WO2023104617A1 - Procédé de relevé d'environnement d'un véhicule automobile - Google Patents

Procédé de relevé d'environnement d'un véhicule automobile Download PDF

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Publication number
WO2023104617A1
WO2023104617A1 PCT/EP2022/083997 EP2022083997W WO2023104617A1 WO 2023104617 A1 WO2023104617 A1 WO 2023104617A1 EP 2022083997 W EP2022083997 W EP 2022083997W WO 2023104617 A1 WO2023104617 A1 WO 2023104617A1
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WO
WIPO (PCT)
Prior art keywords
ultrasonic
motor vehicle
point
classification
echo signal
Prior art date
Application number
PCT/EP2022/083997
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German (de)
English (en)
Inventor
Henrik Starkloff
Andreas Walz
Mohamed Elamir Mohamed
Michael Hallek
Original Assignee
Valeo Schalter Und Sensoren Gmbh
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Publication of WO2023104617A1 publication Critical patent/WO2023104617A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/06—Systems determining the position data of a target
    • G01S15/08—Systems for measuring distance only
    • G01S15/10—Systems for measuring distance only using transmission of interrupted, pulse-modulated waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/06—Systems determining the position data of a target
    • G01S15/46—Indirect determination of position data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/87—Combinations of sonar systems
    • G01S15/876—Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector
    • G01S15/878—Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector wherein transceivers are operated, either sequentially or simultaneously, both in bi-static and in mono-static mode, e.g. cross-echo mode
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
    • G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/523—Details of pulse systems
    • G01S7/526—Receivers
    • G01S7/527—Extracting wanted echo signals
    • G01S7/5273—Extracting wanted echo signals using digital techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/06—Systems determining the position data of a target
    • G01S15/46—Indirect determination of position data
    • G01S2015/465—Indirect determination of position data by Trilateration, i.e. two transducers determine separately the distance to a target, whereby with the knowledge of the baseline length, i.e. the distance between the transducers, the position data of the target is determined

Definitions

  • the present invention relates to ultrasonic measuring technology in motor vehicles and, in particular, to a method for measuring an area surrounding a motor vehicle with one or more ultrasonic transceivers.
  • Motor vehicles are equipped with ultrasonic transceivers, which emit ultrasonic transmission signals into the surroundings of the motor vehicle and receive ultrasonic reception signals from the surroundings of the motor vehicle.
  • a distance to the object in the vicinity of the motor vehicle is determined on the basis of a signal propagation time between the transmission of an ultrasonic transmission signal and the occurrence of an ultrasonic echo in the ultrasonic reception signal, which is due to a reflection of the ultrasonic transmission signal on an object in the vicinity of a motor vehicle.
  • Possible positions of the object, or more precisely of the reflection point form an ellipse whose focal points form the positions of the transmitting and the receiving ultrasonic transceiver and whose semimajor axis corresponds to the determined distance.
  • the actual position of the reflection point is then determined by trilateration or the like using a number of ellipses obtained at different measurement positions.
  • trilateration and the like the problem arises that an object that is essentially point-shaped when viewed in two dimensions, such as a post, is essentially located at the intersection of the ellipses determined from different measurement positions, since in this case the reflections in the multiple measurements are all in the take place essentially at the same point.
  • an extended object such as a wall is usually arranged along a tangent to the ellipses obtained at the various measurement positions, since in this case the multiple reflections usually occur at different points of the extended object.
  • the object of the invention is to further improve the measurement of an environment of a motor vehicle by means of ultrasound.
  • a method for measuring the surroundings of a motor vehicle which has a number of ultrasonic transceivers.
  • the method comprises: a) controlling the number of ultrasonic transceivers for emitting ultrasonic transmit signals into the area surrounding the motor vehicle and receiving ultrasonic received signals from the area surrounding the motor vehicle; b) identifying a plurality of ultrasound echoes in the received ultrasound signals received and acquiring a respective echo signal representation for each of the identified ultrasound echoes; c) determining a classification of each of the plurality of echo signal representations as a reflection of the ultrasonic transmission signal on a punctiform object or as a reflection of the ultrasonic transmission signal on a linear object; and d) generating a number of object features, which indicate a respective object in the area surrounding the motor vehicle, based on the plurality of ultrasonic echoes, taking into account the classifications of the plurality of echo signal representations.
  • the proposed method is based on the idea that, based on a signal form of an individual echo signal (a section of the ultrasonic received signal arranged around an ultrasonic echo), conclusions can be drawn as to whether the ultrasonic echo is due to a reflection of the ultrasonic transmitted signal on an essentially punctiform object or on a reflection of the ultrasonic transmission signal is due to an extended object.
  • This can be caused, for example, by differences in the geometry, in the surface texture and the like, more typically in a two-dimensional view. be approximately point-like objects, such as posts, corners of buildings and the like, on the one hand, and line-like objects, such as extended walls and walls and the like, on the other hand.
  • the object features which at least indicate a position of the respective object in the area surrounding the motor vehicle include, to be determined correctly either on the basis of tangents to or on the basis of intersections of the particular ellipses.
  • an accuracy of the surroundings measurement can be advantageously improved.
  • An ultrasonic transmission signal can in particular be an impulse applied to a carrier frequency.
  • a received ultrasound signal can be received and preferably recorded over a predefined period of time after the transmission of the ultrasound signal.
  • the ultrasonic transmission signal is transmitted with one of the ultrasonic transceivers.
