WO2022106342A1 - Dispositif d'évaluation, programme informatique et procédé mis en œuvre par ordinateur pour entraîner un réseau neuronal à déterminer des coefficients de frottement - Google Patents

Dispositif d'évaluation, programme informatique et procédé mis en œuvre par ordinateur pour entraîner un réseau neuronal à déterminer des coefficients de frottement Download PDF

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Publication number
WO2022106342A1
WO2022106342A1 PCT/EP2021/081647 EP2021081647W WO2022106342A1 WO 2022106342 A1 WO2022106342 A1 WO 2022106342A1 EP 2021081647 W EP2021081647 W EP 2021081647W WO 2022106342 A1 WO2022106342 A1 WO 2022106342A1
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Prior art keywords
vehicle
neural network
roadway
friction
tire
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PCT/EP2021/081647
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German (de)
English (en)
Inventor
Mehrdad Salari
Ulrich Mair
Original Assignee
Zf Friedrichshafen Ag
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Publication of WO2022106342A1 publication Critical patent/WO2022106342A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
    • B60T8/17—Using electrical or electronic regulation means to control braking
    • B60T8/172—Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/068—Road friction coefficient
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T2210/00—Detection or estimation of road or environment conditions; Detection or estimation of road shapes
    • B60T2210/10—Detection or estimation of road conditions
    • B60T2210/12—Friction
    • B60T2210/122—Friction using fuzzy logic, neural computing
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
    • B60W2420/403—Image sensing, e.g. optical camera
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
    • B60W2420/408—Radar; Laser, e.g. lidar
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/54—Audio sensitive means, e.g. ultrasound

Definitions

  • Evaluation device computer program and computer-implemented method for training a neural network for determining the coefficient of friction
  • the invention relates to a computer-implemented method for training a neural network for determining the coefficient of friction, the neural network being executed on an evaluation device of a motor vehicle.
  • An evaluation device interprets the input data, and in the case of vehicles in an autonomous driving mode, a control device takes over both the longitudinal guidance and the lateral guidance of the motor vehicle.
  • the otherwise active driver of the motor vehicle becomes the passive vehicle occupant of the vehicle.
  • Semi-autonomous and in particular autonomous vehicles use sensors to record the road ahead and the area around the vehicle.
  • the sensors preferably provide the evaluation device with input data, such as lane boundaries, lane edges, lane markings and other road markings.
  • Some vehicle systems also use input data from global positioning systems, such as GPS, vehicle-to-vehicle communications, and/or on-board data to automatically control the vehicle.
  • Autonomous driving can be divided into five levels of automation. Automation level one corresponds to assisted driving and automation level five corresponds to fully autonomous driving. In fully autonomous driving, several input data are combined sioned to achieve higher reliability. The higher the level of automation in autonomous driving, the higher the demands on the reliability and quality of the input data. Even a small error in the calculation of driving-specific variables can result in a safety risk for the vehicle, vehicle occupants and external road users in fully autonomous driving mode.
  • the condition of the road surface and the road surface friction between the vehicle's tires and the road are necessary in order to be able to react specifically and appropriately to conditions external to the vehicle.
  • the roadway friction coefficient between the roadway surface and at least one tire of the vehicle is important in particular in the case of high lateral acceleration in curves and during braking and acceleration processes, in order to adapt the speed of the vehicle to the conditions external to the vehicle if necessary.
  • Optical methods for detecting the roadway are already widely known. Road markings and other objects are detected and recognized in DE 10 2015 121 504 A1, for example. Furthermore, rudimentary methods for determining a roadway friction value are known.
  • a road surface friction value can be determined from the tire speed.
  • motor vehicles are equipped with an anti-lock braking system to prevent the wheels from locking in a braking situation.
  • the tire speeds of individual tires are measured using tire speed sensors and compared with one another.
  • the brake pressure on a tire is reduced if its speed during braking falls disproportionately compared to those of the other wheels. Taking into account the change in rotational speed when accelerating or braking, a road surface condition and a road surface friction value when driving over the road can be determined.
  • the control device can react accordingly and dangerous situations are avoided.
  • the earlier and more reliably the coefficient of friction of a section of roadway is predictively determined, the greater the gain in safety and comfort for vehicle occupants.
