WO2024002946A1 - Détermination ultrasonore d'une surface de route - Google Patents

Détermination ultrasonore d'une surface de route Download PDF

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Publication number
WO2024002946A1
WO2024002946A1 PCT/EP2023/067264 EP2023067264W WO2024002946A1 WO 2024002946 A1 WO2024002946 A1 WO 2024002946A1 EP 2023067264 W EP2023067264 W EP 2023067264W WO 2024002946 A1 WO2024002946 A1 WO 2024002946A1
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WO
WIPO (PCT)
Prior art keywords
probability
time series
determining
coating
mathematical model
Prior art date
Application number
PCT/EP2023/067264
Other languages
German (de)
English (en)
Inventor
Henrik Starkloff
Anto Joys Yesuadimai Michael
Jean-Francois Bariant
Original Assignee
Valeo Schalter Und Sensoren Gmbh
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 Valeo Schalter Und Sensoren Gmbh filed Critical Valeo Schalter Und Sensoren Gmbh
Publication of WO2024002946A1 publication Critical patent/WO2024002946A1/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
    • 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
    • G01S7/539—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

Definitions

  • the present invention relates to a method for determining a surface of a roadway using ultrasound, a method for detecting an object in the surroundings of a vehicle using ultrasound, a method for tracking an object in the surroundings of a vehicle using ultrasound, a computer program product, a control device and a Vehicle.
  • the DE 10 2020 100 566 A1 discloses a method for detecting a misalignment of an ultrasonic distance sensor on a surface, the ultrasonic sensor emitting ultrasonic signals, the ultrasonic sensor receiving at least part of the ultrasonic signal reflected from the surface and providing a corresponding received signal, with several analysis areas of the received signal are determined, wherein an amplitude distribution of the received signal is determined in each of the analysis areas, a statistical model being adapted to the amplitude distribution in each of the analysis areas, and thereby at least one statistical parameter is determined in each case, and wherein the statistical parameters are evaluated in order to to determine a type of subsoil.
  • an object of the present invention is to improve a determination of a road surface.
  • a method for determining a surface of a roadway using ultrasound comprises: providing at least one time series that is captured by at least one ultrasonic transducer; determining a maxima number, which is a number of local maxima in a first time period of the at least one time series; Providing a first mathematical model which assigns at least one coating probability to several maxima numbers for the first time period, wherein a coating probability is a probability of the presence of a specific coating of at least two coatings in a detection area. rich of an ultrasonic transducer; and determining at least a first surface probability with respect to an area of the road represented by the at least one time series using the provided mathematical model and the determined maximum number.
  • the method allows a reliable distinction between at least two road surfaces based on the number of local maxima in a time series. By outputting a probability for the presence of a particular road surface, the method can be combined with other methods for determining a surface.
  • a “surface” means, in case of doubt, a road surface.
  • the first time period is preferably a single section of the at least one time series.
  • the first time period can in particular be predetermined.
  • the first distance can range from a predetermined start time and/or a predetermined minimum distance to a predetermined end time and/or a predetermined maximum distance. If the method uses more than one time series, the first time period for all time series is preferably chosen to correspond to one another and in particular to be the same.
  • the method can be used for more than two road surfaces.
  • the method is preferably used for exactly two coverings.
  • the method can be used for asphalt and gravel road surfaces because these two surfaces can lead to very different ultrasound echoes.
  • the "area of the roadway represented by at least one time series” means, in case of doubt, a part of the roadway from which echoes from the roadway and/or objects are reflected to the ultrasonic transducer and are subsequently recorded in the time series.
  • the shape of the area depends on the design. If there are several ultrasonic transducers with a suitably similar orientation and position, the area can be adjusted by varying the emitting ultrasonic transducer and/or emitting ultrasonic transducers and recommended catching ultrasonic transducer and/or receiving ultrasonic transducer can be adjusted.
