Method for operating a detection device with interference treatment using an artificial neural network

Abstract

The invention relates to a method (44) for operating a detection device, in particular a detection device (14) for a vehicle (10), a detection device, a driver assistance system and a vehicle. In the method (44), at least one electromagnetic beam (26) is emitted by the detection device into a monitoring region of the detection device. The detection device receives at least one electromagnetic beam coming from the monitoring region and converts it into at least one detection variable (40) which can be processed with at least one evaluation device. On the basis of at least one detection variable (40), at least one interference treatment is performed using at least one artificial neural network (CNN). In the at least one interference treatment, at least one interference analysis (46) is performed, in which the at least one detection variable (40) is examined for known interference patterns (52) of interference variables using at least one artificial neural network (CNN). If at least one known interference pattern (52) is recognised, the at least one detection variable (40) is corrected by interference variables that belong to the at least one recognised interference pattern (52).

Description

[0001] Description

[0002] Method for operating a detection device with disturbance treatment using an artificial neural network

[0003] Technical area

[0004] The invention relates to a method for operating a detection device, in particular a detection device for a vehicle, in which at least one electromagnetic beam is sent into a monitoring area of ​​the detection device with the detection device, at least one electromagnetic beam coming from the monitoring area is received with the detection device and converted into at least one detection variable which can be processed by at least one evaluation device, at least one disturbance treatment is carried out on the basis of at least one detection variable using at least one artificial neural network.

[0005] Furthermore, the invention relates to a detection device, in particular a detection device for a vehicle, with at least one transmitting device for transmitting electromagnetic rays into a monitoring area of ​​the detection device, with at least one means for receiving electromagnetic rays coming from the monitoring area and for determining detection variables from received electromagnetic rays, with at least one means for carrying out interference treatments on the basis of detection variables, wherein the at least one means has at least one artificial neural network.

[0006] Furthermore, the invention relates to a driver assistance system, in particular a driver assistance system for a vehicle, with at least one detection device, wherein the at least one detection device has at least one transmitting device for transmitting electromagnetic rays into a monitoring area of ​​the at least one detection device, at least one means for receiving electromagnetic rays coming from the monitoring area and for determining detection variables from the received electromagnetic rays, at least one means for carrying out fault actions on the basis of detection variables, wherein the at least one means has at least one artificial neural network.

[0007] Furthermore, the invention relates to a vehicle with at least one detection device, wherein the at least one detection device has at least one transmitting device for transmitting electromagnetic rays into a monitoring area of ​​the at least one detection device, at least one means for receiving electromagnetic rays coming from the monitoring area and for determining detection variables from the received electromagnetic rays, at least one means for carrying out interference actions on the basis of detection variables, wherein the at least one means has at least one artificial neural network.

[0008] State of the art

[0009] DE 10 2020 107 372 A1 discloses a method for operating a radar system with at least two radar sensors. In particular, the following steps are performed, preferably sequentially in the specified order or in any desired sequence, whereby individual and / or all steps can also be performed repeatedly:

[0010] - Carrying out a signal transmission at the radar sensors in order to transmit (by the radar sensors) at least one radar signal each, preferably by at least one transmitting antenna of the respective radar sensor, in particular in the form of an electromagnetic signal, transmitted into an environment outside the radar sensor,

[0011] - Performing signal processing at the radar sensors in order to determine a detection variable from the radar sensors which is specific for the respective radar signal transmitted, in particular for the transmitted radar signal reflected by a target object and delayed by a signal propagation time, which can be received, for example, by at least one receiving antenna of the radar sensor,

[0012] - Carrying out a disturbance evaluation in order to detect at least one disturbance in each of the radar sensors based on the respective detection variable, wherein the disturbance evaluation can preferably be carried out centrally for all of the detection variables or individually for the respective detection variables in the respective radar sensors,

[0013] - Providing at least one or at least two or at least four or at least six adaptation options to avoid the at least one detected disturbance by adapting the signal transmission,

[0014] - carrying out an evaluation of the at least one adaptation option for each of the radar sensors, in particular by each of the radar sensors,

[0015] - Performing a tuning of the adaptation option between the different radar sensors based on the evaluation,

[0016] - Carrying out the adaptation of the signal transmission according to the at least one adaptation option depending on the tuning, in particular only if the adaptation option brings about a reduction in interference for the majority of the radar sensors and / or by selecting the adaptation option which brings about the reduction in interference for the majority of the radar sensors.

[0017] The invention is based on the object of designing a method, a detection device, a driver assistance system, and a vehicle of the type mentioned above, in which the determination of detection variables can be improved. In particular, a signal-to-noise ratio for the detection variables is to be improved. In particular, alternatively or additionally, the determination of the detection variables is to be improved with regard to expenditure, in particular with regard to material expenditure, component expenditure, and / or assembly expenditure, and / or with regard to the validity of the detection variables.

[0018] Disclosure of the invention

[0019] The object is achieved according to the invention in the method in that, during the at least one disturbance treatment, at least one disturbance analysis is carried out, in which the at least one detected variable is examined for known disturbance patterns of disturbance variables using at least one artificial neural network. If at least one known disturbance pattern is detected, the at least one detected variable is corrected for disturbance variables belonging to the at least one detected disturbance pattern. According to the invention, at least one electromagnetic beam is transmitted into a monitoring area. The detection device receives at least one electromagnetic beam coming from the monitoring area and converts it into at least one detected variable.

