WO2024115172A1 - Procédé de compression de données provenant d'au moins un capteur de véhicule - Google Patents

Procédé de compression de données provenant d'au moins un capteur de véhicule Download PDF

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Publication number
WO2024115172A1
WO2024115172A1 PCT/EP2023/082406 EP2023082406W WO2024115172A1 WO 2024115172 A1 WO2024115172 A1 WO 2024115172A1 EP 2023082406 W EP2023082406 W EP 2023082406W WO 2024115172 A1 WO2024115172 A1 WO 2024115172A1
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Prior art keywords
spectrum
sensor data
data
sensor
relevant
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PCT/EP2023/082406
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German (de)
English (en)
Inventor
Maxim Tatarchenko
Kilian Rambach
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Robert Bosch Gmbh
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Publication of WO2024115172A1 publication Critical patent/WO2024115172A1/fr

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Classifications

    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/70—Type of the data to be coded, other than image and sound
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/003—Transmission of data between radar, sonar or lidar systems and remote stations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/417—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section involving the use of neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
    • G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/42—Simultaneous measurement of distance and other co-ordinates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/523—Details of pulse systems
    • G01S7/526—Receivers
    • G01S7/53—Means for transforming coordinates or for evaluating data, e.g. using computers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/523—Details of pulse systems
    • G01S7/526—Receivers
    • G01S7/53—Means for transforming coordinates or for evaluating data, e.g. using computers
    • G01S7/533—Data rate converters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/3066—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction by means of a mask or a bit-map

