Ultrasonic sensor that transmits sensor data to a computer system in a vehicle using a reconstructed ultrasonic echo signal model

Abstract

Ultrasonic sensor - wherein the ultrasonic sensor performs a method for transmitting sensor data from the ultrasonic sensor to a computer system in a vehicle and - wherein the ultrasonic sensor emits an ultrasonic burst (α) and - where the ultrasonic sensor receives an ultrasonic signal and - wherein the ultrasonic sensor generates an ultrasonic receiving signal (β) and - wherein the ultrasonic sensor performs data compression of the received signal to generate compressed data (γ) • using a neural network model (151) and / or • using an HMM model (Hidden Markov model) and / or • using a Petri net and - wherein the ultrasonic sensor performs data compression of the received signal to generate compressed data (γ) • using a reconstructed ultrasound echo signal model (610) and - wherein the ultrasonic sensor transmits the compressed data to a computer system (δ).

Description

Field of invention

[0001] The invention relates to a method for transmitting data from an ultrasonic sensor system for parking assistance systems in vehicles to a computer system and a corresponding device. The basic idea is the detection of potentially relevant structures in the measurement signal and the compression of this measurement signal by transmitting only these detected, potentially relevant structures instead of the measurement signal itself. The actual detection of objects, e.g., obstacles to the parking process, only takes place after reconstruction of the measurement signal as a reconstructed measurement signal in the computer system, where typically several such decompressed measurement signals from several ultrasonic sensor systems are combined. Here, the decompression of the data in the control unit after compression in the sensor is achieved by structure detection in the measurement signal. General introduction

[0002] According to Section 39 of the Patent Act, this disclosure claims priority from German patent application 10 2018 106 248.6 filed March 16, 2018, and priority from German patent application 10 2019 105 648.9 filed March 6, 2019.

[0003] Furthermore, the disclosure presented here claims priority from the prior application with file number 10 2018 106 242.7 of March 16, 2018.

[0004] Ultrasonic sensor systems for parking assistance in vehicles are enjoying increasing popularity. The desire is to transmit increasing amounts of data from the actual measurement signal of the ultrasonic sensor to a central computer system, where this data is combined with data from other ultrasonic sensor systems and other types of sensor systems, e.g., radar systems, to create so-called environment maps through sensor fusion. Therefore, there is a desire to perform object detection not in the ultrasonic sensor system itself, but rather in the computer system through this sensor fusion. This avoids data loss and thus reduces the likelihood of incorrect information, incorrect decisions, and thus accidents. At the same time, however, the transmission bandwidth of the available sensor data buses is limited. Their replacement should be avoided because they have proven themselves in the field.At the same time, there is a desire to limit the amount of data to be transmitted. In short, the information content of the data and its relevance for the subsequent obstacle detection (object detection) performed in the computer system must be increased without having to increase the data rate too much – or better: without having to increase the data rate at all. Quite the opposite: Preferably, the data rate requirement should even be reduced to allow data rate capacities for the transmission of status data and self-test information from the ultrasonic sensor system to the computer system, which is absolutely necessary for functional safety (FuSi). The disclosure presented here addresses this problem.

[0005] Various methods for processing an ultrasonic sensor signal are already known in the state of the art.

[0006] WO 2012 / 016834 A1, for example, discloses such a method for evaluating an echo signal for vehicle environment detection. It proposes transmitting a measurement signal with a predeterminable coding and format and searching for and determining the components of the measurement signal in the received signal by means of a correlation with the measurement signal. The level of the correlation, not the level of the echo signal's envelope, is then evaluated using a threshold value.

[0007] From DE 4 433 957 A1 it is known to periodically emit ultrasonic pulses for obstacle detection and to use the transit time to determine the position of obstacles, whereby during the evaluation echoes that remain correlated over several measuring cycles are amplified, while echoes that remain uncorrelated are suppressed.

[0008] From DE 10 2012 015 967 A1 a method for decoding a received signal received by an ultrasonic sensor of a motor vehicle is known, in which a transmitted signal of the ultrasonic sensor is emitted in coded form and for decoding the received signal is correlated with a reference signal, wherein before correlating the received signal with the reference signal a frequency shift of the received signal with respect to the transmitted signal is determined and the received signal is correlated with the transmitted signal, whose frequency is shifted by the determined frequency shift, as a reference signal, wherein to determine the frequency shift of the received signal the same received signal is subjected to a Fourier transformation and the frequency shift is determined based on a result of the Fourier transformation.

[0009] DE 10 2011 085 286 A1 discloses a method for detecting the surroundings of a vehicle by means of ultrasound, wherein ultrasonic pulses are emitted and the ultrasonic echoes reflected from objects are detected, characterized in that the detection range is divided into at least two distance ranges, wherein the ultrasonic pulses used for detection in the respective distance range are emitted independently of one another and are encoded by different frequencies.

[0010] From WO 2014 108 300 A1, a device and a method for environmental sensing by means of a signal converter and an evaluation unit are known, wherein signals received from the environment with a first impulse response length at a first time during a measuring cycle and with a second longer impulse response length at a second later time within the same measuring cycle are filtered depending on the runtime.

[0011] DE 10 2015 104 934 A1 discloses a method for providing information dependent on an object detected in the surrounding area of ​​a motor vehicle. In the method of DE 10 2015 104 934 A1, the surrounding area of ​​the motor vehicle is detected using a sensor device of the motor vehicle, and the information is provided to a communication interface in the motor vehicle. Raw sensor data is stored in a control unit on the sensor device as information about a free space detected between the sensor device and an object detected in the surrounding area.According to the technical teaching of DE 10 2015 104 934 A, these raw sensor data are provided at a communication interface connected to the control unit on the sensor device side for transmission to and further processing with a further control unit of a processing device comprising the two control units and designed to create a surrounding area map.

[0012] The technical teachings of the above-mentioned patents are all based on the idea of ​​detecting an object in front of the vehicle using the ultrasonic sensor itself, and then transmitting the object data only after the objects have been detected. However, this results in the loss of synergy effects when using multiple ultrasonic transmitters.

[0013] DE 10 2010 041 424 A1 discloses a method for detecting the surroundings of a vehicle using a number of sensors. In this method, at least one piece of echo information about the surroundings is detected by at least one sensor during at least one echo cycle and compressed using an algorithm. The at least one piece of compressed echo information is transmitted to at least one processing unit. While DE 10 2010 041 424 A1 also focuses on the compressed transmission of detected object data, it is already recognized that it is generally useful to transmit data extracted from the received echo signal (echo information) to the control unit in compressed form, without specifying a compression method.

[0014] A method for sensor connection is known from DE 10 2013 226 376 A1. In section

[0006] of DE 10 2013 226 376 A1, the authors of DE 10 2013 226 376 A1 explain the prior art. In claim 1 of DE 10 2013 226 376 A1, the authors of DE 10 2013 226 376 A1 claim this self-cited prior art. DE 10 2013 226 376 A1 discloses a method for operating a sensor system, in particular an ultrasonic sensor system, with an ultrasonic sensor and a control unit. Data is transmitted from the ultrasonic sensor to the control unit in a current-modulated manner. Data is transmitted from the control unit to the at least one ultrasonic sensor in a voltage-modulated manner.

[0015] DE 10 024 959 A1 discloses a device for the unidirectional or bidirectional exchange of data between a measuring unit for measuring a physical quantity and a control / evaluation unit. The device determines the physical quantity with a specified accuracy based on the measurement data provided by the measuring unit. The measuring unit is connected to the control / evaluation unit via a data line. The technical teaching of DE 10 024 959 A1 is based on the task of optimizing data transmission between a measuring unit and a remote control / evaluation unit.The object is achieved in that at least one compression unit is provided which is assigned to the measuring unit and which compresses the measurement data supplied by the measuring unit in a predetermined cycle in such a way that, on the one hand, the information content of the measurement data is transmitted completely or in a reduced form via the data line to the control / evaluation unit in such a way that the physical quantity is determined with the predetermined accuracy, and, on the other hand, that the amount of data transmitted is minimal.

[0016] DE 10 2013 015 402 A1 discloses a method for operating a sensor device of a motor vehicle, in which a transmission signal is transmitted into a surrounding area of ​​the motor vehicle by means of a sensor, in particular an ultrasonic sensor, and a signal component of the transmission signal (11) reflected by a target object in the surrounding area (7) is received as a raw echo signal (12). Data is transmitted between the sensor and a control unit of the sensor device via a data bus. A measured variable relating to the target object is determined by means of the sensor device. The raw echo signal is converted into a digital raw echo signal by means of a converter of the sensor, and the digital raw echo signal is transmitted from the sensor via the data bus to the control unit, which determines the measured variable by relating the digital raw echo signal to a reference signal corresponding to the transmission signal. From DE 10 2013 015 402 A1.However, DE 10 2013 015 402 A1 does not provide for decompression of the data in the control unit.

[0017] A system for sensor fusion is known from US 2006 / 0 250 297 A1.

[0018] The use of feature vectors is known from WO 2018 / 210 966 A1 and the related priority-substantiating European patent application with EPO file number EP 1 717 320.9. However, WO 2018 / 210 966 A1 leaves open how exactly the classification is carried out when multiple signal object features are present. Task

[0019] The invention is therefore based on the object of creating a solution which solves the above problem of reducing the bus bandwidth requirement for the transmission of measurement data from the ultrasonic sensor system to the computer system and possibly has further advantages.

[0020] This object is achieved by an ultrasonic sensor according to claim 1. Further embodiments are the subject of subclaims. Solution

[0021] As explained above, the technical teachings from the prior art are all based on the idea of ​​detecting an object in front of the vehicle using the ultrasonic sensor in the ultrasonic sensor itself and only then transmitting the object data after the objects have been detected. However, since synergy effects are lost when using multiple ultrasonic transmitters, it was recognized during the preparation of the disclosure presented here that it is not sensible to transmit only the echo data from the ultrasonic sensor itself, but rather all data, and then to evaluate the data from several sensors in a central computer system. To do this, however, the data must be compressed differently for transmission via a data bus with a lower bus bandwidth than in the prior art. This can then unlock synergy effects. For example, it is conceivable for a vehicle to have more than one ultrasonic sensor.To distinguish between the two sensors, it is useful for them to transmit with different coding. In contrast to the current state of the art, however, both sensors are now supposed to capture the ultrasonic echoes from both ultrasonic sensors and transmit them, appropriately compressed, to the central computer system, where the received ultrasonic signals are reconstructed. Only after reconstruction (decompression) is the detection of the objects carried out. This also enables the fusion of the ultrasonic sensor data with other sensor systems such as radar, etc.

[0022] A method for transmitting sensor data from a sensor to a computer system is proposed. The method is particularly suitable for use in transmitting data of an ultrasonic reception signal from an ultrasonic sensor to a control unit as a computer system in a vehicle. The method is based on the Fig. 1. According to the proposed method, an ultrasonic burst is first generated and emitted into a free space, typically in the vicinity of the vehicle (step α of the Fig. 1). An ultrasonic burst consists of several consecutive sound pulses at ultrasonic frequency. This ultrasonic burst is created by a mechanical oscillator in an ultrasonic transmitter or ultrasonic transducer slowly oscillating and then oscillating again. The ultrasonic burst emitted by the exemplary ultrasonic transducer is then reflected by objects in the vicinity of the vehicle and received by an ultrasonic receiver or the ultrasonic transducer itself as an ultrasonic signal and converted into an electrical reception signal (step β of the Fig. 1). Particularly preferably, the ultrasonic transmitter is identical to the ultrasonic receiver and is then referred to as a transducer in the following. However, the principle explained below can also be applied to separate receivers and transmitters. The proposed ultrasonic sensor contains a signal processing unit that analyzes and compresses the received electrical signal (step γ of the Fig. 1) in order to minimize the necessary data transmission and to create space for the aforementioned status messages and further control commands from the control computer to the signal processing unit or the ultrasonic sensor system. The compressed electrical reception signal is then transmitted to the computer system (step δ of the Fig. 1).

[0023] The associated method thus serves to transmit sensor data, in particular from an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle. It begins with the transmission of an ultrasonic burst (step α of the Fig. 1). This is followed by receiving an ultrasonic signal and forming an electrical reception signal (step β of the Fig. 1) and performing a data compression of the received signal (step γ of the Fig. 1)to generate compressed data (step γ of the Fig. 1) and the detection of at least two or three or more predetermined properties. Preferably, the electrical received signal is detected by sampling (step ya of Fig. 2) into a sampled received signal consisting of a time-discrete stream of samples. Each sample can typically be assigned a sampling time as a timestamp of this sample. The compression can be achieved, for example, by a wavelet transformation (step yb of the Fig. 2). For this purpose, the received ultrasound signal in the form of the sampled received signal can be compared with predetermined basic signal forms, which are stored, for example, in a library, by forming a correlation integral (see also Wikipedia for this term) between the predetermined basic signal forms and the sampled received signal. The predetermined basic signal forms are also referred to below as signal object classes. By forming the correlation integral, the corresponding spectral values ​​of this prototypical signal object class are determined for each of these prototypical signal object classes. Since this happens continuously, the spectral values ​​themselves represent a stream of time-discrete, instantaneous spectral values, whereby each spectral value can in turn be assigned a timestamp.An alternative, but mathematically equivalent method is the use of optimal filters (matched filters) for each predetermined signal object class (basic signal form). Since several prototypical signal object classes are usually used, which can also be subjected to different temporal spreads (see also “wavelet analysis”), this typically results in a time-discrete stream of multidimensional vectors of spectral values ​​of different prototypical signal object classes and their different respective temporal spreads. Each of these multidimensional vectors is in turn assigned a timestamp. Each of these multidimensional vectors is a so-called feature vector. This is therefore a time-discrete stream of feature vectors. Each of these feature vectors is preferably in turn assigned a timestamp. (Step yb of the . Fig. 2)

[0024] The continuous temporal shift thus also results in a temporal dimension. This allows the feature vector of the spectral values ​​to be supplemented with past values ​​or values ​​that depend on them, for example, temporal integrals or derivatives or filter values ​​of one or more of these values, etc. This can further increase the dimension of these feature vectors within the feature vector data stream. To keep the effort required to a minimum, it is therefore advisable to limit the extraction of the feature vectors from the sampled input signal of the ultrasonic sensor to a few prototypical signal object classes. This allows, for example, matched filters to be used to continuously monitor the occurrence of these prototypical signal object classes in the received signal.

[0025] Particularly simple prototypical signal object classes include the isosceles triangle and the double-pointed triangle. A prototypical signal object class typically consists of a given spectral coefficient vector, i.e., a given prototypical feature vector value.