  • the associated ultrasound reception signal can be received with the same or with a different one of the ultrasound transceivers. That is, a transmitting and a receiving ultrasonic transceiver can be identical or different in a respective measurement. Several measurements can be carried out one after the other or, for example using different carrier frequencies, also simultaneously with different ultrasonic transceivers.
  • the motor vehicle is equipped with only a single ultrasonic transceiver. In this case, at least two measurements are carried out while the motor vehicle is moving, so that the ultrasonic transceiver is arranged at different positions relative to a stationary object in the vicinity of the motor vehicle for the different measurements.
  • the motor vehicle preferably has several, for example four, eight or twelve, ultrasonic transceivers on each side of the vehicle. In this case, at least two measurements can also be carried out when the motor vehicle is stationary using different ultrasonic transceivers.
  • An ultrasonic echo is to be understood in particular as a reflection of the ultrasonic transmission signal on an object in the vicinity of the motor vehicle.
  • the ultrasonic echo can be identified in the measured ultrasonic received signal or in a recording thereof based on the occurrence of an amplitude maximum, for example using a threshold filter.
  • further and complex signal analysis techniques can also be used for identification, which, in addition to an amplitude, also take into account a signal form of a peak at the amplitude maximum and the like.
  • the result of the identification can be a point in time at which the ultrasonic echo occurs or a transit time of the ultrasonic transmission signal between the transmission of the ultrasonic transmission signal by the transmitting ultrasonic transceiver and the receipt of the ultrasonic echo by the receiving ultrasonic transceiver. If further steps of the method take place "based on an ultrasonic echo", this also means in particular "based on the temporal occurrence of the ultrasonic echo in the associated ultrasonic received signal” and/or “based on a signal propagation time of the ultrasonic transmitted signal until the ultrasonic echo occurs" and/or "by a measured distance".
  • an "echo signal representation” is a digital representation of a portion of the
  • the echo signal representation can include a number of sampled values of the received ultrasound signal and/or an amplitude envelope of the received ultrasound signal. "Capturing" the echo signal representation is in particular the generation of the digital representation using the ultrasonic received signal.
  • the identification of the ultrasound echo can also already take place on a digital representation of the ultrasound received signal; in this case, “capturing” the echo signal representation can be understood to mean the provision of a corresponding section of the digital representation of the received ultrasound signal.
  • point and line refer to an idealized shape of the actual object when viewed two-dimensionally in a horizontal plane. This means that the object actually located in the environment does not actually have to be point-shaped or line-shaped. Rather, classification as a "point object” or as a “line object” means that the actual object is described by a two-dimensional point object feature or by a two-dimensional line object feature for the purposes of the method and is thus described with sufficient accuracy.
  • Point-like objects are objects for which the ultrasonic transmission signal is reflected at essentially the same point of the object in the case of different measurements that are carried out with ultrasonic transceivers at different positions.
  • substantially the same means anything that is within an acceptable range
  • Line-shaped objects are walls, walls, and the like.
  • objects are classified as “lines” that can be described by two two-dimensional coordinates, or by one one-dimensional coordinate, a length specification and an angle specification, or the like.
  • linear objects are objects for which the ultrasonic transmission signal is reflected at different points of the object in different measurements that are carried out at different positions of the ultrasonic transceivers involved.
  • the term “object in the area surrounding the motor vehicle” is also based on the representations of the area formed by the object features and not on the object that is actually present.
  • a single object that is actually present such as a building positioned diagonally to the motor vehicle, could be used in the proposed method as a first line-shaped object (first wall), a point-shaped object (corner of the building) and a second line-shaped object (second wall on the other side of the corner) are described.
  • a non-rectilinear object could be approximated by several linear objects.
  • the classification can be done using heuristic databases, by comparison with reference signal representations, by identifying characteristic signal shapes and/or using a neural network, with artificial intelligence and the like.
  • An object feature can be understood as a data set that includes a number of data elements with information about at least one position and preferably further information about the location and orientation of a respective object in the area surrounding the motor vehicle.
  • an object feature is a data record that enables the reconstruction of a two-dimensional representation of a punctiform or linear object on a two-dimensional map of the area surrounding the motor vehicle.
  • a map of the measured surroundings of the motor vehicle can thus be interpreted as a two-dimensional plot of all specific object features.
  • a map determined in this way can be used, for example, to determine whether and to what extent a door of the motor vehicle can be opened. Thanks to the improved accuracy of the proposed method, door damage can be avoided more reliably.
  • the position and the like of a respective object in the area surrounding the motor vehicle can be determined by trilateration or the like of a plurality of measurements.
  • a respective "measurement” includes in particular an identified ultrasonic echo, the associated echo signal representation, and preferably topological parameters of an associated measurement arrangement consisting of a transmitting and a receiving ultrasonic transceiver, with which the ultrasonic echo was generated and measured.
  • the classification of the echo signal representation can be taken into account here, for example, when selecting the measurements used for the trilateration or the like and/or when selecting a suitable trilateration technique, for example intersection point formation or tangent fitting. In this way, the object features can advantageously be determined particularly precisely.
  • the respective echo signal representation includes a number of samplings of an envelope curve of the corresponding ultrasonic received signal before and after a temporal occurrence of the corresponding ultrasonic echo.
  • Such a data-reduced echo signal representation of the respective ultrasonic echo advantageously transports enough information to enable the echo signal representation to be classified as an ultrasonic echo of a punctiform or line-shaped object, while on the other hand it saves enough data to efficiently enable classification using neural networks and the like.