  • the fact that the first mathematical model assigns a coating probability to several maxima numbers can in particular mean that a different coating probability can be assigned to each different maxima number. This can also mean that the same covering probability can be assigned to several different maximum numbers and different covering probabilities can be assigned to other different maximum numbers.
  • the method can, for example, determine and output a probability for a predetermined covering of several coverings, a probability for a more likely covering of several coverings and/or a probability for several coverings.
  • the first mathematical model provided can contain, for example, a classification, a mathematical function, in particular a continuous mathematical function, and/or a probability distribution. Other mathematical models are also conceivable.
  • the mathematical model contains a Poisson distribution. What the options mentioned have in common is that they enable a maxima number to be assigned to a covering probability with little effort.
  • a threshold value curve is used, and only those local maxima or amplitudes that are above the respective threshold value are transmitted to subsequent method steps.
  • These threshold values are usually chosen to distinguish the relatively few echoes of real objects from the often relatively numerous surface echoes.
  • the present method has the great advantage of gaining additional knowledge, namely the recognition of the road surface, from at least one time series. Because the present method evaluates complete time periods, the need for calculations increases compared to conventional methods. Therefore, measures to reduce effort are of practical importance.
  • the method can advantageously additionally comprise: determining a plurality of second time periods of the time series, determining a second covering probability with respect to each second time period by means of an amplitude distribution of the respective second time period, and determining an overall covering probability with respect to the area represented by the at least one time series in Dependence of the first coating probability and the second coating probabilities.
  • This method therefore determines the overall coating probability depending on two mathematically and/or logically independent analyzes of the at least one time series provided. This method is therefore particularly reliable.
  • the method for determining a second covering probability with respect to each second time period can include: Executing the following steps for every second time period: determining an amplitude distribution in the respective second time period; Providing a second mathematical model, wherein the second mathematical model assigns a covering probability to several amplitude distributions of the respective second time period, and determining a second covering probability for the respective second time period using the provided second mathematical model and the determined amplitude distributions.
  • the following can be provided: determining an overall coating probability with respect to the area represented by the at least one time series depending on the first coating probability and the second coating probabilities.
  • the same mathematical model can be provided for every second time period.
  • the same mathematical model can be provided for every second time period, with the same mathematical models differing at most in parameter values. This option enables parallelization and thus reduces effort.
  • Each second mathematical model provided can contain, for example, a classification, a continuous mathematical function and/or a probability distribution, in particular a gamma distribution. In this way, a deposit probability can be assigned to each amplitude distribution with little effort.
  • determining the overall coating probability includes: determining a second overall coating probability as a function of the second coating probabilities, and determining the overall coating probability by combining the first coating probability and the second total coating probability. Probability.
  • the “second overall coating probability” is therefore an intermediate result which is determined depending on the amplitude distributions in the second time periods. This procedure has the advantage that subsequent steps have to continue working with fewer numbers and therefore with less effort. “Combining” can be understood to mean, in particular, adding, weighted adding and/or averaging.
  • the overall coating probability is determined by combining the first coating probability and the second coating probabilities.
  • This method saves effort by eliminating the need to calculate an intermediate result.
  • weighted combining is used, with a weighting of the first covering probability ranging in magnitude, in particular from including a weighting of a second covering probability up to and including twice a sum of all weights of the second covering probabilities.
  • simple addition is used for combining, which makes the method according to the invention particularly efficient.
  • the probabilities for each of the toppings can be added separately to determine the overall topping probability. This also applies to a method that evaluates several time series one after the other, so it is a step that can be used advantageously in several respects.
  • the number of maxima and, if necessary, the amplitude distributions can be determined over the several time series.
  • the determination of the road surface is particularly useful if not only an area in front of an ultrasonic transducer is examined, but an approximately flat section in front of or behind a vehicle is scanned.
  • two ultrasonic transducers can be arranged and set up so that one of the two ultrasonic transducers emits an ultrasonic pulse and the other ultrasonic transducer consequently receives an echo signal.