[0026] To determine the relevance of the spectral coefficients of a feature vector of an ultrasonic echo signal, the magnitude of a distance of these properties, the elements of the vector of the instantaneous spectral coefficients (feature vector), to at least one prototypical combination of these properties (prototype) in the form of a prototypical signal object class is determined, which is symbolized by a given prototypical feature vector (prototype or prototype vector) from a library of given prototypical signal object class vectors (step γd of the Fig. 2). Preferably, the spectral coefficients of the feature vector are normalized before correlation with the prototypes (step yc of the Fig. 2). The distance determined in this distance determination can, for example, consist of the sum of all differences between a spectral coefficient of the given prototypical feature vector (prototype or prototype vector) of the respective prototype and the corresponding normalized spectral coefficient of the current feature vector of the ultrasound echo signal. A Euclidean distance would be calculated by the square root of the sum of the squares of all differences between a spectral coefficient of the given prototypical feature vector (prototype or prototype vector) of the prototype and the corresponding normalized spectral coefficient of the current feature vector of the ultrasound echo signal. However, this distance calculation is generally too complex. Other methods of distance calculation are conceivable. Each given prototypical feature vector (prototype or prototype vector) can then be assigned a symbol and, if necessary, also a parameter, e.g.the distance value and / or the amplitude are assigned before normalization. If the distance determined in this way falls below a first threshold and is the smallest distance between the current feature vector value and one of the predefined prototypical feature vector values ​​(prototypes or values ​​of the prototype vectors), its symbol is used as the recognized prototype. This creates a pairing of the recognized prototype and the timestamp of the current feature vector. The data is then transmitted preferentially (step δ of the . Fig. 2), here the determined symbol that best symbolizes the recognized prototype, and for example the distance as well as the time of occurrence (time stamp) to the computer system only if the value of this distance is below the first threshold and the recognized prototype is a prototype to be transmitted. It may be that unrecognizable prototypes are also stored, for example for noise, e.g. the absence of reflections, etc. This data is irrelevant for obstacle detection and should therefore not be transmitted. A prototype is therefore recognized if the value of the determined distance between the current feature vector value and the specified prototypical feature vector value (prototype or value of the prototype vector) is below this first threshold (step γe of the Fig. 2). Therefore, the ultrasonic echo signal itself is no longer transmitted, but only a sequence of symbols for recognized typical temporal signal profiles and time stamps associated with these signal profiles in a specific time period (step δ of the Fig. 2). Preferably, only one symbol for the detected signal prototype, its parameters (e.g., envelope amplitude (of the ultrasonic echo signal) and / or temporal extension), and a temporal reference point of the occurrence of this signal prototype (the timestamp) as the detected signal object are transmitted for each detected signal object. The transmission of individual sample values ​​or times at which threshold values ​​are exceeded by the envelope of the sampled received signal (of the ultrasonic echo signal), etc., is eliminated. This selection of the relevant prototypes leads to massive data compression and a reduction in the required bus bandwidth.

[0027] Thus, the presence of a combination of properties is quantitatively recorded by forming an estimated value—here, for example, the inverse distance between the representative of the prototypical signal object class in the form of the specified prototypical feature vector (prototype or prototype vector)—and the compressed data is subsequently transmitted to the computer system if the magnitude of this estimated value (e.g., inverse distance) is above a second threshold or the inverse estimated value is below a first threshold. The signal processing unit of the ultrasonic sensor thus performs data compression of the received signal to generate compressed data.

[0028] For greater clarity, the distance determination will be explained again here.

[0029] This distance determination is also known as classification in the signal processing of statistical signals. Examples of classifiers include logistic regression, the cuboid classifier, the distance classifier, the nearest neighbor classifier, the polynomial classifier, the clustering method, artificial neural networks, and latent class analysis.

[0030] A classifier example is in Fig. 7 shown.

[0031] An exemplary physical interface (101) controls an exemplary ultrasonic transducer (100) and causes it to transmit an exemplary ultrasonic transmission pulse or ultrasonic transmission burst. The ultrasonic transducer (100) receives the signal from a non- Fig. 7, with an amplitude change, a delay, and typically with a distortion caused by the nature of the reflecting object. In addition, there are typically several objects in the vicinity of the vehicle that contribute to modifying the reflected ultrasonic wave. The ultrasonic transducer (100) converts the received reflected ultrasonic wave into an ultrasonic transducer signal (102). The physical interface converts this ultrasonic transducer signal, typically by filtering and / or amplification, into an ultrasonic echo signal (1). The feature vector extractor (111) extracts this ultrasonic echo signal (1). Preferably, the physical interface (101) transmits the ultrasonic echo signal (1) to the feature vector extractor (111) as a time-discrete received signal consisting of a sequence of sample values.Each sample value is preferably assigned a time date (time stamp). In the example, the feature vector extractor (111) has the . Fig. 7 a plurality of m (where m is a positive integer) optimal filters (optimal filter 1 to optimal filter m). These serve to determine m intermediate parameter signals (123), each relating to the presence of a respective signal base object preferably assigned to the respective intermediate parameter signal, preferably with the aid of a suitable filter (e.g., an optimal filter) from the sequence of sample values ​​of the ultrasonic echo signal (1). The resulting intermediate parameter signals (123) are also designed as a time-discrete sequence of respective intermediate parameter signal values, which are preferably each correlated with a date (time stamp). Thus, preferably, each intermediate parameter signal value is assigned exactly one time date (time stamp).

[0032] A subsequent significance enhancer (125) performs a matrix multiplication of the vector of intermediate parameter signal values ​​of the intermediate parameter signal (123) with a so-called LDA matrix (126). This is typically determined at the time of construction using statistical methods of statistical signal processing and pattern recognition. The significance enhancer thus generates the feature vector signal (138). It maps the m intermediate parameter signal values ​​to n (where n is an integer) parameter signal values ​​of the feature vector signal (138). Typically, the feature vector signal (138) comprises n parameter signals. Preferably, n <m. An dieser Stelle sei nur angemerkt, dass der Begriff „Feature-Vektor“ in der statistischen Signaltheorie und in der Mustererkennung auch oft als Merkmalsvektor bezeichnet wird. Das Feature-Vektor-Signal (138) ist somit u.a.as a time-discrete sequence of feature vector signal values, each with n parameter signal values ​​of the preferably n parameter signals of the feature vector signal (138), which comprise the parameter signal values ​​and further parameter signal values, each with the same time date (time stamp). Here, n is the dimension of the individual feature vector signal values, which are preferably the same from one feature vector value to the next. A feature vector signal value in this sense is a vector with a time stamp that comprises several, preferably n, parameter signal values. This respective time date (time stamp) is assigned to each feature vector signal value thus formed.

[0033] This is followed by the evaluation of the temporal course of the feature vector signal (138) in the resulting n-dimensional phase space and the conclusion about a recognized signal base object by determining an evaluation value (e.g. the distance).

[0034] For this purpose, a distance determiner (or classifier) ​​(112) compares the current feature vector signal value of the feature vector signal (138) with a plurality of prototypical feature vector signal value prototypes previously stored in a prototype database (115). This will be explained in more detail below. The distance determiner (or classifier) ​​(112) determines an evaluation value for each of the examined feature vector signal value prototypes of the prototype database (115), which indicates the extent to which the respective feature vector signal value prototype of the prototype database (115) resembles the current feature vector signal value. This evaluation value is referred to below as the distance. The feature vector signal value prototypes are preferably each assigned to exactly one basic signal object.The feature vector signal value prototype from the prototype database (115) with the smallest distance to the current feature vector signal value then best matches it. If the distance is smaller than a predefined threshold, this feature vector signal value prototype from the prototype database (115) represents the signal base object (121) detected with the highest probability.

[0035] This recognition process is carried out several times in succession, so that a determined signal basic object sequence results from the temporal sequence of the recognized signal basic objects (121).

[0036] The likely signal object (122) is then determined by determining the sequence of predefined signal object sequences from a signal object database (116) that is most similar to the determined signal object sequence. As previously explained, a signal object consists of a temporal sequence of signal object sequences. A predefined symbol in the signal object database (116) is typically assigned to the signal object.

[0037] For example, this estimation of the signal basic object sequence can be performed by a Viterbi estimator (113). In the simplest case, the number of signal basic objects detected within a given time period that, according to their position in the sequence of detected signal basic objects, match the position of an expected signal basic object in an expected sequence of signal basic objects specified as a given signal object in the signal object database (116) minus the number of signal basic objects detected within the given time period that, according to their position in the sequence of detected signal basic objects, do NOT match the position of an expected signal basic object in an expected sequence of signal basic objects specified as a given signal object in the signal object database (116), is taken as the evaluation value for the match.In this way, the Viterbi estimator determines an evaluation value for each of the predetermined signal objects of the signal object database (116), wherein the signal objects of the signal object database (116) consist of predetermined sequences of expected basic signal objects and are correlated with a respective symbol.

[0038] Figuratively speaking, a check is carried out here to determine whether the point to which the n-dimensional feature vector signal (138) points in the n-dimensional phase space, on its path through the n-dimensional phase space in a predetermined temporal sequence, approaches predetermined points in this n-dimensional phase space closer than a predetermined maximum distance. The feature vector signal (138) therefore has a temporal profile. An evaluation value (e.g. a distance) is then calculated, which can, for example, represent the probability of the presence of a certain sequence. This evaluation value, which is again assigned a temporal date (time stamp), is then compared with a threshold vector, preferably within the Viterbi estimator (113), to form a Boolean result that can have a first and a second value.If this Boolean result has the first value for this time date (time stamp), the symbol of the signal object and the time date (time stamp) associated with this symbol are transmitted from the sensor to the computer system. This transmits the detected signal object (122) preferentially with its parameters. Depending on the detected signal object, additional parameters may be transmitted.

[0039] At this point, for greater clarity, we will once again discuss the processing of the feature vector signal (138). Preferably, the ultrasonic echo signal (1) is a sequence of quantization vectors—the ultrasonic echo signal values—whose components, the measured parameters, are generally not completely independent of each other. Each ultrasonic echo signal value of an ultrasonic echo signal (1) on its own generally exhibits insufficient selectivity for accurate signal object detection in complex contexts.

[0040] This is precisely the deficiency in the prior art. By creating one or more such quantization vectors from the continuous stream of analog physical values ​​of the ultrasonic echo signal (1) at typically regular or regular time intervals through the physical interface (101) (see Fig. 7) the time- and value-quantized multidimensional ultrasonic echo signal value data stream is created in the form of the ultrasonic echo signal (1).

[0041] The resulting multidimensional ultrasound echo signal (1) in the form of one or more streams of quantization vectors is, in a first exemplary processing step, first divided into individual frames of defined length, filtered, normalized, then orthogonalized and, if necessary, suitably distorted by a non-linear mapping - e.g., logarithmization and cepstrum analysis. This is achieved by the optimal filter block in the feature vector extractor (111) of the Fig. 7. Instead of the optimal filter block, other signal processing structures can also be imagined here for generating the intermediate parameter signals (123). For example, derivatives of the thus generated ultrasound echo signal values ​​can also be formed here. Finally, a significance enhancement of the determined intermediate parameter signal (123) takes place in a significance enhancement unit (125) to form the actual feature vector signal (138). This can be achieved, as described, for example, by multiplying the multidimensional quantification sector by a so-called predefined LDA matrix (126).

[0042] The following step of detection in the distance finder (or classifier) ​​(112) can be carried out, for example, in two different methods: a) by a neural network or b) by an HMM recognizer c) by a Petri net

[0043] Here again the HMM recognizer ( Fig. 7) described: Using the aforementioned predefined LDA matrix (126), the intermediate parameter data stream (123) is mapped from the multidimensional input parameter space to a new parameter space by the significance enhancer (125), thereby maximizing its selectivity. The components of the newly obtained transformed feature vectors are selected not according to real physical or other parameters, but according to maximum significance, resulting in said maximum selectivity.

[0044] The LDA matrix (126) is usually calculated beforehand at the time of construction by a training step offline based on example data streams with known signal object data sets, i.e. data sets that were obtained with given structures of the signal curve.

[0045] If care is taken that all elements of the method performed by the distance finder (or the classifier) ​​(112) perform at least locally reversible functions, deviations of the signal curve can be taken into account in the form of an approximately linear transformation function.

[0046] The prototypes from example data streams for the given prototypical signal patterns (prototypical signal base objects) in the coordinates of the new parameter space are calculated during the design phase and stored in a prototype database (115) for later recognition. In addition to this statistical data, this database can also contain instructions for a computer system regarding what should happen in the event of a successful or failed recognition of the respective signal base object prototype. Typically, this computer system will be the computer system of the sensor system.

[0047] The feature vector signal values ​​of the feature vector signal (138) which are output by the feature extractor (111) for these prototypical signal curves of the given basic signal objects of the ultrasonic echo signal (1) in the laboratory are thus saved in this prototype database (115) as basic signal object prototypes.

[0048] In later operation, the feature vector signal values ​​of the feature vector signal (138) are now compared with these previously stored, i.e., learned, basic signal object prototypes (115), for example, by calculating the Euclidean distance between a quantization vector in the coordinates of the new parameter space and all of these previously stored basic signal object prototypes (115) in the distance detector (112). At least two detections are performed: 1. Does the detected feature vector value of the feature vector signal (138) correspond to one of the pre-stored signal basic object prototypes of the prototype database (115) or not and with what probability and reliability? 2. If it is one of the already stored signal basic object prototypes of the prototype database (115), which one is it and with what probability and reliability?

[0049] To facilitate initial detection, dummy prototypes are usually stored in the prototype database (115) of the basic signal object prototypes, which should cover all parasitic parameter combinations encountered during operation to the greatest extent possible. These basic signal object prototypes are stored in the prototype database (115).

[0050] For the basic signal object prototypes in the prototype database (115), the distance can be determined at the time of design according to the method applied in the distance determiner (112) for each pairing of two different basic signal object prototypes in the prototype database (115). This then results in a minimum prototype distance. This is preferably also stored in the prototype database (115) or in the distance determiner, halved as half the minimum prototype distance.

[0051] For example, if this minimum half-prototype distance is undershot by the distance determined by the distance determination (112) between the current feature vector signal value of the feature vector signal (138) and a basic signal object prototype in the prototype database (115), this basic signal object prototype is considered recognized. From this point on, it can be ruled out that further distances to other basic signal object prototypes in the prototype database (115) calculated during a further search could yield even smaller distances. The search can then be aborted, which halves the time on average and thus conserves sensor resources.

[0052] The calculation of the minimum Euclidean distance can be done, for example, using the following formula: DistFV_CbE=MinCb_cnt=Cb_anz1[∑dim_cnt=dim1(FVdim_cnt−CbCb_cnt,din_cnt)2]

[0053] Here, dim_cnt stands for the dimension index that runs up to the maximum dimension of the feature vector (138) dim.

[0054] FV dim_cnt stands for the parameter value of the feature vector signal value of the feature vector (138) corresponding to the index dim_cnt.

[0055] Cb_cnt stands for the number of the signal basic object prototype in the prototype database (115).

[0056] Cb CB_cnt,dim_cnt accordingly stands for the parameter value corresponding to dim_cnt of the entry of the signal basic object prototype in the prototype database (115) which is assigned to the signal basic object prototype corresponding to Cb_cnt.

[0057] Dist FV_CbE represents the obtained minimum Euclidean distance, which is an example here. When searching for the smallest Euclidean distance, the number Cb_cnt that produces the smallest distance is noted.

[0058] For clarification, an example assembly code is provided:

[0059] The confidence measure for a correct detection is derived from the dispersion of the underlying base data streams for a signal basic object prototype and the distance of the current feature vector value of the feature vector signal (138) from their center of gravity.

[0060] Fig. Figure 8 demonstrates various detection cases. For simplicity, the representation is chosen for a two-dimensional feature vector signal, where each feature vector signal value comprises a first parameter value and a second parameter value. This serves only to better illustrate the methodology on a two-dimensional sheet of paper. In reality, the feature vector signal values ​​of the feature vector signal (138) are typically always multidimensional.