Definitions

  • the present invention relates to a method for compressing sensor data from at least one sensor of a vehicle.
  • the invention also relates to a computer program and a device for this purpose.
  • Scene recognition for autonomous vehicles requires precise detection and classification of objects and other road users. It is known from the state of the art to use a radar device on the vehicle for this purpose.
  • Vehicle radar has shown great potential as a sensor for driver assistance systems due to its robustness to weather and lighting conditions.
  • reliable detection and classification of radar object types in real time has proven to be very difficult.
  • a method according to the invention is used in particular for compressing sensor data from at least one sensor of a vehicle.
  • the following steps can be provided, which are preferably carried out one after the other or in any order and/or automatically:
  • the sensor data can be implemented as radar data, for example, preferably as a radar spectrum in which objects in the environment can be evaluated, i.e. detected and/or classified, depending on a spatial position and/or relative speed and/or distance and/or the like.
  • the sensor can be designed as a radar sensor or the like. be designed. It is also possible that the sensor is attached to the vehicle in such a way that the sensor can detect the surroundings of the vehicle. For example, the sensor is aligned in the direction of travel for this purpose.
  • the relevant areas can, for example, be provided for object evaluation and in particular object detection in that at least one object in the surroundings can be evaluated, preferably detected and/or classified, by evaluating the relevant areas.
  • the relevant areas include, for example, data values which indicate a distance and/or a position and/or a relative speed and/or the like of the object.
  • these areas can also overlap, particularly in the case of objects that are close to one another and also when several reflections, such as radar reflections, are measured on one object.
  • the sensor data are not retained in full, but (only) the relevant areas are selected, preferably stored and/or transmitted. If each area were to be selected in full, this could lead to redundancy of the data values if the areas overlap. Therefore, according to the invention, it can be provided that the overlaps are detected in order to at least reduce the redundancy of the information. For example, the overlapping areas are only selected once, e.g. stored and/or transmitted.
  • radar reflections For the evaluation, preferably detection and/or classification, of objects using radar data, methods can generally be used that use either radar reflections or radar spectra or a combination of these, i.e. both radar spectra and radar reflections, as input data.
  • the radar can send out a sensor signal, receive it and calculate the radar spectra from it (e.g. distance, Doppler and/or angle spectra).
  • Possible radar reflections can be detected in the spectrum as peaks in the radar spectra, for example by a peak value detection such as a so-called "constant false alarm detector”.
  • Various attributes such as distance, relative radial velocity, azimuth angle, elevation angle and/or radar cross section (RCS) can be calculated for these reflections.
  • the set of these radar reflections is also called a radar point cloud.
  • the radar spectra generally contain even more information than the detected radar reflections. Therefore, it has proven advantageous to use not only the radar point cloud, but the radar spectra or the combination of radar spectra and radar point cloud as input data for an algorithm, e.g. an artificial neural network (NN).
  • NN artificial neural network
  • a combination of radar spectra and radar point clouds as input data for a NN is used in [2].
  • the relevant areas of the radar spectrum are identified and used together with the radar point cloud.
  • Using radar spectra as input for a neural network (NN) can generally be more computationally and memory intensive than radar point cloud based approaches.
  • the radar spectrum must be stored even though it is very sparse and only a small part of the spectrum is used for further processing. Therefore, it may be useful to use a more memory efficient method to efficiently store the radar spectrum. This is particularly advantageous when the radar spectrum is to be transmitted from the radar sensor to a central processing unit, since only a limited bandwidth may be available for this.
  • a compressed radar spectrum can be transmitted with a lower bandwidth.
  • Using radar spectra as input for a neural network (NN) is therefore often more computationally expensive and requires more memory than radar point cloud based approaches.
  • the method proposed according to the invention has the advantage that it can be identified in advance which part of the radar spectra is relevant for the evaluation such as detection and/or classification of an object. Only This part therefore needs to be stored. A method is therefore proposed in particular to store this part more efficiently and thus reduce the amount of data to be stored.
  • the detected overlap of the relevant areas is used for compression and preferably for redundancy reduction in that, on the basis of the detection, the compressed sensor data are obtained in which the amount of overlap is at least reduced or completely eliminated.
  • the overlapping parts can comprise redundant information, so that the detection is carried out for redundancy reduction.
  • the sensor data are designed as the at least one- or at least two-dimensional (or multi-dimensional) spectrum of the received sensor signal and result from processing of the received sensor signal, wherein the sensor data can be present in an at least one- or at least two-dimensional or multi-dimensional form, wherein the relevant areas each identify an at least one- or at least two- or multi-dimensional area of the sensor data which contains information about at least one detected point of at least or exactly one (detected) object in the surroundings of the vehicle.
  • peak value detection can be used to determine those points in the sensor data which identify at least one (detected) object.
  • the relevant areas can be determined, for example, by selecting an at least one- or at least two-dimensional area around the respective points. This can cause overlaps if points are arranged close to one another.
  • determining the relevant areas includes the following steps:
  • POI points of interest
  • the sensor data and thus also the relevant areas of the sensor data, have several data values in an at least one- or at least two-dimensional data structure, wherein the data values can each be assigned coordinates, wherein the detection of the overlap takes place by an evaluation and in particular a comparison of the coordinates of the data values of the relevant areas.
  • the data structure is, for example, a matrix or the like.
  • the detection is also optionally possible for the detection to be carried out during data transmission and/or data storage of the sensor data, whereby, on the basis of the detection, multiple transmission and/or storage of the overlapping parts is avoided, and preferably, after a first transmission and/or storage, a renewed transmission and/or storage of the overlapping parts is skipped.
