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WO2019179809A1 - Procédé de classification d'un siège de véhicule et système de classification permettant de classifier un siège de véhicule - Google Patents

Procédé de classification d'un siège de véhicule et système de classification permettant de classifier un siège de véhicule Download PDF

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Publication number
WO2019179809A1
WO2019179809A1 PCT/EP2019/056021 EP2019056021W WO2019179809A1 WO 2019179809 A1 WO2019179809 A1 WO 2019179809A1 EP 2019056021 W EP2019056021 W EP 2019056021W WO 2019179809 A1 WO2019179809 A1 WO 2019179809A1
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WO
WIPO (PCT)
Prior art keywords
vehicle seat
seat
classification
standard
standard weight
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/EP2019/056021
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German (de)
English (en)
Inventor
Matthias Franz
Thomas Holzinger
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Bayerische Motoren Werke AG
Original Assignee
Bayerische Motoren Werke AG
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 Bayerische Motoren Werke AG filed Critical Bayerische Motoren Werke AG
Publication of WO2019179809A1 publication Critical patent/WO2019179809A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass
    • G01M99/001—Testing of furniture, e.g. seats or mattresses

Definitions

  • Embodiments of the present disclosure relate to a method of classifying vehicle seats and a classification system configured to classify vehicle seats.
  • a vehicle seat for example, a seat of a motor vehicle, a motor vehicle, a rail nenInstituts, an aircraft or a watercraft.
  • a quality analysis of the vehicle seats installed in the motor vehicle is provided. For example, workstations such as seams or material quality can be tested. It is also possible to test a firmness of the seat cushion, which is approximately relevant for the comfort of a user of the vehicle seat.
  • Seat analyzes are carried out predominantly subjectively. Test persons can assess the sitting feeling on a vehicle seat and indicate the seat comfort they perceive. In such analyzes with volunteers, measuring devices such as pressure measuring mats can be included in order to objectify the results. Based on the result of a seat comfort analysis, a vehicle seat may be classified, for example, it may be determined whether it meets predetermined comfort requirements or not.
  • Embodiments relate to a method for classifying a vehicle seat of a motor vehicle.
  • the method comprises positioning at least one standard weight on the vehicle seat and creating at least one camera image of the vehicle seat with the standard weight.
  • a camera image is created that shows at least a portion of the vehicle seat on which the standard weight is positioned.
  • the method comprises superposing the camera image with model data of the vehicle seat.
  • the model data has classification ranges. A classification of the vehicle seat takes place by means of evaluation of the camera image superimposed with the model data.
  • the standard weight can deform the vehicle seat and this deformation can be determined on the camera image.
  • the model data may include, for example, data on various degrees of deformation. On the basis of the evaluation of the deformation of the vehicle seat with the model data, such as by comparing the actual deformation with the data at various degrees of deformation, the vehicle seat can be classified.
  • the model data includes a mark, which is displayed on a screen, for example, on which a live image of the vehicle seat is displayed (for example, the vehicle seat is filmed and model data superimposed on the filmed image).
  • marking it is possible to position / set the standard weight at a predetermined location, and it is possible to reproduce the positioning without fluctuations due to changing positions of the standard weight.
  • a guidance for positioning can be provided. For example, a light contour can be projected to the predetermined location.
  • the classification of the vehicle seat may include that a strength of a seat cushion is classi fied, for example, the strength of a seat.
  • a seat cushion may be a padding of the vehicle seat on a seat, on a backrest, on a headrest.
  • Inharait zen strength of a seat can be designed so that it is as comfortable as possible for a user of the driving convincing seat and in this case is between a too high hardness and too low hardness. It can be provided a standard strength of the seat, for example, with a me chanical tension of 6 kPa, for example, with a standard tolerance of 20%.
  • a strength may depend on a foam part, a reference voltage or a suspension of the vehicle seat. Due to tolerances in the production process, the strengths may be too soft or too hard for individual vehicle seats, for example, so that they deviate from the standard strength.