[0020] Advantageously, electromagnetic radiation coming from the monitored area can be converted into detection variables in the form of electrical received signals by means of the detection device, in particular by means of at least one receiving device, which may have at least one antenna. Electrical received signals can be processed by electrical means, in particular electrical control and / or evaluation devices.

[0021] Electromagnetic beams that can be received by the detection device can include or consist of electromagnetic echo beams. Electromagnetic echo beams can originate from electromagnetic beams transmitted by the detection device that have been reflected by at least one object. The detection parameters determined from echo beams are specific to the reflecting object. For ease of differentiation, detection parameters that originate exclusively from echo beams can also be referred to as "echo reception parameters."

[0022] Alternatively or additionally, received electromagnetic radiation may contain or consist of interference radiation from interference sources. Interference radiation received by the detection device is converted into corresponding received quantities in a similar way to echo radiation. For ease of differentiation, received quantities that originate exclusively from interference sources can also be referred to as "interference quantities."

[0023] The detection variables can be superpositions of any echo reception variables and any interference variables. If no interference beams are detected, the detection variables consist solely of echo variables, if present. If no echo beams are detected, the detection variables consist solely of interference variables, if present. According to the invention, at least one interference treatment is carried out based on at least one detection variable using at least one artificial neural network in order to reduce the influence of any interference sources and the corresponding interference variables on the determination of information about the monitoring area, in particular object information about objects in the monitoring area.

[0024] Object information can be distance variables, direction variables and / or speed variables, which characterize distances, directions or speeds of objects relative to the detection device or a corresponding reference point or reference system.

[0025] During the at least one fault treatment, at least one fault analysis is performed. During the at least one fault analysis, the at least one recorded variable is examined for known fault patterns. The known fault patterns originate from disturbance variables that were known before the fault analysis was performed. If a known disturbance pattern is detected, the recorded variable is adjusted for the corresponding disturbance variables.

[0026] Known sources of interference can, in particular, be external sources of interference. These external sources of interference can be other radiation sources, in particular radar sources, that emit electromagnetic radiation in the same or an overlapping wavelength range as the detection device according to the invention.

[0027] Interference beams from known interference sources can cause characteristic interference patterns in the corresponding interference variables detected with the detection device. In particular, corresponding noise patterns from known interference sources can be identified in the detected detection variables. Accordingly, if known interference patterns are detected, the detection variables can be adjusted for the corresponding interference variables. This allows the overall signal-to-noise ratio of the detection variables, especially the echo reception variables contained therein, to be improved.

[0028] According to the invention, machine learning is used to analyze the detected variables. Depending on the type of object, the material, the shape, the XYZ coordinates, the environmental conditions (e.g., rain), external noise, the dynamics of the environment or the object, and other factors, the shape of the received electromagnetic radiation varies and also depends on the transmitted electromagnetic radiation.

[0029] The perturbation analysis uses at least one artificial neural network. A suitable multi-layer neural network (deep neural network) can be specified for this purpose, with an input layer, several intermediate layers (hidden layers), and an output layer. For training, several different scenarios can be recorded in advance with the detection device and used to train the neural network. Depending on the type of application and the degree of automation, for example, SAE Level 0 to SAE Level 4, different classes can be used.

[0030] By correcting the at least one detection variable with the aid of at least one disturbance analysis, sufficiently good data can be obtained even with a detection device that has lower-precision components. Components that may inherently be subject to greater noise can also be used. This allows for simpler and less expensive components to be used for the detection device, while still being able to determine sufficiently good detection variables for the application and the corresponding degree of automation, if applicable. By correcting the measurements with the aid of at least one disturbance analysis, the performance of the detection device can be improved.

[0031] By improving the signal-to-noise ratio of the detected variables, the validity of the data obtained therefrom can be improved. Thus, higher security levels can be achieved with the detection device according to the invention. With the detection device according to the invention, data can be determined that satisfy the automation levels SAE 0 to 4 required for autonomous or semi-autonomous driving. The invention can advantageously be used in detection devices for vehicles, in particular motor vehicles. The invention can advantageously be used in detection devices for land vehicles, in particular passenger cars, trucks, buses, motorcycles, or the like, aircraft, in particular drones, and / or watercraft. The invention can also be used in detection devices for vehicles that can be operated autonomously or at least semi-autonomously.However, the invention is not limited to detection devices for vehicles. It can also be used for detection devices in stationary operation, in robotics, and / or in machines, particularly construction or transport machines such as cranes, excavators, or the like.

[0032] The detection device can advantageously be connected to or be part of at least one electronic control device of a vehicle or machine, in particular a driver assistance system and / or a chassis control system and / or a driver information device and / or a parking assistance system and / or a gesture recognition system or the like. In this way, at least some of the functions of the vehicle or machine can be performed autonomously or semi-autonomously using the information obtained with the detection device.