  • the envelope is in particular an amplitude envelope.
  • the number of scans of the envelope curve before the ultrasonic reception signal is preferably 50, particularly preferably 25 and particularly preferably 10.
  • the number of samplings of the envelope curve after the ultrasonic reception signal is preferably 100, particularly preferably 50 and particularly preferably 20.
  • a sampling rate can be suitably selected in this case, so that the echo signal representation essentially includes the entire ultrasonic echo from the moment the envelope curve rises until the envelope curve decays below a predefined threshold value.
  • step c) takes place using a classification device trained by means of machine learning, which is provided with at least the respective echo signal representation as input data and whose output data represent the classification.
  • the inventors have recognized that by using a classification device trained using machine learning, it is possible to classify echo signal representations as ultrasonic echoes from punctiform or linear objects, which is superior to the human eye and thus to a knowledge-based approach based on heuristic databases and the like.
  • one or more topological parameters of a measurement arrangement consisting of a transmitting one of the number of ultrasonic transceivers and a receiving one of the ultrasonic transceivers, using which the respective echo signal representation was recorded, are also provided as input data for the classification device.
  • the respective topological parameter comprises an installation height of the transmitting and/or the receiving ultrasonic transceiver, an elevation angle of a measuring axis of the transmitting and/or the receiving ultrasonic transceiver and an azimuth angle enclosed by the measuring axes of the transmitting and the receiving ultrasonic transceiver.
  • the input data for the classification device include the parameters mentioned, particularly precise classifications can result.
  • a measuring axis of a respective ultrasonic transceiver is in particular the axis along which a transmitted signal lobe has a maximum.
  • An elevation angle is to be understood in particular as the angle that is enclosed by the measuring axis projected onto a vertical plane to a roadway on which the motor vehicle is standing or driving, with the roadway.
  • An azimuth angle is to be understood in particular as meaning that angle which the two measurement axes projected onto the roadway enclose.
  • the classification device comprises an artificial neural network trained by supervised machine learning.
  • the monitored machine learning can take place using reference measurements, in which a plurality of predefined reference objects of known geometry are arranged in an otherwise object-free or otherwise predefined environment of the motor vehicle and associated reference echo signal representations are recorded.
  • the monitored machine learning can also be based on simulation results.
  • the artificial neural network comprises an input neuron layer, a number of hidden convolutional neuron layers, a number of hidden densely connected multilayer perceptron layers and an output
  • neuron layer Such a structure of the neural network delivers particularly good results if the input data of the neural network includes a respective echo signal representation on the one hand and the topological parameters of the associated measurement arrangement on the other.
  • the convolutional neuron layers are advantageous in identifying similarities based on the echo signal representations comprising a plurality of continuously adjacent samples, while the multilayer perceptron layers can particularly advantageously take into account the disjoint topological parameters.
  • the neural network can correspond to an hourglass model, in which the input neuron layer and the output neuron layer have more neurons than the intervening hidden layers.
  • a total number of neurons of the neural network can be from 1000 to 10000, particularly preferably from 2000 to 5000, very particularly preferably from 2000 to 3000.
  • step d) comprises: selecting a first ultrasound echo and one or more further ultrasound echoes from the plurality of identified ultrasound echoes; determining a locus of possible reflection points for each of the selected ultrasonic returns; Determining an intersection of the determined loci and/or determining a tangent to the determined loci; validating the determined intersection point and/or the determined tangent; and generating a point feature based on the validated intersection and/or a line feature based on the validated tangent.
  • the classification of at least the echo signal representation for the first ultrasonic echo is made when a decision is made as to whether an intersection point or a tangent is determined and/or when a decision is made as to whether a point object feature or a line object feature is generated and/or when a Decision as to which one or which several further ultrasonic echoes are selected is taken into account.
  • a decision is made as to whether an intersection point or a tangent is determined and/or when a decision is made as to whether a point object feature or a line object feature is generated and/or when a Decision as to which one or which several further ultrasonic echoes are selected is taken into account.
  • the selection of the measurements (ultrasonic echoes and associated echo signal representations, topological parameters and the like) on the basis of which an attempt is to be made to construct an object feature can be based on spatial criteria - selection of measurements with neighboring ultrasonic transceivers - or temporal criteria - selection of only the most recent measurements - or a combination of these.
  • the locus of possible reflection points for a given ultrasonic echo can be a circle whose center is the position of the ultrasonic transceiver, whose radius is given by the measured distance is, or more precisely a semicircle section of this circle outside the motor vehicle.
  • the locus of possible reflection points can be an ellipse whose focal points are the positions of the two ultrasonic transceivers and whose semimajor axis is given by the measured distance , or more precisely a semi-ellipse section of this ellipse outside the motor vehicle.
  • the point of intersection and/or the tangent can be determined by fitting, for example using the least squares method or the like. Because, in a general case where more than two measurements are selected, there may not exist an exact intersection of more than two ellipses and there may not exist a tangent drawn exactly to more than two ellipses. Thus, when determining the locus curve, in addition to the coordinates of the point of intersection or the tangent, a measurement uncertainty can result from the adjustment, which can serve as a measure of the accuracy of the adjustment or the measurement. The intersection point and/or the tangent can thus be validated, for example, based on the measurement uncertainty determined when determining the intersection point and/or the tangent.
