WO2019115662A1

WO2019115662A1 - Method for determining a friction value for a contact between a tyre of a vehicle and a roadway, and method for controlling a vehicle function of a vehicle

Info

Publication number: WO2019115662A1
Application number: PCT/EP2018/084664
Authority: WO
Inventors: Simon GEISLER; Christian Lellmann
Original assignee: Robert Bosch Gmbh
Priority date: 2017-12-13
Filing date: 2018-12-13
Publication date: 2019-06-20
Also published as: DE102017222568A1; EP3724041A1

Abstract

The invention relates to a method for determining a friction value for a contact between a tyre of a vehicle (102) and a roadway. The method involves a step of processing sensor signals (140) using a processing rule in order to generate processed sensor signals. The sensor signals (140) represent status data input by at least one detection device (104, 109) and which can be correlated with the friction value. The processed sensor signals represent at least one provisional friction value relating to at least one measured sub-section of the roadway. The method also involves a step of determining the friction value for a sought partial section of the roadway using the processed sensor signals and a geostatistical process. The friction value is determined using a trend function defined as a linear combination of deterministic functions and a random variable. The trend function represents a local trend of status data. In addition, the process describes, for example, the processing of different provisional friction values from different sensors, which are used in a weighted combination for determining the friction value for a sought partial section of the roadway.

Description

description

title

Method for determining a coefficient of friction for a contact between a

Tire of a vehicle and a roadway and method for controlling a

Vehicle function of a vehicle

State of the art

The invention is based on a device or a method according to the preamble of the independent claims. The subject of the present invention is also a computer program.

For vehicle movements, inter alia, the coefficient of friction between the tires of a vehicle and the road surface may be important. For the direct active friction value measurement in special situations, such as an airfield frictional value determination, measuring vehicles with Reibwertmesstechnik can be used. The Reibwertmesstechnik such measuring vehicles based on a force measurement in which the friction force using the known normal force, for example by means of the necessary braking force and the required braking torque is determined.

Disclosure of the invention

Against this background, with the approach presented here, a method, furthermore a device, / or / a control device, which uses this method, and finally a corresponding computer program according to the main claims are presented. The measures listed in the dependent claims advantageous refinements and improvements of the independent claim device are possible.

The current developments in the area of networked vehicles enable the exchange of sensor data, for example, on the current lane, the speed of the vehicles and the traffic situation. The vehicle is provided with information about the environment, which it alone could not generate with its own sensors. In this context also plays the

Road fragrant a big role. The coefficient of friction can be used here, for example to automatically set vehicle speeds, for example, before bends, especially in difficult road conditions such as wet or snow. The challenge lies in the development of suitable systems and algorithms that make it possible to estimate a good coefficient of friction from the sensor data. The approach presented here describes such a method for interpolating a good coefficient of friction using the methods of geo-statistics for modeling the spatial relationships between coefficients of friction.

According to embodiments, a coefficient of friction between a roadway and a vehicle can be determined in particular by a time-sequence-based statistical approach. In this case, the friction value can be determined, for example, by using sensor data or sensor signals as an estimated value or a probability distribution of friction values, in particular using the processes from geo-statistics for modeling spatial relationships between coefficients of friction. The coefficient of friction can be used to control a vehicle function of a vehicle, in particular an assistance function. In particular, a server-based creation of a friction coefficient map can be realized using a geostatistical process. The sensor data obtained from many different vehicles can be sent for example via mobile networks to a server backend and there, for example, under

Consideration of further state data are processed so that a location-dependent coefficient of friction can be aggregated.

Advantageously, according to embodiments, in particular an accurate and reliable assessment of a friction between the vehicle and the roadway can be made possible. In this case, for example, data from a plurality of sources can be used and thus swarming knowledge can be used. In particular, effects of possible sensor errors can also be reduced, and results of statistical evaluation for the friction value determination can be improved. Furthermore, for example, a large group of users can be addressed. Also, a setup effort for the benefit of Reibwertbestimmung be kept low and cost, especially in comparison to dedicated friction coefficient sensor. Optionally, the

Friction value determination can be combined with other connectivity functions. In particular, the friction coefficient determination results on road sections Also provide for vehicles that have not traveled such sections of the road itself.