  • the method can include, for example: Executing the preceding method step several times, each time determining a coating probability, the method including: Smoothing a specific coating probability as described above.
  • a method for detecting an object in the surroundings of a vehicle using ultrasound comprises: determining a surface of a roadway in the area surrounding the vehicle using the above-described method for determining a surface of a roadway using ultrasound; selecting a pattern and/or filter depending on the particular roadway; and detecting at least one object or object candidate by comparing a time series with the selected pattern and/or filtering the time series using the selected filter.
  • a method for tracking an object in the vicinity of a vehicle using ultrasound includes: providing a time series acquired by at least one ultrasonic transducer; Detecting an object by comparing the time series with a threshold curve; and repeating providing and recognizing.
  • the method has and/or carries out the additional steps: detecting at least one object candidate using the above method for detecting an object in the environment of a vehicle using ultrasound; and re-recognizing the object by comparing the last recognized object with the at least one object candidate.
  • object detection for weakly reflecting objects is advantageously achieved. For example, a beverage can can be tracked on a gravel road.
  • Comparing an object with an object candidate can in particular include comparing a respective position taking into account a position tolerance.
  • a computer program product such as a computer program means
  • a control device for a vehicle is proposed. This is set up to carry out one of the methods described above and/or to run the computer program product. The control device thus implements the advantages of the respective method.
  • a vehicle which contains the control device described above.
  • the vehicle according to the invention therefore also implements the advantages of the respective method.
  • Fig. 1 shows schematically a top view of a vehicle
  • FIG. 4 shows a flow chart of a method for determining a surface of a roadway using ultrasound according to a second embodiment of the invention
  • FIG. 6 shows a flowchart of a method for detecting an object in the vicinity of a vehicle using ultrasound
  • FIG. 7 shows a flowchart of a method for tracking an object in the vicinity of a vehicle using ultrasound according to a fourth embodiment of the invention.
  • the vehicle 100 is, for example, a car that is arranged in an environment 102.
  • the vehicle 100 is, for example, a passenger car or a truck.
  • the method 200 is carried out on the control device 106, for example.
  • the method 200 is carried out repeatedly on the control device 106 at regular intervals.
  • the method 200 can be executed on the control device 106 once or several times per second.
  • the method 200 is only carried out when the control unit 106 determines that the vehicle is moving at a speed up to a threshold value, in particular at a maximum of 50 km/h or preferably at a maximum of 30 km/h, in order to save computing time and energy.
  • the method 200 begins with a step S1.
  • a time series 202 is provided.
  • the time series 202 can, for example, be output by one of the ultrasonic transducers 104 as the sensor signal.
  • the time series 202 is preferably post-processed.
  • a raw signal from the ultrasonic transducer 104 can be post-processed by a low-pass filter in order to be output as an envelope curve, as is also shown in the upper part of FIG. 3.
  • the low-pass filter is preferably integrated into each ultrasonic transducer 104 in order to reduce a computing load on the part of the control device 106.
  • the diagram in the upper part of FIG. 3 is an amplitude-time diagram of an amplitude value A over a time t.
  • the time series 202 therefore represents, for example, an average amplitude of an echo signal which is transmitted by an ultrasonic transducer 104 as a result of an ultrasonic sound pulse is received.
  • a threshold value curve 204 is also shown as an example.
  • the threshold curve 204 is designed to distinguish reflections of real objects in the environment 102 from reflections, for example, of the road surface. For example, if a local maximum in the course of the time series 202 exceeds the respective threshold value in the course of the threshold value course 204, an object is detected at the corresponding distance from the ultrasonic transducer 104.
  • the time series 202 appears to contain no echo from an object, but several echoes from a road surface in the area 102.
  • a time period 206 is determined.
  • the time period 206 is in particular a single time period, so that the method 200 is carried out using a single portion of the time series 202.
  • the time period 206 is preferably predetermined. For example, a minimum distance and a maximum distance, a minimum time and a maximum time and/or a minimum index and a maximum index are stored in the control device 106.