  • the respective measurement uncertainties can, for example, be compared with a threshold value that must not be exceeded.
  • Other types of validation are conceivable, for example plausibility checks. If, for example, multiple executions of step d) result in object features that contradict one another, the contradictory object features cannot be validated, for example.
  • Heuristic databases can also be used for validation in order to compare the determined object features with combinations of object features that are known to be plausible or implausible.
  • the validation does not necessarily take place before a respective object feature is generated.
  • a number of object feature hypotheses can also initially be generated by repeatedly selecting and determining loci and points of intersection and/or tangents without validating them. Validation can only take place after several object feature hypotheses have been generated. The individual object feature hypotheses can then either be discarded or confirmed or accepted as generated object features.
  • a point-object feature is in particular a data set that includes an x-coordinate, a y-coordinate and an indication of a measurement uncertainty.
  • the measurement uncertainty can also be understood as an indication of an "extension” of the "point”, or in other words the radius of a circle around the position determined by the x and y coordinates.
  • the point-object feature can thus enable the reconstruction of a two-dimensional circle that indicates or delimits a position of the point-like object.
  • a line feature is, in particular, a data record that contains an x-coordinate, a y-coordinate (beginning of the line), an angle (direction of the line) and a length. would be (length of the line).
  • a line object feature can, for example, also have two x-coordinates and two y-coordinates (beginning and end of the line).
  • the line feature can include a measurement uncertainty, which can be understood as a "thickness" of the "line", or in other words, a distance between two lines parallel to the line.
  • the line object feature can thus allow the reconstruction of a two-dimensional bar (length: distance between the reflection points 16, 17; thickness: measurement uncertainty) or a two-dimensional ellipse (major semi-axis: half the distance between the reflection points 16, 17; minor semi-axis: half the measurement uncertainty) that indicates or delimits a position of the linear object.
  • the generation of erroneous object features can advantageously be further contained. Further possibilities as to how the classification of the echo signal representation of the first ultrasonic echo and preferably also of the further echo signal representations can advantageously be taken into account are discussed in more detail on the basis of the following embodiments.
  • both the intersection and the tangent are determined and, if only the intersection is successfully validated, the point feature is created; if only the tangent is successfully validated, the line feature is created; and if both the intersection and the tangent are successfully validated, generating either the point object feature or the line object feature depending on the classification of at least the echo signal representation for the first ultrasonic echo.
  • the decision of whether to generate a line feature based on the tangent or an intersection based on the tangent can be made using computational validation techniques.
  • the classification thus needs advantageously to be performed and taken into account only when a computational validation technique does not provide an unambiguous result, and in this case can advantageously bring about a decision for the correct type of object feature.
  • step d) depending on the classification of at least the echo signal representation for the first ultrasonic echo, either the intersection point is determined and, if the intersection point is successfully validated, the point object feature is generated, or the tangent is determined and, if the tangent is successfully validated, the line feature is created.
  • only those ultrasound echoes are selected as the one or more further ultrasound echoes whose echo signal representations have the same classification as the echo signal representation of the first ultrasound echo.
  • Intersections and tangents are thus advantageously only constructed and validated if this makes sense based on the classification of the echo signals. Calculation effort can be reduced and the quality of the measurement can be improved.
  • the method also includes e) controlling an operating process of the motor vehicle as a function of the generated object features.
  • the operating process is not restricted and can denote any partially or fully automatic operating process that depends directly or indirectly on the presence and position of objects in the area surrounding the motor vehicle.
  • the proposed method is carried out when the motor vehicle is stationary.
  • a method for training a classification device for use in the method of the first aspect when using the classification device comprises: arranging a punctiform or linear object in a real or simulated environment of a motor vehicle; Determination of ultrasonic received signals, each of which includes a number of ultrasonic echoes that have arisen through reflection of a respective transmitted ultrasonic signal on the object in the vicinity of the motor vehicle; identifying a plurality of ultrasound echoes in the received ultrasound signals received and acquiring a respective echo signal representation for each of the identified ultrasound echoes; Performing supervised machine learning with the classification device using at least one respective echo signal representation as training input data and using a classification as a reflection on a point-like object or a reflection on a line-like object, according to the kind of the arranged object, as training output data.
  • the step of determining the ultrasonic received signals can be carried out, for example, by controlling a plurality of ultrasonic transceivers in the motor vehicle to emit ultrasonic transmitted signals into the real environment of the motor vehicle and receiving ultrasonic received signals from the real environment.
  • the real environment can be specially prepared and, apart from the arranged object, cannot have any further objects within a measuring range of the ultrasonic transceiver.
  • the real environment can also be an acoustic absorber chamber.
  • the step of determining the ultrasonic received signals can take place by simulating the propagation of ultrasonic transmitted signals in the simulated environment of the motor vehicle and the reception of the ultrasonic received signals.
  • a classification device for use in the method of the first aspect using the classification device trained according to the method of the second aspect.
  • the classification device can be embodied in hardware or in software and can comprise a neural network, for example.
  • a motor vehicle having a plurality of ultrasonic transceivers and the classifying device of the third aspect.
  • the motor vehicle can also include a control unit that is set up to carry out the method of the first aspect.
  • the motor vehicle may be an automobile, truck, motorcycle, electric bicycle, and the like.
  • a computer program product which comprises instructions which, when executed by a control unit of a motor vehicle of the fourth aspect, cause the control unit to carry out the method of the first aspect.
