Ultrasonic measuring unit

Abstract

The invention relates to an ultrasonic measuring unit (1), in particular for use in a vehicle (2), comprising a control unit (3) and an evaluation unit (4). The control unit (3) is designed to control first ultrasonic transducers for transmitting ultrasonic signals (10.1) and to receive echoes, resulting from the ultrasonic signals (10.1) on the basis of the signal paths of the ultrasonic signals (10.1), from second ultrasonic transducers, and the evaluation unit (4) is designed to evaluate the echoes in order to detect the position of an object (6) causing the echoes. The evaluation is carried out on the basis of the positions of the first and second ultrasonic transducers and the signal paths using a multilateration in the form of values of multilateration parameters, said multilateration parameters characterizing hyperbolas or ellipses (31, 32, 33), the evaluation unit (4) has a trained artificial intelligence module (AI module) (4) which is designed to evaluate the relevance of the hyperbolas or of the ellipses (31, 32, 33) on the basis of the receipt of the values, and the evaluation unit (4) is configured to detect the position on the basis of the evaluation.

Description

[0001] Description

[0002] Ultrasonic measuring unit

[0003] Technical field

[0004] The invention relates to an ultrasonic measuring unit, particularly for use in a vehicle, comprising a control unit and an evaluation unit. The invention further relates to a method for operating an ultrasonic measuring unit.

[0005] State of the art

[0006] It is known to use ultrasonic sensors to detect objects in the vicinity of a vehicle. For example, DE 10 2016 218 064 A1 describes an ultrasonic sensor system for detecting the environment of a vehicle using ultrasound. The ultrasonic sensor system described therein comprises an ultrasonic transmitter, an ultrasonic receiver, and a control unit for controlling the operation of the ultrasonic sensor system. The control unit is configured to execute an operating procedure in which ultrasonic signals are emitted at a plurality of successive time points. Furthermore, the operating procedure provides for receiving ultrasonic echo signals received from one or more objects in the environment, whereby a track is assigned to each emitted and received ultrasonic signal, and a distance value is determined for each existing ultrasonic echo.In this operating procedure, the ultrasound echoes are grouped into tracks of multiple temporally successive ultrasound signals if the distance values ​​of the ultrasound echoes follow a predetermined pattern. Ultrasound echoes that do not belong to any group are classified as interference.

[0007] Disclosure of the invention

[0008] The invention is based on the objective of providing an improved ultrasonic measuring unit, an improved method for operating an ultrasonic measuring unit, and a corresponding computer program. The objectives of the invention are achieved by the features of the independent claims. An ultrasonic measuring unit, particularly for use in a vehicle, is proposed. The ultrasonic measuring unit comprises a control unit and an evaluation unit. The control unit is configured to drive first ultrasonic transducers to transmit ultrasonic signals. Furthermore, the control unit is configured to receive echoes resulting from the ultrasonic signals from second ultrasonic transducers due to the signal paths of the ultrasonic signals. For example, the control unit can receive the echoes resulting from reflections of the ultrasonic signals from the second ultrasonic transducers.The echoes can be sent from the second ultrasound transducers to the control unit in the form of electrical signals, especially in the form of amplitude signals.

[0009] The evaluation unit is designed to analyze the echoes to detect the position of the object generating them. The echoes are caused, in particular, by the reflection of ultrasound signals emitted by at least one of the first ultrasound transducers at the object. Depending on the application, the object's position includes, in particular, its relative position with respect to one of the first and second ultrasound transducers. It is assumed that the positions of the first and second ultrasound transducers are known. The evaluation is performed based on the positions of the first and second ultrasound transducers and the signal paths using multilateration in the form of values ​​of multilateration parameters. For this purpose, the ultrasound measuring unit is preferably configured to perform a multilateration procedure.

[0010] The multilateration method involves measuring the transit times of the ultrasound signals between the first and second ultrasound transducers. For the purposes of this disclosure, the first ultrasound transducers are considered transmitters of the ultrasound signals, and the second ultrasound transducers are considered receivers of the ultrasound signals. In this context, one of the transmitters and one of the receivers may be configured as a single ultrasound sensor. In this case, the same ultrasound sensor can operate as a transmitter in a first time interval and as a receiver in a second time interval.

[0011] To perform multilateration, the evaluation unit is preferably configured to record the respective time of transmission of the respective ultrasound signal from each of the first ultrasound transducers, hereinafter referred to as the respective transmission time. The ultrasound measuring unit may have a communication link between the control unit and the evaluation unit for recording the respective transmission time.

[0012] Furthermore, the evaluation unit is advantageously configured to record a set of reception times for each ultrasound signal. This is based on the assumption that in most applications, the respective ultrasound signal can be received by two or more of the second ultrasound transducers at the respective different reception times of the respective set of reception times of the respective ultrasound signal, hereinafter also referred to as the respective set of reception times, since the second ultrasound transducers are at different distances from the object.

[0013] Advantageously, the evaluation unit is configured to calculate a set of transit times for each ultrasound signal. The evaluation unit can calculate one transit time of the set from the difference between the respective reception time and the respective transmission time. With three first and three second ultrasound transducers, the evaluation unit could calculate at least nine transit times if the transmitted ultrasound signals are received by all receivers. If at least one of the ultrasound signals is reflected more than once and received by one of the second ultrasound transducers, the number of transit times will be correspondingly higher than nine.To differentiate the ultrasound signals, they can be transmitted sequentially, such that a subsequent signal is emitted after a previous one has been received. Alternatively or additionally, the ultrasound signals can also have different characteristics. For example, they can differ in their pulse sequences and / or frequencies, such as different chirps.