  • a renewed transmission and/or storage of the overlapping parts is skipped.
  • the overlaps and preferably the overlapping parts can accordingly designate those data values of the relevant areas that have the same coordinates in the data structure.
  • the compressed sensor data can, for example, be transmitted to a control unit of the vehicle and/or to a server outside the vehicle, where it can be decompressed and evaluated.
  • the vehicle can be designed, for example, as a motor vehicle and/or passenger vehicle and/or autonomously driving vehicle. Furthermore, the control of the vehicle can be carried out, for example, by a driver assistance system and/or by an autonomous driving function and/or by a braking function such as emergency braking based on the evaluation of the relevant areas and/or the object evaluation and/or the object detection.
  • the sensor data are implemented as a radar spectrum or an ultrasound spectrum or a lidar spectrum.
  • the invention also relates to a computer program, in particular a computer program product, comprising instructions which, when the computer program is executed by a computer, cause the computer to carry out the method according to the invention.
  • the computer program according to the invention therefore brings with it the same advantages as have been described in detail with reference to a method according to the invention.
  • the invention also relates to a device for data processing which is set up to carry out the method according to the invention.
  • the device can be, for example, a computer which executes the computer program according to the invention.
  • the computer can have at least one processor for executing the computer program.
  • a non-volatile data memory can also be provided in which the computer program is stored and from which the computer program can be read by the processor for execution.
  • the invention can also relate to a computer-readable storage medium which comprises the computer program according to the invention.
  • the storage medium is designed, for example, as a data storage device such as a hard disk and/or a non-volatile memory and/or a memory card.
  • the storage medium can, for example, be integrated into the computer.
  • the method according to the invention can also be implemented as a computer-implemented method.
  • Fig. 1 is a schematic representation of the sensor data according to embodiments of the invention.
  • Fig. 2+3 further exemplary sensor data in the form of radar spectra
  • Fig. 5 an evaluation of the method according to embodiments of the invention
  • Fig. 6 shows the method according to embodiments of the invention.
  • a method 100 for compressing sensor data 300 of at least one sensor 40 of a vehicle 1 is shown schematically.
  • a first method step 101 Determination of the sensor data 300 can be provided, for example by receiving the sensor data 300 by a processing function which processes a sensor signal received by the at least one sensor 40 in order to output an at least one-dimensional or at least two-dimensional spectrum 300 of the sensor signal.
  • a second method step 102 relevant regions 302 in the sensor data 300 can be determined, wherein the relevant regions 302 can be provided for object evaluation and in particular detection of one or more objects in an environment 2 of the vehicle 1.
  • these relevant regions 302 should be fed as relevant parts of the spectrum 300 to an algorithm for object evaluation and preferably detection.
  • the relevant regions 302 can partially overlap.
  • detection of the overlap of the relevant regions 302 is therefore provided for compressing the sensor data 300.
  • the detected overlapping parts of the relevant regions 302 are stored and/or transmitted only once.
  • the method 100 can thus provide an efficient compression algorithm and preferably storage algorithm.
  • a computer program 15 and a data processing device 10 are shown in Fig. 6.
  • the proposed method 100 can use the special structure of the sensor data 300 and in particular of radar spectra and radar point clouds to efficiently store the relevant parts of the "sparse" sensor data 300. Furthermore, the special structure can be used to efficiently process the data.
  • the proposed method 100 is also not only applicable to radar, but also to other sensors where peaks can be detected in a spectrum, such as lidar or ultrasound.
  • the method 100 is described in more detail below using the example of a radar spectrum.
  • the sensor data 300 are shown schematically in the form of a radar spectrum 300.
  • the detected radar reflections 301 are shown, which are also referred to as “points of interest” (POI for short) or, within the scope of the invention, as relevant points.
  • POI points of interest
  • the relevant areas 302 of the spectrum 300 around the radar reflections 301 are shown, which can also be referred to as a "patch".
  • the remaining area of the spectrum 300 may not be relevant for further evaluation.
  • the positions of the detected radar reflections 301, i.e. the POIs 301, as well as the other relevant areas 302 of the spectrum 300 are stored.
  • the storage can be carried out in such a way that the points at which the areas
  • the proposed method 100 is described in connection with one- or at least two-dimensional radar data, it is also applicable for data of higher dimensions (e.g. 4D for range, Doppler, azimuth, elevation spectra). It is also applicable for sensors other than radar, e.g. for lidar or ultrasound.
  • the spectrum 300 results from a measurement of an environment 2, preferably a vehicle 1, preferably by a sensor 40 such as a radar, lidar or ultrasound sensor 40.
  • the method 100 can also be used for object evaluations such as classification tasks or a detection of objects or a semantic segmentation based on the sensor data 300 such as the spectra, e.g. with regard to pedestrians, other vehicles 1 or smaller objects.
  • a measurement of a distance, a speed and/or an acceleration is carried out based on the sensor data 300 such as the spectra. Tracking of objects can also be provided if necessary.
  • a control signal e.g. for a vehicle 1
  • the control signal can be used, e.g. for braking and/or steering and/or accelerating the vehicle 1, e.g. for emergency braking and/or for a driver assistance system and/or for an autonomous driving function.
  • the method 100 according to embodiments of the invention can be used to generate training data for training a neural network. In particular, the effort for storing and/or processing the training data can be reduced by the method 100.
  • Embodiments of the invention can also be used to construct radar hardware that efficiently uses memory and available bandwidth.
  • sensor data 300 from several sensors are combined and processed in a central control unit.
  • the bandwidth with which the sensor data 300 can be sent from a sensor 40 to the central control unit is limited. It is therefore advantageous to transmit the sensor data 300 in a compressed form and thereby save bandwidth or manage with a limited bandwidth. It is therefore an advantage of embodiments of the invention that the relevant areas 302 of the radar spectrum can be efficiently compressed and stored.