  • the classification according to the proposed method makes it possible to classify vehicle seats efficiently and inexpensively, for example with regard to the strength of seat cushions. Thus, it is possible to recognize vehicle seats that deviate from a given standard, and for example not to be installed in motor vehicles, or rule out of their respective motor vehicles.
  • the proposed method makes it possible that a classification of the vehicle seat takes place directly in a motor vehicle, the vehicle seat does not have to be removed from the vehicle to do so.
  • a classification of vehicle seats according to the method is carried out by means of digitalization in vehicle production or production, in particular using augmented reality, virtual data being added to a camera image.
  • the classification can be carried out with respect to different quality classes or comfort classes. For example, vehicle seats that are subject to a desired specification of strength, e.g. the standard strength of their seat upholstery by less than 5% or less than 3% are assigned to a highest quality class, vehicle seats deviating from a target specification by more than 5% and less than 10% are assigned to a middle quality class and vehicle seats, which deviate from a target specification by more than 10% to a low quality class.
  • a desired specification of strength e.g. the standard strength of their seat upholstery by less than 5% or less than 3%
  • vehicle seats deviating from a target specification by more than 5% and less than 10% are assigned to a middle quality class
  • vehicle seats which deviate from a target specification by more than 10% to a low quality class.
  • the classification of vehicle seats is carried out according to the method using a standard weight and a camera image.
  • the standard weight which is positioned on the vehicle seat, the vehicle seat deforms at the respective location depending on, for example, the strength of his seat cushion. Since the mass of the standard weight is known, can be deduced based on the strength or the degree of deformation on a strength of the seat cushion.
  • model data such as virtual elements, for example, based on augmented reality methods extended.
  • Vehicle seat model data may be design data or digital models of the vehicle seat.
  • Model data may be, for example, computer aided design (CAD) data.
  • the Mo del l schemes can represent shapes, contours and size of the vehicle seat, if this is not burdened with a standard weight, especially if it is unloaded.
  • the model data can represent contours of the vehicle seat, for example, which, in the event that the vehicle seat to be classified is unloaded, ie no weight is positioned on the vehicle seat, trace the contours of the vehicle seat to be classified.
  • the model data may include markings indicative of a deformation of the vehicle seat by the standard weight at a standard strength of the seat cushion. For example, if the actual deformation deviates from the mark, it may be determined that the seat cushion of the vehicle seat has a different strength than the standard and the vehicle seat is classified accordingly.
  • the overlaying of the model data on the camera image of the vehicle seat to be classified may include adapting the model data in relation to a size, a rotation and / or a translation as a function of a camera perspective from which he takes place the camera image.
  • the vehicle seat to be classified may be referred to as a real vehicle seat, whereas the model data may be referred to as a virtual vehicle seat.
  • overlaying may include maximizing a correlation function of the model data and the real vehicle seat.
  • the real vehicle seat and its position on the camera image can be detected by means of tracking software or image recognition.
  • a further camera image of the unloaded real vehicle seat can be created before the positioning of the standard weight for the superposition with model data. As a result, it may be particularly easy to superimpose the model data on the camera image.
  • the model data of the vehicle seat which are superimposed on the camera image of the vehicle seat with the standard weight, can provide a classification basis. For example, it can be evaluated for classifying by what degree, for example, a contour or a surface of the real vehicle seat deviates from a contour or surface of the model data of the vehicle seat. A deviation results from the deformation of a seat cushion by the standard weight. It is possible that the model data have a standard deviation, for example a standard deviation range, for a load of the vehicle seat with the standard weight. If it is detected on the camera image extended by the model data that the deviation is within the standard deviation, the vehicle seat can be classified as comfortable become. If the determined deviation is outside the standard deviation, the driving seat can be classified, for example, as uncomfortable.