[0033] The detection device can be used to detect stationary or moving objects, in particular vehicles, persons, animals, plants, obstacles, road surface irregularities, in particular potholes or stones, road markings, traffic signs, open spaces, in particular parking spaces, precipitation or the like, and / or movements and / or gestures.

[0034] In an advantageous embodiment of the method, the at least one disturbance analysis can be performed multiple times, and the adjusted detection variables determined from the respective disturbance analyses can be combined to form at least one combined detection variable. In this way, the signal-to-noise ratio of the detection variables can be further improved.

[0035] Advantageously, the at least one disturbance analysis can be performed between two and ten times, in particular four times. With each pass of the at least one disturbance analysis, the signal-to-noise ratio improves further. When performed four times, the signal-to-noise ratio improves, in particular, by a factor of 2.

[0036] In a further advantageous embodiment of the method, several different electromagnetic beams can be sent into the same scene of the surveillance area and respective detection variables can be determined, at least one disturbance analysis can be carried out for at least some of the plurality of detection variables thus determined, and respective adjusted detection variables can be determined for at least some of the different electromagnetic beams sent, and at least some of the plurality of adjusted detection variables thus determined can be combined to form at least one combined detection variable.

[0037] In this way, the signal-to-noise ratio of the detection variables can be further improved.

[0038] To capture the same scene, the different electromagnetic beams can be sent within a correspondingly small time window.

[0039] Advantageously, several different electromagnetic beams can be transmitted consecutively into the same scene of the surveillance area. This prevents mutual interference between the transmitted electromagnetic beams.

[0040] Advantageously, four different electromagnetic beams can be sent into the same scene, the respective detection parameters determined, and the respective disturbance analyses performed. This allows a correspondingly small time window to be realized, so that changes in the detected scene are minimized.

[0041] The different electromagnetic beams can differ in shape, wavelength, pulse duration, transmission duration, transmission power, coding, and so on. By varying the transmitted electromagnetic beams, the corresponding echo signals can be better distinguished from any interference. This allows the interference to be more easily identified and removed.

[0042] In a further advantageous embodiment of the method, an artificial convolutional neural network can be used as at least one artificial neural network. An artificial convolutional neural network (CNN) is a machine learning concept inspired by biology with the goal of extracting features. Since noise has patterns, in particular interference patterns, that are different from patterns, in particular object patterns, of regular signals, in particular echo beams, the detected detection variables can be reduced accordingly after the interference patterns have been detected. It is even possible to vary the electromagnetic beams that are sent into the monitored area by the detection device for scanning. In this way, the at least one interference analysis can be used to determine which electromagnetic beams originate from beams that the detection device has sent.From this knowledge, disturbing noise can be identified and eliminated accordingly.

[0043] In a further advantageous embodiment of the method, in the at least one disturbance analysis, the at least one detection variable is first examined for known object patterns which are caused by electromagnetic echo beams which are reflected by known objects, and upon detection of at least one known object pattern, an echo detection variable corresponding to the at least one known object pattern is removed from the at least one detection variable, then the at least one detection variable freed from the detected at least one echo detection variable is examined with at least one artificial neural network for known interference patterns of interference variables, and upon detection of at least one known interference pattern, the original at least one detection variable, which can contain the at least one echo detection variable, is used to remove interference variables which belong to the at least one detected interference pattern.This way, the signal-to-noise ratio can be further improved.

[0044] By first removing echo detection variables from at least one detection variable using known object patterns, corresponding interference patterns can be identified even more effectively. This can improve the removal of interference variables from the original detection variables.

[0045] In a sense, the object patterns caused by objects whose pattern is already known can first be subtracted from the at least one original detection variable. The detection variable freed of the object patterns can then be subjected to at least one further disturbance analysis, in which the disturbances with known disturbance patterns can be identified. The detected disturbances can then be removed from the at least one original detection variable, so that this cleaned detection variable ideally contains only echoes from objects, provided all disturbances have been identified.

[0046] In a further advantageous embodiment of the method, predetermined interference patterns and / or, if applicable, object patterns can be used for the at least one interference analysis, and / or interference patterns and / or object patterns learned during operation of the detection device can be used for the at least one interference analysis. In this way, the method can access a larger number of known interference patterns and / or known object patterns more flexibly.

[0047] Predefined interference patterns and / or object patterns can be used in the at least one interference analysis. These patterns can be learned in advance, particularly under laboratory conditions, and stored in appropriate storage media, particularly storage media of the detection device. This allows for faster access to the corresponding interference patterns and / or object patterns when performing the method.

[0048] Alternatively or additionally, learned interference patterns and / or object patterns can be used during operation. In this way, the number of known interference patterns and / or object patterns can be continuously increased. This also allows the method to be continuously improved. In a further advantageous embodiment of the method, received signals, in particular electrical received signals, which are converted from electromagnetic radiation by a receiving device of the detection device, can be used as detection variables, and / or

[0049] Object information about objects detected during measurements with the detection device is used as detection variables, wherein the object information is determined from received signals, in particular electrical received signals, which are converted from electromagnetic radiation by a receiving device of the detection device. In this way, the at least one disturbance analysis can be performed at a suitable processing level.