[0014] The multilateration parameters characterize hyperbolas or ellipses. For example, the values ​​of the multilateration parameters can specify a measure of a major axis and a measure of a minor axis of the respective ellipse, and preferably a position of the foci of the respective ellipse. The hyperbolas or ellipses preferably describe possible locations of the object relative to at least one of the first and second ultrasound transducers.

[0015] Preferably, the evaluation unit is configured to calculate a corresponding set of values ​​for describing the respective ellipse or hyperbola, depending on the runtime of the respective set of runtimes. For example, the evaluation unit determines the respective set of values ​​for describing the ellipse or hyperbola for each runtime, hereinafter also referred to as the respective multilateration parameter set or, more simply, the respective parameter set. In this way, the values ​​of the multilateration parameters can be structured in the form of multilateration parameter sets, whereby the respective parameter set is assigned to the corresponding ellipse or hyperbola based on the respective runtime at which the parameter set is calculated. Thus, the respective ellipse or hyperbola is preferably also assigned to the respective runtime at which the parameter set for describing the ellipse or hyperbola is calculated.

[0016] Each ellipse is characterized in particular by the fact that the sum of the distances between any point on the ellipse and its foci is equal to the product of the associated travel time and the speed of sound in the space between the ultrasonic measuring unit and the object. A first focal point of the ellipse is defined by the position of the first ultrasonic transducer from which the ultrasonic signal is transmitted. A second focal point of the ellipse is defined by the position of the second ultrasonic transducer from which the respective ultrasonic signal is received.

[0017] According to one variant, the object's position can be determined by calculating the intersection point of two of the ellipses. The evaluation unit can select any two of the ellipses for this purpose.

[0018] In principle, the evaluation unit can be configured to approximate the object's position relative to the first and / or second ultrasound transducers, depending on the parameter sets. Advantageously, the evaluation unit is designed to minimize distances between the approximated object's position and a selected set of ellipses or hyperbolas.

[0019] Alternatively or additionally, the evaluation unit can approximate the object's position by minimizing the distances between the selected ellipses or hyperbolas to a locus. The locus preferably represents points on the object that extend along a single direction on the object's surface, for example, if the object is a wall.

[0020] Minimizing the distances between the approximated position or locus and the selected ellipses or hyperbolas can be achieved through an iterative process using a termination criterion. If the termination criterion is met, the last approximated position can represent the object's position detected by the evaluation unit. The ultrasonic measuring unit is preferably configured to send the detected position to a vehicle control unit for further processing. Similarly, in the iterative process, the last approximated locus can represent points of the object detected by the evaluation unit, thus indicating the object's detected position. In the iterative process, the evaluation unit preferably changes the position of the object's approximate position or the approximate locus at each new iteration step and recalculates the distances.The position can be changed, for example, depending on derivations of the distances according to the dimensions of the space between the ultrasonic measuring unit and the object.

[0021] According to the invention, the evaluation unit comprises a trained artificial intelligence module (AI module) configured to assess the relevance of hyperbolas or ellipses based on the received values ​​of the multilateration parameters. Furthermore, the evaluation unit is configured to perform position detection based on this assessment. Preferably, the evaluation unit is configured to assess the relevance of the hyperbolas or ellipses by using the AI ​​module to select a subset of the hyperbolas or ellipses for determining the object's position, or by weighting the hyperbolas or ellipses differently. Thus, according to one possible embodiment, the evaluation unit can be configured to determine the set of selected ellipses or hyperbolas depending on the values ​​of the multilateration parameters, particularly depending on the sets of multilateration parameters.It is equally possible for the evaluation unit to weight the ellipses or hyperbolas depending on the values ​​of the multilateration parameters, particularly depending on the specific set of multilateration parameters. In this case, the approximate position can be determined iteratively using the weighted ellipses or hyperbolas as described above. Therefore, detecting the position based on the assessment can mean that the position is determined using the selected or weighted ellipses or hyperbolas.

[0022] The advantage of assessing the relevance of ellipses or hyperbolas based on the values ​​of the multilateration parameters and then detecting the object's position based on this assessment could be that inaccurate measurements, and especially interference signals, are weighted less heavily in determining the object's position, hereinafter referred to as position determination. This can increase the accuracy of object determination. Furthermore, assessing relevance using the AI ​​module could allow for an assessment based on a larger number of parameters than could be considered using programmed software without an AI module.The trained AI module allows for the identification of relationships between multilateration parameters and the "quality" of the ellipses or hyperbolas, and thus the "quality" of the measurements from which these ellipses or hyperbolas result. These relationships can only be explicitly captured using mathematical formulas with considerable effort. Based on the relevance assessed by the AI ​​module, inaccurate measurements or measurements affected by disturbances can be automatically discarded for position determination. This can be achieved by selecting the ellipses or hyperbolas as described above. Reducing the number of ellipses or hyperbolas decreases the computation time when performing the iterative procedure described above. This, in particular, enables real-time position determination.

[0023] Since, as described above, each ellipse or hyperbola is assigned to its respective runtime and parameter set, assessing the relevance of that ellipse or hyperbola can be equated with assessing the relevance of the runtime or parameter set. For position determination, it is, in most cases, equally important whether the respective ellipse or the parameter set assigned to it is selected or weighted. In most applications, position determination is performed based on the values ​​of the parameter sets. Selecting the respective ellipse means that the evaluation unit determines the object's position using its associated parameter set.

[0024] According to a preferred embodiment, the KL module is configured to assess the relevance of hyperbolas or ellipses depending on the signal strength of the echoes received by the second ultrasound transducer. This configuration makes it possible to suppress interference signals even more reliably or to prevent their consideration in position determination, since interference signals often have a lower signal strength than the transmitted ultrasound signals. In particular, the evaluation unit can weight the respective parameter set more strongly for position determination the greater the signal strength of the respective received ultrasound signal. In an advantageous embodiment, the evaluation unit generates the aforementioned parameter sets for those echoes whose signal strength exceeds a predefined threshold. Echoes whose signal strength is less than or equal to the threshold are considered irrelevant in this embodiment.Ignored for position determination.