  • the classification of the vehicle seat can be determined manually on the basis of the image detected on the camera image deviation of a shape of the vehicle seat of superimposed model data. It is also possible that a classification takes place automatically, for example by means of a pattern recognition software, which is designed to determine the degree of deviation of the vehicle seat from the model data. For example, in an automated classification, if a deviation is within the standard deviation, a green signal may be output so that a quality of the vehicle seat is OK or a red signal is output if a deviation is outside the standard deviation , A yellow or orange signal may be output if a deviation is in a limit adjacent to the standard deviation.
  • the proposed method makes it possible to classify vehicle seats by means of a camera evaluation. As a result, a quality or a user comfort of the vehicle seat can be classified, which may depend on a strength of a seat cushion.
  • Such Klassifi coding is possible in particular in an already manufactured vehicle, without the vehicle seat, for example, must be changed or removed.
  • a classification is also possible from a distance in which there is an optical connection between the seat and camera, example, through a window of a climate chamber, which are reduced by the method technical requirements of the camera, since it can be operated outside the climate chamber.
  • the method makes classification quick and efficient, therefore it can be used in a series production for vehicle seat quality assurance.
  • Insbesonde re in an automated classification, for example, to carry out the method only a manual positioning of the standard weight on the vehicle seat necessary.
  • steps in a classification can be reduced, in particular a classification can be carried out without test subjects and on the basis of an objective evaluation scale.
  • the standard weight is positioned on a seat of the driving convincing seat.
  • the seat can have approximately a foam part, which can be coated with a coating of a textile material or of a leather material.
  • the seat can be set for classification at a predetermined inclination angle.
  • the predetermined angle of inclination may, for example, correspond to a co-location (construction position) of the vehicle seat, or be inclined by 0 °, 10 °, or 20 ° with respect to a horizontal floor surface.
  • the standard weight can also be positioned on all other seat cushion surfaces of the vehicle seat. For example, it can be positioned on a backrest or a headrest.
  • camera images of the backrest or the head restraint can in each case be created in these cases and the model data comprise these parts of the vehicle seat.
  • a backrest can be placed vertically, so that a positioned standard weight remains in the previously provided position and does not slip off the backrest.
  • attach the standard weight for classifying a strength of the backrest by means of a suitable position onshalterung on the headrest.
  • a standard weight can be positioned analogously to the headrest.
  • An angle of inclination of the backrest can also correspond to a co-location, or be inclined by 10 °, 20 °, or 30 ° relative to a vertical.
  • an adjustment of movable components of the vehicle seat may correspond to a representation of the model data, so that an overlay is possible.
  • vehicle seats with respect to their various components for example, all upholstery can be classified. This makes a more differentiated objective classification possible.
  • the model data has at least information about the unloaded vehicle seat.
  • the model data may have different classification ranges. For example, it may be provided in a vehicle seat for a sports car, that a strength of the Anlagenit ze is higher than, for example, in a vehicle seat for a minibus.
  • image recognition software it can be recognized which vehicle seat model is involved and, depending on the detected vehicle seat, model data assigned to the vehicle seat can be loaded and used from a database, for example.
  • the information about the unloaded vehicle seat are about a shape contour or surface of the vehicle seat when no weight loaded the driving convincing seat.
  • the model data correspond to a shape of the vehicle seat to be classified if it is not loaded with the standard weight.
  • the classification areas are assigned to a respective standard weight and detect the degree of plastic deformation of a surface of the vehicle seat by the respective standard weight.
  • an identification mark for example a QR code
  • the respective standard weight can be recognized by means of image recognition software.
  • associated classification ranges can be used due to the recognized standard weight.
  • classification ranges may be selected differently for a first standard weight than for a second standard weight if the first standard weight is a lower mass, for example has as the second standard weight.
  • a recognition of the standard weight can be automated by the identifi cation mark on the standard weight about easier.
  • At least three classification ranges are used, wherein a strength greater than the standard strength is assigned to a first classification range, a second classification range is assigned a strength corresponding to the standard strength, and a third classification range has a lower strength than a standard strength of a vehicle seat surface the standard strength is assigned.