[0050] Advantageously, received signals can be used as acquisition variables. This allows disturbance analysis to be performed directly on the received signals at a lower processing level. This allows disturbances to be eliminated very early on.

[0051] Advantageously, electrical received signals, in particular electrical voltages or the like, can be used as detection variables. The electrical received signals are generated during the conversion of the electromagnetic radiation by means, in particular receiving devices, of the detection device. Electrical received signals can be processed by electrical means, in particular electrical evaluation devices or the like.

[0052] The received signals can be echo received signals resulting from echo rays, interference signals resulting from interference rays or a superposition of echo received signals and interference signals.

[0053] Alternatively or additionally, object information can be used as a detection variable. This allows for perturbation analysis to be performed at a higher processing level. This can improve the validity of images containing object information, especially distance images.

[0054] In a further advantageous embodiment of the method, the method can be used to operate a detection device in the form of a radar sensor with which electromagnetic rays are transmitted in the form of radar beams.

[0055] Radar sensors are highly variable in terms of the radar beams they transmit. This allows the detection parameters to be varied to improve the discrimination between object patterns and clutter patterns. This allows the same scene to be scanned with different radar beams, particularly one after the other. This can improve the overall signal-to-noise ratio for the adjusted detection parameters.

[0056] Advantageously, at least one receiving device of the detection device, in particular of the radar sensor, can be designed to receive electromagnetic rays, in particular radar rays, of the same type as the electromagnetic rays which are transmitted with the detection device.

[0057] Advantageously, a wavelength range in which the at least one receiving device can receive electromagnetic beams can comprise the wavelength range in which the electromagnetic beams, in particular the radar beams, are emitted by the detection device. This ensures that at least echoes of the emitted electromagnetic beams, in particular the radar beams, can be received.

[0058] In a further advantageous embodiment of the method, at least one adjusted detection variable, in particular optionally at least one adjusted combined detection variable, can be further processed, in particular subjected to image processing, and / or on the basis of the at least one adjusted detection variable, in particular optionally the at least one adjusted combined detection variable, at least one transmitting device and / or at least one receiving device of the detection device can be adapted.

[0059] Advantageously, at least one adjusted detection variable, in particular, if appropriate, at least one adjusted combined detection variable, can be further processed. In this way, further information about the monitored area can be obtained.

[0060] Advantageously, at least one item of object information, in particular at least one distance variable, at least one direction variable, and / or at least one speed variable, can be determined from at least one adjusted detection variable, in particular optionally from at least one adjusted combined detection variable. In this way, the detected scenes can be characterized more precisely.

[0061] Advantageously, at least one adjusted detection variable, in particular, if appropriate, at least one adjusted combined detection variable, can be subjected to image processing. In this way, further interference effects can be removed.

[0062] Alternatively or additionally, at least one transmitting device and / or at least one receiving device of the detection device can be adjusted based on the at least one adjusted detection variable, in particular, if appropriate, the at least one adjusted combined detection variable. In this way, the performance of the detection device can be adapted to the prevailing situation.

[0063] Furthermore, the object is achieved according to the invention in the detection device in that the detection device has at least some of the means for carrying out the method according to the invention.

[0064] According to the invention, the detection device has at least one fault analysis means with which a fault analysis according to the invention can be carried out.

[0065] Advantageously, the detection device can comprise at least one artificial neural network, in particular an artificial convolutional neural network. When performing disturbance analyses, the artificial neural network can be used to examine recorded variables for known disturbance patterns of disturbance variables. If known disturbance patterns are detected, the recorded variables can be adjusted for disturbance variables belonging to the detected disturbance patterns. Artificial convolutional neural networks allow for even better detection of disturbance patterns.

[0066] In an advantageous embodiment, the detection device can be a radar sensor. A radar sensor can be used to monitor a surveillance area for objects without contact. Radar sensors can be variably adapted to the emitted radar beams. In particular, the shape, pulse duration, length, and / or coding, or the like, of radar beams can be varied. In this way, radar sensors can determine a larger number of detection variables for the same detected scene by transmitting different radar beams into the same scene. This can further improve the identification of interference patterns.

[0067] Furthermore, the object is achieved according to the invention in the driver assistance system in that the driver assistance system has at least some means for carrying out the method according to the invention.

[0068] According to the invention, the driver assistance system has at least one detection device and at least some of the means for carrying out the method according to the invention for operating the at least one detection device.

[0069] With the driver assistance system, a vehicle can be operated autonomously or semi-autonomously.

[0070] With a detection device, at least one surveillance area in the surroundings of the vehicle and / or in the interior of the vehicle can be monitored for objects. With the at least one detection device, distance variables, direction variables and / or speed variables, which characterize distances, directions or speeds of detected objects, can be determined. The information obtained with the at least one detection device can be used with the driver assistance system for autonomous or semi-autonomous operation of the vehicle. According to the invention, the driver assistance system has at least some means for carrying out the method according to the invention. Advantageously, at least one detection device of the driver assistance system can have at least some means for carrying out the method according to the invention.If the at least one detection device is part of the driver assistance system, the part of the means of the at least one detection device for carrying out the method according to the invention is also part of the driver assistance system, i.e., also part of the means of the driver assistance system for carrying out the method according to the invention. This applies accordingly to the means of the vehicle, which has at least one driver assistance system and / or at least one detection device.