[0061] The centers of gravity of various prototypes (141, 142, 143, 144) are shown. As described above, half the minimum distance between these basic signal object prototypes can now be stored in the prototype database (115). This would then be a global parameter that would be equally valid for all basic signal object prototypes in the prototype database (115). This decision is made using this minimum distance. However, it presupposes that the dispersion of the basic signal object prototypes, which are marked by their center of gravity positions (141, 142, 143, 144), are more or less identical. If the distance determination (or other evaluation) by the distance determiner (112) (or classifier) ​​is optimal, then this is also the case. This would correspond to a circle around the center of gravity (141, 142, 142, 144) of each of the signal basic object prototypes of the prototype database (115).

[0062] However, this is rarely achievable in reality. An improvement in recognition performance can therefore be achieved if the scatter width for the basic signal object prototype in the prototype database (115) were also stored. This would correspond to a circle around each of the basic signal object prototypes in the prototype database (115) with a radius specific to the basic signal object prototype. The disadvantage is an increase in computing power.

[0063] A further improvement in recognition performance can be achieved if the scatter width for the basic signal object prototype in the prototype database (115) is modeled using an ellipse. Instead of the radius as before, the major axis diameters of the scatter ellipse and their tilt relative to the coordinate system must now be stored in the prototype database (115) for each of the basic signal object prototypes in the prototype database (115). The disadvantage is a further, massive increase in computing power and memory requirements.

[0064] Of course, the calculation can be further complicated, but this usually only increases the effort massively and does not significantly increase the recognition performance for the signal basic object prototypes of the prototype database (115).

[0065] It is therefore recommended to use the simplest of the options described.

[0066] The position of the distance determination (112) in the exemplary two-dimensional parameter space of the Fig. The current feature vector signal values ​​of the feature vector signal (138) determined by 8 can now be highly different. It is conceivable that such a first exemplary feature vector signal value (146) lies too far from the center of gravity coordinates (141, 142, 143, 144) of the center of gravity of any basic signal object prototype in the prototype database (115). This distance threshold can, for example, be the aforementioned minimum half prototype distance. It is also possible that the scatter ranges of the basic signal object prototypes overlap around their respective centers of gravity (143, 142), and a second exemplary determined current feature vector signal value (145) of the feature vector signal (138) lies in the overlap range. In this case, a hypothesis list can contain both basic signal object prototypes with different probabilities as an attached parameter, due to different distances.Thus, instead of passing a single signal base object as the most probable one to the Viterbi estimator (113), a vector of potentially present signal base objects is passed to the Viterbi estimator (113). From the chronological sequence of these hypothesis lists, the Viterbi estimator then searches for the possible sequence that has the greatest probability of one of the given signal base object sequences in its signal object database compared to all possible paths through the signal base objects (121) recognized as possible from the hypothesis lists received from the distance determiner (112) by the Viterbi estimator (113). In such a path, exactly one recognized signal base object prototype from this hypothesis list must be traversed through this path for each hypothesis list.

[0067] In the best case, the current feature vector signal value (148) lies in the scatter range (threshold ellipsoid) (147) around the center of gravity (141) of a single signal basic object prototype (141), which is thus reliably recognized by the distance detector (112) and passed on to the Viterbi estimator (113) as a recognized signal basic object (121).

[0068] For improved modeling of the scatter range of an individual basic signal object prototype, it is conceivable to model it using multiple, here circular, signal object prototypes with associated scatter ranges. Thus, multiple signal object prototypes in the prototype database (115) can represent the same basic signal object prototype in the sense of a basic signal object class. The risk here is that, due to the distribution of the probability of a basic signal object prototype among multiple such sub-basic signal object prototypes, the probability of the individual sub-basic signal object prototype may become smaller than that of another basic signal object prototype whose probability was smaller than that of the original basic signal object prototype. Thus, this other basic signal object prototype may falsely prevail.

[0069] Another major problem is the computing power required to reliably recognize the signal basic object prototypes from the prototype database (115). This will be discussed briefly:

[0070] A crucial point is that the computational effort increases with Cb_anz * dim.

[0071] For a non-optimized HMM recognizer, the number of assembly instructions that must be executed to calculate a vector component is approximately 8 steps.

[0072] The number A_Abst of necessary assembly steps to calculate the distance of a single signal basic object prototype (CbE) of the prototype database to a single feature vector signal value (FV) is calculated approximately as follows: A_Abst=FV_Dimension*8+8

[0073] This results in the number A_CB of assembler steps for determining the signal basic object prototype of the prototype database (115) with the smallest distance: A_CB=Cb_anz*(A_Abst)+4=Cb_anz*(FV_Dimension*8+8)+4

[0074] Using the example of a medium-sized HMM recognizer with 50,000 signal basic object prototypes (number of signal basic object prototype entries in the prototype database = CB_anz) and 24 FV_dimensions (number of parameter values ​​in a feature vector signal value = feature vector dimension = FV_dimension), the number of steps is: 50000*(24*8+8)+4∼10 million operations per feature vector signal value of the feature vector signal(138)

[0075] At a relatively low sampling rate of 8kHz = 8000 FV per second (feature vector signal values ​​of the feature vector signal (124) per second), a computing power of 8GIpS (8 billion instructions per second) is required.

[0076] Given the challenges of saving energy in electromobility and / or reducing the CO2 footprint, this is not acceptable.

[0077] When an optimized HMM recognition method is performed by the distance detector (112) or the classifier (112), the smallest distance between two signal basic object prototypes of the prototype database (115) is precalculated, as already mentioned, and stored in the prototype database (115) or in the distance calculation (112). This has the advantage that the search can be aborted by the distance detector (112) if a distance between a current feature vector signal value of the feature vector signal (138) and a signal basic object prototype of the prototype database (115) is found by the distance detector (115) that is less than half of this smallest distance. This halves the average search time for the distance detector (112).Further optimizations can be achieved by sorting the prototype database (115) according to the statistical occurrence of the signal base object prototypes in real ultrasonic echo signals (1). This ensures that the most common signal base object prototypes are found much faster, which further shortens the computing time of the distance detector (112) or classifier (112) and further reduces power consumption.

[0078] For a distance finder executing such an optimized HMM detection process, the computing power requirements are now as follows: Again, there are 8 steps to calculate the distance of a vector component. The steps for calculating the distance A_Distance from a signal basic object prototype entry (CbE) in the prototype database (115) to the current feature vector signal value (FV) of the feature vector signal (138) are again: A_Abst=FV_Dimension*8+8

[0079] The number of steps for determining the signal basic object prototype entry of the prototype database (115) with the smallest distance A_CB with optimization is slightly higher: A_CB=Cb_anz*(A_Abst)+4=Cb_anz*(FV_Dimension*8+10)+4

[0080] The two additional assembler instructions are necessary to check whether the determined distance between the current feature vector signal value and the currently examined signal basic object prototype of the prototype database (115) is below half the smallest distance between the signal basic object prototypes of the prototype database (115).

[0081] Furthermore, the number CB_Anz of the signal basic object prototype entries of the prototype database (115) for mobile and energy-autonomous applications is limited to 4000 prototype database entries of signal basic object prototypes in the prototype database (115) or even less.

[0082] In addition, the number of feature vector signal values ​​per second within the feature vector signal (138) is reduced by filtering in the feature vector extractor (111) and reducing the sampling rate in the feature vector extractor (111).

[0083] This is explained using a simple example: The said distance finder (112) or classifier (112), which executes a medium HMM recognition method, is now operated with a prototype database (115) with only a tenth of the entries, e.g. with 4000 entries (CbE) and still with 24 feature vector signal dimensions (i.e. 24 parameter signals).

[0084] The number of steps is now 4000*(24*8+10)+4∼808004_ operations per feature vector signal value of the feature vector signal(138)

[0085] If the feature vector signal value rate is reduced to 100 feature vector signal values ​​per second extracted from a 10 ms time window in the feature vector extractor (111) over, for example, 80 sample values ​​each and the search is aborted when the distance of the current feature vector signal value to the processed signal basic object prototype of the prototype database (115) is less than half the smallest prototype database entry distance, the effort is at least halved with suitable sorting of the prototype database (115).

[0086] The required computing power then drops to <33 DSP MIPS (33 million operations per second). In reality, prototype database sorting results in even lower computing power requirements, for example, 30 MIPS. This makes the system real-time capable and allows it to be integrated into a single IC, and thus into a sensor.

[0087] The search space can be narrowed down by pre-selection. A prerequisite for this is an even distribution of the data, i.e., the centroids of the quadrants are in the geometric quadrant center.

[0088] The necessary reduction in the size of the prototype database (115) has advantages and disadvantages: Reducing the number of entries in the prototype database (115) increases both the false acceptance rate (FAR), i.e. the incorrect signal basic object prototypes that are accepted as signal basic object prototypes, and the false rejection rate (FRR), i.e. the actually existing signal basic object prototypes that are not recognized.

[0089] On the other hand, this reduces the resource requirements (computing power, chip area, memory, power consumption, etc.).

[0090] Furthermore, the history, ie, the previously detected signal basic object prototypes, can be used in hypothesis formation by the distance estimator (112). A suitable model for this is the so-called Hidden Markov Model (HMM).

[0091] For each signal basic object prototype, a confidence measure and a distance to the measured current feature vector signal value of the feature vector signal (138) can be derived, which can then be further processed by the Viterbi estimator (113). It is also useful to output a hypothesis list for each detected signal basic object prototype (121), which, for example, contains the ten most probable signal basic object prototypes with the respective probability and reliability of detection.

[0092] Since not every temporal and spatial signal basic object sequence can be assigned to a signal object and is therefore meaningful, it is possible to evaluate the temporal sequence of the hypothesis lists of the consecutive frames using a Viterbi estimator (113).

[0093] Here, the signal basic object prototype sequence path through the successive hypothesis lists has to be found which has the highest probability and is present in the signal object database (116) of the Viterbi estimator (113).

[0094] Here, too, at least two detections are made: 1. Is the most probable sequence of signal basic object prototypes one of the already stored sequences of signal basic object prototypes or not and with what probability and reliability? 2. If it is one of the already stored sequences of signal basic object prototypes, which one is it and with what probability and reliability?

[0095] For this purpose, on the one hand, such a prototype can be entered into a signal object database (116) in the form of an entry consisting of a predetermined sequence of basic signal object prototypes by means of a training program; on the other hand, this can also be done manually using a type-in ​​tool that enables the input of these sequences of basic signal object prototypes via a keyboard.

[0096] Using the Viterbi estimator (113), the most probable of the predefined sequences of signal basic object prototypes for a sequence of recognized signal basic object prototypes (121) can be determined from the sequence of hypothesis lists of the distance determiner (112) or the classifier (112). This applies in particular if individual signal basic object prototypes were incorrectly recognized by the distance determiner (112) or the classifier (112) due to measurement errors. Therefore, it is very useful for the Viterbi estimator (113) to adopt sequences of signal basic object prototype hypothesis lists from the distance determiner (112) or the classifier (112), as described above. The result is the signal object (122) identified as the most probable or, analogous to the previously described emission calculation of the distance estimator (112) or classifier (112), a signal object hypothesis list.

[0097] Finally, we consider the functional components of the signal object detection engine. This is described in Fig. 7 as a Viterbi estimator (113). This search of the Viterbi estimator (113) accesses the signal object database (116). The signal object database (116) is fed, on the one hand, by a training tool and, on the other hand, by a tool in which these sequences of basic signal object prototypes can be defined through textual input. The possibility of a download in production is mentioned here only for the sake of completeness.

[0098] The basis for sequence recognition for the temporal sequence of the signal basic object prototypes in the Viterbi estimator (119) is, for example, a hidden Markov model. The model is built up from different states. Fig. In the example given in Figure 9, these states are symbolized by numbered circles. In the example in Fig. 9, the circles are numbered from Z1 to Z6. There are transitions between the states. These transitions are described in the Fig. 9 is denoted by the letter a and two indices i, j. The first index i denotes the number of the source node, the second index j the number of the destination node. In addition to the transitions between two different nodes, there are also transitions a ii or a jj which lead back to the starting node. In addition, there are transitions that allow to skip nodes from the sequence, thus resulting in a probability of a k-th observable b k actually observed. This results in sequences of observables that can be calculated with pre-calculable probabilities b k can be observed.

[0099] It is important to note that every Hidden Markov Model consists of unobservable states q i exists. Between two states q i and q jthe transition probability a ij .

[0100] Thus, the probability p for the transition from q i on q j write as: p(qnj|qn−1i)≡aij

[0101] Here, n represents a discrete point in time. The transition takes place between step n and state q i and the step n+1 with the state q j instead of.

[0102] The emission distribution b i (Ge) depends on the state q i As already explained, this is the probability of observing the elementary gesture Ge (the observable) when the system (Hidden Markov Model) is in state q i located: p(Ge|qi)≡bi(Ge)

[0103] To start the system, the initial states must be determined. This is done by a probability vector π i . It can then be stated that a state qi with probability π i an initial state is: p(qi1)≡πi

[0104] It is important that a new model must be created for each sequence of signal basic object prototypes. In a model M, the observation probability for a temporal observation sequence of signal basic object prototypes Ge→=(Ge1,Ge2,…GeN) be determined

[0105] This corresponds to a temporal sequence of states that cannot be directly observed, which corresponds to the following sequence: Q→=(q1,q2,……qN)

[0106] The probability p of observing this state sequence Q, which depends on the model M, the temporal state sequence Q and the temporal observation sequence Ge, is: p(Ge→|Q→,M)=p(Ge1,Ge2,…GeN|q1,q2,……qN)=p(Ge1|q1)⋅p(Ge2|q2)⋅……p(GeN|qN)=∏n=1Np(Gen|qn)=∏n=1Nbn(Gen)

[0107] This results in the probability of a sequence of states Q→=(q1,q2,……qN) in model M: p(Q→|M)=p(q1,q2,……qN|M)=p(q1)⋅p(q2|q1)⋅p(q3|q1,q2)⋅…… p(qN|q1,q2,…qN−1)=p(q1)∏n=2Np(qn|qn−1)=π1∏n=2Na(n−1)n

[0108] Thus, the probability of detecting a signal object is equal to a sequence of signal basic object prototypes (see also Fig. 10): p(Ge→|Mj)=∑allQkp(Ge→|Qk,Mj)p(Q→k|Mj)=∑allQk(∏n=1Nbn(Gen))(π1∏n=2Na(n−1)n)

[0109] The most probable signal object model (signal object) for the observed emission Ge is determined by summing the individual probabilities over all possible paths Q k that lead to this observed sequence of signal basic object prototypes Ge. p(Ge→|Mj)=∑allQkp(Ge→|Qk,Mj)p(Q→k|Mj)=∑allQk(∏n=1Nbn(Gen))(π1∏n=2Na(n−1)n)

[0110] The summation over all possible paths Q is not without its problems due to the potential computational effort. Therefore, the process is usually aborted very early. It is therefore suggested to consider only the most probable path Q k to use. This will be discussed below.

[0111] The calculation is done by recursive calculation. The probability a n (i) at time n the system is in state q i to observe can be calculated as follows: αn(i)=p(Ge1,Ge2,……Gen:qn=qi)≡p(Gein,qni)αn+1(j)=[∑i=1Sαn(i)⋅aij]bj(Gen+1)

[0112] Here, all S possible paths leading to the state q are summed up. i+1 lead in

[0113] It is assumed that the total probability in the state q i n+1 to reach the best path is dominated. Then the sum can be simplified with little error. αn+1*(j)=[maxi(αn*(i)⋅aij)]bj(cn+1)

[0114] By tracing back from the last state, you now get the best path.