  • a deformation of the vehicle seat by the standard weight may cause a standard deviation of a seat surface to model data of the vehicle seat.
  • the classification may associate the vehicle seat with the second classification area, so that it may be known that the vehicle seat provides high user comfort. If a deformation is outside the standard deviation, the classification may assign the vehicle seat to the first or third classification area, so that it may be known that a user comfort of the vehicle seat may be limited.
  • the method further comprises determining a measurement temperature and a measurement humidity when creating the camera image and normalizing a classification of the vehicle seat to a standard temperature and a standard humidity using a material table comprising at least one material of the vehicle seat.
  • a material table comprising at least one material of the vehicle seat.
  • information about how a strength of a material of a seat cushion changes depending on the temperature can be provided in a material table. For example, if a measurement is made for a classification at a first temperature other than a standard temperature, the result of the classification based on the material table may be converted or adjusted or normalized to a result that would have been achieved at a standard temperature.
  • a relationship of the classification result and the temperature dependence of a material hardness of a cushioning material may be approximately non-linear, since other factors, e.g. a reference voltage of the vehicle seat affect the classification result.
  • a temperature may be determined, for example, by means of an optical thermometer, such as a laser thermometer. By taking into account the temperature and / or the humidity, it is possible to enable an increased objectivity of a classification of vehicle seats also in different measuring environments.
  • the standard weight has at least one marking which, for comparison with the model data and assignment of a classification area is formed.
  • a marker can have multiple marker lines.
  • a jewei time marking line can be assigned to a respective strength of a seat cushion. If the standard weight sinks into the seat cushion during positioning on the seat cushion, for example, one of the respective marking lines may lie on a contour line of the model data of the vehicle seat superimposed on the camera image. Due to the development, it is possible to determine a strength of the seat cushion thereby, the one marking line, which lies on the contour line of the model data, read. As a result, a classification can be particularly easily possible, for example, a classification can be carried out particularly easily by a trained computer program with image recognition. For example, the different marking lines have different colors. By way of example, the marking has a color spectrum which changes continuously over a spatial extent, so that a classification of the vehicle seat with continuous classification values is possible.
  • the camera image is created after a period of time longer than, for example, 60 s after the positioning of the standard weight on the vehicle seat.
  • the duration may be longer than 10 seconds, longer than 30 seconds, longer than 60 seconds, longer than 90 seconds, or longer than 120 seconds. It may be that a seat pad of the vehicle seat deforms only after a certain time, so that a static deformation sets only after a predetermined period ⁇ Waiting for the creation of the camera image by the time until the static deformation has set, can Quality of a classification of the vehicle seat can be increased.
  • the time can be selected depending on a material of the vehicle seat so that the camera image is only created when a stati cal deformation of the material has been adjusted.
  • the standard weight has a mass of at least 3 kg and a maximum of 20 kg.
  • the standard weight has a mass of more than one kilo, more than two kilos, more than five kilos or more than ten kilos.
  • the standard weight has a mass of less than 18 kilos, less than 15 kilos or less than ten kilos.
  • the standard weight has a predetermined size.
  • a size of a Aufla ge Chemistry the standard weight on the vehicle seat may correspond to a size of a seat bone, for example, have a diameter of more than 5 cm and less than 15 cm. This can improve a realistic classification.
  • a height of the standard weight may be used as a mark for evaluation with the model data
  • a standard deviation of a deformation may be defined by an upper edge of the standard weight in a vehicle seat having a standard strength on a respective shape contour of the model data.
  • the standard weight is positioned on a headrest, a backrest and / or a side support cushion of the vehicle seat.
  • the standard weight depending on a distance of the side support cushion of the vehicle seat dimensi oned, so that it rests on the two Sohaltpolstem the vehicle seat. In this way, Kings also strengths or comfort classes of side cushion, headrest and / or backrest of the vehicle seat can be classified.