[0071] Furthermore, the object is achieved according to the invention in the vehicle in that the vehicle has at least some of the means for carrying out the method according to the invention.

[0072] Advantageously, the vehicle can have at least one driver assistance system, in particular at least one driver assistance system according to the invention. With the driver assistance system, the vehicle can be operated autonomously or semi-autonomously.

[0073] Alternatively or additionally, the vehicle may have at least one detection device, in particular at least one detection device according to the invention. A detection device can be used to monitor at least one monitoring area in the surroundings of the vehicle and / or in the interior of the vehicle for objects.

[0074] Advantageously, at least one detection device, in particular at least one detection device according to the invention, can be connected to a driver assistance system, in particular at least one driver assistance system according to the invention, or can be part of such a system. In this way, information obtained with the at least one detection device can be used by the driver assistance system for autonomous or semi-autonomous operation of the vehicle. Furthermore, the features and advantages presented in connection with the method according to the invention, the detection device according to the invention, the driver assistance system according to the invention, and the vehicle according to the invention and their respective advantageous embodiments apply to one another accordingly, and vice versa.The individual features and advantages can of course be combined with each other, whereby further beneficial effects can arise that go beyond the sum of the individual effects.

[0075] Short description of the drawings

[0076] Further advantages, features, and details of the invention will become apparent from the following description, in which exemplary embodiments of the invention are explained in more detail with reference to the drawings. Those skilled in the art will expediently consider the features disclosed in the drawings, the description, and the claims in combination individually and combine them to form useful further combinations. The figures show schematically:

[0077] Figure 1 is a front view of a vehicle with a driver assistance system having a radar sensor;

[0078] Figure 2 is a functional diagram of the driver assistance system with the radar sensor of Figure 1;

[0079] Figure 3 shows the time course of an electrical raw received signal, which is determined from a radar echo signal and electromagnetic interference rays with a receiving device of the radar sensor from Figures 1 and 2, and the time courses of the corresponding electrical echo received signal and the electrical interference signals;

[0080] Figure 4 shows the time course of the raw received signal from Figure 3;

[0081] Figure 5 shows the time course of the echo reception signal from Figure 3;

[0082] Figure 6 is a flowchart for a method for operating the radar sensor from Figures 1 and 2.

[0083] In the figures, identical components are provided with identical reference symbols.

[0084] Embodiment(s) of the invention

[0085] Figure 1 shows a front view of a vehicle 10 in the form of a passenger car. The vehicle 10 has a driver assistance system 12. With the driver assistance system 12, the vehicle 10 can be operated autonomously or semi-autonomously. Figure 2 shows a functional diagram of the driver assistance system 12.

[0086] The driver assistance system 12 comprises a detection device in the form of a radar sensor 14. Furthermore, the driver assistance system 12 has a central processor unit 16.

[0087] The radar sensor 14 is arranged, for example, in the front bumper of the vehicle 10 and directed into a monitoring area 18 in front of the vehicle 10 in the direction of travel. The radar sensor 14 can also be arranged at a different location on the vehicle 10, even with a different orientation. The driver assistance system 12 can also have multiple radar sensors 14, which can be arranged at different locations on the vehicle 10 with different orientations. In addition, the driver assistance system 12 can also have other types of detection devices.

[0088] The invention is explained by way of example with reference to the radar sensor 14 shown in Figures 1 and 2. However, the invention can also be used for other radar sensors or other types of detection devices that use electromagnetic radiation to monitor a corresponding surveillance area.

[0089] The radar sensor 14 comprises a transmitting device 20 with, for example, a transmitting antenna Tx, a receiving device 22 with, for example, a receiving antenna Rx and, for example, an electronic control and evaluation device 24.

[0090] The transmitting device 20 and the receiving device 22 are each functionally connected to the control and evaluation device 24. In this way, information can be exchanged between the transmitting device 20, the receiving device 22, and the control and evaluation device 24.

[0091] The control and evaluation device 24 is connected to the central processor unit 16 of the driver assistance system 12. In this way, information can be exchanged between the radar sensor 14, or the control and evaluation device 24, and the central processor unit 16.

[0092] The radar sensor 14 can also be equipped with multiple transmit antennas (Tx) and multiple receive antennas (Rx). The radar sensor 14 can be configured as a multiple input multiple output (MIMO) radar sensor.

[0093] The transmitting device 20 can, for example, generate electrical scanning signals, which can be transmitted into the surveillance area 18 by the transmitting antenna Tx as electromagnetic scanning beams in the form of radar signals 26. The radar signals 26 can, for example, be transmitted as radar pulses in the form of chirps. The transmitted radar signals 26 can be varied using the transmitting device 20. For example, the shapes, pulse durations, signal durations, and / or codings or the like of the radar signals 26 can be varied.

[0094] The radar signals 26 can be reflected by objects 28 located in the surveillance area 18.