  • 1 shows an exemplary motor vehicle with multiple ultrasonic transceivers; 2 illustrates details of a control unit of the example motor vehicle;
  • FIG. 3 illustrates steps of a method according to a first embodiment
  • Figure 7 illustrates a point object feature
  • Figure 8 illustrates a line object feature
  • FIG. 11 illustrates steps of a method according to a third embodiment
  • FIG. 1 shows an exemplary motor vehicle 1 with a plurality of ultrasonic transceivers 2 and a control unit 3 .
  • the plurality of ultrasonic transceivers 2 are arranged along one side of the motor vehicle 1 and are connected to the control unit 3 via a communication line 4 .
  • the control unit 3 comprises a control device 5 and a classification device 6.
  • the control device 5 is set up to carry out the method described below.
  • the control device 5 carries out processing steps, sends control signals to the ultrasonic transceivers 2 and receives measurement signals from them, as described in detail below.
  • the classification device 6 is used for the classification of echo signal representations, which will be described later.
  • the control unit 3 (control device 5, classification device 6) is implemented by one or more control units or ECUs of the motor vehicle 1, for example.
  • FIG. 3 illustrates steps of a method for measuring an environment of motor vehicle 1 according to a first exemplary embodiment. Reference is made to FIGS. 1 to 3 .
  • step S1 the control device 5 controls the ultrasonic transceivers 2 and thereby causes them to emit an ultrasonic transmission signal into the lateral surroundings of the motor vehicle 1 in each case.
  • each of the ultrasonic transceivers 2 is caused to emit a control signal at a different carrier frequency.
  • the ultrasonic transceivers 2 can be controlled at different times.
  • the control device 5 receives an ultrasonic reception signal from each of the ultrasonic transceivers 2 .
  • Each of the ultrasonic transceivers 2 delivers to the control device 5 an ultrasonic received signal that essentially only contains ultrasound with that carrier frequency with which the same ultrasonic transceiver 2 previously transmitted the ultrasonic transmitted signal.
  • Fig. 4 shows a plot of an ultrasonic reception signal 7 supplied by one of the ultrasonic transceivers 2. More precisely, in Fig.
  • the amplitude envelope of the ultrasound received signal 7 is plotted.
  • the time t since the transmission of the ultrasonic transmission signal is plotted on the horizontal axis, and the amplitude A is plotted on the vertical axis.
  • the reverberation has subsided.
  • the peak has subsided again.
  • step S2 the control device 5 identifies the maximum in the ultrasonic received signal 7 at t3 as an ultrasonic echo 8, which was created by reflection of the transmitted ultrasonic signal on an object in the vicinity of the motor vehicle 1.
  • the control device 5 detects a number of scans of the envelope of the ultrasound received signal 7 in the area of the ultrasound echo 8, more precisely from time t2 to time t4 as an echo signal representation 9 of the ultrasound echo 8. For example, 10 scans before and 20 scans after the maximum are detected.
  • a distance to a point on the object in the vicinity of the motor vehicle 1, from which the ultrasonic echo was reflected is determined.
  • the position of objects in the area surrounding motor vehicle 1 is to be determined by trilateration using a number of such measurements, which are carried out from different positions, as described below.
  • 5 and 6 illustrate measurements on a punctiform object 10 and on a linear object 11 in an area surrounding motor vehicle 1.
  • FIGS. 5 and 6 show a schematic projection of motor vehicle 1 and objects 10 and 11, respectively a two-dimensional road plane when viewed in the vertical direction z from above.
  • a first ultrasonic transceiver 2a has determined a distance a from the object 10, 11 in the manner described above by identifying an ultrasonic echo 8 (FIG. 4).
  • a point 16, 17 of the object 10, 11 at which the ultrasonic echo 8 (FIG. 4) was reflected is located on a circle 12 whose center is the position of the ultrasonic transceiver 2a and whose radius is the measured distance a.
  • the circle 12 is an example of a locus of possible reflection points 16, 17.
  • a second ultrasonic transceiver 2b has determined a distance b from the object 10, 11.
  • a circle 13 with the second ultrasound transceiver 2b as the center and the distance b as the radius forms another locus of possible reflection points 16, 17 of the ultrasound transmission signal emitted by the ultrasound transceiver 2b.
  • the object is the punctiform object 10 shown in FIG. 5 - such as a post or the like -
  • the reflection points 16, 17 at which the circles 12, 13 touch the punctiform object 10 are close to one another and close at the point of intersection 14 of the circles 12 and 13.
  • the point of intersection 14 therefore represents a good approximation for the position of the object 10, which is in the form of a point to a first approximation.
  • the object is the extended, line-shaped object 11 shown in FIG. 6—such as a wall or the like—but otherwise the same distances a and b were determined, as shown in FIG Reflection points 16, 17, at which the circles 12, 13 touch the line-shaped object 11, not even approximately together, but are far apart.
  • the point of intersection 14 of the circles 12 and 13 does not represent a good approximation for the position of the line-shaped object 11. Rather, the position of the line-shaped object 11 in this Case better described by a line on a tangent 15 to the loci 12, 13.
  • Typical linear objects 10, whose position is better approximated by the tangent 15 to several loci 12, 13, are walls, walls, hedges and the like, and also each have a characteristic surface structure that differs from the surface structure of the punctiform objects.
  • the surface structure of a particular type of object affects the amplitude and the specific signal form of the ultrasonic echo 8 in the received ultrasonic signal 7 .