[0025] In an advantageous advanced training, the Kl module is trained to assess the relevance of hyperbolas or ellipses depending on at least one relative distance between one of the first ultrasound transducers emitting the respective ultrasound signal and one of the second ultrasound transducers receiving the respective ultrasound signal. The assessment of the relevance of each hyperbola or ellipse depending on the relative distance between the transmitter and receiver of the respective ultrasound signal can be carried out, for example, by weighting the respective parameter set resulting from the relative distance more heavily in the position determination the greater the relative distance. This allows a measurement using the respective ultrasound signal to be weighted more heavily the more accurate the measurement is. This can increase the accuracy of the ultrasound measurement unit.It is also possible that the ellipse or hyperbola assigned to the respective runtime is ignored when determining the position if the relative distance is below a specified minimum distance.

[0026] In another embodiment, the evaluation unit is configured to determine the object's height relative to the ultrasonic measuring unit using the trained AI module. In this embodiment, the AI ​​module can preferably determine the height as a function of the echo's temporal profile, particularly the amplitude signal. Using the AI ​​module, the evaluation unit determines the object's height preferably as a function of at least one time interval between two maxima of the temporal profile. It is assumed that the time interval between two maxima is greater the taller the object is. If the object's height is known, further echoes generated by reflections of additional ultrasonic signals from the object can be detected more easily. This increases the accuracy of the ultrasonic measuring unit.

[0027] In another embodiment, the evaluation unit is configured to perform the evaluation of the echoes using an iterative method, in particular the iterative method described above. In this embodiment, the AI ​​module is configured to assess the relevance of the hyperbolas or ellipses depending on residuals calculated using the iterative method. This allows the iterative method to converge more quickly. As a result, real-time position determination of the vehicle in complex traffic situations may only become possible in the first place. Preferably, the AI ​​module is configured to weight the respective hyperbola or ellipse more heavily for position determination the lower the residual calculated using the respective hyperbola or ellipse, in particular using the respective parameter set assigned to the respective hyperbola or ellipse.

[0028] The residual calculated using the respective ellipse or hyperbola is preferably larger the greater the distance of the respective ellipse or hyperbola from the approximated position of the object or the approximated locus. Residuals of ellipses or hyperbolas that lie above a predefined residual threshold are preferably ignored for position determination. According to an advantageous embodiment, the evaluation unit is configured to recalculate the approximate position of the object or the approximate locus at each new iteration step of the iterative procedure, depending on the ellipses or hyperbolas that have been reweighted or selected based on the residuals.

[0029] In a further training course, the AI ​​module is configured to assess the relevance of hyperbolas or ellipses based on the vehicle's motion data during a current measurement cycle, within which the evaluation unit determines the object's position. This allows for a further increase in the accuracy of the ultrasonic measuring unit. In many cases, the travel times of the ultrasonic signals are not determined simultaneously, but rather with a time delay. The higher the relative speed of the vehicle to the object, the less accurate the position determination typically becomes when the position is determined based on sequentially measured travel times. This effect can be taken into account using the AI ​​module by assessing the relevance of the hyperbolas or ellipses depending on the motion data, particularly the relative speed.The lower the relative speed, the more weight can be given to the hyperbolas or ellipses. Alternatively or additionally, the weighting of the hyperbolas or ellipses can be increased the lower the absolute speed of the vehicle. This assumes that lower absolute speeds of the vehicle generally also result in lower relative speeds.

[0030] According to a further embodiment, the evaluation unit is configured to store a previous position of the object determined in a previous measurement cycle and, using the K-module, assess the relevance of hyperbolas or ellipses in the current measurement cycle, during which the evaluation unit detects the object's position, based on the object's previous position. The position determination is preferably performed repeatedly at regular time intervals, i.e., in measurement cycles. Each measurement cycle can have a duration of approximately 50 milliseconds. The object's position determined in the current measurement cycle is hereinafter referred to as the current position.

[0031] The evaluation unit can use the previous position, preferably taking the vehicle's speed into account, to calculate the expected transit time of the ultrasonic signals in the current measurement cycle. This expected transit time can be compared with the transit time of each set of transit times for the respective ultrasonic signal. If the difference between the expected transit time and the transit time of each set of transit times for the respective ultrasonic signal falls within a predetermined permissible deviation, the evaluation unit preferably weights the ellipse or hyperbola associated with that transit time more heavily than those ellipses or hyperbolas for which this difference exceeds the permissible deviation. The evaluation unit determines the current position using these weighted ellipses or hyperbolas.According to a preferred variant, such weighting can be summed over several measurement cycles.

[0032] In a further embodiment, the AI ​​module is configured to assess the relevance of hyperbolas or ellipses based on the position of a previous object calculated in a prior measurement cycle. The previous object differs from the object mentioned above. The ultrasonic signals emitted by the first ultrasonic transducers can be reflected by the current object and the previous object during the current measurement cycle. If the ultrasonic signals are reflected by two objects, this further embodiment allows the accuracy of the ultrasonic measuring unit to be increased. To improve object detection in this case, the AI ​​module is preferably configured to give less weight to, or ignore, those parameter sets generated based on reflections from the previous object.

[0033] The evaluation unit can use the position of the previous object, preferably taking into account the vehicle's speed, to calculate the expected further travel time of those ultrasonic signals generated by reflections from the previous object in the current measurement cycle. This expected further travel time can be compared with the travel time of the respective subset of the respective ultrasonic signal. If the difference between the expected further travel time and the travel time of the respective subset of the respective ultrasonic signal is within a predetermined permissible further deviation, the evaluation unit preferably weights the respective ellipse or hyperbola associated with that travel time less heavily than those ellipses or hyperbolas for which this difference exceeds the permissible deviation.The evaluation unit determines the current position using the weighted ellipses or hyperbolas. According to a preferred embodiment, this weighting can be summed over several measurement cycles.