[0095] The radar sensor 14 can be used, for example, to detect stationary or moving objects 28, for example vehicles, persons, animals, plants, obstacles, road surface irregularities, for example potholes or stones, road markings, traffic signs, open spaces, for example parking spaces, precipitation or the like, and / or movements and / or gestures.

[0096] Radar signals 26, which are reflected by the objects 28 in the direction of the radar sensor 14, can be received as electromagnetic rays in the form of radar echo signals 30 by the receiving antenna Rx of the receiving device 22.

[0097] The receiving device 22 can convert the received radar echo signals 30 into detection variables in the form of electrical echo reception signals 38. Figures 3 and 5 show the time profile of an exemplary electrical echo reception signal 38, which results from the radar echo signals 30 of an exemplary radar signal 26. Depending on the propagation time of a transmitted radar signal 26 until the reception of the corresponding radar echo signal 30, object information about the detected object 28 can be determined. For example, distance variables 32, direction variables, and speed variables can be determined, which characterize the distances, directions, or speeds of detected objects 28 within a reference system, for example, relative to the radar sensor 14. An indirect or direct propagation time method can be used.When using a MIMO radar sensor 14, distance quantities 32 can be determined from phase differences between electrical scanning signals used to generate the radar signals 26 and the electrical echo reception signals 38 of the detected radar echo signals 30.

[0098] The object information is determined in the control and evaluation device 24.

[0099] The receiving antennas Rx of the receiving device 22 receive not only the radar echo signals 30 originating from detected objects 28, but also electromagnetic interference rays 34, which may originate, for example, from external interference sources 42. The electromagnetic interference rays 34 are converted into electrical interference signals 36 by the receiving device 22.

[0100] The interference sources 42 can, for example, be other radar sensors that emit interference beams 34 in the form of radar beams. Figure 2 shows three examples of interference sources 42, whose reference symbols are provided with the indices 1, 2, and 3 for easier differentiation. The reference symbols of the corresponding electrical interference signals 36, whose temporal profiles are indicated in Figure 3, are correspondingly designated with the indices 1, 2, and 3.

[0101] The electrical echo reception signals 38, which originate from echo signals 30, and the electrical interference signals 36 are superimposed to form a detection variable in the form of an electrical raw reception signal 40. Figures 3 and 4 show, by way of example, the temporal profile of the raw reception signal 40 for the scene shown in Figure 2 with the three interference sources 42i, 42i and 423. The raw reception signal 40 depends on the type, material, shape and spatial position, for example the position in a defined reference system, of the reflecting object 28. Furthermore, the raw reception signal 40 depends on the ambient conditions, for example prevailing precipitation or the like, external noise, the dynamics of the environment or the detected object 28. In addition, the raw reception signal 40 depends on the radar signals 26 used.

[0102] For comparison purposes, Figure 3 shows the time courses of the exemplary electrical raw received signal 40, the corresponding echo received signal 38 and the three electrical interference signals 36i, 362 and 363.

[0103] The electrical interference signals 36i, 362 and 36a originate from the three interference sources 42i, 422 and 423, which each emit electromagnetic interference rays 34i, 342 and 343, respectively.

[0104] Figure 4 shows only the time course of the raw received signal 40 from Figure 3. Figure 5 shows the time course of the electrical echo received signal 38 from Figure 3 after interference treatment, in which the interference signals 36i, 362 and 363 were removed according to a method explained in more detail below.

[0105] The interference signals 36 degrade the signal-to-noise ratio for the echo reception signals 38. This degrades the accuracy of the object information determined about objects 28 detected by the radar sensor 14.

[0106] In order to be able to determine the most accurate object information about objects 28, for example exact distance values ​​32, exact direction values ​​and / or exact speed values ​​for objects 28, it is necessary to improve the signal-to-noise ratio.

[0107] For this purpose, a fault handling procedure is performed in a method 44 for operating the radar sensor 14. The method 44 is shown as a flowchart in Figure 6.

[0108] In the case of disturbance processing, disturbance analyses 46 are performed using an artificial neural network. The neural network is implemented, for example, as a convolutional neural network (CNN).

[0109] In the method 44, four disturbance analyses 46 are performed, for example. More or fewer disturbance analyses 46 can also be performed. The signal-to-noise ratio improves with the number of disturbance analyses 46.

[0110] For each of the disturbance analyses 46, a radar signal 26 is transmitted, and the corresponding echo signals 30 are detected and converted into raw received signals 40. The four disturbance analyses 46 are performed at short intervals for the same scene in the surveillance area 18. For each of the disturbance analyses 46, a different variation of a radar signal 26 is used, so that four different variations of radar signals 26 are used for the four disturbance analyses 46. For ease of differentiation, the reference symbols of the four different variations of the radar signals 26 are provided with the indices 1, 2, 3, and 4, respectively.

[0111] In the flowchart of Figure 6, the four disturbance analyses 46 are shown at the same height for clarity. The disturbance analyses 46 and the corresponding radar measurements take place sequentially. The sequence and principle of the four disturbance analyses 46 are identical. Therefore, the same reference numerals are used in the illustrations. Representative of all four disturbance analyses 46, the disturbance analysis 46 for the radar signal 26i, shown on the left in Figure 6, is explained in more detail below using the example of the scene shown in Figure 2.