  • the amplitude and signal form can be read from the echo signal representation 9 recorded in step S3.
  • step S4 the control device 5 therefore uses the classification device 6 in order to classify the recorded echo signal representations 9 .
  • the classification device 6 receives at least one respective echo signal representation 9 as an input and provides a classification as an output that indicates whether the echo signal representation 9 contains an ultrasonic echo 8 that was reflected from a point-shaped object 10, or contains an ultrasonic echo 8 that was reflected from a line-shaped object 1 1 was reflected.
  • the classification device 6 can determine the classification by comparison with previously stored reference echo signal representations and/or by means of artificial intelligence or the like.
  • step S5 the control device 5 creates a number of object features 18, 19 (FIGS. She 5 and 6, calculates points of intersection 14 and/or tangents 15 and takes into account the classification determined in step S4 when deciding whether, based on a respective point of intersection 14, a point-object feature 18 (FIG. 7) or based on a respective tangent 15 a line object feature 19 (FIG. 8) is created.
  • object features 18, 19 FIGS. She 5 and 6
  • FIG. 7 illustrates a point-object feature 18 that indicates the point-like object 10.
  • the point-object feature 19 includes the x-coordinate 20 and the y-coordinate 21 of the point of intersection 14 of the loci 12, 13 as well as a measurement uncertainty 22 of the coordinates 20, 21.
  • the measurement uncertainty 22 is determined, for example, as the least square of an adjustment of the point of intersection 14 in a case where more than two loci 12, 13 do not intersect exactly at a single intersection 14.
  • the point-object feature 18 thus allows the reconstruction of a circle with the coordinates 20, 21 of the point of intersection 14 and a radius determined by the measurement uncertainty 22 in a two-dimensional map of the area surrounding the motor vehicle 1. As can be seen in Fig.
  • a circle are slightly closer to the motor vehicle 1 around the intersection 14 than the actual punctiform object 10. However, it is never further away from the motor vehicle 1 than the actual punctiform object 10.
  • the circle that can be reconstructed from the point-object feature 18 is therefore a conservative one Estimation of the position of the point object 10.
  • FIG. 8 illustrates a line object feature 19 which indicates the line-shaped object 11.
  • the line object feature 20 includes the x-coordinate 23 and the y-coordinate 24 of the first reflection point 16 at which the tangent 15 touches the locus 12, and also includes an angle specification 25 and a length specification 26.
  • the angle specification 25 and the length specification 26 are set in such a way that a line from the first reflection point 16 to the second reflection point 17 can be reconstructed from the line object feature 19 .
  • the line object feature includes a measurement uncertainty 27.
  • the measurement uncertainty 27 is determined, for example, as the square error of an adjustment of the tangent 15 in a case with more as two loci 12, 13, in which the tangent 15 is adapted to the plurality of loci 12, 13 with the least possible error square.
  • a bar or an ellipse can thus be constructed from the line object feature 19 which extends from the first to the second reflection point 16 to the second reflection point 17 and has a thickness determined by the measurement uncertainty 27 . Due to the consideration of the measurement uncertainty 27, the line object feature 19 also represents a conservative estimate of the position of the extended or linear object 10.
  • the corners 28 , 29 of the linear object 11 are not covered by the two-dimensional feature (bar or ellipse) that can be reconstructed from the line object feature 19 .
  • these corners would be detected as separate punctiform objects in a measurement actually carried out with a sufficient number of ultrasonic transceivers 2, so that at the location of the corners 28, 29 further circles would be reconstructed from further point object features and overall a sufficiently exact representation of the object 1 1 arises from several object features.
  • one ultrasonic transceiver 2a, 2b emits an ultrasonic transmission signal and the ultrasonic reception signal with the associated ultrasonic echo received therein.
  • a measurement arrangement to include a first of the ultrasonic transceivers 2 and a second ultrasonic transceiver 2 that differs therefrom.
  • the first ultrasound transceiver 2 transmits an ultrasound transmission signal
  • the second ultrasound transceiver 4 which is different therefrom, receives an ultrasound reception signal which contains the associated ultrasound echo 8 of the transmitted ultrasound transmission signal.
  • a locus curve 12, 13 of possible reflection points results as an ellipse with the positions of the two ultrasonic transceivers 2 involved as the focal point, whose large semi-axis is equal to the specific "distance" (here more correctly as "half the distance of the ultrasonic signal from transmission to reception” to denote) a, b is.
  • a, b is.
  • Netes permuting of the ultrasonic transceivers 2 which can be done sequentially or simultaneously in frequency multiplex, with a number n ultrasonic transceivers 2, a total of n*(n-1) different measurement arrangements are formed from one transmitting and one associated receiving ultrasonic transceiver 2.
  • more locus curves can be determined and, correspondingly, more object features can also be generated in the area surrounding motor vehicle 1 for more precise measurement.
  • FIG. 9 illustrates the step of classifying an echo signal representation 9 with an exemplary classification device 6 according to an advantageous development of the first exemplary embodiment.
  • the classification device 6 in Fig. 9 comprises an artificial neural network, which is trained by means of monitored machine learning and is thus set up to classify the echo signal representation 9 based on an input data vector 30, which comprises at least the echo signal representation 9, and as an output data vector 31, in each case either to output a classification 32 as a reflection on a punctiform object or a classification 33 as a reflection on a linear object.