  • anticipatory driving increases the vehicle's efficiency.
  • the object of the present invention to specify an improved method for predictively determining the roadway friction coefficient between the tires of a vehicle and a roadway.
  • the method for the predictive determination of the road surface friction value should be improved during use.
  • tire wear is to be recognized and taken into account when determining the road surface friction value.
  • a computer-implemented method for training a neural network for determining the friction coefficient of the roadway is carried out on the evaluation device of a motor vehicle and includes the acquisition of a first input data set using at least one first sensor arrangement, the predictive calculation of a first road surface friction value using the neural network as a function of the first input data set, the acquisition of a second data set using at least one second sensor arrangement, that Determining the second roadway friction value, determining a weighting error of the neural network taking into account the first input data set and the second roadway friction value and that Train the neural network to minimize the weighting error.
  • the coefficient of friction of a tribological system When determining the coefficient of friction of the roadway, the coefficient of friction of a tribological system, preferably between at least one tire of the vehicle and the roadway, is to be determined.
  • the term roadway friction value also includes the synonyms coefficient of friction, coefficient of friction and coefficient of friction.
  • the friction coefficient between the tires and the road is lower than on a dry road. Impurities on the roadway also lead to a lower roadway friction coefficient between the two bodies.
  • the properties of the tire also influence the coefficient of friction.
  • tire properties can be taken into account when determining the condition of the road surface.
  • the tire condition can be taken into account and also monitored when training the neural network.
  • a road ahead of the vehicle and to be traveled on is to be detected.
  • vehicle-specific data such as vehicle speed, steering angle, assistance system information with additional data such as the planned route guidance, GPS data, curves and elevation profile can be recorded and combined with one another. Based on the combined data, it can be predicted- which lane the vehicle is traveling on and for which lane the computer-implemented method for training the neural network for determining the friction coefficient of the lane is applied.
  • a plurality of input data sets are advantageously provided for training the neural network. At least one first input data set and at least one second data set are preferably available to the neural network for training.
  • the computer-implemented method for training the neural network for determining the coefficient of friction includes providing the first input data set.
  • This first input data record is recorded using at least one first sensor arrangement.
  • This at least one first sensor arrangement is advantageously aligned in the direction of travel and detects the road ahead. Multiple data can be taken into account and combined with each other during acquisition. In this way, the condition of the road surface, the outside temperature and other data can be recorded.
  • the at least one first sensor arrangement can preferably include a camera, an ultrasonic sensor, a radar system, a lidar system and/or the like.
  • the first sensor arrangement can advantageously detect further emitted or reflected radiation in the non-visible spectral range, such as infrared radiation.
  • the roadway ahead can be subdivided into individual logical roadway sections for the predictive calculation of the first roadway friction value.
  • the at least one first sensor arrangement can provide a first input data set for each logical roadway section.
  • the logical lane sections to be detected can advantageously be chosen to be as small as desired. The smaller the individual logical lane sections are defined, the greater the sum of the logical lane sections for a lane. The sum of the first input data records for a lane also increases accordingly. Consequently, the amounts of data for the lane to be determined increase as the logical lane sections become smaller.
  • the calculation of all first roadway friction values for smaller logical roadway sections is required more computationally intensive than for larger logical lane sections.
  • Smaller logical lane sections have the advantage that the neural network can be trained more precisely.
  • a further advantage is a finely resolved predictive determination of the roadway friction coefficient, which is made available to the control unit when controlling the vehicle.
  • the first roadway friction value between at least one tire of the vehicle and the roadway should be calculated using the first input data set.
  • the calculation of the road surface friction between a tire such as B. the rubber of a tire and a driven road a complex function depending on the forces and the friction interface between the two media.
  • Existing bumps, the distribution of these bumps, the material composition of the tire and the road, and the size of the contact surface of the two media affect the calculation and the driving characteristics of the vehicle on the road.
  • the properties of the road surface and the tires are temperature-dependent. If the roadway is covered by a layer of snow or ice, the roadway friction coefficient between the vehicle's tires and the roadway changes.
  • the data from a number of sensors are advantageously recorded and made available to the evaluation device.
  • the data can be merged and evaluated in the evaluation device.