A method is provided for determining a coefficient of friction for a contact between a tire of a vehicle and a road, the method comprising the following steps:

The processing of sensor signals using a

A processing rule for generating processed sensor signals, the sensor signals representing friction coefficient correlatable state data read by at least one sensing device, the processed sensor signals representing at least a provisional friction coefficient with respect to at least one measured subsection of the roadway; and

Determining the coefficient of friction for a sought subsection of the roadway using the processed sensor signals and a geostatistical process, wherein the friction value is determined using a trend function defined as a linear combination of deterministic functions, the trend function representing a local trend of state data.

This method can be implemented, for example, in software or hardware or in a mixed form of software and hardware, for example in a control unit. Here, the coefficient of friction may be determined as an estimated value and additionally or alternatively as a probability distribution of a friction at a specific location or region of the roadway. The coefficient of friction can also represent a range of values, the coefficient of friction

For example, represents an average and a confidence interval or the like. The coefficient of friction may be intended for use for triggering a vehicle function of a vehicle, in particular a vehicle

Assistance function or an assistance system of a vehicle. The status data can represent physical measured values obtained by the at least one detection device, for example weather data, geographical or topographical data or road data. A

Detection device can be designed to detect the status data in the form of the sensor signals and to provide additionally or alternatively. A provisional coefficient of friction may be one for a measured subsection of

Lane represent specific coefficient of friction. A preliminary coefficient of friction can represent a valid coefficient of friction for a measured section of the roadway. The method may also include a step of reading the sensor signals from an interface to the at least one

Have detection device. Also, the method may include a step of providing a friction value in the form of a control signal for output to an interface to at least one vehicle. In order to determine the coefficient of friction using a trend function defined as a linear combination of deterministic functions and a random variable, the coefficient of friction can be modeled as the sum of the trend function and the random variable. In addition, a connection of diverse

Sensor signals are modeled.

According to one embodiment, in the step of determining the coefficient of friction for the sought subsection of the roadway using Kriging and / or co-Kriging is determined as geostatistischem process. In this case, a relationship between the coefficient of friction for the searched subsection of the roadway and the at least one provisional friction value with respect to at least one measured subsection of the roadway can be modeled using a weighting factor. The weighting factor can be determined by kriging and / or co-kriging. The so-called Kriging can also be called Krigen or Gaussian regression. The Kriging can be designed as so-called Universal Kriging or universal Kriging or be and / or co-Kriging. Such an embodiment offers the advantage that friction coefficients can also be determined precisely for gaps between measuring points. Thus, a creation of a spatial map can be made possible by correlations between measuring locations are determined in order to predict coefficients of friction at desired points between measuring points optimal. According to the embodiment presented here, the coefficient of friction is calculated using a linear combination

deterministic functions, whereby the trend function represents a local trend of state data. The

Condition data may be, for example, environmental attributes and / or road attributes. In the present case, an attribute can be understood as meaning a value or information that qualitatively and / or quantitatively depicts or represents a state of the environment or of the road. Thus, such a spatial map advantageously also have other influencing variables in the form of state data, which allow an even more precise determination of the coefficient of friction. Also, in the step of determining a spatial relationship between sections of the roadway using a Semivariogramms be modeled. In particular, the above-mentioned weighting factor can be determined by means of the semivariogram. Such

Embodiment offers the advantage that a spatial variance of a geographical area can be safely and accurately taken into account. Thus, the coefficient of friction may advantageously be determined as a weighted average with variance optimized to a minimum.

According to the embodiment presented here, in the step of determining the modeling of a spatial relationship can be extended by further influencing variables in the form of state data. The state data can be modeled, for example, in the trend function, or the determination of the spatial relationship can be modeled using a semivariogram extended by a so-called cross-variogram.

Furthermore, in the step of determining the coefficient of friction for the desired

Subsection of the road from provisional coefficients of friction with respect to measured sections of the roadway are interpolated using the geostatistical process. Additionally or alternatively, in the step of determining preliminary coefficients of friction with respect to a measured subsection of the roadway using the geostatistical process can be combined into a coefficient of friction. Additionally or alternatively, further influencing variables in the form of the state data, for example environmental attributes or road attributes for prediction of the coefficient of friction can also be used in the step of determining. Such an embodiment offers the advantage that coefficients of friction for spaces between measuring points can be reliably and accurately determined or gaps in a friction coefficient map can be reliably and accurately closed. In addition, the prediction of the coefficient of friction by the possibility of taking into account further influencing variables becomes more reliable and more accurate.