[0034] Furthermore, a method for operating an ultrasonic measuring unit, particularly for use in a vehicle, is proposed. The ultrasonic measuring unit comprises a control unit and an evaluation unit and can be configured according to one of the variants described above. The method comprises the following steps. In a first step, the control unit activates the first ultrasonic transducers to transmit ultrasonic signals. In a second step, the control unit receives echoes resulting from the ultrasonic signals based on the signal paths of the ultrasonic signals.In a third step, the echoes are evaluated using the evaluation unit to detect the position of the object generating them. This evaluation depends on the positions of the first and second ultrasound transducers and the signal paths, using multilateration in the form of multilateration parameters. These multilateration parameters characterize hyperbolas or ellipses. In a fourth step, the relevance of the hyperbolas or ellipses is assessed based on the received values ​​using a trained artificial intelligence module (AI module) of the evaluation unit. In a fifth step, the position is detected based on this assessment by the evaluation unit. The numbering of the steps does not necessarily dictate a sequence. For example, the fourth step is preferably performed during the third step.

[0035] Furthermore, a method for training an artificial intelligence module (AI module) of an evaluation unit of an ultrasound measurement unit is proposed. The ultrasound measurement unit comprises a control unit and an evaluation unit and can be configured according to one of the variants described above. The training method includes the following training steps. In a first training step, the first ultrasound transducers are controlled by the control unit to transmit training ultrasound signals, hereinafter referred to as training signals. According to a possible further variant of the AI ​​module training, interference signals can be transmitted during the first training step using the first ultrasound transducers or using additional ultrasound transducers, in addition to the training signals.In a second training step, training echoes resulting from the training signals are received by a second set of ultrasound transducers via the control unit. These echoes are derived from the training signals and their path lengths. In a further training variant, the training echoes received by the control unit from the second set of ultrasound transducers can also result from interfering signals. The training echoes are caused by the reflection of ultrasound signals, which carry the training signals, from a training object. The distance between the training object and the ultrasound measurement unit is known during the training process.

[0036] In a third training step, the training echoes are evaluated. This evaluation, which depends on the positions of the first and second ultrasound transducers, is performed using multilateration and results in values ​​of multilateration parameters. These multilateration parameters characterize training hyperbolas or training ellipses. The training hyperbolas or training ellipses describe possible locations of the training object.

[0037] In a fourth training step, the relevance of the training hyperbolas or training ellipses for determining the position of the training object is assessed. The more precisely the position of the training object can be determined using the respective training hyperbola or training ellipse, the higher the relevance of that respective training hyperbola or training ellipse is rated. This can be achieved, for example, by weighting the respective training hyperbola or training ellipse more heavily the more precisely the position of the training object can be determined using that hyperbola or training ellipse. To verify how accurately the position of the training object can be determined using the respective training hyperbola or training ellipse, the distance between the known position of the training object and the respective training hyperbola or training ellipse can be calculated.

[0038] The weighting of a given training hyperbola or ellipse can be achieved by assigning it a weighting value, which can be, for example, a number between zero and one. The weighting can also be binary. In this case, according to one possible implementation, the weighting value of the respective training hyperbola or ellipse can be one if it is suitable for determining the position of the training object, and zero otherwise.

[0039] Each set of multilateration parameters describing the respective training hyperbola or training ellipse forms a training input dataset for the AI ​​module during training. The relevance determined for each training hyperbola or training ellipse, for example in the form of the weighting value, forms a training output dataset for training the AI ​​module. A training dataset is then created from each training input dataset and each training output dataset.

[0040] In a fifth training step, the AI ​​module is trained using the training data sets.

[0041] Brief description of the drawings

[0042] Further advantages, features, and details of the disclosure will become apparent from the following description, in which examples are explained in more detail with reference to the drawing. The diagram schematically illustrates...

[0043] Figure 1 shows a vehicle with an ultrasonic measuring unit, comprising a control unit and an evaluation unit with a KL module;

[0044] Figure 2a shows the course of a first amplitude signal generated with the aid of a first ultrasound receiver, in particular the ultrasound measuring unit shown in Fig. 1;

[0045] Figure 2b shows the course of a second amplitude signal generated with the aid of a second ultrasound receiver, in particular the ultrasound measuring unit shown in Fig. 1;

[0046] Figure 2c shows the course of a third amplitude signal generated with the aid of a third ultrasound receiver, in particular the ultrasound measuring unit shown in Fig. 1; Figure 3 shows several ellipses indicating possible locations, in particular of the object shown in Fig. 1, in relation to the vehicle shown in Fig. 1;

[0047] Figure 4 shows a data flow diagram illustrating the processing of input data, in particular using the Kl module shown in Fig. 1;

[0048] Figure 5 shows a traffic situation with the vehicle and object shown in Fig. 1 and a previous object detected in a previous measurement cycle;

[0049] Figure 6 Steps of a method for operating the ultrasonic measuring unit shown in particular in Fig. 1.

[0050] In the figures, similar components are labelled with the same reference symbols.

[0051] Fig. 1 shows an ultrasonic measuring unit 1 for use in a vehicle 2. The ultrasonic measuring unit 1 has a control unit 3 and an evaluation unit 4.