  • the control device takes over the control of the vehicle depending on the predictively calculated first road friction value of the evaluation device, a comfort and safety gain is provided compared to vehicles without predictive calculation of the road surface friction value.
  • the first input data set is made available to the evaluation device by the at least one first sensor arrangement.
  • the evaluation device can process the input data record and/or other data in a number of processes within the neural network.
  • the evaluation device advantageously includes tely a computing unit, such as a processor, which, thanks to its processor architecture, is designed to process several processes in parallel, such as those found in neural networks.
  • a large number of processor cores enable efficient parallel processing.
  • a graphics processor can be used particularly advantageously, since graphics processors are usually designed for parallel process calculations.
  • the at least one computing unit includes a memory and/or a memory connection to an external memory.
  • the at least one computing unit accesses a computer program via an interface and uses a neural network to calculate a first road surface friction value, which can also be referred to as a calculated actual data set, taking into account the first input data set.
  • the calculation of the first road surface friction includes a pattern recognition algorithm and/or an object recognition algorithm.
  • first input data sets of the front camera, the ultrasonic sensor of the radar system, and/or the like are generated and made available to the pattern recognition algorithm and/or the object recognition algorithm.
  • Sensor data can preferably be evaluated in a fused manner by the evaluation device when a first input data set is generated, and the first roadway friction coefficient can thereby be calculated more precisely and predictively.
  • a second data set for each roadway section is recorded by means of at least one second sensor arrangement.
  • the second sensor arrangement records a second data record when driving over a predetermined, logical roadway section.
  • the first input data records and the predictively calculated roadway friction values are already available for these logical roadway sections.
  • the second set of data is recorded by a second sensor arrangement.
  • the second data set can preferably be generated with a slip measurement. In a slip measurement, the slip which occurs on at least one tire of the vehicle is measured by the second sensor arrangement. In general, slip means the deviation between theoretical speed and actual speed.
  • slip is to be understood as meaning the deviation in the speeds of elements in frictional contact with one another, in particular surfaces.
  • the slip can be determined from the ratio of the speed of a driven tire that is subject to slip to the speed of a non-driven tire that is therefore rotating positively without tire slip.
  • the second set of data can also include the tire speed of at least one tire.
  • the second data record is made available to the evaluation device. This calculates a second roadway friction coefficient for each logical roadway section, taking into account the second data set.
  • a first input data record from the first sensor arrangement and a second data record from the second sensor arrangement are advantageously available for each logical roadway section. Assuming that the calculated second roadway friction value acts as a reference value and the first input data set acts as an actual value, a weighting error of the neural network is determined for each logical roadway section, taking into account the first input data set.
  • a machine learning method in particular a reinforcing, supervised, machine learning method, can be used to train the neural network.
  • a backpropagation algorithm which is also referred to as an error feedback algorithm
  • the algorithm is provided with input data and an expected result. The algorithm links these two quantities together.
  • the algorithm can independently determine results for unknown data.
  • the output of network is directly dependent on these weights.
  • an artificial neural network is trained by changing the weightings in order to minimize an error function.
  • Reinforcement learning can be used to work with a reward and/or punishment system, with weights being adjusted within the neural network and corresponding incentives being created as a result.
  • the neural network should be trained to minimize the weighting error.
  • the training of the neural network by minimizing the weighting error is advantageously carried out by the evaluation device for each logical roadway section.
  • the accuracy of the predictively calculated road friction coefficient increases steadily.
  • the vehicle and the vehicle occupants there is an increase in safety and comfort while at the same time increasing the efficiency of the vehicle.
  • the weighting error can advantageously be determined taking into account a difference between the first input data set and the second road surface friction value. If a weighting is changed as part of the training of the neural network, this has an immediate effect on all subsequent neurons within the neural network. This influences the output of the neural network.
  • the weightings can be determined via an error function by calculating the partial derivative of the error function based on weighting parameters.
  • i 2 and ßi can look like this:
  • E is the calculated weighting error, from which adjustments to individual weightings in the neural network can be derived
  • n is the set of input data sets for which a road surface friction value is calculated.