[0112] In a measurement step 48, a radar measurement is performed using the radar signal 26i. The corresponding echo signals 30 and the interference beams 34 of the interference sources 42 shown as examples in Figure 2 are received by the receiving antenna Rx of the receiving device 22 and converted into an electrical raw received signal 40. The temporal progression of the raw received signal 40 is shown in Figures 3 and 4.

[0113] The raw received signal 40 is transmitted to the CNN neural network. Furthermore, known interference patterns 52 of known electrical interference signals and known object patterns 54 of known objects 28 are transmitted from a pattern memory 50 to the CNN neural network. The pattern memory 50 is, for example, part of the control and evaluation device 24.

[0114] An interference pattern 52 is characterized by the temporal progression of an electrical interference signal 36. The known interference patterns 52 can be patterns of interference signals 36 that typically occur during operation of the vehicle 10. For example, the known interference signals 36 can originate from interference beams 34 emitted by radar sensors of other vehicles.

[0115] An object pattern 54 is characterized by the temporal progression of an electrical echo reception signal 38. The known object patterns 54 can be, for example, patterns of echo reception signals 38 from objects 28 that typically occur during operation of the vehicle 10. Known objects 28 can be, for example, vehicles, people, animals, plants, obstacles, road surface irregularities, for example, potholes or stones, road markings, traffic signs, open spaces, for example, parking spaces, or the like.

[0116] The known interference patterns 52 and the known object patterns 54 are determined in advance, for example, at the end of a production line, through reference measurements with known interference sources 42 or known objects 28, respectively, and stored in the pattern memory 50. The reference measurements can be performed, for example, under laboratory conditions. Alternatively or additionally, known interference patterns 52 and / or known object patterns 54 can also be recorded, for example, "learned," during regular operating situations of the vehicle 10.

[0117] In the described embodiment, it is assumed that corresponding known interference patterns 52 are stored in the pattern memory 50 for the interference signals 36 from the scene in Figure 2. Furthermore, it is assumed that corresponding known object patterns 54 are stored in the pattern memory 50 for the echo reception signals 38 of the object 28 shown there, for example, a street sign.

[0118] In the CNN neural network, the raw received signal 40 is compared with the known object patterns 54 in an object cleaning step 56. For this purpose, pattern recognition methods can be performed, for example. If a match with a known object pattern 54 is detected—in this case, the object pattern 54 of the road sign—the raw received signal 40 is reduced by the identified echo received signal 38 of the known object pattern 54, namely the road sign, and fed to a disturbance analysis step 60 as a reduced received signal 58.

[0119] In the interference analysis step 60, the reduced received signal 58 is compared with the known interference patterns 52. For this purpose, pattern recognition methods can be carried out, for example. If a match with known interference patterns 52 is detected, the original raw received signal 40 is reduced by the interference signals 36 of the corresponding known interference patterns 52 in a cleanup step 62. In the exemplary embodiment shown, for example, the patterns of the interference signals 36 caused by the interference beams 34i, 34i, and 34a of the three interference sources 42i, 42i, and 42a shown in the scene in Figure 2 match the corresponding known interference patterns 52 stored in the pattern memory 50. The original raw received signal 40 is thus reduced by the interference signals 36i, 36i, and 36a.

[0120] After removing the effect of the detected interference signals 36i, 36i and 36a, in the event that all interference signals 36i, 36i and 36a occurring during the measurement are identified via the known interference patterns 52, only the interference-free echo reception signal 38 remains, which originates from the reflecting object 28, namely the road sign.

[0121] The cleaned echo reception signals 38 determined in each of the four exemplary disturbance analyses 46 are combined in a superposition step 64 to form a combined echo reception signal 66.

[0122] In an information determination step 68, the object information, for example, the distance variables 32, the directional variables, and / or speeds, for the detected object 28 are determined from the combined echo reception signal 66. Optionally, the object information can be subjected to further processing, for example, image processing.

[0123] The object information, for example the distance values ​​32, are transmitted to the central processor unit 16 of the driver assistance system 12.

[0124] Optionally, the settings of the transmitting device 20 and / or the receiving device 22 can be adapted to the current scene on the basis of the combined echo reception signal 66.

[0125] Instead of using the raw received signals 40 as the detection variables, the disturbance analysis 46 can also be performed using object information, such as distance variables 32, direction variables, and / or speed variables, as the detection variables. In this case, the object formations are determined in advance based on the corresponding raw received signals 40.

Claims

Claims 1. A method (44) for operating a detection device (14), in particular a detection device (14) for a vehicle (10), in which at least one electromagnetic beam (26) is transmitted into a monitoring area (18) of the detection device (14) by means of the detection device (14), at least one electromagnetic beam (30, 34i, 34z, 34a) coming from the monitoring area (18) is received by the detection device (14) and converted into at least one detection variable (40) that can be processed by at least one evaluation device (24), at least one disturbance treatment is carried out on the basis of at least one detection variable (40) using at least one artificial neural network (CNN), characterized in that at least one disturbance analysis (46) is carried out during the at least one disturbance treatment,in which the at least one detection variable (40) is examined with at least one artificial neural network (CNN) for known interference patterns (52) of interference variables (36) and, upon detection of at least one known interference pattern (52), the at least one detection variable (40) is corrected for interference variables (36) belonging to the at least one detected interference pattern (52).