[0052] The control unit 3 is configured to control first ultrasound transducers, such as a first ultrasound transmitter 1 1 1.1, a second ultrasound transmitter 1 1 1.2, and a third ultrasound transmitter 1 1 1.3, to transmit ultrasound signals. For example, the control unit 3 can control the first ultrasound transducers such that the first ultrasound transmitter 1 1 1 transmits the first ultrasound signals 10.1, the second ultrasound transmitter 1 1.2 transmits the second ultrasound signals 10.2, and the third ultrasound transmitter 1 1.3 transmits the third ultrasound signals 10.3. According to one variant, the ultrasound signals 10.1, 10.2, and 10.3 have different frequencies.

[0053] Furthermore, the control unit 3 is configured to receive echoes resulting from the ultrasound signals via signal paths from second ultrasound transducers, such as a first ultrasound receiver 12.1, a second ultrasound receiver 12.2, and a third ultrasound receiver 12.3. The echoes can be in the form of electrical signals that are transmitted from the second ultrasound transducers to the control unit 3 via lines shown as solid lines in Fig. 1.

[0054] The evaluation unit 4 is configured to evaluate the echoes to detect the position of an object 6 that generates the echoes. According to one possible embodiment, the echoes can be sent from the control unit 3 to the evaluation unit 4 in the form of amplitude signals over time, as shown in Fig. 2. Evaluation by the evaluation unit 4 is preferably performed based on the positions of the first and second ultrasound transducers and the signal paths by means of multilateration in the form of values ​​of multilateration parameters.

[0055] Fig. 2a shows an example of a first waveform 20.1 of a first amplitude signal 21.1 over a time axis 22. The first waveform 20.1 can, for example, be generated using the control unit 3 with the help of first electrical signals generated by the first ultrasound receiver 12.1. The first waveform 20.1 has a maximum at a first time point 22.1, which is generated by receiving the first ultrasound signal reflected from the first object 6 by the first ultrasound receiver 12.1.

[0056] Fig. 2b shows a second waveform 20.2 of a second amplitude signal 21.2 over the time axis 22. The second waveform 20.2 can, for example, be generated by the control unit 3 using second electrical signals generated by the second ultrasound receiver 12.2. The second waveform 20.2 exhibits a maximum at a second time point 22.2, which is generated by receiving the first ultrasound signal reflected from the first object 6 using the second ultrasound receiver 12.2.

[0057] Fig. 2c shows a third waveform 20.3 of a third amplitude signal 21.3 over the time axis 22. The third waveform 20.3 can, for example, be generated by the control unit 3 using third electrical signals generated by the third ultrasound receiver 12.3. The third waveform 20.3 exhibits a maximum at a third time point 22.3, which is generated by receiving the first ultrasound signal reflected from the first object 6 using the third ultrasound receiver 12.3.

[0058] As can be seen in Figures 2a, 2b, and 2c, the first ultrasound signals 10.1 are received later by the second ultrasound receiver 12.2 than by the first ultrasound receiver 12.1. Likewise, the first ultrasound signals 10.1 reflected by the object 6 are received later by the third ultrasound receiver 12.3 than by the second ultrasound receiver 12.2. This is because the second ultrasound receiver 12.2 is located farther from the first ultrasound transmitter 12.1 than the first ultrasound receiver 12.1 is from the first ultrasound transmitter. The same applies to the second ultrasound receiver 12.2 compared to the third ultrasound receiver 12.3.

[0059] The evaluation unit 4 can preferably calculate a first set of transit times for the first ultrasound signal as part of an evaluation of the echoes. The respective transit time of the first set is the time that elapses between the transmission of the first ultrasound signal, i.e., the transmission of the first ultrasound signals 10.1, and the reception of the reflected first ultrasound signal by the respective ultrasound receiver 12.1, 12.2, 12.3.

[0060] For this purpose, the control unit 3 can, for example, send a start signal to the evaluation unit 4 to measure the transit times when the control unit 3 activates the first ultrasound transmitter 1 1.1 to transmit the first ultrasound signal 10.1. Based on the transmission time and the first reception time 22.1, the evaluation unit 4 can determine a first transit time of the first sentence, which the first ultrasound signal requires to travel from the first ultrasound transmitter 1 1.1 to the first ultrasound receiver 12.1 by reflection from the object 6. Similarly, the evaluation unit 4 preferably determines a second transit time of the first sentence based on the transmission time and the second reception time 22.2. Likewise, the evaluation unit 4 preferably determines a third transit time of the first sentence based on the transmission time and the third reception time 22.3.

[0061] Advantageously, the evaluation unit 4 is configured to calculate parameter sets to describe a first set of ellipses shown in Fig. 3, depending on the first set of transit times and the relative positions of the ultrasound receivers 12.1, 12.2, 12.3 to the first ultrasound transmitter 1.1. The first set of ellipses comprises, for example, a first ellipse 31, a second ellipse 32, and a third ellipse 33. A first focus of the first ellipse 31 is defined by the point on the ultrasound measuring unit 1 where the first ultrasound transmitter 11.1 is located. A second focus of the first ellipse 31 can be specified by the point on the ultrasound measuring unit 1 where the first ultrasound receiver 12.1 is located. Fig. 3 shows a principal axis 31.1 and a minor axis 31.2 of the first ellipse 31.

[0062] A first multilateration parameter set, hereinafter referred to as the first parameter set, for describing the first ellipse 31 includes, for example, information about the position of the first focus and the second focus of the first ellipse 31 and a dimension of the major axis 31.1 and the minor axis 31.2 of the first ellipse 31. Similarly, the evaluation unit 4 is preferably configured to generate a second parameter set for describing the second ellipse 32 and a third parameter set for describing the third ellipse 33. Accordingly, the second parameter set includes information about the positions of the foci of the second ellipse 32, a dimension of a major axis, and a dimension of a minor axis of the second ellipse 32. The third parameter set similarly includes information about the positions of the foci of the third ellipse 33 and dimensions of a major axis and a minor axis of the third ellipse 33.