  • i 2 is the calculated second roadway friction value and is the predictively calculated first roadway friction value. So that small errors are included less intensively than large errors in the weighting error and thus in the training of the neural network, a medium quadratic error can be determined. This is also known as the mean squared error.
  • a control device is designed to determine and carry out vehicle actions, taking into account the predictively calculated coefficient of road friction, and in particular to provide a fully autonomous driving mode.
  • the control device takes over the longitudinal and lateral control of the vehicle.
  • At least one tire of the vehicle is slipping and/or slip is actively generated and the second sensor arrangement detects this slip and a second road surface friction value is determined from the second data set.
  • slip is actively generated in an advantageous embodiment.
  • a defined amount of slip a target slip, is generated. If one or more tires are already slipping, the actively generated slip is additional slip. Otherwise, the actively generated slip is the total slip present.
  • Acceleration can advantageously be understood to mean a positive acceleration, also referred to as an acceleration process, or a negative acceleration, also referred to as a braking process.
  • a positive acceleration to generate slip causes a positive change in the speed of the vehicle in the direction of travel.
  • a drive torque of a motor rotationally accelerates tires in order to positively change the speed of the vehicle.
  • the slip-generating braking process in contrast to positive acceleration to generate slip causes a negative change in the speed of the vehicle in the direction of travel.
  • Brakes can be used to negatively change the amount of speed of the vehicle.
  • a minimum braking process is carried out, during which brake slip occurs in a secured manner.
  • each tire is designed so that it can be accelerated separately and independently of other tires.
  • each tire can be individually accelerated negatively by means of electronic brake force distribution. Separation of all tire brake circuits is required.
  • the acceleration of the individual tires can counteract a yawing of the vehicle.
  • a tire condition of at least one tire is preferably determined taking into account the first input data set and the determined second roadway friction coefficient, in particular taking into account the weighting error. If the tires of the vehicle are worn during the service life of the vehicle and the properties of the tire rubber compound change over time, the trained neural network can record this continuous change over the service life. A change in the tire properties usually results in a change in the first input data set and in the second coefficient of friction determined on the road. This results in a weighting error, which in turn is used to train the neural network. If the weighting error is stored over the useful life of the vehicle, it can be determined from the history of the weighting error, a change in the tire properties can be determined. It is known, for example, which second roadway friction coefficient tires deliver given known first input data sets.
  • the control device which uses the first predictively calculated roadway friction coefficient to control the vehicle, takes into account a change in the tire properties using the computer-implemented method for training the neural network.
  • a threshold value for the weighting error is preferably defined. If the threshold value is exceeded, this is detected by the evaluation device. When the threshold value in the trained neural network is exceeded, the condition of at least one tire is advantageously classified as critical. The weighting error of the neural network can be minimized while the vehicle is being driven and the computer-implemented method carried out in the process. If the evaluation device of the trained neural network detects a high weighting error that exceeds the established threshold value, the evaluation device can determine a probability of a critical tire condition, taking into account all first input data sets and second data sets.
  • a critical tire condition of at least one tire can be, for example, a tire pressure drop as a result of tire damage.
  • the detection of a critical tire condition is advantageously forwarded to the control device, so that it can adjust vehicle parameters accordingly.
  • the vehicle occupants are preferably informed of a critical tire condition.
  • the safety of the vehicle and the vehicle occupants is further increased.
  • the invention relates to an evaluation device for a vehicle, which is designed to carry out the method described.
  • the computer-implemented method for training the neural network is preferably executed on the evaluation device.
  • the evaluation device is also designed to calculate individual processes within a neural network.
  • the input data is a first input data set and a second data set.
  • the neural network is designed to predictively calculate a road surface friction value as a function of this first sensor data and to determine a weighting error taking into account the second data set.
  • the evaluation device preferably has an input interface in order to receive data from the at least one first sensor arrangement, preferably from the camera, radar, lidar, infrared and/or ultrasonic sensors and/or the like.
  • the input interface can connect the first and the second sensor arrangement to the evaluation device for data exchange.
  • the exchange can also be wired or wireless.
  • the artificial neural network can be executed on the evaluation device.
  • the invention also relates to a computer program for a vehicle. This is designed to carry out the method described.
  • Computer programs usually include a sequence of instructions that cause hardware to carry out a specific method. When the computer program in question is used on a computer, it executes predefined command sequences.