[0115] The probability of this path is a product. Therefore, a logarithmic calculation reduces the problem to a pure summation problem. The probability of detecting a signal object corresponds to the detection of a model M j corresponds to the determination of the most probable signal object model for the observed emission X. This is now done exclusively via the best possible path Q best p(Ge→|Mj)=∑allQk(∏n=1nbn(Gen))(π1∏n=2Na(n−1)n)

[0116] This will then become p(Ge→|Mj)=p(Ge→|Qbest,Mj)p(Q→best|Mj)=exp(ln(π1)+ln(b1(Ge1))+∑n=2Nln(bn(Gen))+ln(a(n−1)n))

[0117] It is now of particular importance that the prototype database (115) contains only signal basic object prototypes.

[0118] The detected signal objects are transmitted with the detected parameters instead of the sampled values. This results in data compression without deviating from the signal character.

[0119] It is particularly important that no detection of objects located in the vicinity of the vehicle takes place here. Rather, structures within the ultrasonic echo signal (1) are detected and used for compression.

[0120] Only this enables the evaluation-free reconstruction of the signal in the control unit after the data has been received.

[0121] In contrast to the state of the art, the aim of the present proposal is not to detect and classify objects located in the vicinity of the vehicle and thereby bring about data compression, but to compress and transmit the ultrasonic echo signal (1) itself with as little loss as possible by limiting it to application-relevant signal form components.

[0122] The ultrasonic sensor then transmits the compressed data, preferably only the coding (symbols) of the detected prototypes, their amplitude and / or temporal extension, and the time of occurrence (time stamp), to the computer system. This minimizes the EMC impact caused by data transmission via the data bus between the ultrasonic sensor and the computer system, and status data from the ultrasonic sensor for system error detection can be transmitted to the computer system in the time intervals via the data bus between the ultrasonic sensor and the computer system, which improves latency. During the development of the proposal, it was recognized that the transmission of data via the data bus must be prioritized. However, this prioritization does not affect prioritization over other bus participants, as is known from the state of the art.Rather, the data connection between the sensor and the vehicle's control computer is usually a point-to-point connection. Therefore, prioritization here should be understood as determining which piece of data acquired by the sensor system must be transmitted to the control unit first. Messages about safety-critical errors from the sensor—in this case, the ultrasonic sensor, for example—to the computer system have the highest priority, as they are highly likely to affect the validity of the ultrasonic sensor's measurement data. This data is sent from the sensor to the computer system. Requests from the computer system to perform safety-relevant self-tests have the second highest priority. Such commands are sent from the computer system to the sensor. The data from the ultrasonic sensor itself has the third highest priority, as the latency cannot be increased.All other data has lower priority for transmission over the data bus.

[0123] It is particularly advantageous if the method for transmitting sensor data, in particular of an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle, comprising the transmission of an ultrasonic burst with a beginning (57) and end (56) of the transmission of the ultrasonic burst and comprising the reception of an ultrasonic signal and forming a reception signal for a reception time (T E) at least from the end (56) of the transmission of the ultrasonic burst and comprising the transmission of the compressed data via a data bus, in particular a single-wire data bus, to the computer system, is designed in such a way that the transmission (54) of the data from the sensor to the computer system begins with a start command (53) from the computer system to the ultrasonic sensor via the data bus and before the end (56) of the transmission of the ultrasonic burst, or begins after a start command (53) from the computer system to the sensor via the data bus and before the start (57) of the transmission of the ultrasonic burst. The transmission (54) then takes place periodically after the start command (53) until the end of the data transmission (58). This end of the data transmission (58) then lies after the end of the reception time (T E ).

[0124] A further variant of the proposed method thus provides the first step of data compression, forming a feature vector signal (a stream of feature vectors with n feature vector values ​​and n as the dimension of the feature vector) from the received signal. Such a feature vector signal can comprise multiple analog and digital data signals. It thus represents a temporal sequence of more or less complex data / signal structures. In the simplest case, it can be understood as a vector signal consisting of several sub-signals.

[0125] For example, it may be useful to form a first and / or higher time derivative of the received signal or the simple or multiple integral of the received signal, which are then sub-signals within the feature vector signal.

[0126] An envelope signal of the received signal (ultrasonic echo signal) can also be formed, which can then be a partial signal within the feature vector signal (138).

[0127] Furthermore, it may be useful to convolve the received signal with the transmitted ultrasonic signal, thus creating a correlation signal, which can then be a sub-signal within the feature vector signal. This can be done either by using the transmitted ultrasonic signal used to control the driver for the transmitter, or by using a signal measured at the transmitter, which better corresponds to the actual emitted sound wave.

[0128] Finally, it can be useful to use matched filters to detect the occurrence of predetermined signal objects and to generate a matched filter signal for the respective signal object of some of the predetermined signal objects. A matched filter is a filter that optimizes the signal-to-noise ratio (SNR). The predefined signal objects are to be detected in the disturbed ultrasound received signal. In the literature, the terms correlation filter, signal-matched filter (SAF), or simply matched filter are also frequently found. The matched filter is used to optimally determine the presence (detection) of the amplitude and / or position of a known signal shape, the predetermined signal object, in the presence of disturbances (parameter estimation).

[0129] These disturbances can be, for example, signals from other ultrasonic transmitters and / or ground echoes.

[0130] The optimal filter output signals are then preferably sub-signals within the feature vector signal.

[0131] Certain events can be signaled in separate sub-signals of the feature vector signal. These events are basic signal objects within the meaning of this disclosure. Basic signal objects therefore do not include signal shapes such as rectangular pulses, wavelets, or wave trains, but rather distinctive points in the course of the received signal and / or in the course of signals derived therefrom, such as an envelope signal (ultrasonic echo signal), which can be obtained from the received signal, for example, by filtering.

[0132] Another signal, which may be a sub-signal of the feature vector signal, can, for example, detect whether the envelope of the received signal, the ultrasonic echo signal, crosses a predetermined third threshold. This is a signal that indicates the presence of a basic signal object within the received signal and thus the feature vector signal.

[0133] Another signal, which may be a sub-signal of the feature vector signal, can, for example, detect whether the envelope of the received signal, the ultrasonic echo signal, crosses a predetermined fourth threshold, which may be identical to the third threshold, in an ascending direction. This is therefore a signal that indicates the presence of a basic signal object within the received signal and thus the feature vector signal.

[0134] Another signal, which may be a sub-signal of the feature vector signal, can, for example, detect whether the envelope of the received signal, the ultrasonic echo signal, crosses a predetermined fifth threshold, which may be identical to the third or fourth threshold, on a downward slope. This is therefore a signal that indicates the presence of a basic signal object within the received signal and thus the feature vector signal.

[0135] Another signal, which may be a sub-signal of the feature vector signal, can, for example, detect whether the envelope of the received signal, the ultrasonic echo signal, exhibits a maximum above a sixth threshold, which may be identical to the aforementioned third to fifth thresholds. This is therefore a signal that indicates the presence of a basic signal object within the received signal and thus the feature vector signal.

[0136] Another signal, which may be a sub-signal of the feature vector signal, can, for example, detect whether the envelope of the received signal, the ultrasonic echo signal, exhibits a minimum above a seventh threshold, which may be identical to the aforementioned third to sixth thresholds. This is therefore a signal that indicates the presence of a basic signal object within the received signal and thus the feature vector signal.

[0137] In this case, it is preferably evaluated whether at least one preceding maximum of the ultrasonic echo signal is at a minimum distance from the minimum to avoid noise detection. Other filtering options are conceivable at this point. It can also be checked whether the time interval between this minimum and a preceding maximum is greater than a first minimum time interval. Fulfillment of these conditions sets a flag or signal, which itself is preferably a sub-signal of the feature vector signal.

[0138] Similarly, it should be checked in an analogous manner whether the temporal and amplitude distances of the other signal objects meet certain plausibility requirements, such as minimum temporal and / or minimum amplitude distances. These tests can also be used to derive additional, analog, binary, or digital sub-signals, thus further increasing the dimensionality of the feature vector signal.

[0139] If necessary, the feature vector signal can be transformed into a significant feature vector signal in a significance enhancement step, e.g., using a linear mapping or a higher-order matrix polynomial. However, practice has shown that this is not yet necessary, at least for current requirements.

[0140] According to the proposed method, the detection and classification of signal objects into detected signal object classes within the received signal follow based on the feature vector signal or the significant feature vector signal.

[0141] If, for example, the amplitude of the output signal of a matched filter, and thus of a sub-signal of the feature vector signal, lies above an eighth threshold value that may be specific to the matched filter, then the signal object that the matched filter is designed to detect can be considered detected. Other parameters are preferably also taken into account. For example, if an ultrasonic burst was transmitted with an increasing frequency during the burst (called chirp-up), then an echo is also expected that exhibits this modulation property. If the signal shape of the ultrasonic echo signal, for example a triangular signal shape of the ultrasonic echo signal, matches an expected signal shape locally in time, but not the modulation property, then it is not an echo from the transmitter, but an interference signal that may originate from other ultrasonic transmitters or from over-range.In this respect, the system can then distinguish between intrinsic and extraneous echoes, whereby one and the same signal shape is assigned to two different signal objects, namely intrinsic and extraneous echoes. The transmission of intrinsic echoes via the data bus from the sensor to the computer system is preferably prioritized over the transmission of extraneous echoes, since the former are generally safety-relevant and the latter are generally not.

[0142] Typically, during detection, at least one assigned signal object parameter is assigned to each detected signal object or determined for this signal object. This is preferably a timestamp that indicates when the object was received. The timestamp can, for example, refer to the temporal start of the signal object in the received signal or the temporal end or the temporal position of the temporal center of gravity of the signal object, etc. Other signal object parameters, such as amplitude, stretching, etc. are also conceivable. In a variant of the proposed method, at least one of the assigned signal object parameters is thus transmitted with a symbol for the at least one detected signal object class. The signal object parameter is preferably a time value as a timestamp and indicates a temporal position that is suitable for inferring the time since the transmission of a previous ultrasound burst.Preferably, this is later used to determine a determined distance of an object as a function of a time value determined and transmitted in this way.

[0143] Finally, the detected signal object classes are transmitted in a prioritized manner in the form of assigned symbols with time stamps, preferably together with the associated signal object parameters. The transmission can also be performed in more complex data structures (records). For example, it is conceivable to first transmit the times of the detected safety-relevant signal objects (e.g., identified obstacles) and then the detected signal object classes of the safety-relevant signal objects. This further reduces latency.

[0144] The proposed method, at least in one variant, comprises determining a chirp value as an associated signal object parameter, which indicates whether the detected signal object is an echo of an ultrasonic transmission burst with chirp-up, a chirp-down, or a no-chirp characteristic. Chirp-up means that the frequency within the received signal object increases in the received signal. Chirp-down means that the frequency within the received signal object decreases in the received signal. No-chirp means that the frequency within the received signal object remains essentially the same in the received signal. Reference is made to the as yet unpublished German patent applications DE 10 2017 100 837.3 and DE 10 2017 100 835.7, which are incorporated in their entirety by this disclosure.

[0145] In one variant of the method, a confidence signal is also generated by forming the correlation, e.g., by forming a continuous-time or discrete-time correlation integral, between the received signal, or instead of the received signal, with a signal derived from the received signal, on the one hand, and a reference signal, e.g., the ultrasonic transmission signal or another expected wavelet, on the other. The confidence signal is then typically a sub-signal of the feature vector signal, i.e., a component of the feature vector, which consists of a sequence of vector samples (feature vector values).

[0146] In a variant of the method, a phase signal is also generated on this basis, indicating the phase shift of, for example, the received signal or a signal generated from it (e.g., the confidence signal) relative to a reference signal, such as the ultrasonic transmitted signal and / or another reference signal. The phase signal is then typically also a sub-signal of the feature vector signal, i.e., a component of the feature vector, which consists of a sequence of vector samples.

[0147] Similarly, in another variant of the proposed method, a phase confidence signal can be generated by establishing the correlation between the phase signal or a signal derived therefrom and a reference signal, and used as a sub-signal of the feature vector signal. The phase confidence signal is then typically also a sub-signal of the feature vector signal, i.e., a component of the feature vector consisting of a sequence of vector samples.

[0148] When evaluating the feature vector signal, it is then useful to compare the phase confidence signal with one or more threshold values ​​to generate a discretized phase confidence signal, which itself can become a sub-signal of the feature vector signal.

[0149] In a variant of the proposed method, the evaluation of the feature vector signal and / or the significant feature vector signal can be performed by forming one or more distance values ​​between the feature vector signal and one or more signal object prototype values ​​for recognizable signal object classes. Such a distance value can be Boolean, binary, discrete, digital, or analog. Preferably, all distance values ​​are linked to one another in a nonlinear function. Thus, if a triangular chirp-up echo is expected, a triangular chirp-down echo received can be discarded. This discarding is a nonlinear process within the meaning of this disclosure.

[0150] Conversely, the triangle in the received signal can vary in its form. This primarily affects the amplitude of the triangle in the received signal. If the amplitude in the received signal is sufficient, the optimal filter assigned to this triangular signal, for example, delivers a signal above a predefined ninth threshold. In this case, a detected signal object can then be assigned to this signal object class (triangular signal) at the time of crossing. In this case, the distance value between the feature vector signal and the prototype (here, the ninth threshold) falls below one or more predetermined binary, digital, or analog distance values ​​(here, 0 = intersection).

[0151] In another variant of the method, at least one signal object class is a wavelet, which is estimated and thus detected by estimation devices (e.g., optimal filters) and / or estimation methods (e.g., estimation programs running in a digital signal processor). The term wavelet refers to functions that can be used as the basis for a continuous or discrete wavelet transform. The word "wavelet" is a neologism from the French "ondelette," meaning "small wave," and has been translated into English partly literally ("onde" → "wave") and partly phonetically ("-lette" → "-let"). The term "wavelet" was coined in the 1980s in geophysics (Jean Morlet, Alex Grossmann) for functions that generalize the short-time Fourier transform, but since the late 1980s it has been used exclusively in its current sense.In the 1990s, a veritable wavelet boom emerged, triggered by the discovery of compact, continuous (up to arbitrary order of differentiability) and orthogonal wavelets by Ingrid Daubechies (1988) and the development of the Fast Wavelet Transform (FWT) algorithm using multiscale analysis (MultiResolution Analysis - MRA) by Stéphane Mallat and Yves Meyer (1989).

[0152] In contrast to the sine and cosine functions of the Fourier transform, the most commonly used wavelets exhibit locality not only in the frequency spectrum but also in the time domain. "Locality" is understood here as small dispersion. The probability density is the normalized square of the magnitude of the function under consideration or of its Fourier transform. The product of both variances is always greater than a constant, analogous to the Heisenberg uncertainty principle. This limitation gave rise to the Paley-Wiener theory (Raymond Paley, Norbert Wiener), a precursor to the discrete wavelet transform, and the Calderón-Zygmund theory (Alberto Calderón, Antoni Zygmund), which corresponds to the continuous wavelet transform, in functional analysis.