  • two standard weights for classifying a vehicle seat are positioned at two different locations of the vehicle seat.
  • a plurality of weights may also be positi on a plurality of locations of the vehicle seat.
  • the standard weight may consist of a plurality of elements that can emulate a body of a user of the vehicle seat. Such a standard weight may partly have the form of a dummy.
  • various standard weights of various masses can be positioned on the seat surface of the vehicle seat, so that deformation of the seat surface with unequal mass distribution can be examined and the vehicle seat can be classified accordingly. In this way, the behavior of the vehicle seat in cornering with centrifugal forces can be classified.
  • the classification further comprises a check of a functionality of a movement device of the vehicle seat, wherein a movement of the standard weight caused by the activated movement device is determined by means of evaluation of at least two camera images, the functionality being classified based on the movement of the standard weight.
  • Vehicle seats may include active movement devices, such as massage functions.
  • the seat surface of a vehicle seat may include pneumatic chambers that can move a pelvic area of a user of the vehicle seat.
  • a classification of the moving means may be to test a functionality of the moving means. When the moving means is activated and the standard weight is positioned in a certain vicinity, for example above the moving means, the standard weight is moved following a movement of the moving means.
  • the model data may have an upper limit and a lower limit within which the standard weight must at least move with an activated mover in order for the mover to be classified as functional.
  • the camera images can then be determined if the standard weight is at extreme positions of the movement, for example at extreme positions of a flub amplitude. For example, a plurality of camera images can be determined to facilitate the detection of the extreme positions. For example, a video recording with superimposed or overlaid model data can be used become. Due to the development, it is possible to classify other functions of the vehicle seat in addition to a strength of a seat cushion of a vehicle seat.
  • the classification system comprises at least a standard weight and a Schmaus evaluation device.
  • the image evaluation device comprises at least one camera and an evaluation device, wherein the evaluation device has model data for at least one driving seat and is adapted to superimpose a camera image of the camera with the model data.
  • the classification system is thus designed to export a method described above.
  • a classification system may be provided in a portable device, such as a tablet, or may be a distributed or a stationary system.
  • the camera may be connected to the evaluation device via a wireless connection, such as WLAN or Bluetooth, or may be connected via a network connection.
  • a central evaluation device contains model data of the plurality of vehicle seats.
  • a further development of the classification system further comprises a classification device, which is designed to output a classification result of the vehicle seat based on the camera image transferred by the evaluation device with the model data.
  • the development of the classification system can allow higher automation in a classification of driving seat seats.
  • the disclosure also includes the further developments of the disclosed classification system having one or more features as already described in connection with the further developments of the disclosed method. For this reason, the corresponding Witer educations of the disclosed classification system are not described here again, but also apply to them as disclosed.
  • Fig. 1 is a schematic representation of a method for classifying a vehicle seat
  • FIG. 2 shows a classification system with a standard weight arranged on a vehicle seat and an image evaluation device
  • FIG. and Fig. 3 is a further schematic representation of a method for classifying a vehicle seat, wherein the vehicle seat is associated with a quality level or a comfort class.
  • the method comprises positioning at least one standard weight 11 on the vehicle seat, creating at least one camera image 12 of the vehicle seat with the standard weight, overlaying the camera image 13 with model data of the vehicle seat, the model data having classification ranges and classifying the vehicle seat 14 by means of evaluation of the model data overlaid camera image.
  • the method makes it possible to classify vehicle seats quickly and inexpensively with camera images that are augmented by virtual elements.
  • the Jardinechellesvorrich device 23 may include a camera 24 and a computing unit 25, which may be an evaluation device and, for example, processor, memory, image analysis software and model data.
  • the camera 24 is designed to len a camera holder 26 of the vehicle seat 21 Vietnamesel, wherein the camera holder 26 is a photo or an image recording.
  • the camera mount 26 can also be a video.
  • the vehicle seat 21 includes a seat surface 27, a seat back 28 and a headrest 29.
