el vehículo en cuestión. Se simula una percepción del escenario por sensores en las ubicaciones del sensor. El escenario incluye además un modelo de un vehículo de división de carril. La localización del vehículo de división de carriles y las salidas simuladas de los sensores que perciben el escenario se introducen en un algoritmo de aprendizaje de máquina que entrena un modelo para detectar la ubicación de un vehículo de división de carril basado en las salidas de sensor. Un controlador de vehículo incorpora entonces el modelo de aprendizaje de máquina y estima la presencia y/o la ubicación de un vehículo de división de carril basado en las entradas de los sensores reales introducidas en el modelo de aprendizaje de la máquina.
MX2017001202A 2016-01-26 2017-01-26 Algoritmo de entrenamiento para evitar colisiones. MX2017001202A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US15/007,024 US10474964B2 (en) 2016-01-26 2016-01-26 Training algorithm for collision avoidance

Publications (1)

Publication Number Publication Date
MX2017001202A true MX2017001202A (es) 2018-07-25

Family

ID=58462924

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2017001202A MX2017001202A (es) 2016-01-26 2017-01-26 Algoritmo de entrenamiento para evitar colisiones.

Country Status (6)

Country Link
US (1) US10474964B2 (es)
CN (1) CN107031622B (es)
DE (1) DE102017101106A1 (es)
GB (1) GB2547997A (es)
MX (1) MX2017001202A (es)
RU (1) RU2017100440A (es)

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Also Published As

Publication number Publication date
US20170213149A1 (en) 2017-07-27
GB201701278D0 (en) 2017-03-08
CN107031622A (zh) 2017-08-11
US10474964B2 (en) 2019-11-12
GB2547997A (en) 2017-09-06
DE102017101106A1 (de) 2017-07-27
RU2017100440A (ru) 2018-07-11
CN107031622B (zh) 2022-04-12

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