[0153] Although the integral of a wavelet function is always 0 in technical terms, the wavelet function usually takes the form of outwardly tapering (decreasing) waves (i.e., "wavelets" = ondelettes = wavelets). For the purposes of this disclosure, however, wavelets with an integral other than 0 are also permissible. Examples include the rectangular and triangular wavelets described below. This further interpretation of the term "wavelet" is common in the American language and is therefore well known. This further interpretation shall also apply here.

[0154] Important examples of wavelets with a 0-integral are the Haar wavelet (Alfréd Haar 1909), the Daubechies wavelets named after Ingrid Daubechies (around 1990), the Coiflet wavelets also constructed by her, and the more theoretically important Meyer wavelet (Yves Meyer, around 1988).

[0155] Wavelets exist for spaces of any dimension, typically using a tensor product of a one-dimensional wavelet basis. Due to the fractal nature of the two-scale equation in MRA, most wavelets have a complex shape; most do not have a closed form. This is particularly important because the aforementioned feature vector signal is multidimensional, thus allowing the use of multidimensional wavelets for signal object detection.

[0156] A special variant of the proposed method therefore involves the use of multidimensional wavelets with more than two dimensions for signal object detection. In particular, the use of appropriate optimal filters for detecting such wavelets with more than two dimensions is proposed in order to supplement the feature vector signal with additional subsignals suitable for detection, if necessary.

[0157] A particularly suitable wavelet, for example, is a triangular wavelet. This wavelet is characterized by a starting time of the triangular wavelet, a temporally essentially linear increase in the wavelet amplitude up to a maximum of the triangular wavelet's amplitude following the starting time of the triangular wavelet, and a temporally essentially linear decrease in the wavelet amplitude up to the end of the triangular wavelet following the maximum of the triangular wavelet.

[0158] Another particularly suitable wavelet is a rectangular wavelet, which, within the meaning of this disclosure, also includes trapezoidal wavelets. A rectangular wavelet is characterized by a starting time of the rectangular wavelet, which is followed by an increase in the wavelet amplitude of the rectangular wavelet with a first temporal steepness of the rectangular wavelet up to a first plateau time of the rectangular wavelet. The first plateau time of the rectangular wavelet is followed by a persistence of the wavelet amplitude with a second steepness of the wavelet amplitude up to a second plateau time of the rectangular wavelet. The second plateau time of the rectangular wavelet is followed by a decrease with a third temporal steepness up to the temporal end of the rectangular wavelet. The magnitude of the second temporal steepness is less than 10% of the magnitude of the first temporal steepness and less than 10% of the magnitude of the third temporal steepness.

[0159] Instead of the wavelets described above, it is also possible to use other two-dimensional wavelets, such as a half-sine wavelet, which also has an integral not equal to 0.

[0160] It is proposed that, when using wavelets, the temporal shift of the respective wavelet of the detected signal object be used as a signal object parameter. For example, this shift can be determined by correlation. It is further proposed that, when using wavelets, the point in time at which the output level of an optimal filter suitable for detecting the respective wavelet exceeds a predefined tenth threshold value for this signal object or this wavelet is preferably used. Preferably, the ultrasonic echo signal (the envelope) of the received signal and / or a phase signal and / or a confidence signal, etc., are evaluated.

[0161] Another possible signal object parameter that can be determined is a temporal compression or expansion of the respective wavelet of the detected signal object. The amplitude of the wavelet of the detected signal object can also be determined.

[0162] During the development of the proposal for the method disclosed here, it was recognized that it is advantageous to first transmit the data of the detected signal objects from the very rapidly arriving echoes from the sensor to the computer system and only then the subsequent data of the later detected signal objects. Preferably, at least the detected signal object class and a timestamp are always transmitted, which should preferably indicate when the signal object arrived back at the sensor. During the detection process, the various signal objects that are considered for a section of the received signal can be assigned scores that indicate the probability assigned to the presence of this signal object according to the estimation algorithm used. In the simplest case, such a score is binary. However, it is preferably a complex, real, or integer number.This could, for example, be the determined distance. If several signal objects have a high score value, it is sometimes useful to also transmit the data of detected signal objects with lower score values. To enable the computer system to handle this correctly, not only the date (symbol) of the detected signal object and the timestamp for the respective signal object should be transmitted in this case, but also the determined score value. Instead of just transmitting the date (symbol) of the detected signal object and the timestamp for the signal object corresponding to this symbol, the date (symbol) of the signal object with the second smallest distance and its timestamp for the signal object corresponding to this second most likely symbol can also be transmitted.Thus, in this case, a hypothesis list consisting of two detected signal objects and their temporal positions, as well as additionally assigned score values, is transmitted to the computer system. Of course, a hypothesis list consisting of more than two symbols for more than two detected signal objects and their temporal positions, as well as additionally assigned score values, is also possible.

[0163] Preferably, the transmission of data from the detected signal object class and the associated data, such as timestamps and score values ​​of the respective detected signal object classes, i.e., the associated signal object parameters, is carried out according to the FIFO principle. This ensures that the reflection data from the nearest objects is always transmitted first, thus prioritizing the safety-critical case of a vehicle collision with an obstacle based on probability.

[0164] In addition to the transmission of measurement data, the transmission of sensor error states can also take place. This can also occur during a reception time (T E) can occur if the sensor detects through self-test devices that there is a defect and the previously transmitted data could potentially be incorrect. This ensures that the computer system can become aware of any change in the evaluation of the measured data at the earliest possible time and can reject it or handle it differently. This is of particular importance for emergency braking systems, as emergency braking is a safety-critical intervention that may only be initiated if the underlying data has a corresponding trustworthiness level. In contrast, the transmission of the measured data, for example the date of the detected signal object class and / or the transmission of the one assigned signal object parameter, is therefore postponed and thus given a lower priority. Of course, it is conceivable that the transmission could be aborted if an error occurs in the sensor.In some cases, however, it may be the case that an error appears possible, but is not certain. Therefore, continuing the transmission may be advisable in such cases. The transmission of safety-critical sensor errors is therefore preferably given a higher priority.

[0165] In addition to the wavelets with an integration value of 0 already described and the signal sections with an integration value other than 0, which are additionally referred to here as wavelets, specific points in time in the course of the received signal can also be considered basic signal objects within the meaning of this disclosure. These points in time can be used for data compression and can be transmitted instead of samples of the received signal. We will refer to this subset of the set of possible basic signal objects below as signal times. The signal times are therefore a special form of basic signal objects within the meaning of this disclosure.

[0166] A first possible signal time point and thus a signal base object is a crossing of the amplitude of the ultrasonic echo signal (1) with the amplitude of an eleventh threshold signal (SW) in an ascending direction.

[0167] A second possible signal time point and thus a signal base object is a crossing of the amplitude of the ultrasonic echo signal (1) with the amplitude of a twelfth threshold signal (SW) in descending direction.

[0168] A third possible signal time point and thus a signal base object is a maximum of the amplitude of the ultrasonic echo signal (1) above the amplitude of a thirteenth threshold signal (SW).

[0169] A fourth possible signal time point and thus a signal base object is a minimum of the amplitude of the ultrasonic echo signal (1) above the amplitude of a fourteenth threshold signal (SW).

[0170] It may be useful to use signal time point type-specific threshold signals (SW) for these four example types of signal time points and other types of signal time points.

[0171] The temporal sequence of signal basic objects is typically not arbitrary. This is exploited in this disclosure, since the preferred method is not to transmit the signal basic objects, which are of a simpler nature, but rather to transmit recognized patterns of sequences of these signal basic objects, which then represent the actual signal objects. For example, if a triangular wavelet of sufficient amplitude is expected in the ultrasonic echo signal (1), then in addition to a corresponding minimum level at the output of an optimal filter suitable for the detection of such a triangular wavelet, 1. the occurrence of a first possible signal time with a crossing of the amplitude of the ultrasonic echo signal (1) with the amplitude of a threshold signal (SW) in an ascending direction and following this in time 2. the occurrence of a third possible signal time with a maximum of the amplitude of the ultrasonic echo signal (1) above the amplitude of a threshold signal (SW) and following it in time 3. the occurrence of a second possible signal time point with a crossing of the amplitude of the ultrasonic echo signal (1) with the amplitude of a threshold signal (SW) in a descending direction in temporal correlation with the exceeding of the said minimum level at the output of the said optimal filter is expected. The exemplary signal object of a triangular wavelet in this example thus consists of the predefined sequence of three basic signal objects, which is recognized, replaced by a symbol, and transmitted as this symbol, preferably together with its time of occurrence, the time stamp. This exceeding of the said minimum level at the output of the said optimal filter is, incidentally, another example of a fifth possible signal time point and thus another possible basic signal object.

[0172] The resulting grouping and temporal sequence of detected basic signal objects can itself be recognized, for example by a Viterbi decoder, as a predefined, expected grouping or temporal sequence of basic signal objects and can thus itself represent a basic signal object. Thus, a sixth possible signal time and thus a basic signal object is the occurrence of such a predefined grouping and / or temporal sequence of other basic signal objects.

[0173] If such a grouping of basic signal objects or a temporal sequence of such signal object classes is recognized in the form of a signal object, the symbol of this recognized summary signal object class and at least one associated signal object parameter are preferably transmitted instead of the individual signal basic objects, as this saves considerable data bus capacity. However, there may also be cases in which both are transmitted. In this case, the data item (symbol) of the signal object class of a signal object is transmitted, which is a predefined temporal sequence and / or grouping of other signal basic objects. In order to achieve compression, it is advantageous if at least one signal object class (symbol) of at least one of these other signal basic objects is not transmitted.

[0174] A temporal grouping of basic signal objects occurs especially when the temporal separation of these basic signal objects does not exceed a predefined interval. In the previously mentioned example, the propagation time of the signal in the matched filter should be considered. Typically, the matched filter is slower than the comparators. Therefore, the change in the output signal of the matched filter should be in a fixed temporal relationship to the temporal occurrence of the relevant signal instants.

[0175] Thus, a method is proposed for transmitting sensor data, in particular from an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle. This method begins with the transmission of an ultrasonic burst and the reception of an ultrasonic signal, and the formation of a time-discrete received signal consisting of a sequence of sample values. Each sample value is assigned a time datum (time stamp). This is followed by the determination of at least two parameter signals, each relating to the presence of a basic signal object assigned to the respective parameter signal, with the aid of at least one suitable filter (e.g., an optimal filter) from the sequence of sample values ​​of the received signal. The resulting parameter signals (feature vector signals) are also designed as a time-discrete sequence of respective parameter signal values ​​(feature vector values), each of which is correlated with a datum (time stamp).Thus, each parameter signal value (feature vector value) is preferably assigned exactly one time date (time stamp). These parameter signals together are referred to below as the feature vector signal. The feature vector signal is thus designed as a time-discrete sequence of feature vector signal values, each with n parameter signal values, which consist of the parameter signal values ​​and further parameter signal values, each with the same time date (time stamp). Here, n is the dimensionality of the individual feature vector signal values, which are preferably the same from one feature vector value to the next feature vector value. Each feature vector signal value formed in this way is assigned this respective time date (time stamp). This is followed by the evaluation of the temporal course of the feature vector signal in the resulting n-dimensional phase space and the conclusion about a detected signal object by determining an evaluation value (e.g. the distance).As explained previously, a signal object consists of a temporal sequence of basic signal objects. A predefined symbol is typically assigned to the signal object. Figuratively speaking, a check is carried out here to determine whether the point to which the n-dimensional feature vector signal points in the n-dimensional phase space, as it moves through the n-dimensional phase space in a predetermined temporal sequence, approaches predetermined points in this n-dimensional phase space closer than a predetermined maximum distance. The feature vector signal therefore has a temporal profile. An evaluation value (e.g. a distance) is then calculated, which can, for example, represent the probability of a certain sequence occurring. This evaluation value, which is again assigned a temporal date (time stamp), is then compared with a threshold vector to form a Boolean result that can have a first and a second value.If this Boolean result has the first value for this time date (timestamp), the symbol of the signal object and the time date (timestamp) associated with this symbol are transferred from the sensor to the computer system. Depending on the detected signal object, additional parameters may be transferred.

[0176] Particularly advantageous is the reconstruction of a reconstructed ultrasonic echo signal model (610) from the detected signal objects (122). This ultrasonic echo signal model (610) is generated from the summed, linearly superimposed signal waveform models of the individual detected signal objects. It is then subtracted from the ultrasonic echo signal (1). This creates a residual signal (660). This allows for better suppression of signal objects similar to the selected signal object detected with a higher probability. The weaker signal objects stand out better in the residual signal (660) and can be more easily detected. (See also Fig. 18). Thus, in a method according to the invention, a subtraction of the ultrasonic echo signal model (610) reconstructed from the detected signal objects from the ultrasonic echo signal (1) is preferably also provided to form a residual signal (660). The residual signal (660) thus formed is then used again to form the feature vector signal (138), and the next signal object with the next higher probability is detected. Since the first detected signal object was removed from the input signal according to its weighting, it can no longer influence this detection. This form of detection thus delivers a better result.

[0177] However, this detection method is generally slower. Therefore, it is advisable to first perform a direct initial object detection without subtraction while the measurement is still in progress. Then, after all sample values ​​of an ultrasonic echo response from the vehicle's surroundings have been acquired, and after transmitting an ultrasonic pulse or ultrasonic burst, perform another pattern detection with subtraction of the ultrasonic echo signal model (610). This takes longer but is more precise.

[0178] Preferably, this reducing classification of the ultrasonic echo signal (1) into signal objects with the aid of the ultrasonic echo signal model (610) is terminated when the magnitudes of the sample values ​​of the residual signal (660) are below the magnitudes of a predetermined threshold curve.

[0179] Particularly preferably, data transmission in the vehicle takes place via a serial, bidirectional, single-wire data bus. The electrical return line is preferably ensured by the vehicle body. The sensor data is preferably sent to the computer system in a current-modulated manner. The data for controlling the sensor is preferably sent to the sensor by the computer system in a voltage-modulated manner. According to the invention, it was recognized that the use of a PSI5 data bus and / or a DSI3 data bus is particularly suitable for data transmission. Furthermore, it was recognized that it is particularly advantageous to transmit the data to the computer system at a transmission rate of > 200 kbit / s and from the computer system to the at least one sensor at a transmission rate of > 10 kbit / s, preferably 20 kbit / s.Furthermore, it was recognized that the transmission of data from the sensor to the computer system should be modulated with a transmit current on the data bus whose current should be less than 50 mA, preferably less than 5 mA, and preferably less than 2.5 mA. These buses must be adapted accordingly for these operating values. However, the basic principle remains.

[0180] To carry out the methods described above, a computer system with a data interface to said data bus, preferably said single-wire data bus, is required, which supports the decompression of the thus compressed data. However, the computer system will generally not perform complete decompression, but will instead, for example, only evaluate the timestamp and the detected signal object type. The sensor required to carry out one of the methods described above has at least one transmitter and at least one receiver for generating a received signal, which can also be combined as one or more transducers. Furthermore, it has at least devices for processing and compressing the received signal and a data interface for transmitting the data via the data bus, preferably said single-wire data bus, to the computer system.For compression, the compression device preferably has at least one of the following sub-devices:. • Optimal filter, • Comparators, • Threshold signal generating devices for generating one or more threshold signals (SW), • Differentiator for forming derivatives, • Integrator for forming integrated signals, • other filters, • Envelope former for generating an envelope signal (ultrasonic echo signal) from the received signal, • Correlation filter for comparing the received signal or signals derived from it with reference signals.