  • the computer program is preferably designed to carry out the predictive calculation of the roadway friction coefficient using the neural network. Furthermore, the computer-implemented method for training a neural network can be carried out.
  • the invention relates to a machine-readable storage medium for a vehicle.
  • the machine-readable storage medium may include one or a combination of volatile memory elements and non-volatile memory elements.
  • Volatile storage elements may include, for example, random access memory and/or erasable programmable read only memory included.
  • Non-volatile memory elements may include, for example, hard drives and/or compact disk read-only memories.
  • storage may include electronic, magnetic, optical, or other types of storage media.
  • the machine-readable storage medium may include one or more separate computer programs. The machine-readable storage medium is advantageously connected to the evaluation device.
  • the invention relates to a control device for a vehicle. This is designed to carry out the method described.
  • This control device is also designed to control the vehicle. It can be connected to the evaluation device and take into account the determined road surface friction data when controlling the vehicle.
  • the invention relates to a vehicle.
  • This is designed to be able to carry out the method described.
  • a vehicle which is designed to carry out the method described can advantageously drive fully autonomously.
  • the decision-making process of the vehicle's control unit is based on the predictively calculated first road friction value. Measurements from sensors serve as the input variable.
  • the main task of the sensors is to record the vehicle environment in every driving situation and to make the data available to the evaluation device.
  • the vehicle can be optimally controlled by the control unit, taking into account the driving dynamics limits, the vehicle-specific data and the predictively calculated road surface friction value.
  • the invention relates to a neural network. This is designed to carry out the method described. The calculation of individual processes of the neural network is carried out by the evaluation device. Measurements from sensors serve as input data. The sensors record the vehicle surroundings and tire slip in every driving situation. Further advantages, features and details of the invention result from the following description of exemplary embodiments and figures. show:
  • FIG. 1 shows an exemplary embodiment of a vehicle according to the invention
  • FIG. 2 shows an exemplary embodiment of a neural network according to the invention
  • FIG 3 shows an exemplary embodiment of a method according to the invention.
  • FIG. 1 A vehicle 100 with an evaluation device 120 is shown in FIG. 1 .
  • Vehicle 100 is advantageously an autonomous vehicle.
  • Evaluation device 120 accesses a computer program 130 in order to predictively calculate a first road surface friction value 242 from a first input data set 210 via a neural network 200 . This predictively calculated first roadway friction value 242 is made available to a control device 140 .
  • First input data set 210 is provided by at least one first sensor arrangement 102, a vehicle communication interface 104, a vehicle sensor 106 and/or an environment sensor system 108.
  • the at least one first sensor arrangement 102 advantageously includes a camera and is aimed at the roadway ahead.
  • the vehicle communication interface 104 is designed to record vehicle-external data and forward it to the evaluation device.
  • data from other vehicles can be acquired by the vehicle communication interface 104 .
  • the vehicle communication interface can call up vehicle-external data. This data is preferably stored on external servers.
  • the vehicle sensor 106 collects vehicle-specific data.
  • the vehicle sensor 106 collects vehicle-specific data.
  • Vehicle sensors 106 can include a number of sensors for this purpose.
  • Vehicle sensors 106 are preferred already integrated in individual assemblies such as the drive train, transmission, engine, exhaust system, steering and electrical system of the vehicle.
  • the vehicle-specific data are made available to the evaluation device 120 in order to determine the dynamic driving limits of the vehicle there. Furthermore, the data can be advantageous when calculating the predictive road surface friction coefficient.
  • Surroundings sensor system 108 captures surrounding information outside of the vehicle.
  • light sensors, moisture sensors and temperature sensors can measure, for example, the brightness, the outside temperature and/or the air humidity in the vehicle environment and forward it to the evaluation device 120 .
  • a further, second data set is recorded using at least one second sensor arrangement 110.
  • This second sensor arrangement 110 is preferably embodied in multiple configurations, with at least one second sensor arrangement 110 being assigned to each tire 112 of the vehicle.
  • the second sensor arrangement 110 is advantageously embodied as an active tire speed sensor in order to measure a tire speed and in particular a slip on the respectively associated tire.