[0063] To determine the position of object 6 relative to the ultrasonic measuring unit 1, the evaluation unit 4 is preferably configured to determine an intersection point of at least two of the three ellipses of the first set of ellipses. For this purpose, an algorithm for calculating intersection points of ellipses is preferably implemented in the evaluation unit 4. Fig. 3 shows an example of a first intersection point 34, which results from a search for intersection points between the ellipses of the first set of ellipses.

[0064] The ellipses 31, 32, 33 shown in Fig. 3 represent a special case in which the transit times of the first set of transit times are recorded very precisely and within very short time intervals. This results in the three ellipses 31, 32, 33 intersecting at the common first intersection point 34. However, when the ultrasonic measuring unit 1 is used under real-world conditions, the three ellipses 31, 32, 33 do not intersect at the common first intersection point 34. For example, under real-world conditions, the third ellipse 33 can be shaped like the further ellipse 33.2 shown in Fig. 3. This occurs, for instance, when a further transit time, used to determine the shape of the further ellipse 33.2, is longer than the third transit time used to construct the third ellipse 33.Since determining the position of object 3 by identifying the intersection points of the ellipses is not possible in many applications, the evaluation unit 4 is advantageously configured to perform the position determination, i.e., the determination of the position of object 6, using an iterative method, in particular the iterative method described above. In the iterative method, an approximate position of object 6 is advantageously determined at each new iteration step. By way of example, the approximate position of object 6 is shown in Fig. 3 in the form of a second point 35.

[0065] In the application example shown in Fig. 3, the second point 35 is located at a greater distance from the further ellipse 33.2 than from the first ellipse 31 and the second ellipse 32. The evaluation unit 4 is advantageously configured to minimize the distances between the approximate position of the object 6 and the ellipses of the first set of ellipses. Preferably, a sum of the distances or an average of the distances can be minimized. It is also possible to minimize the squares of the respective distances, in particular to minimize the sum of the squares of the distances.

[0066] In the same way that the evaluation unit 4 can calculate the first set of transit times and thus the first, second and third parameter sets as described above, based on the first ultrasound signal received at the different times 22.1, 22.2, 22.3, the evaluation unit 4 can calculate a second set of transit times depending on further first times at which the second ultrasound signal is received by means of the ultrasound receivers 12.1, 12.2, 12.3 and, for this purpose, calculate values ​​of further parameter sets to describe further ellipses that indicate further possible locations of the object 6.

[0067] Similarly, the evaluation unit 4 can also calculate second further ellipses and values ​​of second further parameter sets for describing these second further ellipses based on a third set of transit times, which result from receiving the third ultrasound signal at different times using the ultrasound receivers 12.1, 12.2, 12.3. It goes without saying that in the iterative procedure, not only the ellipses of the first set of ellipses, but also the first further ellipses and preferably the second further ellipses, or the values ​​of the parameter sets for describing the first further ellipses or the second further ellipses, can be used.

[0068] As described above, the computational effort for the iterative procedure could be significantly reduced if the number of ellipses, for each of which a respective distance to the approximate position of object 6 is to be minimized, is reduced.

[0069] For this purpose, the evaluation unit has a trained AI module 5, which is configured to assess the relevance of the ellipses based on the received values ​​of the multilateration parameters, in particular the values ​​of the parameter sets, such as the first, second, third, and first subsequent parameter sets, and the second subsequent parameter sets. The evaluation unit 4 is further configured to detect the position of object 6 based on the assessment of the relevance of the ellipses.

[0070] Figure 4 schematically shows the AI ​​module 5. The AI ​​module 5 is configured to calculate an output data set 42 based on an input data set 41. The AI ​​module 5 preferably comprises mathematical functions with parameters, wherein the values ​​of the parameters can be adapted to the training data sets during training of the AI ​​module 5, as described above. The trained AI module 5 is thus specified by the fact that the values ​​of the parameters of the mathematical functions are adapted to the training data sets. The mathematical functions can, for example, comprise a chain of artificial neurons arranged in several hidden layers. In this embodiment, connection weights between the neurons, particularly between neurons from different hidden layers, can represent the values ​​of the parameters of the mathematical functions.Collectively, the neurons can form an artificial neural network. This neural network can be, for example, a multi-layer perceptron network, a radial basis function (RBF) network, or a convolutional neural network (CNN).

[0071] The following describes how the Kl module 5 can be used. A calculation module of the evaluation unit 4 (not shown in the figures) calculates the values ​​of the parameter sets and preferably stores these values ​​in a database. A processor of the evaluation unit 4 (also not shown in the figures) can read values ​​of the respective parameter set from the database and generate the input data set 41 from them. In a simple variant, the input data set 41 contains the values ​​of a single parameter set from the parameter sets mentioned above, used to describe one of the ellipses mentioned above. Upon receiving the input data set 41, the Kl module 5 calculates the output data set 42. The output data set 42 contains the weighting value described above. As described above, determining the weighting value provides one possible way to determine the relevance of the respective ellipse.To determine the relevance of several of the aforementioned ellipses simultaneously, the input data set 41 can also contain values ​​from several of the parameter sets. In this case, the output data set 42 comprises several weighting values, each weighting the respective parameter set.

[0072] According to one possible embodiment, the evaluation unit 4 is configured to re-determine the approximate position of object 6 in a new iteration step of the iterative procedure described above, depending on the weighting values. For example, a weighting of the respective ellipse can be used to multiply the respective distance between the ellipse and the approximate position of object 6 by the respective weighting value. This means that when minimizing the sum of the distances or the squared distances, the distances are factored in with their respective weighting values ​​of the corresponding ellipse.