  • the standard weight 22 of the classification system 20 may be positioned on the seat surface 27 of the vehicle seat 21.
  • a cross-sectional view A of the seat surface 27 is shown.
  • the cross-sectional view A shows side support pads 30, 30 'of the vehicle seat 21.
  • a virtual seat surface 31 and a real seat surface 32 are shown.
  • the real seating surface 32 may be determined from the camera receptacle 26.
  • the virtual seat surface 31 may be stored in CA (computer aided design) data of the classification system 20 superimposed with the camera shot.
  • CA computer aided design
  • the arithmetic unit 25 of the classification system 20 may be configured to superimpose the virtual seat surface 31 (the CA data) in the camera receptacle 26 with the real seating surface 32 in such a way that real and virtual seating surfaces may be overlaid. Surface when the seat surface is not weighted by a weight, or to overlap in undeformed places, such as the backrest when the seat cushion is deformed. Based on a comparison of the real seat surface 32, which may be deformed by the weight 22, for example, and the virtual seat surface 31, the arithmetic unit 25 can determine in example, with what strength or to what degree the seat cushion is deformed by the weight of Normge. Based on the determination of the deformation and the mass of the standard weight, the arithmetic unit can classify, for example, a strength of the seat cushion of the seat surface 27.
  • FIG. 2b shows an enlargement 35 of the cross-section A of the vehicle seat 21.
  • the virtual seat surface 31 and deformed by the standard weight 22 real seat surface 32 are beaut det.
  • the standard weight 22 has a first measuring point 36, a second measuring point 37 and a third measuring point 38.
  • the measuring points are measuring lines that can indicate classification areas. For example, a position of a measuring point on the camera image 26 in the United ratio to the virtual seat surface 31 for a classification of the strength of the seat cushion ver used.
  • the seat cushion may be soft, ie have a low strength, so that it is greatly deformed by the weight 22.
  • the first measurement point 36 may lie on a contour line of the virtual seat surface 31, and the vehicle seat 21 may be classified as a vehicle seat having a soft seat cushion.
  • the standard being e.g. can be displayed by the second measuring point 27.
  • the seat cushion of the seat surface 27 is hard so that the weight 22 less deforms the seat cushion.
  • a distance of the real seat surface 32 to the virtual seat surface 31 is smaller than in the first example.
  • the measuring point 38 may be a measuring line which lies in the camera image 26 on the virtual seat surface 31.
  • the measuring point 38 may be associated with a hard seat cushion or high strength of the seat cushion, so that when the measurement point 38 is on the virtual seat surface 31, the vehicle seat 21 or the seat cushion of the seat surface 27 of the vehicle seat 21 is considered hard, e.g. Harder than a standard, can be classified.
  • FIG. 3 shows a further schematic representation of a method 40 for classifying a vehicle seat, wherein a quality level and / or a comfort class can be assigned to the vehicle seat.
  • the method 40 includes importing augmented reality for classifying vehicle seats, for example by overlaying CA data 41 of the vehicle seat in a co-location with the real seat.
  • the real seat may be the vehicle seat received by the camera.
  • the CA data may be a virtual design, such as a digital design model of the vehicle seat. Overlaying may include adapting the CA data to the captured image of the vehicle seat depending on the camera's perspective, for example, in terms of size, angle of rotation or translation angle, or adjusted for optical distortion of the camera.
  • the method 40 includes, for example, an optical evaluation 42, which may be a qualitative assessment of the seat adjustment. For example, it can be checked whether the vehicle seat is set in the co-location.
  • the method 40 includes, for example, positioning the reference weight 43. It may further include overlaying a boundary layer 44 of the CA model with the captured image of the vehicle seat. For example, a limit position of the CA model describing a surface of a seat cushion of the vehicle seat is superimposed on the surface of the seat cushion of the vehicle seat on an image taken by the camera. By way of example, this makes it possible to check the position of the reference weight 45.