  • the input data vector 30 preferably also includes at least three topological parameters 34-36 that specify the measurement arrangement with which the echo signal representation 9 was recorded.
  • the first topological parameter 34 preferably includes an installation height of the transmitting and/or the receiving ultrasonic transceiver 2 ( FIG. 1 ) of the measuring arrangement.
  • the second topological parameter 36 preferably includes an elevation angle of a measurement axis of the transmitting and/or the receiving ultrasonic transceiver 2 ( FIG. 1 ).
  • the third topological parameter 37 preferably comprises an azimuth angle enclosed by the measurement axes of the transmitting and the receiving ultrasonic transceiver 2 ( FIG. 1 ). In this way, the topology of the measurement arrangement can also be advantageously taken into account during classification.
  • the artificial neural network of the classification device 6 preferably comprises, in addition to an input neuron layer to which the input data vector 31 is input and an output neuron layer which outputs the classification 32 or 33, at least three hidden, convolutional neuron layers and at least three hidden, densely connected multilayer perceptron layers.
  • the layers may correspond to an hourglass model, in which intermediate hidden layers have fewer neurons than outer layers. Good results have been obtained with a total number of neurons in the low four-digit range, e.g. B. from 2000 to 3000 achieved.
  • FIG. 10 illustrates steps of a method according to a second embodiment.
  • the second exemplary embodiment is based on the method of the first exemplary embodiment, with the proviso that not just two but a large number of measurements are carried out with a large number of different measuring arrangements.
  • Sub-steps S11-S16 of a specific embodiment of step S4 (FIG. 3) of determining the object features 18, 19 (FIGS. 7, 8) are described on the basis of the second exemplary embodiment. Referring particularly to Fig. 10 and Figs. 4-6.
  • a first measurement and one or more further measurements are selected from the plurality of measurements.
  • a measurement in this case includes an ultrasonic echo 8 and the associated echo signal representation 9.
  • a set of measurements is selected in this way that was recorded or updated in a most recent measurement run.
  • step S12 a locus curve 12, 13 of possible reflection points 16, 17 is determined for each selected measurement in the manner described for the first exemplary embodiment.
  • step S13 in the manner described with reference to FIG. 5, an intersection 14 of the loci 12, 13 of all selected measurements, which is adapted as best as possible, and an associated measurement uncertainty of the intersection 14 are determined.
  • step S14 a tangent 15 is adapted to the loci 12, 13 of all selected measurements in the manner described with reference to FIG. 6, and an associated measurement uncertainty of the tangent 15 is determined.
  • step S15 the tangents 15 and points of intersection 14 determined are validated at least on the basis of the measurement uncertainties determined. Further heuristic considerations can be included in the validation.
  • step S15 branches to step S161.
  • step S161 a point object feature 18 (FIG. 7) is generated based on the intersection point 14 and the measurement uncertainty of the intersection point 14.
  • step S15 branches to step S162.
  • step S162 a line object feature 19 (FIG. 8) is generated based on the tangent 15, the reflection points 16, 17 and the measurement uncertainty of the tangent 15.
  • step S163 recourse is made to the classifications 32, 33 of the echo signal display 9 of the selected measurements. In this case, only the classification 32, 33 of the echo signal representation 9 of the first selected ultrasonic echo 8 can be taken into account, or that classification 32, 33 which occurs most frequently among the selected measurements can be taken into account. If the classification 32, 33, which is taken into account in this way, a classification 32 as a reflection on a is a point object, a point object feature 18 (FIG. 7) is performed. If the classification 32, 33 taken into account is a classification 33 as a reflection on a linear object, a line object feature 19 (FIG. 8) is generated.
  • the classifications 32, 33 determined by the classification device 6 are used to decide ambiguous cases if the validation in step S15 alone does not indicate whether a point object feature 18 or a line object feature 19 is to be generated.
  • FIGS. 11 and 12 illustrate steps of a method according to a third embodiment.
  • the third embodiment is based on the method of the first embodiment in the same manner as the second embodiment.
  • Sub-steps S11-S16 of a further possible embodiment of step S4 (FIG. 3) of determining the object features are described on the basis of the third exemplary embodiment.
  • step S11 a first measurement and one or more further measurements are selected from the plurality of measurements.
  • further measurements are selected from the same or earlier measurement runs, which were acquired by means of ultrasonic transceivers 2 which are in close proximity to the ultrasonic transceiver 2 with which the first selected measurement was acquired.
  • step S11 only measurements are selected whose respective echo signal representation 9 has a classification 32 as a reflection on a point-like object.
  • step S12 a locus curve 12, 13 of possible reflection points 16, 17 is determined for each selected measurement in the manner described for the first exemplary embodiment.
  • step S13 in the manner described with reference to FIG. 5, an intersection 14 of the loci 12, 13 of all selected measurements, which is adapted as best as possible, and a measurement uncertainty of the intersection 14 are determined.
  • step S15 the point of intersection 14 determined is validated at least on the basis of the measurement uncertainties determined. If the intersection point 14 was successfully validated in step S15, the process continues with step S16. Otherwise no feature is generated.
  • step S16 a point object feature 18 (FIG. 7) is generated based on the intersection point 14 and the measurement uncertainty of the intersection point 14.
  • FIG. 7 a point object feature 18 (FIG. 7) is generated based on the intersection point 14 and the measurement uncertainty of the intersection point 14.