  • step S2 The course of the time series 202 in the time period 206 is further processed as a result of step S2.
  • a next step S3 local maxima 208 are determined in the time period 206.
  • the local maxima 208 are each marked with a cross. Strictly speaking, a local maximum 208 is a value whose neighboring values have a smaller magnitude. For the purposes of this method 200, to distinguish local maxima 208, for example, the criterion that neighboring values of a value to be checked at least do not have a larger amount may be sufficient.
  • the method in step S3 uses a mathematical method such as Fourier analysis or the like to determine a number of local maxima 208.
  • a maxima number 210 namely the number of local maxima 208 in the time period 206.
  • the maximum number 210 is "eight" in the example in FIG. 3 and is illustrated in FIG. 3 by an arrow in the lower half of the diagram.
  • a mathematical model 212 is provided in a next step S4.
  • An exemplary mathematical model 210 is shown in the lower part of FIG. 3.
  • the aim of the method 200 is to be able to distinguish between two different coverings or types of coverings on the road.
  • These two surfaces are preferably asphalt and gravel.
  • Asphalt is a representative of very smooth road surfaces that reflects echoes with only a small amplitude.
  • Gravel is a representative of very rough, coarse road surfaces that reflect echoes with a relatively large amplitude.
  • the problem here for example, is that objects that generate weak echoes, such as a beverage can or a bottle, are difficult to distinguish from echoes caused by deposits.
  • the mathematical model 212 is suitable for using the maximum number 210 to determine a probability that the coating in the area from which echoes are reflected back to the ultrasonic transducer 104 and reproduced as the time series 202 is a specific coating.
  • the mathematical model 212 for example, is suitable for using the maximum number 210 to evaluate whether the
  • Time series 202 contains echoes from an asphalt area or a gravel area.
  • the mathematical model 212 is preferably complete. This means that, for example, a probability X of the presence of a road surface can be determined. It follows that with probability 1 -X the other road surface is present. This embodiment is efficient, for example, if the mathematical model 212 is only intended to differentiate between two road surfaces.
  • the lower diagram of FIG. 3 shows two exemplary Poisson distributions 214, 216 as the mathematical model 212. More precisely, the lower diagram shows a coating probability W over a maxima number n.
  • the first exemplary Poisson distribution 214 has a maximum value at approximately 1 echo and its high probability range ranges from approximately 0 echoes to approximately 3 echoes.
  • This first Poisson distribution 214 corresponds, for example, to the probability that a certain maximum number 210 is present in the time period 206 if there is asphalt in an area represented by the time series 202.
  • the second exemplary Poisson distribution 216 has a maximum value at approximately 6 echoes and its high probability range ranges from approximately 4 echoes to approximately 10 echoes. This second Poisson distribution 216 corresponds to the probability that a certain maximum number 210 is present in the time period 206 if there is gravel in the area represented by the time series 202.
  • the mathematical model 212 assigns a coating probability to each maxima number 210.
  • the mathematical model 212 can in particular be stored as a table and/or as a mathematical or statistical function.
  • the mathematical model 212 is preferably specifically determined during an application of the ultrasonic transducer 104 using a test environment and a pre-production vehicle.
  • Step S4 can take place before, during or after one of steps S1, S2 and S3.
  • a deposit probability is determined based on the mathematical model 212 provided in step S4 and the maxima number 210 determined in step S3.
  • step S5 it is determined which probabilities the two Poisson distributions 214, 216 indicate for the maximum number 210.
  • the Poisson distribution 214 for the maximum number 210 results in a low first probability 218, and the Poisson distribution 216 for the maximum number 210 results in a significantly higher second probability 220.
  • the method outputs a first coating probability as a result of step S5.
  • the first surface probability output indicates, for example, the probability that a road surface detected by the ultrasonic transducer 104 is gravel.