  • an intended target position of the reference weight can be stored and an image recognition device can be designed to determine a position of the reference weight on the camera image.
  • An optical adjustment 46 of the CA boundary layers with a deformation, for example a deformation of the seat cushion of the vehicle seat, by the reference weight can be performed.
  • a comfort class 47 For example, the classification into the comfort class depends on the deformation of the seat cushion.
  • a CA-KO layer with the real seat for example the vehicle seat on a camera image.
  • a opti shear boundary adjustment 49 in which based on the CA data, a boundary position adjustment (for example, tolerance levels) of about Kedem, filing or stitching of the vehicle seat Runaway leads can be.
  • a boundary position adjustment for example, tolerance levels
  • it can be determined based on the camera image of the vehicle seat without a Refe rence weight by matching with the CA data and pattern recognition of the recorded Bil of whether boundary layers, such as seams of the vehicle seat, are attached to a designated position.
  • the intended position can be stored in the CA data.
  • a classification into quality level 50 is possible, for example if an actual limit position of the vehicle seat on the camera image deviates slightly, for example less than 5 mm, less than 3 mm or less than 1 mm, from the limit position of the superimposed CA data, a quality level can be high, eg the vehicle seat is assigned a highest value of a quality scale.
  • method 40 it is possible according to method 40 to record the evaluation basis, for example the classification into quality level 50 or the classification into comfort classes 47 by means of documentation 51. swei-
  • the camera image can be stored with the superimposed CA data or an evaluation of the camera image can be documented, for example by what degree a seat cushion of the vehicle seat has been deformed by arranging the reference weight on the vehicle seat.
  • a Kamerys system eg in a tablet / Hololense / smartphone
  • a software with deposited seat boundary pattern data and a hardware reference object (weight) perform a comparison of the deformations of a vehicle seat virtually and with hardware.
  • a hardware reference object weight
  • About the reference weight on the seat and its generated deformation in the seat foam (sinking) is objectified and predicted over the optical from the same seating comfort.
  • compliance with the comfort specification can be monitored in series operation / production without costly subject studies.
  • the proposed method may allow for time saving and objectification in the classification of vehicle seats, as well as providing the possibility of rapid series monitoring in the manufacture of vehicles regarding the quality of the vehicle seats.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Seats For Vehicles (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

Selon des modes de réalisation, la présente invention concerne un procédé de classification d'un siège de véhicule dans un véhicule à moteur. Le procédé consiste à positionner au moins un poids standard sur le siège du véhicule et à générer au moins une image caméra du siège du véhicule avec le poids standard. La superposition de l'image caméra avec des données modèle du siège du véhicule permet de classifier le siège de véhicule au moyen de l'évaluation de l'image caméra superposée avec les données modèle. Selon d'autres modes de réalisation, l'invention concerne un système de classification permettant la classification d'un siège de véhicule.
PCT/EP2019/056021 2018-03-23 2019-03-11 Procédé de classification d'un siège de véhicule et système de classification permettant de classifier un siège de véhicule Ceased WO2019179809A1 (fr)

Applications Claiming Priority (2)

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DE102018204495.3A DE102018204495A1 (de) 2018-03-23 2018-03-23 Verfahren zum Klassifizieren eines Fahrzeugsitzes und Klassifizierungssystem zum Klassifizieren eines Fahrzeugsitzes
DE102018204495.3 2018-03-23

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WO2019179809A1 true WO2019179809A1 (fr) 2019-09-26

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Cited By (1)

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CN111160270A (zh) * 2019-12-31 2020-05-15 中铁大桥科学研究院有限公司 一种基于智能视频识别的桥梁监测方法

Families Citing this family (2)

* Cited by examiner, † Cited by third party
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DE102019128950A1 (de) * 2019-10-28 2021-04-29 Bayerische Motoren Werke Aktiengesellschaft Vorrichtung und Verfahren zur Qualitätsprüfung eines Fahrzeugsitzes
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