[0181] In a particularly simple form, the proposed method for transmitting sensor data, in particular of an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle, is carried out as follows: It begins with the transmission of an ultrasound burst. This is followed by the reception of an ultrasound signal, typically a reflection, and the formation of a time-discrete received signal consisting of a temporal sequence of sample values. Each sample value is assigned a time date (time stamp). This typically represents the time of sampling. On the basis of this data stream, a first parameter signal of a first property is determined from the sequence of sample values ​​of the received signal using a first filter. Preferably, the parameter signal is again formed as a time-discrete sequence of parameter signal values. Each parameter signal value is again assigned exactly one time date (time stamp). This date preferably corresponds to the most recent time date of a sample value that was used to form this respective parameter signal value.At the same time, at least one further parameter signal of a property associated with this further parameter signal is preferably determined from the sequence of sample values ​​of the received signal using a further filter associated with this further parameter signal, wherein the further parameter signals are each again embodied as time-discrete sequences of further parameter signal values. Here, too, each further parameter signal value is assigned the same time date (time stamp) as the corresponding parameter signal value.

[0182] The first parameter signal and the additional parameter signals are collectively referred to below as the feature vector signal. This feature vector signal (or parameter vector signal) thus represents a time-discrete sequence of feature vector signal values, each consisting of the parameter signal values ​​and additional parameter signal values, each with the same time date (time stamp). Thus, each feature vector signal value (=parameter signal value) thus formed can be assigned this respective time date (time stamp).

[0183] The comparison of the feature vector signal values ​​of a temporal date (time stamp) with a threshold vector, which is preferably a prototype vector, is then preferably carried out quasi-continuously, forming a Boolean result that can have a first and a second value. For example, it is conceivable to compare the absolute value of the current feature vector signal value, which represents, for example, a first component of a feature vector signal value, with a fifteenth threshold value, which represents a first component of the threshold vector, and to set the Boolean result to a first value if the absolute value of the feature vector signal value is smaller than this fifteenth threshold value, and to a second value if this is not the case.If the Boolean result has a first value, it is then further conceivable to compare the magnitude of the further feature vector signal value, which, for example, represents a further component of this feature vector signal, with a further threshold value, which represents a further component of the threshold vector, and to leave the Boolean result at the first value if the magnitude of the further feature vector signal value is smaller than this further threshold value, and to set the Boolean result to the second value if this is not the case. In this way, all further feature vector signal values ​​can be checked. Of course, other classifiers are also conceivable. Comparison with several different threshold vectors is also possible. These threshold vectors therefore represent the prototypes of predefined signal shapes. They originate from the aforementioned library. Each threshold vector is preferably assigned a symbol.

[0184] The final step in this case is to transfer the symbol and, if applicable, also the feature vector signal values ​​and the time date (time stamp) assigned to this symbol or feature vector signal value from the sensor to the computer system if the Boolean result for this time date (time stamp) has the first value.

[0185] This means that all other data is no longer transmitted. Furthermore, the multidimensional analysis prevents interference.

[0186] On this basis, a sensor system is proposed, comprising at least one computer system capable of implementing one of the previously presented methods and at least two sensors that are also capable of implementing one of the previously presented methods, such that these at least two sensors can communicate with the computer system through signal object detection and are also capable of transmitting extraneous echoes in a compacted form and making this information additionally available to the computer system. Accordingly, the sensor system is typically designed so that the data transmission between the at least two sensors and the computer system takes place or can take place according to the previously described methods.Within the at least two sensors of the sensor system, typically one ultrasonic reception signal, i.e., at least two ultrasonic reception signals, is compressed using one of the previously proposed methods and transmitted to the computer system. Within the computer system, the at least two ultrasonic reception signals are reconstructed into reconstructed ultrasonic reception signals. The computer system then uses the reconstructed ultrasonic reception signals to detect objects in the vicinity of the sensors. In contrast to the prior art, the sensors do not perform this object detection.

[0187] The computer system preferably also carries out object detection of objects in the vicinity of the sensors with the aid of the reconstructed ultrasonic reception signals and additional signals from other sensors, in particular the signals from radar sensors.

[0188] As a final step, the computer system preferably creates an environment map for the sensors or a device of which the sensors are a part based on the detected objects.

[0189] The proposed ultrasonic sensor system, as exemplified in the Fig. 7 and Fig. 12, thus preferably comprises an ultrasonic transducer (100), a feature extractor (111) and an estimator (150, 151) or classifier and a physical interface (101). The ultrasonic transducer (100) is preferably provided and / or configured to receive an acoustic ultrasonic wave signal and, depending thereon, to form an ultrasonic transducer signal (102). The feature vector extractor (111) is provided and / or configured to form a feature vector signal (138) from the ultrasonic transducer signal (102). The ultrasonic sensor system is preferably configured and provided to detect signal objects in the ultrasonic echo signal (1) by means of the estimator (151, 150) and to classify them into signal object classes, wherein a signal object class can also comprise only one signal object.Preferably, at least one associated signal object parameter and a symbol corresponding to the signal object class assigned to this signal object are assigned to each signal object (122) thus identified and classified, or at least one associated signal object parameter and a symbol for this signal object are determined for each signal object (122) thus identified and classified. At least the symbol of a identified signal object class and at least one associated signal object parameter of this identified signal object class are transmitted to a higher-level computer system via a data bus.

[0190] The estimator (150) preferably comprises a distance detector (112) and a prototype database (115). Likewise, the estimator (150) preferably comprises a Viterbi estimator and a signal object database. The estimator (151) can also use a neural network model.

[0191] A proposed method for operating an ultrasonic sensor therefore comprises the steps according to Fig. 11: • Waiting for an ultrasonic reception signal that is different from the background noise, • Switching to a “no signal object basic prototype” state if the ultrasonic reception signal is different from background noise, and performing a process for detecting and classifying signal basic prototypes; • Change to a sequence of states that are assigned to the signal basic prototypes of a given sequence of signal basic prototypes when the first signal basic prototype of this sequence is recognized; • Following the sequence of basic signal prototypes until the end of the sequence of basic signal prototypes is reached; • Inferring the presence of one of this sequence of basic signal prototypes and a signal object associated with this sequence when the end of this sequence of basic signal prototypes is reached and signaling this signal object; • Abort (not in Fig. 11) of this sequence in case of timeout and / or single or multiple detection of a basic signal prototype at a position of this sequence at which this basic signal prototype is not expected or at which this basic signal prototype is not expected in the following position. • Return to the state “no signal object basic prototype”.

[0192] The compression method described above corresponds to an associated decompression method, which is preferably used to decompress ultrasonic reception data compressed and transmitted in this signal-object-oriented manner in the control computer after it has been received from the sensor via the aforementioned data interface. After receiving new data to be decompressed in the control computer from the sensor, an ultrasonic echo signal model is provided that initially has no signal ( Fig. 15a). This ultrasonic echo signal model is then slowly filled in signal object by signal object by successively adding the respective prototypical, parameterized signal curves of the signal objects (160) contained in the ultrasonic reception data, and the ultrasonic echo signal model (610) is approximated to the measured signal curve. Preferably, only the signal curves of signal objects that satisfy specified conditions, such as a specified chirp direction, are added ( Fig. 13b). Of course, summation without a selection condition is also possible. ( Fig. 15 and Fig. 16) From the summed signal profiles of the prototypical signal objects detected by the sensor, the reconstructed ultrasonic received signal is preferably formed in the sensor's control computer based on the ultrasonic echo signal model. This can be done by forming samples of a reconstructed envelope of the received signal (ultrasonic echo signal). This reconstructed ultrasonic received signal (reconstructed ultrasonic echo signal) can then be used much more effectively in the control computer for more complex processes such as sensor fusion than the supposed object data transmitted by the sensor to the control unit. Advantage of the invention

[0193] Such compressed data transmission via the data bus between the sensor and the computer system reduces the data bus load and thus the criticality with regard to EMC requirements. It also creates free data bus capacity during the receive time for the transmission of control commands from the computer system to the sensor and for the transmission of status information and other data from the sensor to the computer system. The proposed prioritization ensures that safety-relevant data is transmitted first, thus avoiding unnecessary sensor downtime. List of characters Fig. 1 shows the basic process of signal compression and transmission (see description above); Fig. 2 shows in more detail the basic process of signal compression and transmission (see description above); Fig. Figure 3 a shows a conventional ultrasonic echo signal (1) and its conventional evaluation. Fig. Figure 3 b shows a conventional ultrasonic echo signal (1) and its evaluation, where the amplitude is also transmitted. Fig. Figure 3c shows an ultrasonic echo signal including the chirp direction. Fig. 3d shows detected signal objects (triangle signals) in the signal of the Fig. 1c discarding unrecognized signal components. Fig. 4e shows the unclaimed conventional transmission Fig. 4f shows the unused transmission of analyzed data after complete reception of the ultrasonic echo Fig. Figure 4g shows the claimed transmission of compressed data, where in this example symbols for basic signal objects are transmitted largely without compression according to the state of the art. Fig. Figure 5h shows the claimed transmission of compressed data, where in this example symbols for signal basic objects are compressed to symbols for signal objects. Fig. Figure 6 shows the claimed transmission of compressed data, where, in this example, symbols for basic signal objects are compressed into symbols for signal objects. In this process, not only the envelope signal (ultrasound echo signal) but also the confidence signal is evaluated. Fig. 7 shows an exemplary device in the form of an ultrasonic sensor with detection of signal objects. The data interface, the data bus and the control unit are not shown for simplification. Fig. 8 serves to explain the selection of the signal basic object prototypes of the prototype database (115) by the distance determiner (112). Fig. 9 serves to explain the HMM method applied by the Viterbi estimator (113) to identify the signal object as the most probable sequence of signal basic object prototypes based on a sequence of recognized signal basic object prototypes (121) as the recognized signal object (122). Fig. 10 shows an exemplary state sequence in the Viterbi estimator (113) for the detection of a single signal object. Fig. 11 shows an exemplary, preferred state sequence in the Viterbi estimator (113) for the continuous detection of one of signal objects, as is typically necessary for detection in autonomous driving tasks. Fig. 12 shows an alternative embodiment with an estimator (151) executing a neural network model. Fig. 13 shows the decompression of the transmitted envelope of the ultrasonic signal (ultrasonic echo signal) limited to the chirp-down signal components; Fig. 14 shows the decompression of the transmitted envelope of the ultrasonic signal (ultrasonic echo signal) limited to the chirp-up signal component; Fig. 15 and Fig. 16 shows the decompression of the transmitted envelope of the ultrasonic signal (ultrasonic echo signal) with chirp-up signal components and chirp-down signal components; Fig. 17 shows the original signal (a), the reconstructed signal (b) and their superposition (c). Fig. 18 shows an improved device for improved compression. Description of the other characters

[0194] The Fig. 1 and Fig. 2 have already been described above. Figure 3 a

[0195] Fig. Figure 3a shows the unused temporal profile of a conventional ultrasonic echo signal (1) and its unused conventional evaluation in freely selected units. Starting with the transmission of the transmit burst (SB), a threshold signal (SW) is carried along. Whenever the envelope signal (ultrasonic echo signal (1)) of the ultrasonic receive signal exceeds the threshold signal (SW), the output (2) is set to logic 1. This is a temporal analog interface with a digital output level. Further evaluation is then handled by the sensor's control unit. Signaling errors or controlling the sensor is not possible via this state-of-the-art analog interface. Figure 3 b

[0196] Fig. Figure 3b shows the unused temporal profile of a conventional ultrasonic echo signal (1) and its unused conventional evaluation in freely selected units. Starting with the transmission of the transmission burst (SB), a threshold signal (SW) is carried along. Whenever the envelope signal (ultrasonic echo signal (1)) of the ultrasonic reception signal exceeds the threshold signal (SW), the output (2) is now set to a level corresponding to the amplitude of the detected reflection. This is a temporally analog interface with an analog output level. Further evaluation is then handled by the sensor's control unit. Signaling of errors or controlling the sensor is not possible via this state-of-the-art analog interface. Figure 3c

[0197] shows the ultrasonic echo signal for explanation, with the chirp direction (e.g. A=Chirp-Up; B=Chirp down) marked. Figure 3d

[0198] In Fig. 3d explains the principle of symbolic signal transmission. Instead of the signal from Fig. 3c, only two types of triangular objects are transmitted here as an example. Specifically, these are a first triangular object (A) for the chirp-up case and a second triangular object (B) for the chirp-down case. At the same time, the time and amplitude of the triangular object are transmitted. If the signal is reconstructed based on this data, a signal corresponding to Fig. 3d. The signal components that did not correspond to the triangular signals were removed from this signal. This results in the discarding of unrecognized signal components and massive data compression. Figure 4e

[0199] shows the unclaimed conventional analog transmission of the intersection points of the ultrasonic echo signal (1) of the ultrasonic reception signal with the threshold signal (SW). Figure 4f

[0200] shows the unused transmission of analyzed data after complete reception of the ultrasonic echo. Figure 4g

[0201] shows the claimed transmission of compressed data, whereby in this example symbols for basic signal objects are transmitted largely without compression. Figure 5h

[0202] shows the claimed transmission of compressed data, wherein in this example symbols for basic signal objects are compressed into symbols for signal objects. First, a first triangular object (59) is identified and transmitted, characterized by the characteristic temporal sequence of a threshold value being exceeded, followed by a maximum and a threshold value being undershot. Then, a double peak with a saddle point (60) above the threshold signal is identified. Characteristic here is the sequence of the ultrasonic echo signal (1) exceeding the threshold signal (SW), followed by a maximum of the ultrasonic echo signal (1), followed by a minimum above the threshold signal (SW), followed by a maximum above the threshold signal (SW), followed by a threshold signal (SW) being undershot. After detection, the symbol for this double peak with a saddle point is transmitted. A timestamp is also transmitted.Preferably, further parameters of the double peak with saddle point are also transmitted, such as the positions of the maxima and minimum or a scaling factor. This is followed by the detection of a triangular signal as the basic signal object as the ultrasonic echo signal (1) exceeds the threshold signal (SW), followed by a maximum of the ultrasonic echo signal (1), followed by the ultrasonic echo signal (1) falling below the threshold signal (SW). This is followed by the detection of a double peak again, but this time the minimum of the ultrasonic echo signal (1) is below the threshold signal (SW). This double peak can therefore, for example, be treated as a separate signal object. As is easily apparent, this type of signal treatment leads to a massive data reduction. Figure 6

[0203] shows the claimed transmission of compressed data according to Fig. 3, where in this example not only the envelope signal (ultrasonic echo signal (1)) but also the confidence signal is evaluated. Figure 7

