Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
  • 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
  • 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
  • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
  • G01S13/862—Combination of radar systems with sonar systems
• • 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
  • G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
• • 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
• • 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
  • G01S15/87—Combinations of sonar systems
• • 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
• • 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
  • 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
  • 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
  • G01S2013/9316—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles combined with communication equipment with other vehicles or with base 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
  • 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
  • G01S2013/9324—Alternative operation using ultrasonic waves
• • 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
  • G01S7/411—Identification of targets based on measurements of radar reflectivity
  • G01S7/412—Identification of targets based on measurements of radar reflectivity based on a comparison between measured values and known or stored values

Definitions

• the invention relates to a method for determining a road condition, a computer program, one or more machine-readable storage media and one or more control devices.
• the invention relates to a method for determining a road condition of a motor vehicle (ie the condition of the road on which the motor vehicle is traveling), with a first sensor system depending on first input variables (rl, r2) and dependent on second input variables (r3 , r4) by means of a distributed machine learning system, in particular a distributed neural network, a variable (z) characterizing the road condition is determined.
• Neural networks are constructed in such a way that there is an input layer (English: “input layer”), one or more hidden layers (English: “hidden layer”) and an output layer (English: “output layer”).
• input layer English: “input layer”
• hidden layer English: “hidden layer”
• output layer English: “output layer”
• basic functions so-called neurons, are calculated, which get values from the previous layer, evaluate them and pass them on to the next layer.
• neural networks English: “Deep Neural Networks", also short: "DNN”
• DNN Deep Neural Networks
• the architecture of such a DNN is of crucial importance for the training of these models, e.g. the question of how many layers there are and which tasks they take on (e.g. folding operations or similar).
• the first sensor system and / or second sensor system can in particular be arranged in the motor vehicle.
• the first sensor system and / or the second sensor system can each include an ultrasonic sensor, a radar sensor, or an optical sensor (in particular a lidar or video).
• the first sensor system and / or the second sensor system can each comprise a plurality of sensors.
• the distributed neural network is distributed to at least two, in particular structurally separate, control units of the motor vehicle.
• the distributed neural network is partially implemented in a sensor control device.
• the sensor control unit is an ultrasound sensor control unit or a radar sensor control unit or a control unit of an optical sensor.
• a wheel speed sensor is also possible.
• Such a sensor control device can be provided to receive (raw) data from the sensors assigned to it and to carry out preprocessing. This preprocessed data can then be fed to a central processing unit of the motor vehicle. It is possible that the sensor control device and at least one assigned sensor are structurally integrated,
• the distributed neural network is also partially implemented in a central processing unit of a motor vehicle.
• the central processing unit determines the variable (z) characterizing the state of the road. That is, the part of the neural network that runs in the central processing unit comprises the starting layer of the neural network.
• the motor vehicle is actuated, for example by actuating the drive and / or brake and or steering, for example to initiate a speed adjustment or an evasive maneuver.
• a road signal system is activated depending on the size (z) characterizing the state of the road.
• a warning system can be activated when bad road conditions are detected.
• the information about the road condition can be transmitted to other motor vehicles, which in turn can initiate corresponding reactions.
• the neural network is trained before determining the variable (z) that characterizes the road condition.
• a neural network trained in this way as a whole is particularly efficient.
• the parts of the neural network that are implemented in sensor control devices have the function of compress the sensor data. These are then decompressed again in the central computer by the parts of the neural network located there.
• the functions of detecting the state of the road and compressing the data are fused with one another in the parts of the neural network which are arranged in the sensor control devices.
• the neural network thus fused can get by with less computing time and memory for the same result.
• the parts of the neural network that perform the decompression and sensor data fusion are fused together in the central control unit. This saves resources in the central control unit.
• the training is carried out in such a way that a feature vector to be averaged between separate parts of the neural network is as small as possible.
• That the architecture of the DNN can be optimized using an automated process such as AutoML are performed so that transitions of different neurons from layer to layer are created in such a way that these transitions also correspond to the hardware-specific interfaces of the individual devices.
• the feature vector which is as small as possible, ensures an optimally compressed flow of information between the devices.
• the figure illustrates the structure of an embodiment of the invention in a motor vehicle (100) which has ultrasonic sensors (1, 2, 3, 4) and radar sensors (5).
• the ultrasonic sensors (1, 2, 3, 4) determine their respective raw signals and transmit them to an ultrasonic sensor control unit (10) on which a first part (11) of the distributed neural network is arranged. These raw signals lie there as signals (rl, r2) at the input layer of the first part (11) of the distributed neural network.
• the first part (11) of the distributed neural network can be implemented, for example, in a computer program which is stored on a machine-readable storage medium (12) of the ultrasound sensor control device (10).
• the radar sensors (5) likewise determine their respective raw signals and transmit them to a radar sensor control unit (20) on which a second part (21) of the distributed neural network is arranged. These raw signals are there as signals (r3, r4) at the input layer of the second part (21) of the distributed neural network.
• the second part (21) of the distributed neural network can be implemented, for example, in a computer program that is stored on a machine-readable storage medium (22) of the radar sensor control unit (20).
• the first part (11) now determines output signals that act as the first feature vector
• the second part (12) also determines output signals which are transmitted as the second feature vector (y) from the radar sensor control unit (20) to the central control unit (30), preferably via the same bus.
• a third part (31) of the distributed neural network is implemented in the central control device (30).
• the first feature vector (x) and the second feature vector are implemented in the central control device (30).
• the third part (31) uses this to determine an output variable (z) which characterizes the state of the road on which the motor vehicle is traveling.
• the third part (31) of the distributed neural network can be implemented, for example, in a computer program that is stored on a machine-readable storage medium (32) of the central control device (30).
• a speed of the motor vehicle is then reduced in the exemplary embodiment if the output variable (z) shows that the road is wet, is dirty, has a reduced coefficient of friction, ground waves, or potholes.
• the distributed neural network (11, 21, 31) is also learned in a targeted manner with errors in an electrical system of the motor vehicle (100). That means in training, for example, error patterns in the transmission of the first and / or the second
• Characteristic vectors (x, y) are simulated and injected, and these error patterns can then be trained by means of monitored learning. This makes it possible to detect incorrect road conditions due to electrical faults in the system, e.g. to avoid a loose contact on one of the data transmission lines.
• the distributed neural network can also be trained to recognize the errors and to enter them in an error memory in order to be able to provide appropriate information when repairing the motor vehicle (100).
• the neural network is also at least partially executed in the sensors themselves.
• the neural network can also be extended to a computer located remotely from the motor vehicle and thus to a plurality of vehicles traveling on the same road.

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• 2018
  • 2018-06-14 DE DE102018209595.7A patent/DE102018209595A1/de active Pending
• 2019
  • 2019-05-22 CN CN201980038830.4A patent/CN112204418A/zh active Pending
  • 2019-05-22 WO PCT/EP2019/063209 patent/WO2019238367A1/de active Application Filing

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