  • a further application for embodiments of the invention can be provided by temporarily storing radar spectra in a radar sensor when algorithms access them at a later point in time. If a new radar measurement is carried out in parallel during this time, the new spectra 300 must also be stored. It is therefore advantageous if the radar spectra are stored in a compressed form in order to save storage space.
  • the method 100 is also applicable to various radar spectra 300, e.g. non-coherently integrated spectra, so-called beamformed spectra, or the complex spectra of the various reception channels.
  • radar spectra 300 e.g. non-coherently integrated spectra, so-called beamformed spectra, or the complex spectra of the various reception channels.
  • complex spectra the real and imaginary parts or the magnitude and phase of the complex number for the respective value under consideration can be stored.
  • the method is also applicable to data from other sensors 40.
  • a rectangular patch as shown in Fig.1, is used as an example as the relevant area 302.
  • the storage algorithm preferably a compression algorithm, can also be easily extended to other shapes of the patch, e.g. an ellipse or a trapezoid.
  • the storage algorithm is shown below as an example as pseudocode: def compress_spectrum(pois, spectrum):
  • the function receives as input variables the one or two-dimensional coordinates of the POI 301 in the spectrum 300 as well as the spectrum 300 that is being compressed. For each POI 301 specified with "p", the corresponding patch coordinates are iterated over. If a pixel value has not yet been saved in the respective patch, it is saved. A mask stores which pixel values have already been saved so that no value is saved twice.
  • the information with which the spectrum 300 can be decompressed again is contained in the list with the coordinates of the POI 301 "pois", the compressed spectrum “spectrum_compressed” and the size of the spectrum “spectrum_size”. It is advantageous that the mask does not need to be saved, as it can be calculated on-the-fly during decompression.
  • the compression algorithm can be executed more memory efficiently during compression.
  • the compressed result is still the same.
  • the mask can be omitted. This reduces the amount of data storage required during the calculation. Instead, once a value from the spectrum has been stored, this value in the spectrum is replaced by an invalid value. value. This information can be used to avoid storing these values twice in the future. To do this, you can compare whether the current pixel value being considered is equal to invalid_value. It must be noted that invalid_value is a value that cannot occur in real measurements.
  • decompression algorithm can work analogously to the compression algorithm and is simplified below using pseudocode: def decompress_spectrum(pois, spectrum_compressed, spectrum_size):
  • the spectrum is initialized as an array with 0 in a given size.
  • the POIs and the respective patch coordinates x,y are iterated over. For each pixel value that has not yet been saved in "spectrum_decompressed", the next value of spectrum_compressed is saved to the current coordinate x,y.
  • Fig. 2 shows an example spectrum with typical parameters of a radar sensor.
  • the size of the spectrum is 32x256, with 250 POIs and a patch size of 5x9 pixels.
  • the values in the patches were randomly generated.
  • uncompressed refers to uncompressed storage
  • saving as sparse matrix refers to a method in which the position of the pixel and its value are stored for each value not equal to 0
  • compressed is the proposed method according to embodiments of the invention.
  • Other known compression methods are listed as a further comparison:
  • the table shows the storage space required for 8 bits or 32 bits per stored value and 16 bits per stored index. It can be seen that for this example, "Saving as sparse matrix" is not advantageous and actually requires more storage space than the uncompressed method. This is because the data is not sparse enough.
  • the other methods Block Sparse Row, Compressed Sparse Row, Compressed Sparse Column are also not suitable.
  • the proposed method "compressed” requires the least storage space, only about 76% of the storage space for 32 bit values and 85% of the storage space for 8 bit values compared to the uncompressed method.
  • “Saving as sparse matrix” requires less memory for 32-bit values than the uncompressed method, but it is still unsuitable for 8-bit values and requires more memory than the uncompressed method.
  • the other methods also all require more memory than the proposed method.
  • the proposed method requires the least memory, at around 41% and 45% compared to the uncompressed methods.
  • Fig. 4 shows a spectrum from a real measurement in a city.
  • the spectrum size is 256x32 bins and a patch size of 5x9 bins.
  • the patches are shown in lightly shaded points, the POIs in darker points in the center of the lightly shaded patches. It can be seen that the spectrum is sparse and many of the patches overlap, analogous to the simulated radar spectra above. This means that the proposed method can also be advantageously applied to real measurements.
  • the indices of the bins in the spectrum under consideration in which the POIs are located can be calculated from these attributes. In this case, it is not necessary to store the indices again, i.e. the memory requirements of the proposed method can be further reduced. During decompression, the required indices can be calculated on-the-fly.
  • the proposed method can be combined with a "run length encoding".
  • the sequence of spectrum values is not saved, but rather the number of times a value occurs in succession and then the value itself is saved. Depending on the spectrum being considered, this can lead to further storage space savings.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

L'invention concerne un procédé (100) de compression de données de capteur (300) provenant d'au moins un capteur (40) de véhicule (1), comprenant les étapes suivantes : l'acquisition de données de capteur (300) propres à un spectre au moins unidimensionnel (300) d'un signal de capteur reçu par au moins un capteur (40) ; la détermination de zones pertinentes (302) dans les données de capteur (300) qui sont fournies pour l'évaluation d'objets dans une zone environnante (2) du véhicule (1), les zones pertinentes (302) se chevauchant en partie ; la détection du chevauchement des zones pertinentes (302) afin de compresser les données de capteur.
PCT/EP2023/082406 2022-11-29 2023-11-20 Procédé de compression de données provenant d'au moins un capteur de véhicule WO2024115172A1 (fr)

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DE102018109001A1 (de) 2018-04-16 2019-10-17 Infineon Technologies Ag Verfahren zum Erzeugen einer kompakten Darstellung von Radardaten, eine Radarvorrichtung und eine Radardatenverarbeitungsschaltung
US20200292658A1 (en) 2019-03-12 2020-09-17 Semiconductor Components Industries, Llc Methods and apparatus for data compression and transmission
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