In addition, in the step of determining, at least one parameter of the geostatistical process can be set for each measured subsection of the roadway. In particular, the at least one parameter of the geostatistical process can be set depending on a type, property and additionally or alternatively origin of the status data. Such an embodiment offers the advantage that the geostatistical process to the type of

Sensor signals or detection devices can be adapted to account for available in a deployment environment data sources for accurate determination of the coefficient of friction.

According to one embodiment, in the step of determining as friction data correlatable state data at least one environmental attribute,

in particular the pollen density and / or the leaf fall and / or the

Soil texture and / or topology can be used. By the expansion of the model by environmental factors such as pollen or

Foliage cover, for example in forest areas in conjunction with the (annual) time dependency, the friction coefficient estimation can be further improved. Due to the influence of these variables on the available coefficient of friction, it is thus possible to estimate more accurate friction coefficients which are better adapted to the environment and the season.

In addition, according to an embodiment, in the step of determining as friction-correlatable state data at least one road attribute, in particular the road surface and / or the road type and / or the traffic density and / or structural features and / or the wear of the road and / or a scattering Salt can be used. The nature of

Road surface, whether the road is an asphalt road or a gravel road, for example, can have as much influence on the coefficient of friction as the road

Road type (highway, main road, secondary road), the density of traffic or structural features such as a bridge, an underpass or a tunnel and the scattering with salt. The wear of the road can also be modeled in the calculation of the coefficient of friction.

Advantageously, by taking into account these factors, the coefficient of friction can be determined more accurately. On the one hand, it is possible by means of the attributes to delimit an actually physically possible coefficient of friction, for example by the road attributes "road surface" and "wear of the road". On the other hand, local-temporal modeling of the coefficient of friction can be better represented.

There is also a method of controlling a vehicle function of a vehicle

Vehicle, the method comprising the following steps: Receiving a control signal generated using a coefficient of friction determined according to one embodiment of the above method; and

Driving the vehicle function using the received

Control signal.

This method can be implemented, for example, in software or hardware or in a mixed form of software and hardware, for example in a device or a control device. The vehicle function can be a

Represent the assistance function of an assistance system of the vehicle. The vehicle may be a vehicle for highly automated driving.

The approach presented here also provides a device which is designed to implement the steps of a variant of a method presented here

to implement, control or implement appropriate facilities. Also by this embodiment of the invention in the form of a device, the object underlying the invention can be solved quickly and efficiently.

For this purpose, the device may comprise at least one computing unit for processing signals or data, at least one memory unit for storing signals or data, at least one interface to a sensor or an actuator for reading sensor signals from the sensor or for outputting data or control signals to the sensor Actuator and / or at least one

Communication interface for reading or outputting data embedded in a communication protocol. The arithmetic unit may be, for example, a signal processor, a microcontroller or the like, wherein the memory unit may be a flash memory, an EEPROM or a magnetic memory unit. The communication interface may be configured to read in or output data wirelessly and / or by line, wherein a communication interface that can input or output line-based data may, for example, electrically or optically send this data from a corresponding data transmission line or output to a corresponding data transmission line. In the present case, a device can be understood as meaning an electrical device which processes sensor signals and outputs control and / or data signals in dependence thereon. The device may have an interface, which may be formed in hardware and / or software. In the case of a hardware-based embodiment, the interfaces can be part of a so-called system ASIC, for example, which contains a wide variety of functions of the device. However, it is also possible that the interfaces are their own integrated circuits or at least partially consist of discrete components. In a software training, the interfaces may be software modules that are present, for example, on a microcontroller in addition to other software modules.

Also of advantage is a computer program product or computer program with program code which can be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory and for carrying out, implementing and / or controlling the steps of the method according to one of the above

described embodiments, in particular when the program product or program is executed on a computer or a device.

Embodiments of the approach presented here are shown in the drawings and explained in more detail in the following description. It shows:

1 is a schematic representation of a networked system according to an embodiment;

Fig. 2 is a schematic representation of parts of the system of Fig. 1;

3 is a flow chart of a method for determining according to an embodiment; and

4 is a flowchart of a method for controlling according to an exemplary embodiment. Before embodiments are described in more detail below with reference to the figures, first background and principles of embodiments will be briefly explained.