  • the second sensor arrangement 110 provides the determined data to the evaluation device 120 . Both forward and backward movements of the tire are advantageously detected via the second sensor arrangement 110 .
  • Evaluation device 120 includes a processing unit 122, a machine-readable storage medium 124 and at least one interface 126.
  • Processing unit 122 includes a central processing unit, such as a processor unit and/or a graphics processing unit, the central processing unit and the graphics processing unit including a plurality of processing cores. Furthermore, the processing unit can have its own memory, such as a cache memory. Advantageously, individual processes are divided among several computing cores in order to process processes efficiently.
  • the machine-readable storage medium 124 can be embodied as a volatile and/or non-volatile memory.
  • the machine-readable storage medium is advantageously embodied as a temperature, shock and vibration-resistant memory in order to meet the requirements of a vehicle.
  • the evaluation device accesses the computer program 130 and the neural network 200 via an interface 126 .
  • the computer-implemented method for training the neural network is executed on the computing unit.
  • the vehicle also includes a control device 140 for controlling the vehicle 100.
  • Vehicle actions are stored on the control device 140 for each input data set and in particular for each road surface friction value.
  • Control device 140 selects a vehicle action, taking into account driving dynamics limits, the vehicle-specific data and in particular the roadway friction coefficient predictively calculated by neural network 200 . By training the neural network 200, the vehicle can be optimally controlled by the control unit 140.
  • FIG. 2 schematically shows a multi-layer neural network 200 comprising a number of interconnected neurons.
  • the output of a neuron is always the input of the neuron connected to it.
  • the input layer 220 is followed by a plurality of intermediate layers 230. Beginning with the input layer 220, information flows through one or more intermediate layers 230 to the output layer 240.
  • the first input data record 210 comprises data from one or more sensor arrangements.
  • This data includes vehicle data 212, such as engine speed and speed of the vehicle, vehicle environment data 214, such as outside temperature and outside air humidity in the immediate vicinity of the vehicle.
  • vehicle data 212 such as engine speed and speed of the vehicle
  • vehicle environment data 214 such as outside temperature and outside air humidity in the immediate vicinity of the vehicle.
  • the input data set 210 includes data from a first sensor arrangement 216, it being possible for the first sensor arrangement 216 in turn to include data from one or more sensors. Camera, ultrasound, radar, lidar data and/or the like are advantageously available as data from the first sensor arrangement.
  • external data 218, such as GPS or data is provided to the vehicle-to-vehicle communication interface 214 .
  • This first input data record 210 is provided to the input layer 220 of the neural network 200 and is received by neurons in this layer.
  • the data is forwarded to neurons in the intermediate layer.
  • neurons of the input layer 220 pass on the data of the first input data record 210 to the neurons of the first intermediate layer 230 .
  • a weighting is carried out.
  • the number of intermediate layers 230 in the artificial neural network 200 is unlimited. In practice, each intermediate layer 230 also consumes computing power that is necessary for the operation of the neural network.
  • the output layer 240 lies after the intermediate layers 230 of the neural network 200 in the processing of the information.
  • the output layer represents the endpoint of the information flow in a classic artificial neural network 200 and contains the result of the information processing by the network.
  • a supervised machine learning technique may be used to train the neural network 200 .
  • the information as to which roadway friction value was predictively calculated is output in the output layer 240 of the neural network 200.
  • This information is compared with the result from the second data set, the second road surface friction value 244, and the deviation is then given as a weight error 246 is propagated backwards into the network to adjust the neurons accordingly.
  • the weights between the individual neurons are adjusted.
  • a combination of first input data sets 210 and second data sets 244 are compared as data pairs. This comparison results in weighting errors 246 which, during training after a number of training cycles, can predictively calculate a road surface friction value for all first input data sets 210 and also for new, first input data sets 210 that have not yet been known.
  • a back propagation method can advantageously be carried out as a method for training and learning the artificial neural network.
  • the weights within the neural network are changed depending on their influence on the error. This guarantees an approximation to the desired output in an iterative process when the method is carried out again.
  • the mean value of the squared deviation (Squared Mean Error SME) or another error function can be determined.
  • FIG. 3 shows a flowchart for training a neural network 200.
  • a roadway section on a roadway ahead of the vehicle is detected.