  • This probability can be further used by another method in the control device 106 or another control device, for example to track an object that weakly reflects ultrasound and/or to execute a program for autonomous driving in a manner adapted to the surface.
  • several time series 202 are provided in step S1. These may have been received and output one after the other by the same ultrasonic transducer 104. These can be received and output by several ultrasound transducers 104 that detect the same or adjacent areas of the environment 102. These multiple time series 20 can also be received and output by the multiple ultrasonic transducers 104 over a certain period of time.
  • FIGS. 4 and 5 A second embodiment of the invention is described below with reference to FIGS. 4 and 5 described.
  • Fig. 4 shows a flow chart of the second embodiment
  • Fig. 5 shows diagrams for clarification. Only the differences from the first embodiment will be discussed below.
  • steps S1 to S5 of a method 300 for determining a surface of a roadway using ultrasound according to the second embodiment correspond to steps S1 to S5 of the first embodiment.
  • step S1 time series 202 is provided for all subsequent steps.
  • the subsequent process steps can be carried out functionally in parallel, i.e. independently of one another in terms of time.
  • the subsequent process steps can also be carried out functionally and in parallel in order to save time.
  • the method steps can also be carried out in serial parallel, for example by first carrying out all steps S6 and then all steps S7 and so on.
  • step S6 shows three process branches with steps S6 to S9 parallel to the process branch of steps S2 to S5. This is an example, and more or less than three process branches can be carried out in addition to steps S2 to S5.
  • a second time period 302 of the time series 202 is determined.
  • a different second time period 302 is determined.
  • the second time periods 302 adjoin one another, but this is not mandatory. According to one option, some of the second time periods 302 partially overlap.
  • step S8 a second mathematical model 306 is provided. Similar to step S4 and the first mathematical model 212, step S8 can also take place earlier.
  • a second deposit probability is determined based on the second mathematical model 306 provided in step S8 and the amplitude distribution 304 determined in step S7.
  • the two Poisson distributions 214, 216 are again used as the first mathematical model 212 in step S5, and one or more gamma distributions are used as the second mathematical models 306 in steps S9.
  • Fig. 5 The lower diagram of Fig. 5 corresponds to the lower diagram of Fig. 3.
  • the middle diagram of Fig. 5 corresponds to the upper diagram of Fig. 3.
  • the upper diagram of Fig. 5 shows several amplitude distributions 304 over the second time ranges 302. It is therefore a diagram of amplitude distributions V over time ranges B.
  • probabilities W are shown over amplitude distributions V for each second time range 302. This is illustrated by the dashed arrow in the third of the four diagram parts of the diagram above.
  • a first coating probability which was determined based on the first mathematical model 212 and the maxima number 210 on the basis of the first time period 206
  • several second coating probabilities which was determined based on the respective second mathematical model 306 and the respective amplitude distribution 304 on the basis of the respective second time period 302.
  • these coating probabilities are combined.
  • all coating probabilities indicate the probability of the same specific coating being present.
  • the first deposit probability and the second deposit probabilities can simply be added to form an overall deposit probability for the presence of this particular deposit.
  • step S10 several time series 202 are provided in step S10.
  • a method 400 for detecting an object in the surroundings of a vehicle using ultrasound is described below with reference to FIG. 6. Otherwise, reference is made to the description above.
  • step S20 of the method 400 the method 200 is carried out with steps S1 to S5 or the method 300 with steps S1 to S10.
  • the result of step S21 is therefore either the first deposit probability or the entire deposit probability.
  • a pattern and/or a filter is selected based on the deposit probability from step S21.
  • the pattern indicates an expected course for a time series emitted by the ultrasonic transducer 104 when the probable deposit is present.
  • the filter provides a function for filtering out or hiding a time series emitted by the ultrasonic transducer 104 when the probable amount is present.
  • a step S22 the time series 202 is compared with the pattern and/or filtered using the filter.
  • a weak echo signal with a local maximum 208 below the threshold curve 204 can be recognized as an object or an object candidate.