2. Method according to claim 1, characterized in that the at least one fault analysis (46) is carried out several times and the adjusted detection variables (38) determined from the respective fault analyses (46) are combined to form at least one combined detection variable (66).

3. Method according to claim 1 or 2, characterized in that several different electromagnetic beams (26i, 262, 263, 264) are transmitted into the same scene of the surveillance area (18) and respective detection variables (40) are determined, at least one disturbance analysis (46) is carried out for at least some of the thus determined several detection variables (40), and respective adjusted detection variables (38) are determined for at least some of the transmitted different electromagnetic beams (26i, 262, 263, 264), and at least some of the thus determined several adjusted detection variables (38) to form at least one combined detection variable (66).

4. Method according to one of the preceding claims, characterized in that an artificial convolutional neural network is used as at least one artificial neural network (CNN).

5. Method according to one of the preceding claims, characterized in that in the at least one disturbance analysis (46), the at least one detection variable (40) is first examined for known object patterns (54) which are caused by electromagnetic echo beams (30) reflected by known objects (28), and upon detection of at least one known object pattern (54), an echo detection variable (38) corresponding to the at least one known object pattern (54) is removed from the at least one detection variable (40), then the at least one detection variable (58) freed from the detected at least one echo detection variable (38) is examined with at least one artificial neural network (CNN) for known interference patterns (52) of interference variables (36), and upon detection of at least one known interference pattern (52), the original at least one detection variable (40), which may contain the at least one echo detection variable (38),is adjusted for disturbance variables (36) belonging to the at least one detected disturbance pattern (52).

6. Method according to one of the preceding claims, characterized in that predetermined interference patterns (52) and / or optionally object patterns (54) are used for the at least one interference analysis (46) and / or interference patterns (52) and / or object patterns (54) learned during operation of the detection device (14) are used for the at least one interference analysis (46).

7. Method according to one of the preceding claims, characterized in that reception signals, in particular electrical reception signals, which are converted from electromagnetic rays (30, 34i, 342, 34a) by a reception device (22) of the detection device (14), are used as detection variables (40), and / or Object information (32) relating to objects (28) which are detected during measurements with the detection device (14) is used as detection variables (40), wherein the object information is determined from received signals (38), in particular electrical received signals, which are converted from electromagnetic rays (30) by a receiving device (22) of the detection device (14).

8. Method according to one of the preceding claims, characterized in that the method (44) is used to operate a detection device (14) in the form of a radar sensor with which electromagnetic rays (26) are transmitted in the form of radar beams.

9. Method according to one of the preceding claims, characterized in that at least one adjusted detection variable (40), in particular optionally at least one adjusted combined detection variable (66), is further processed, in particular is subjected to image processing, and / or on the basis of the at least one adjusted detection variable (38), in particular optionally the at least one adjusted combined detection variable (66), at least one transmitting device (20) and / or at least one receiving device (22) of the detection device (14) is adapted.

10. Detection device (14), in particular a detection device (14) for a vehicle (10), with at least one transmitting device (20) for transmitting electromagnetic rays (26) into a monitoring area (18) of the detection device (14), with at least one means (22) for receiving electromagnetic rays (30, 34i, 342, 34a) coming from the monitoring area (18) and for determining detection variables (40) from received electromagnetic rays (30, 34i, 342, 343), with at least one means (24) for carrying out interference treatments based on detection variables (40), wherein the at least one means (24) has at least one artificial neural network (CNN), characterized in that the detection device (14) comprises at least some of the means for carrying out the method according to one of claims 1 to 9.

11. Detection device (14) according to claim 10, characterized in that the detection device (14) is a radar sensor.

12. Driver assistance system (12), in particular a driver assistance system (12) for a vehicle (10), with at least one detection device (14), wherein the at least one detection device (14) has at least one transmitting device (20) for transmitting electromagnetic rays (26) into a monitoring area (18) of the at least one detection device (14), at least one means (22) for receiving electromagnetic rays (30, 34i, 342, 34a) coming from the monitoring area (18) and for determining detection variables (40) from the received electromagnetic rays (30, 34i, 342, 343), at least one means (24) for carrying out fault actions based on detection variables (40), wherein the at least one means (24) has at least one artificial neural network (CNN), characterized in that the driver assistance system (12) has at least some of the means for carrying out the method according to one of the claims 1 to 9.

13. Vehicle (10) with at least one detection device (14), wherein the at least one detection device (14) has at least one transmitting device (20) for transmitting electromagnetic rays (26) into a monitoring area (18) of the at least one detection device (14), at least one means (22) for receiving electromagnetic rays (30, 34i, 342, 343) coming from the monitoring area (18) and for determining detection variables (40) from the received electromagnetic rays (30, 34i, 342, 343), at least one means (24) for carrying out interference actions based on detection variables (40), wherein the at least one means (24) has at least one artificial neural network (CNN), characterized in that the vehicle (10) has at least some of the means for carrying out the method according to one of claims 1 to 9.

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