  • Step S14 The second section of the method of the third exemplary embodiment from FIG. 12 corresponds to the first section from FIG Step S13 Step S14 is executed, in which a tangent 15 is adapted to the loci 12, 13 and a measurement uncertainty of the tangent 15 is determined, that in step S15 the tangent 15 is validated, and that if the validation is successful in step S16, a line -Object feature 19 (Fig. 8) based on the tangent 15, the reflection points 16, 17 and the measurement uncertainty of the tangent 14 is generated.
  • the classification 32, 33 of each echo signal representation 9 is taken into account from the start.
  • Point object features 18 are generated only from measurements classified as reflections on point objects, and line object features 19 are generated only from measurements classified as reflections on line objects. The accuracy can be further improved.
  • FIG. 13 illustrates an example method for training a classifier 6, and FIG. 14 illustrates steps of the example method for training.
  • a reference object which can be a punctiform object 10 or a linear object 11, is arranged in the surroundings of the motor vehicle 1. The arrangement can take place physically, or a simulated reference object 10, 11 can be arranged in a simulated environment of the motor vehicle 1.
  • step S22 received ultrasonic signals 7 are determined, each of which includes one or more ultrasonic echoes 8 that were created by reflection of an emitted ultrasonic transmit signal on reference object 10, 11 in the vicinity of motor vehicle 1.
  • the ultrasonic reception signals 7 can be measured in the same way as described for the first exemplary embodiment of the measurement method. As an alternative to this, the propagation of the ultrasonic transmission signals, their reflections on the reference object 10, 11 and the respective resulting ultrasonic reception signal 7 can be simulated.
  • step S23 ultrasonic echoes 8 are identified in each of the ultrasonic received signals 7 determined in step S22 and the associated echo signal representations 9 are recorded in the same way as described with reference to the first exemplary embodiment of the measurement method.
  • step S24 the artificial neural network of the classification device 6 is trained using monitored machine learning.
  • the artificial neural network of the classification device 6 is provided with a respective echo signal representation 9 and preferably also the associated topological parameters 34-36 as training input data 37, and the classification device 6 is provided with training output data 38 in accordance with whether the reference object is a punctiform object 10 or a linear object 11, either the classification 32 as a reflection on a punctiform object or the classification 33 as a reflection on a linear object is provided.
  • a supervised machine learning step is then performed on the training input data 37 and the training output data 38 .
  • Steps S21 to S24 are repeated many times with different reference objects, different measurement arrangements and so on until the artificial neural network of the classification device 6 is sufficiently trained.
  • a map of the surroundings of the motor vehicle 1 can be created, which consists of circles (points with radii that correspond to measurement inaccuracy) and bars (lines with a thickness that corresponds to measurement inaccuracy or ellipses with a minor axis, which corresponds to half the measurement uncertainty).
  • This map of the surroundings of motor vehicle 1 can then be used to determine whether and to what extent a door (not shown) of the motor vehicle can be opened, whether it is safe for moving motor vehicle 1 to change lanes, whether parking maneuvers for motor vehicle 1 are possible,
  • the parking process of the motor vehicle 1 can be controlled partially or fully automatically using the map created in this way, and the like.
  • the above-mentioned operating processes of the motor vehicle can thus be controlled as a function of the object features 18, 19 generated.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)

Abstract

L'invention se rapporte à un procédé de relevé d'environnement d'un véhicule automobile (1) comprenant un certain nombre d'émetteurs-récepteurs à ultrasons (2). Le procédé consiste : a) à actionner (S1) le nombre d'émetteurs-récepteurs ultrasonores (2) afin d'émettre des signaux d'émission ultrasonores dans l'environnement du véhicule automobile (1) et de recevoir des signaux de réception ultrasonores (7) en provenance de l'environnement du véhicule automobile (1) ; b) à identifier (S2) une pluralité d'échos ultrasonores (8) dans les signaux de réception ultrasonores reçus (7) et à enregistrer une représentation de signal d'écho pertinent (9) pour chacun des échos ultrasonores identifiés (8) ; c) à déterminer (S3) une classification (32, 33) de chaque représentation de la pluralité de représentations de signaux d'écho (9) en tant que réflexion du signal d'émission ultrasonore sur un objet ponctiforme (10) ou en tant que réflexion du signal d'émission ultrasonore sur un objet linéaire (11) ; et d) à générer (S4) un certain nombre de caractéristiques d'objet (18, 19) indiquant un objet pertinent (10, 11) dans l'environnement du véhicule automobile (1) en fonction de la pluralité d'échos ultrasonores (8), en tenant compte des classifications (32, 33) de la pluralité de représentations de signaux d'écho (9).
PCT/EP2022/083997 2021-12-06 2022-12-01 Procédé de relevé d'environnement d'un véhicule automobile WO2023104617A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102013021837A1 (de) * 2013-12-21 2015-06-25 Valeo Schalter Und Sensoren Gmbh Verfahren zum Klassifizieren eines Objekts, Sensoreinrichtung und Kraftfahrzeug

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
AIT OUFROUKH N ET AL: "Ultrasonic multi-transducer processing for pattern recognition", PROCEEDINGS OF IEEE SENSORS 2002. ORLANDO, FL, JUNE 12 - 14, 2002; [IEEE INTERNATIONAL CONFERENCE ON SENSORS], NEW YORK, NY : IEEE, US, vol. 2, 12 June 2002 (2002-06-12), pages 1329 - 1334, XP010605310, ISBN: 978-0-7803-7454-6 *

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