[0204] shows an exemplary device in the form of an ultrasonic sensor with detection of signal objects. The data interface, the data bus and the control unit are not shown for the sake of simplicity. The ultrasonic transducer (100) is controlled and measured by means of an ultrasonic transducer signal (102) via a physical interface (101). The physical interface (101) serves to drive the ultrasonic transducer (100) and to condition the ultrasonic transducer signals (102) received by the ultrasonic transducer (100) into the ultrasonic echo signal (1) for the subsequent signal object classification. The ultrasonic echo signal (1) is preferably a digitized signal with time-spaced sample values. The feature vector extractor (111) has various devices, here for example n optimal filters (optimal filter 1 to optimal filter n) with n as a positive integer.The outputs of the optimal filters used here as examples form the intermediate parameter signal (123). Instead of the optimal filters, or in addition to them, the following can also be used. • Integrators and / or • Differentiator and / or • Filter and / or • Logarithmizer and / or • FF and DFFT devices and / or • Correlators and / or • Demodulators that multiply their input signal with a given signal and then filter it, and / or • other signal processing sub-devices and / or • combinations of which are used depending on the application, which then generate the n-dimensional intermediate parameter signal (123). The blocks labeled “optimal filter” in the drawings are therefore only placeholders for such signal processing blocks. A signal processing block labeled “optimal filter” can also have more than one output, which therefore contributes to the n-dimensional intermediate parameter signal (123) multiple times with more than one signal. A following, exemplary significance enhancer serves to map the n-dimensional space of the intermediate parameter signal (123) onto an m-dimensional space of the feature vector signal (138). Here, m is a positive integer. As a rule, m is less than n. This serves, on the one hand, to maximize the selectivity of the parameter values ​​from which each feature vector signal value of the feature vector signal (138) results.This is preferably achieved by a linear mapping using an offset value determined in the laboratory using statistical methods, which is added to the values ​​of the intermediate parameter signals, and a so-called LDA matrix, with which the respective vector of the respective sample values ​​of the intermediate parameter signals (123) is multiplied to form the feature vector signal (138). The distance determiner (112) preferably compares each resulting feature vector signal value of the feature vector signal (138) with each signal base object prototype of the prototype database (115). For this purpose, the prototype database (115) contains an entry with a center of gravity vector (e.g., 141, 142, 143, 144) of the respective signal base object prototype of the prototype database (115) for each of the signal base object prototypes of the prototype database (115).Preferably, a distance of the currently examined feature vector signal value of the feature vector signal (138) to the currently examined centroid vector of the prototype database (115) is calculated by the distance determiner (112). However, the distance determiner (112) can also calculate an evaluation of the similarity between the centroid vector (e.g., 141, 142, 143, 144) of the respective signal basic object prototype of the prototype database (115) and the currently examined feature vector signal value of the feature vector signal (138) in the form of an evaluation value, which we always call distance here for the sake of simplicity. The distance determiner (112) determines in this way whether a centroid vector (e.g.,141, 142, 143, 144) of the basic signal object prototypes of the prototype database (115) is sufficiently similar to the current feature vector signal value of the feature vector signal (138), i.e., has a sufficiently small distance, and, if this is the case, which centroid vector (e.g., 141, 142, 143, 144) of the basic signal object prototypes of the prototype database (115) is most similar to the current feature vector signal value of the feature vector signal (138), i.e., has the smallest distance. If necessary, the distance determiner (112) determines a list of centroid vectors (e.g., 141, 142, 143, 144) of the basic signal object prototypes of the prototype database (115) that are sufficiently similar to the current feature vector signal value of the feature vector signal (138), i.e., have a sufficiently small distance. Preferably, these are sorted by distance and passed on to the Viterbi estimator as a hypothesis list with their corresponding distance.The recognition result of the distance determiner (also referred to here as a classifier) ​​is passed on in the case of a single recognized signal basic object (121) as a symbol (e.g., as a prototype database address pointing to the recognized signal basic object prototype in the prototype database (115).) and, in the case of a hypothesis list, preferably as a list of pairs consisting of a symbol of the recognized signal basic object (e.g., the prototype database address pointing to the recognized signal basic object prototype in the prototype database (115).) and the distance to the center of gravity of this recognized signal basic object. The Viterbi estimator then searches for the sequence of signal basic object prototypes that best corresponds to a predefined sequence of signal basic object prototypes in its signal object database (116).For example, matches are counted positively and mismatches are counted negatively, so that for a temporal sequence of recognized signal basic object prototypes, an evaluation value is obtained for each entry in the signal object database (116). In the case of hypothesis lists, the Viterbi estimator preferably checks all possible paths through the temporal sequence of hypothesis lists. It preferably determines its evaluation result taking into account the previously determined distances. This can be done, for example, by dividing the added values ​​by the distance before the addition when forming the evaluation value. In this way, the Viterbi estimator determines the recognized signal objects (122), which are then transmitted via the data bus, possibly provided with suitable parameters.It is important to note that this procedure is not about detecting objects physically present in the measuring space of the ultrasonic transducer (100) and transmitting this information, but about detecting structures within the ultrasonic echo signal (1) and transmitting them. Figure 8

[0205] serves to explain the selection of the signal basic object prototypes of the prototype database (115) by the distance determiner (112).

[0206] The position of the distance determination (112) in the exemplary two-dimensional parameter space of the Fig. 8 determined current feature vector signal values ​​of the feature vector signal (138) can vary greatly. It is conceivable that such a first exemplary feature vector signal value (146) lies too far from the center of gravity coordinates (141, 142, 143, 144) of the center of gravity of any basic signal object prototype in the prototype database (115). This distance threshold can, for example, be the said minimum half prototype distance among the basic signal object prototypes in the prototype database (115). It is also possible that the scatter ranges of basic signal object prototypes in the prototype database (115) overlap around their respective centers of gravity (143, 142), and a second exemplary determined current feature vector signal value (145) of the feature vector signal (138) lies in such an overlap range.In this case, a hypothesis list can contain both signal basic object prototypes with different probabilities as an attached parameter, due to different distances. These probabilities are preferably represented by the distances. In this case, instead of one signal basic object being passed to the Viterbi estimator (113) as the most probable, a vector of possibly present signal basic objects is passed to the Viterbi estimator (113). From the chronological sequence of these hypothesis lists, the Viterbi estimator then searches for the possible sequence that has the greatest probability of one of the predetermined signal basic object sequences in its signal object database compared to all possible paths through the signal basic objects (121) recognized as possible in the hypothesis lists received by the distance determiner (112) via the Viterbi estimator (113).In such a path, exactly one recognized signal basic object prototype of this hypothesis list must be traversed through this path for each hypothesis list.

[0207] In the best case, the current feature vector signal value (148) lies in the scatter range (threshold ellipsoid) (147) around the center of gravity (141) of a single signal basic object prototype (141), which is thus reliably recognized by the distance detector (112) and passed on to the Viterbi estimator (113) as a recognized signal basic object (121).

[0208] For improved modeling of the scattering range of a single basic signal object prototype, it is conceivable to model it using multiple, here circular, basic signal object prototypes with associated scattering ranges. Thus, multiple basic signal object prototypes in the prototype database (115) can represent the same basic signal object prototype in the sense of a basic signal object class.

[0209] The distance determiner (112) preferably compares each resulting feature vector signal value of the feature vector signal (138) with each basic signal object prototype of the prototype database (115). For this purpose, the prototype database (115) contains, for each of the basic signal object prototypes of the prototype database (115), an entry with a centroid vector (e.g., 141, 142, 143, 144) of the respective basic signal object prototype of the prototype database (115). Preferably, a distance of the currently examined feature vector signal value of the feature vector signal (138) to the currently examined centroid vector of the prototype database (115) is calculated by the distance determiner (112). However, the distance determiner (112) can also evaluate the similarity between the centroid vector (e.g.,141, 142, 143, 144) of the respective signal basic object prototype of the prototype database (115) and the currently examined feature vector signal value of the feature vector signal (138) in the form of an evaluation value, which we always call distance here for the sake of simplicity. The distance determiner (112) thus determines whether a centroid vector (e.g. 141, 142, 143, 144) of the signal basic object prototypes of the prototype database (115) is sufficiently similar to the current feature vector signal value of the feature vector signal (138), i.e., has a sufficiently small distance, and, if so, which centroid vector (e.g. 141, 142, 143, 144) of the signal basic object prototypes of the prototype database (115) is most similar to the current feature vector signal value of the feature vector signal (138), i.e., has the smallest distance. If necessary, the distance determiner (112) determines a list of centroid vectors (e.g.,141, 142, 143, 144) of the signal basic object prototypes of the prototype database (115) which are sufficiently similar to the current feature vector signal value of the feature vector signal (138), i.e. have a sufficiently small distance. Figure 9

[0210] serves to explain the HMM method used by the Viterbi estimator (113) to identify the signal object as the most probable sequence of signal basic object prototypes based on a sequence of recognized signal basic object prototypes (121) as the recognized signal object (122). Fig. 9 is explained above in the text. Figure 10

[0211] shows an example state sequence in the Viterbi estimator (113) for the detection of a single signal object. Fig. 10 is explained above in the text. Figure 11

[0212] shows an exemplary, preferred state sequence in the Viterbi estimator (113) for the continuous detection of a signal object, as is typically required for detection in autonomous driving tasks. Fig. 11 is explained above in the text. Figure 12

[0213] shows an alternative embodiment with an estimator (151) that executes a neural network model. The data interface, the data bus, and the control unit are omitted for simplicity. The ultrasonic transducer (100) is controlled and measured by means of an ultrasonic transducer signal (102) through a physical interface (101). The physical interface (101) serves to drive the ultrasonic transducer (100) and to condition the ultrasonic transducer signal (102) received by the ultrasonic transducer (100) into the ultrasonic echo signal (1) for subsequent signal object classification. The ultrasonic echo signal (1) is preferably a digitized signal with temporally spaced sample values. The feature vector extractor (111) again has various devices, here, for example, n optimal filters (optimal filter 1 to optimal filter n) with n as a positive integer.The outputs of the optimal filters used here as examples form the intermediate parameter signal (123). Instead of the optimal filters, or in addition to them, integrators, filters, differentiators, logarithmizers, and / or other signal processing sub-devices and combinations thereof can be used depending on the application, which then generate the n-dimensional intermediate parameter signal (123). A subsequent, exemplary significance enhancer serves to map the n-dimensional space of the intermediate parameter signal (123) to an m-dimensional space of the feature vector signal (138). Here, m is a positive integer. Typically, m is less than n. This serves, on the one hand, to maximize the selectivity of the parameter values ​​from which each feature vector signal value of the feature vector signal (138) results.This is preferably achieved by a linear mapping using an offset value determined in the laboratory using statistical methods, which is added to the values ​​of the intermediate parameter signals, and a so-called LDA matrix, with which the respective vector of the respective sample values ​​of the intermediate parameter signals (123) is multiplied to form the feature vector signal (138). The estimator (151) then attempts to recognize the signal objects in the data stream of the feature vector signal values ​​of the feature vector signal (138) using a neural network model (151). The basic signal object prototypes of each signal and the signal objects are encoded within the neural network model via the networking of the nodes within the neural network model and the parameterization of the neural network model. The estimator (151) then outputs the recognized signal objects (122). Figure 13

[0214] shows the exemplary decompression of the transmitted exemplary envelope signal (ultrasonic echo signal (1)) of the ultrasonic received signal limited to the chirp-down signals.

[0215] First, an ultrasonic echo signal model is generated that has no signal. ( Fig. 13a). The ultrasound echo signal model is parameterized with a model parameter (SA) that is preferentially correlated with the time (t) since the transmission of the ultrasound pulse or burst.

[0216] Then, this ultrasonic echo signal model is preferably supplemented by adding the first exemplary signal object (160) transmitted from the sensor to the control unit with data transmission priority 1, which describes a triangular shape with chirp down (A), and suitably parameterized. ( Fig. 13b)

[0217] The second exemplary signal object (161) in triangular form with chirp up characteristic (B) transmitted with data priority 2 is not taken into account here for the reconstruction of the chirp down signal.

[0218] The third exemplary signal object (162) transmitted with data priority 3 in double peak form with chirp up characteristic (B) is not taken into account here for the reconstruction of the chirp down signal.

[0219] Then, the already supplemented ultrasonic echo signal model is preferably supplemented by adding the fourth exemplary signal object (163) transmitted from the sensor to the control unit with data transmission priority 4, which describes a triangular shape with chirp down (A), suitably parameterized. ( Fig. 13c)

[0220] Then, the already supplemented ultrasonic echo signal model is preferably supplemented by the fifth exemplary signal object (164) transmitted from the sensor to the control unit with data transmission priority 5, which describes a triangular shape with chirp down (A), suitably parameterized. ( Fig. 13d)

[0221] The sixth exemplary signal object (165) transmitted with data priority 6 in double peak form with chirp up characteristic (B) is not taken into account here for the reconstruction of the chirp down signal.

[0222] The resulting reconstructed envelope signal is the reconstructed chirped-down ultrasonic echo signal.

[0223] The decompressed and reconstructed ultrasonic echo signal is then typically used for object detection in the control unit or at another location in the vehicle. Figure 14

[0224] shows the exemplary decompression of the transmitted exemplary envelope signal (ultrasonic echo signal (1)) of the ultrasonic received signal limited to the chirp-up signals.

[0225] First, an ultrasonic echo signal model is generated that has no signal. ( Fig. 14a). The ultrasound echo signal model is parameterized with a model parameter (SA) that is preferentially correlated with the time (t) since the transmission of the ultrasound pulse or burst.

[0226] The first exemplary signal object (160) in triangular form with chirp down characteristic (A) transmitted with data priority 1 is not taken into account here for the reconstruction of the chirp up signal.

[0227] Then, this ultrasonic echo signal model is preferably supplemented by adding the second exemplary signal object (161) transmitted from the sensor to the control unit with data transmission priority 2, which describes a triangular shape with chirp up (B), suitably parameterized. ( Fig. 14b)

[0228] Then, this ultrasonic echo signal model is preferably supplemented by adding the third exemplary signal object (161) transmitted from the sensor to the control unit with data transmission priority 3, which describes a double peak shape with chirp up (B), suitably parameterized. ( Fig. 14c)

[0229] The fourth exemplary signal object (162) in triangular form with chirp down characteristic (A) transmitted with data priority 4 is not taken into account here for the reconstruction of the chirp up signal.

[0230] The fifth exemplary signal object (163) in triangular form with chirp down characteristic (A) transmitted with data priority 5 is not taken into account here for the reconstruction of the chirp up signal.

[0231] Then, this ultrasonic echo signal model is preferably supplemented by adding the sixth exemplary signal object (165) transmitted from the sensor to the control unit with data transmission priority 6, which describes a triangular shape with chirp up (B), suitably parameterized. ( Fig. 14d)

[0232] The resulting reconstructed envelope signal is the reconstructed chirped-up ultrasonic echo signal.

[0233] The decompressed and reconstructed ultrasonic echo signal is then typically used for object detection in the control unit or at another location in the vehicle. Figures 15 and 16

[0234] show the decompression of the transmitted envelope signal (ultrasonic echo signal (1)) of the ultrasonic received signal with chirp-up signals and chirp-down signals;

[0235] First, an ultrasonic echo signal model is generated that has no signal. ( Fig. 15a). The ultrasound echo signal model is parameterized with a model parameter (SA) that is preferentially correlated with the time (t) since the transmission of the ultrasound pulse or burst.