  • the recognized object or the recognized object candidate will be output. No object and/or no object candidate and/or multiple objects and/or multiple object candidates can also be recognized and output.
  • the result of this method 400 can be further used, for example, when tracking an object in the environment 102 of the vehicle 100 and/or in a method for autonomous driving.
  • a method 500 for tracking an object in the vicinity of a vehicle using ultrasound is briefly described below with reference to FIG. 7. Otherwise, reference is made to the description above.
  • a time series 202 is provided.
  • a next step S31 an object is recognized in a manner known per se based on a comparison of the time series 202 with the threshold value curve 204.
  • step S32 it is checked whether an object was recognized in step S31, which should have been recognized after going through step S32 earlier. In other words: it is checked, for example, whether all previously recognized objects are approximately at the expected location.
  • step 32 ends with a positive result, ie if all objects were recognized again, method 500 begins again with step S30. However, if step S32 with ends with a negative result, ie if an object was not recognized again, the method 500 continues to a step S33.
  • step S33 the method 400 described above is carried out.
  • objects and/or object candidates and/or no object and no object candidate are available to the method 500.
  • step S34 the object that was not previously recognized in step S31 is re-recognized based on the objects and/or object candidates recognized in step S33.

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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

La présente invention concerne un procédé (200, 300) pour déterminer une surface d'une route au moyen d'ultrasons, comprenant les étapes consistant à : fournir (S1) au moins une série temporelle (202) qui est mesurée par au moins un transducteur ultrasonore (104) ; déterminer (S3) un nombre maximal (210) qui est un nombre de maximums locaux (208) dans une première période de temps (206) de la ou des séries temporelles (202) ; fournir (S4) un premier modèle mathématique (212) qui attribue une probabilité de surface à chacun d'une pluralité de nombres maximaux (210) pour la première période de temps (206), une probabilité de surface étant une probabilité (218, 220) de la présence d'une surface d'au moins deux surfaces dans une région de détection d'un transducteur ultrasonore (104) ; et déterminer (S5), au moyen du modèle mathématique fourni (212) et du nombre maximal déterminé (210), une première probabilité de surface par rapport à une région de la route représentée par la ou les séries temporelles (202).
PCT/EP2023/067264 2022-06-30 2023-06-26 Détermination ultrasonore d'une surface de route WO2024002946A1 (fr)

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Application Number Priority Date Filing Date Title
DE102022116373.3A DE102022116373A1 (de) 2022-06-30 2022-06-30 Ultraschall-Bestimmen eines Fahrbahnbelags
DE102022116373.3 2022-06-30

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE4117091A1 (de) * 1991-05-25 1992-11-26 Fraunhofer Ges Forschung Verfahren zur erstellung eines klassifizierungssystemes zur erkennung der beschaffenheit des fahrbahnbelages
DE102019123827A1 (de) * 2019-09-05 2021-03-11 Valeo Schalter Und Sensoren Gmbh Verfahren zum Klassifizieren des Bodenbelags durch ein Fahrunterstützungssystem
DE102020100566A1 (de) 2020-01-13 2021-07-15 Valeo Schalter Und Sensoren Gmbh Verfahren zur Erkennung einer Fehlausrichtung eines Ultraschallsensors eines Fahrzeugs

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE4117091A1 (de) * 1991-05-25 1992-11-26 Fraunhofer Ges Forschung Verfahren zur erstellung eines klassifizierungssystemes zur erkennung der beschaffenheit des fahrbahnbelages
DE102019123827A1 (de) * 2019-09-05 2021-03-11 Valeo Schalter Und Sensoren Gmbh Verfahren zum Klassifizieren des Bodenbelags durch ein Fahrunterstützungssystem
DE102020100566A1 (de) 2020-01-13 2021-07-15 Valeo Schalter Und Sensoren Gmbh Verfahren zur Erkennung einer Fehlausrichtung eines Ultraschallsensors eines Fahrzeugs

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