  • a first input data record 210 is provided for this detected roadway section.
  • This first input data set 210 advantageously includes data from a number of sensors for providing data.
  • step S12 data of the vehicle communication interface, in step S14 vehicle data and in step S16 vehicle surroundings data are provided. These data together form the first input data set 210. Taking this first input data set 210 into account, a first roadway friction value 242 is predictively calculated in step S40 by means of the neural network 200.
  • a second sensor arrangement 110 provides a further, second data set 213 as a comparison value, which can also be referred to as a target value or reference value.
  • the roadway section detected from S1 for which a first predictive roadway friction value is present, is detected a second time by means of the second sensor arrangement when the vehicle drives over it.
  • slip can be actively generated on at least one tire in a further step S20 in order to determine the roadway friction value of this roadway section.
  • the actively generated slip is measured by the second sensor arrangement 110 on at least one tire 112 and the second data set is provided in S22.
  • a second roadway friction value 242 for the roadway section can be determined taking into account the second data set.
  • First input data record 210 and second roadway friction value 244 are available to evaluation device 120 .
  • the second roadway friction value 244 and the first input data set 210 are compared and a weighting error within the neural network 200 is calculated in S44.
  • the weight within the neural network is corrected. In doing so, the weighting error can be changed. This correction trains the neural network and improves the quality of the predictive determination of the first predictive road surface friction coefficient 242 .
  • the vehicle 100 is controlled by the control device 140 taking into account the first predictive roadway friction value 242 .
  • the flowchart from FIG. 3 can be repeated in an iterative process for each road section and the first input data record 210 recorded for it. This iterative process trains the neural network and improves the predictive determination of the road friction coefficient in the vehicle. reference sign

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  • Engineering & Computer Science (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Regulating Braking Force (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)

Abstract

L'invention concerne un dispositif d'évaluation (120), un programme informatique et un procédé mis en œuvre par ordinateur pour entraîner un réseau neuronal (200) à déterminer des coefficients de frottement d'un pneumatique de véhicule automobile. Le procédé est mis en œuvre sur le dispositif d'évaluation (120) d'un véhicule automobile (100) et comprend les étapes consistant à acquérir un premier ensemble de données d'entrée (210) au moyen d'au moins un premier agencement de capteur (102), calculer de manière prédictive un premier coefficient de frottement de chaussée (242) au moyen du réseau neuronal (200) sur la base du premier ensemble de données d'entrée (210), acquérir un second ensemble de données (213) au moyen d'au moins un deuxième agencement de capteur (110), déterminer un second coefficient de frottement de chaussée (244), déterminer au moins une erreur de pondération (246) du réseau neuronal (200) en prenant en considération le premier coefficient de frottement de chaussée (242) et le second coefficient de frottement de chaussée (244) et entraîner le réseau neuronal (200) pour minimiser la ou les erreurs de pondération (246).
PCT/EP2021/081647 2020-11-20 2021-11-15 Dispositif d'évaluation, programme informatique et procédé mis en œuvre par ordinateur pour entraîner un réseau neuronal à déterminer des coefficients de frottement WO2022106342A1 (fr)

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DE102020214620.9A DE102020214620A1 (de) 2020-11-20 2020-11-20 Auswerteeinrichtung, Computerprogramm und computerimplementiertes Verfahren zum Trainieren eines neuronalen Netzes zur Reibwertbestimmung

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DE102022207029A1 (de) 2022-07-11 2024-01-11 Zf Friedrichshafen Ag Vorrichtung und Verfahren zum Bestimmen eines aktuellen maximalen Reibwerts
CN115081927A (zh) * 2022-07-18 2022-09-20 东南大学 路面摩擦系数评估与预测方法
DE102023113873B3 (de) 2023-05-26 2024-09-05 Audi Aktiengesellschaft Verfahren zum Betrieb eines Kraftfahrzeugs und Kraftfahrzeug

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DE102013222634A1 (de) * 2013-11-07 2015-05-07 Volkswagen Aktiengesellschaft Verfahren zur Prognostizierung eines Fahrbahn-Reibungsbeiwerts sowie Verfahren zum Betrieb eines Kraftfahrzeugs
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