[0073] If the weighting values ​​are binary, meaning they can only take the value 0 or 1, then the ellipses mentioned above can be selected using the Kl module 5. The iterative procedure is then carried out taking the selected ellipses into account.

[0074] According to an advantageous further development, the input data set 41 includes, in addition to the values ​​of the multilateration parameters, such as the values ​​of the parameter sets, a signal strength of an echo received by the second ultrasound transducer, on the basis of which the respective ellipse is calculated. Advantageously, the evaluation unit 4 also captures a signal strength of the maximum of the amplitude signal curve as part of the above-described determination of the transit time of the respective set of transit times. The K-module 5 can, for example, be trained such that the weighting values ​​are higher the higher the respective signal strength.

[0075] Furthermore, the input data set 41 can contain information about the relative distance between the transmitter and the receiver with which the respective ultrasound signal was transmitted or received. It is self-evident that each ellipse, and thus each transit time, can be assigned a pair of ultrasound transducers, comprising one of the first ultrasound transducers and one of the second ultrasound transducers, between which the respective ultrasound signal is transmitted. The evaluation unit 4 advantageously assigns the relative distance between the ultrasound transducers of the respective pair of ultrasound transducers to the respective ellipse and writes this to the input data set 41.

[0076] According to a specific configuration, the evaluation unit 4 is set up to determine the height of object 6 relative to the ultrasound measuring unit 1 using the trained AI module 5. In this configuration, the output data set 42 includes information about the height of object 6. Advantageously, the height of object 6 can be determined using AI module 5 by considering the values ​​of several parameter sets from the parameter sets mentioned above. For example, with comparatively tall objects, reflections of ultrasound signals generally occur at longer time intervals than with comparatively smaller objects. Therefore, if several of the parameter sets represent ellipses originating from comparatively significantly different travel times, a comparatively large height of object 6 can be inferred. However, this is generally difficult to express using mathematical formulas.For this reason, the trained AI module 5 is particularly well suited to determining the height of object 6 based on the values ​​of the parameter sets. According to an advantageous further development, the height of object 6 determined in this way can also be used as an input value, i.e., as one of the values ​​of the input data set 41, in a subsequent iteration step of the iterative procedure. In this way, if object 6 has already been detected, those ellipses calculated from reflections of the ultrasound signals at the already detected object 6 can be given greater weight.

[0077] As described above, according to a further possible variant, the Kl module 5 can be configured to assess the relevance of the hyperbolas or ellipses depending on the movement data of vehicle 2 during a current measurement cycle, within which the evaluation unit 4 determines the position of object 6. In this configuration, the input data set 41 contains at least one movement data point of vehicle 2, such as an absolute speed of vehicle 2 or a relative speed of vehicle 2 in relation to object 6.

[0078] In a further advantageous embodiment, the input data set 41 can contain the previous position of object 6 described above, which was determined in a previous measurement cycle using the evaluation unit 4. In this way, the current position of object 6 can be determined in the current measurement cycle as a function of the previous position of object 6 and, in particular, as a function of the relevance of the ellipses, which is determined as a function of the previous position of object 6.

[0079] Fig. 5 shows a traffic situation in which, in addition to object 6, a previous object 50 is located in front of vehicle 2. The previous object 50 was detected by the evaluation unit 4 in a previous measurement cycle, which precedes the current measurement cycle. In particular, the position of the previous object 50 within the previous measurement cycle was calculated. In a further advantageous embodiment, the input data set 41 now contains the position of the previous object 50 calculated in the previous measurement cycle. In this case, the K-module 5 is configured to determine the relevance of the ellipses as a function of the position of the previous object 50. Fig. 6 shows steps of a method for operating the ultrasonic measuring unit 1, in particular for use in vehicle 2.In a first step 601, the first ultrasound transducers are controlled to transmit the ultrasound signals using the control unit 3. In a second step 602, the second ultrasound transducers receive echoes resulting from the ultrasound signals based on the signal paths of the ultrasound signals, using the control unit 3.

[0080] In a third step 603, the echoes are evaluated using the evaluation unit 4 to detect the position of the object 6 causing the echoes. For this purpose, the evaluation unit 4 evaluates, in particular, the first waveform 20.1, the second waveform 20.2, and the third waveform 20.3. When evaluating these waveforms, the evaluation unit 4 preferably checks at which point each waveform exhibits a maximum. In principle, such an evaluation of these waveforms of the amplitude signals 21.1, 21.2, 21.3 can also be carried out using the control unit 3. The control unit 3 is configured accordingly for this purpose. Evaluating the echoes using the evaluation unit 4 includes at least one execution of a multilateration procedure, whereby the values ​​of the multilateration parameters, in particular the parameter sets mentioned above, are used.

[0081] In a fourth step 604, the relevance of the ellipses is assessed based on the received values ​​of the multilateration parameters, in particular the values ​​of the parameter sets, using the trained AI module 5. In a fifth step 605, the detection of the position of object 6, that is, the determination of the position of object 6, is carried out based on the assessment of the relevance of the ellipses using the evaluation unit 4. In this step, a further iteration of the iterative procedure described above can be performed. The calculated position of object 6 that results after the last iteration of the iterative procedure can be considered the position of object 6 detected using the evaluation unit 4.

[0082] In another possible configuration, the parameter sets can also include the centers of the ellipses.

[0083] The distances between the approximate position of object 6 and the ellipses can be minimized, for example, using a Gauss-Newton method. Within the framework of the Gauss-Newton algorithm, the aforementioned residuals of the ellipses can be stored in a matrix. According to one possible embodiment, the evaluation unit 4 can be configured to determine a covariance matrix based on this matrix. In another possible variant, the input data set 41 can also include entries of the matrix, the covariance matrix, and / or a determinant of the covariance matrix and / or a determinant of the matrix.