[0236] Then, this ultrasonic echo signal model is preferably supplemented by adding the first exemplary signal object (160) transmitted from the sensor to the control unit with data transmission priority 1, which describes a triangular shape with chirp down (A), and suitably parameterized. ( Fig. 15b)

[0237] Then, this ultrasonic echo signal model is preferably supplemented by adding the second exemplary signal object (161) transmitted from the sensor to the control unit with data transmission priority 2, which describes a triangular shape with chirp up (B), suitably parameterized. ( Fig. 15c)

[0238] Then, this ultrasonic echo signal model is preferably supplemented by adding the third exemplary signal object (162) transmitted from the sensor to the control unit with data transmission priority 3, which describes a double peak shape with chirp up (B), suitably parameterized. ( Fig. 15d)

[0239] Then, this ultrasonic echo signal model is preferably supplemented by adding the fourth exemplary signal object (163) transmitted from the sensor to the control unit with data transmission priority 4, which describes a triangular shape with chirp down (A), suitably parameterized. ( Fig. 16d)

[0240] Then, this ultrasonic echo signal model is preferably supplemented by the fifth exemplary signal object (164) transmitted from the sensor to the control unit with data transmission priority 5, which describes a triangular shape with chirp down (A), suitably parameterized. ( Fig. 16f)

[0241] Then, this ultrasonic echo signal model is preferably supplemented by adding the sixth exemplary signal object (165) transmitted from the sensor to the control unit with data transmission priority 6, which describes a triangular shape with chirp up (B), suitably parameterized. ( Fig. 16g)

[0242] For clarification, Fig. 16h the reconstructed envelope signal is shown in bold. Otherwise, Fig. 16h with the Fig. 16g.

[0243] The resulting reconstructed envelope signal is the reconstructed chirp-up / chirp-down ultrasonic echo signal.

[0244] The decompressed and reconstructed ultrasonic echo signal is then typically used for object detection in the control unit or at another location in the vehicle. Figure 17

[0245] shows the original signal (a), the reconstructed signal (b), and their superposition (c). The deviations between the reconstructed ultrasonic echo signal (166) and the ultrasonic echo signal are very small with the correct procedure and skillful selection of the signal base objects and signal objects. Figure 18

[0246] Fig. 18 shows an improved device for improved compression. The Fig. The reconstructor (600), whose function is explained in Figures 13 to 17, can be used not only in the control unit, but also in the sensor itself before data transmission. For this purpose, the reconstructor (600) generates a reconstructed ultrasonic echo signal model (610) as shown in Figures Fig. 13 to 17 depending on the purpose. This is subtracted from the previously received ultrasonic echo signal (1) in a subtractor (602). For this purpose, the ultrasonic echo signal (1) is preferably stored in the form of digital samples in a memory (601), preferably in a chronological order. A stored sample of the ultrasonic echo signal (1) preferably corresponds to exactly one value of the reconstructed ultrasonic echo signal model (610), which is preferably also stored in a reconstruction memory (603). The reconstruction memory (603) can be part of the reconstructor (600). This additional feedback branch (600, 610, 603, 602, 601) can also be used for other classifiers such as the device of Fig. 12 can be used. List of reference symbols α Emission of the ultrasonic burst; β Receiving the ultrasonic burst reflected from an object and converting it into an electrical reception signal; γ compression of the electrical received signal; ya Sampling the electrical input signal and forming a sampled electrical input signal, wherein preferably each sample value of the electrical input signal can be assigned a time stamp. γb Determination of multiple spectral values, e.g., using matched filters for prototypical signal object classes. These multiple spectral values ​​together form a feature vector. This formation preferably occurs continuously, resulting in a stream of feature vector values. Each feature vector value can preferably be assigned a timestamp value. γc optional, but preferably performed, normalization of the feature vector spectral coefficients of the respective feature vector of a timestamp value before correlation with the prototypical signal object classes in the form of predefined prototypical feature vector values ​​of a prototype library; γd Distance determination between the current feature vector value and the values ​​of the prototypical signal object classes in the form of predefined prototypical feature vector values ​​of a prototype library; γe Selection of the most similar prototypical signal object class in the form of a given prototypical feature vector value from a prototype library with preferably minimal distance to the current feature vector and adoption of the symbol of this signal object class as a recognized signal object together with the timestamp value as compressed data. If necessary, further data, in particular signal object parameters, such as δ, for example, whose amplitude is transferred as compressed data. This compressed data then forms the compressed received signal; transmission of the compressed electrical received signal to the computer system: 1 Envelope of the received ultrasonic signal, here also referred to as ultrasonic echo signal; 2 Output signal (transmitted information) of an IO interface according to the state of the art; 3 transmitted information of a LIN interface according to the state of the art; 4 exemplary first intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction.; 5 exemplary first intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction; 6 exemplary first maximum of the ultrasonic echo signal (1) above the threshold signal (SW); 7 exemplary second intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction; 8 exemplary second intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction; 9 exemplary second maximum of the ultrasonic echo signal (1) above the threshold signal (SW); 10 exemplary first minimum of the ultrasonic echo signal (1) above the threshold signal (SW); 11 exemplary third maximum of the ultrasonic echo signal (1) above the threshold signal (SW); 12 exemplary third intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction; 13 exemplary third intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction; 14 exemplary fourth maximum of the ultrasonic echo signal (1) above the threshold signal (SW); 15 exemplary fourth intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction; 16 exemplary fourth intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction; 17 exemplary fifth maximum of the ultrasonic echo signal (1) above the threshold signal (SW); 18 exemplary fifth intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction; 19 exemplary fifth intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction; 20 exemplary sixth maximum of the ultrasonic echo signal (1) above the threshold signal (SW); 21 exemplary sixth intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction; 22 exemplary sixth intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction; 23 exemplary seventh maximum of the ultrasonic echo signal (1) above the threshold signal (SW); 24 exemplary seventh intersection point of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction; 25 Envelope during the ultrasonic burst 26 Transmission of the data of the exemplary first intersection point (4) of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction via the preferably bidirectional data bus; 27 Transmission of the data of the exemplary first intersection point (5) of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction via the preferably bidirectional data bus; 28 Transmission of the data of the exemplary first maximum (6) of the ultrasonic echo signal (1) above the threshold signal (SW) via the preferably bidirectional data bus; 29 Transmission of the data of the exemplary second intersection point (7) of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction via the preferably bidirectional data bus; 30 Transmission of the data of the exemplary second intersection point (8) of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction via the preferably bidirectional data bus; 31 Transmission of the data of the exemplary second maximum (9) of the ultrasonic echo signal (1) above the threshold signal (SW) and the data of the exemplary first minimum (10) of the ultrasonic echo signal (1) above the threshold signal (SW) via the preferably bidirectional data bus; 32 Transmission of the data of the exemplary third maximum (11) of the ultrasonic echo signal (1) above the threshold signal (SW) via the preferably bidirectional data bus; 34 Transmission of the data of the exemplary third intersection point (12) of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction via the preferably bidirectional data bus; 35 Transmission of the data of the exemplary third intersection point (13) of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction via the preferably bidirectional data bus; 36 Transmission of the data of the exemplary fourth maximum (14) of the ultrasonic echo signal (1) above the threshold signal (SW) via the preferably bidirectional data bus; 37 Transmission of the data of the exemplary fourth intersection point (15) of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction via the preferably bidirectional data bus; 38 Transmission of the data of the exemplary fourth intersection point (16) of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction via the preferably bidirectional data bus; 39 Transmission of the data of the exemplary fifth maximum (17) of the ultrasonic echo signal (1) above the threshold signal (SW) via the preferably bidirectional data bus; 40 Transmission of the data of the exemplary fifth intersection point (18) of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction via the preferably bidirectional data bus; 41 Transmission of the data of the exemplary fifth intersection point (19) of the ultrasonic echo signal (1) with the threshold signal (SW) in the upward direction via the preferably bidirectional data bus; 42 Transmission of the data of the exemplary sixth intersection point (21) of the ultrasonic echo signal (1) with the threshold signal (SW) in the downward direction via the preferably bidirectional data bus; 43 State-of-the-art transmission of the data of the received echoes on the LIN bus after the end of reception; 44 State-of-the-art transmission of data on the LIN bus before transmission of the ultrasonic burst; 45 State-of-the-art transmission of data via the IO interface before transmission of the ultrasonic burst; 46 Effect of the ultrasonic transmission burst on the output signal of the IO interface according to the state of the art; 47 Signal of the first echo (5, 6, 7) on the IO interface according to the state of the art; 48 Signal of the second echo (8, 9, 10, 11, 12) on the IO interface according to the state of the art; 49 Signal of the third and fourth echo (13, 14, 15) on the IO interface according to the state of the art; 50 Signal of the fifth echo (16, 17, 18) on the IO interface according to the state of the art; 51 Signal of the sixth echo (19, 20, 21) on the IO interface according to the state of the art; 52 Signal of the seventh echo (22, 23, 24) on the IO interface according to the state of the art; 53 Start command from the computer system to the sensor via the data bus; 54 periodic automatic data transmission between sensor and computer system, preferably according to DSI3 standard; 55 diagnostic bits after the measurement cycle; 56 End of transmission of the ultrasonic burst (end of transmission burst). Preferably, the end of the ultrasonic burst coincides with point 4. 57 Start of transmission of the ultrasonic burst (start of the transmission burst). 58 End of data transfer 100 ultrasonic transducer, which may, for example, also comprise a separate ultrasonic transmitter and a separate ultrasonic receiver; 101 physical interface for driving the ultrasonic transducer (100) and for processing the ultrasonic transducer signal (102) received by the ultrasonic transducer (100) into the ultrasonic echo signal (1) for the subsequent signal object classification; 102 ultrasonic transducer signal; 111 Feature Vector Extractor (111); 112 distance finder (or classifier); 113 Viterbi estimators. 115 prototype database; 116 signal object database; 122 detected signal objects with signal object parameters; 123 intermediate parameter signals; 125 significance enhancers; 126 LDA matrix; 138 feature vector signal or feature vector signal; 141 Center of gravity coordinate of a first exemplary prototypical signal base object of the prototype database (115); 142 Center of gravity coordinate of a second exemplary prototypical signal base object of the prototype database (115); 143 Center of gravity coordinate of a third exemplary prototypical signal base object of the prototype database (115); 144 Center of gravity coordinate of a specified exemplary prototypical signal base object of the prototype database (115); 145 current feature vector signal value of the feature vector signal (138) in the overlap region of the scatter ranges of the two exemplary signal basic objects of the prototype database (115) with the center of gravity coordinates 142 and 143; 146 exemplary feature vector signal value that is too far from the exemplary center of gravity coordinates (141, 142, 143, 144) of the center of gravity of any exemplary signal basic object prototype of the prototype database (115); 147 Scattering range (threshold ellipsoid) (147) around the center of gravity (141) of a single exemplary signal basic object prototype (141); 148 exemplary current feature vector signal value that lies within the scatter range (threshold ellipsoid) (147) around the center of gravity (141) of a single exemplary signal base object prototype (141) and can thus be reliably detected by the distance detector (112) and passed on to the Viterbi estimator (113) as a detected signal base object (121). 150 Estimator with an HMM model; 151 Estimator with a neural network model; 160 first exemplary signal object in triangular form with chirp down (A) and data transmission priority 1; 161 second exemplary signal object in triangular form with Chirp Up (B) and data transmission priority 2; 162 third exemplary signal object in double peak form with chirp up (B) and data transmission priority 3 (curbstone profile); 163 fourth exemplary signal object in triangular form with chirp down (A) and data transmission priority 4; 164 fifth exemplary signal object in triangular form with chirp down (A) and data transmission priority 5; 165 sixth exemplary signal object in triangular form with Chirp UP (B) and data transmission priority 6; 166 echo signal transmitted and decompressed using signal objects; 600 reconstructors; 601 Memory for the ultrasonic echo signal (1). The memory preferably comprises the data of the echo of an ultrasonic pulse or burst; 602 subtractor for subtracting the reconstructed samples of the ultrasonic echo signal calculated by the reconstructor (600), which form the reconstructed ultrasonic echo signal model (610), from the samples of the ultrasonic echo signal (1) stored in the memory (601) to form the residual signal (660) which serves as an alternative input signal for the feature vector extractor (111) instead of the ultrasonic echo signal (1); 603 Reconstruction memory for the reconstructed ultrasound echo signal model (610). The reconstruction memory is typically implemented as part of the reconstructor (600). 610 reconstructed ultrasound echo signal model; 660 residual signal. The residual signal represents the compression error. The more objects are detected, the smaller the compression error becomes. Compression is usually aborted when all samples of the residual signal (660) are below a threshold curve. a for compression. The corresponding abort signal is not shown in the figures. Information transmitted for the transmission of the received ultrasonic echoes via a state-of-the-art IO interface; A Example of a detected object with chirp-down; au "arbitrary units" = freely selected units B Example of a detected object with chip-up; b Transmitted information for the transmission of the received ultrasonic echoes using a state-of-the-art LIN interface; c Transmitted information for the transmission of the received ultrasonic echoes by means of the proposed method and the proposed device with envelope (ultrasonic echo signal (1)) for comparison; d Transmitted information for the transmission of the received ultrasonic echoes by means of the proposed method and device without envelope; e schematic signal forms during the transmission of the received echo information via an IO interface from the state of the art; En amplitude of the envelope (ultrasonic echo signal (1)) of the received ultrasonic signal; f schematic signal forms during the transmission of the received echo information via a LIN interface of the state of the art; g schematic signal forms during the transmission of the received echo information via a bidirectional data interface; SA Memory address in the memory (601) or reconstruction memory (603). This is typically correlated with a time since the transmission of an ultrasound pulse and / or ultrasound burst. SB transmit burst SW threshold t time; T EReception time. The reception time typically begins with the end (56) of the ultrasound burst transmission. It is possible to begin reception earlier. However, this may cause problems that may require additional measures.

Claims

[1] Ultrasonic sensor, - wherein the ultrasonic sensor carries out a method for transmitting sensor data of the ultrasonic sensor from the ultrasonic sensor to a computer system in a vehicle and - wherein the ultrasonic sensor emits an ultrasonic burst (α) and - wherein the ultrasonic sensor receives an ultrasonic signal and - wherein the ultrasonic sensor forms an ultrasonic reception signal (β) and - wherein the ultrasonic sensor performs data compression of the received signal to generate compressed data (γ) • using a neural network model (151) and / or • using an HMM model (Hidden Markov Model) and / or • using a Petri net and - wherein the ultrasonic sensor performs data compression of the received signal to generate compressed data (γ) • using a reconstructed ultrasound echo signal model (610) and - wherein the ultrasonic sensor transmits the compressed data to a computer system (δ). [2] Ultrasonic sensor according to claim 1 - The ultrasonic sensor transmits the data via a bidirectional single-wire data bus, • where the sensor sends the data to the computer system in current modulation and • in which the computer system sends voltage-modulated data to the sensor. [3] Ultrasonic sensor according to one or more of claims 1 to 2, characterized in that the ultrasonic sensor uses a PSI5 data bus and / or a DSI3 data bus for data transmission. [4] Ultrasonic sensor according to one or more of claims 1 to 3 - the ultrasonic sensor transmits the data to a computer system with a transmission rate of > 200 kBit / s and - wherein the computer system transmits the data to the ultrasonic sensor at a transmission rate > 10 kbit / s, preferably 20 kbit / s. [5] Ultrasonic sensor according to one or more of claims 1 to 4 - wherein the ultrasonic sensor modulates a transmission current onto the data bus to transmit data from the ultrasonic sensor to the computer system - and wherein the current intensity of the transmitting current is < 50 mA, preferably < 5 mA.

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