[0084] It is within the scope of the invention that the evaluation unit 4 detects whether the second ultrasound transducers, i.e., the ultrasound receivers 12.1, 12.2, 12.3, are receiving only the first ultrasound signals 10.1 or, in addition to the first ultrasound signals 10.1, also the second ultrasound signals 10.2 and / or the third ultrasound signals 10.3. In this variant, the evaluation unit 4 can, for example, set a flag indicating whether only the first ultrasound signals 10.1 are being received by the ultrasound receivers. If the flag is set to 1, this can, for example, mean that only the first ultrasound signals 10.1 are being received. If the flag is set to 0, this can mean that the ultrasound receivers are receiving several different ultrasound signals, i.e., the first ultrasound signals 10.1 and at least the second ultrasound signals 10.2 and / or the third ultrasound signals 10.3.In this embodiment, the input data set 41 can preferably have the value of the flag. This allows the K module 5 to also consider, for the assessment of the relevance of the ellipses, whether the object 6 is detected using only a single ultrasonic transmitter or using two or more ultrasonic transmitters.

[0085] It is also possible that the input data set 41 contains information about whether vehicle 2 is moving or not. If vehicle 2 is moving, the travel times can be corrected using a known speed of vehicle 2. However, if object 6 is moving relative to vehicle 2 and vehicle 2 is stationary, then, in general, detecting the position of object 6 is less accurate because its speed is unknown. If the input data set 41 contains information about whether vehicle 2 is moving or not, this effect can be taken into account when evaluating the relevance of the ellipses. In an advantageous further development, the output data set 42 can also contain a confidence score that indicates the probability of how correctly the relevance of the respective ellipse was assessed.

Claims

Patent claims 1. Ultrasonic measuring unit (1), in particular for use in a vehicle (2), comprising a control unit (3) and an evaluation unit (4), wherein - the control unit (3) is designed to control first ultrasound transducers to send ultrasound signals (10.1) and to receive echoes resulting from the ultrasound signals (10.1) due to signal paths of the ultrasound signals (10.1) from second ultrasound transducers, - the evaluation unit (4) is configured to evaluate the echoes to detect the position of an object (6) causing the echoes, wherein the evaluation is carried out depending on the positions of the first and second ultrasound transducers and the signal paths by means of multilateration in the form of values ​​of multilateration parameters, wherein the multilateration parameters characterize hyperbolas or ellipses (31 , 32, 33), wherein the evaluation unit (4) has a trained artificial intelligence module (AI module) (4) which is configured to assess the relevance of the hyperbolas or ellipses (31 , 32, 33) based on the reception of the values, and the evaluation unit (4) is configured to carry out the detection of the position based on the assessment.

2. Ultrasound measuring unit (1) according to claim 1, wherein the Kl module (4) is configured to assess the relevance of the hyperbolas or ellipses (31, 32, 33) depending on the signal strength of the echoes received by means of the second ultrasound transducer.

3. Ultrasound measuring unit (1) according to claim 1 or 2, wherein the Kl module (4) is configured to assess the relevance of the hyperbolas or ellipses (31, 32, 33) as a function of at least one relative distance between one of the first ultrasound transducers emitting the respective ultrasound signal and one of the second ultrasound transducers receiving the respective ultrasound signal.

4. Ultrasound measuring unit (1 ) according to one of the preceding claims, wherein the evaluation unit (4) is configured to determine a height of the object relative to the ultrasound measuring unit (1 ) using the trained AI module (4).

5. Ultrasound measuring unit (1) according to one of the preceding claims, wherein the evaluation unit (4) is configured to perform the evaluation of the echoes using an iterative procedure and the K-module (4) is configured to assess the relevance of the hyperbolas or ellipses (31, 32, 33) depending on residuals calculated using the iterative procedure.

6. Ultrasonic measuring unit (1) according to one of the preceding claims, wherein the KL module (4) is configured to assess the relevance of the hyperbolas or ellipses (31, 32, 33) depending on the movement data of the vehicle (2) of a current measurement cycle, within which the evaluation unit (4) determines the position of the object (6).

7. Ultrasound measuring unit (1) according to one of the preceding claims, wherein the evaluation unit (4) is configured to store a previous position of the object (6) determined in a previous measurement cycle and, using the Kl module (4), to assess the relevance of the hyperbolas or ellipses (31, 32, 33) depending on the previous position of the object (6) in a current measurement cycle within which the evaluation unit (4) detects the position of the object.

8. Ultrasound measuring unit (1) according to one of the preceding claims, wherein the Kl module (4) is configured to assess the relevance of the hyperbolas or ellipses (31, 32, 33) depending on a position of a previous object (50) calculated in a previous measurement cycle.

9. Method for operating an ultrasonic measuring unit (1), in particular for use in a vehicle, wherein the ultrasonic measuring unit (1) comprises a control unit (3) and an evaluation unit (4), the method comprising the following steps: - Controlling first ultrasound transducers to send ultrasound signals using the control unit (3); - Receiving echoes resulting from the ultrasound signals due to the signal paths of the ultrasound signals from second ultrasound transducers using the control unit (3); - Evaluating the echoes to detect the position of an object causing the echoes using the evaluation unit (4), wherein the evaluation is performed depending on the positions of the first and second ultrasound transducers and the signal paths by means of multilateration in the form of values ​​of multilateration parameters, wherein the multilateration parameters characterize hyperbolas or ellipses; - Assessing the relevance of the hyperbolas or ellipses based on receiving the values ​​using a trained artificial intelligence module (Kl module) of the evaluation unit (4); - Performing position detection based on assessment using the evaluation unit (4).

10. Computer program product comprising instructions executable by a processor, wherein the execution of the instructions causes the processor to perform the method according to claim 9.

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