Developments in the area of networked vehicles make it possible, for example by means of so-called connectivity units, to exchange sensory data, for example via traffic or the condition of the road. By processing such data and resulting information gain over road sections, for example, highly automated driving and predictive driver assistance systems can be operated with a gain in safety. In particular, a vehicle may have information about a

Be provided environment that could not be generated by the vehicle alone with its own sensors. In this context is also the

Frictional value a contact between the road and the road and vehicle significant. In passenger cars and the like, no dedicated friction sensors are usually installed. In particular, it is by server-side processing of many sensor data from many different vehicles, eg. B. acceleration sensors, in combination with weather sensors and street-side sensors, z. As smoothness sensors, according to embodiments possible to determine or estimate a coefficient of friction for road sections. Such

Information about the coefficient of friction can then be used for further information

Functional development can be used with the aim of increasing safety and comfort.

In the following description of favorable embodiments of the present invention are for the in the various figures

represented and similar elements acting the same or similar

Reference numeral used, wherein a repeated description of these elements is omitted.

1 shows a schematic representation of a networked system 100 according to one exemplary embodiment. The system 100 is configured to determine a road friction coefficient and to make it usable.

By way of example, the system has four vehicles 102 and vehicle sensors 104 in the form of travel data sensors and / or vehicle-mounted ones

Environmental sensors on. Also exemplary are environmental attributes 106 and road attributes 108 as further influencing variables of the coefficient of friction in the form of Access data shown, and at least one data source 109 on the Internet. To determine the coefficient of friction, the system 100 has at least a first one

Device 110 and a second device 120 on. Furthermore, a server device 130, a so-called server backend 130, a data cloud 130 or a so-called cloud 130 is associated and / or assigned to the system 100. A signal-transmissive network within the system 100 may be realized, for example, by radio or other data transmission.

In this case, the first device 110 is realized as a part of the server device 130. The second device 120, for illustrative purposes in FIG. 1, is merely exemplified in one of the vehicles 102, which may be referred to herein as a receiver vehicle 102. The driving data sensors 104 are arranged merely by way of example in three of the vehicles 102, which may be referred to here as transmitter vehicles 102. The receiver vehicle 102 may also include a vehicle sensor 104. The transmitter vehicles 102 may each have a second device 120.

The first device 110 is configured to determine a coefficient of friction for contact between a tire of a vehicle 102 and a roadway. Here, the first device 110 is configured to read sensor signals 140 from the vehicle sensors 104, and state data of the environmental attributes 106 and the road attributes 108 and the data source 109. The sensor signals 140 represent state data or physical measured values, for example environmental data for an environmental region of the environmental attributes 106, and / or infrastructure data and / or environmental data for the environment

Surrounding area of the road attributes 108 and / or driving data and / or environmental data of the vehicles 102 from the vehicle sensors 104. Further, the first device 110 is configured to determine the coefficient of friction using the sensor signals 140 and provide a friction signal representing or having the friction coefficient 150 issue. The second device 120 is configured to control a vehicle function of the vehicle 102, here the receiver vehicle 102, using the control signal 150.

The system 100 is constructed such that many vehicles 102 transmit the sensor signals 140 or sensor data to the server via a mobile radio network, for example. Send backend 130 and the first device 110 realized in the same. In addition, infrastructure data, such as road sensor data, as well as environmental data, such as weather data that can be queried. By means of the first device 110, the sensor signals 140 are processed according to an embodiment, for example by means of a Kringing method, such as ordinary kinging or co-kinking in time sequences to aggregate a location-dependent coefficient of friction, also referred to herein provisional coefficient of friction. This aggregated coefficient of friction can be passed on in the form of the control signal 150 to other vehicles 102 in a location-specific manner so as to give participating vehicles 102 information about the current coefficient of friction in a respective region or a respective surrounding area.

FIG. 2 shows a schematic representation of parts of the system from FIG. 1. In the representation of FIG. 2, only the first device 110 and the receiver vehicle 102 with the second device 120 and a vehicle function are exemplary of the system of FIG 260 shown. The vehicle function 260 is, for example, an assistance function of an assistance system of the receiver vehicle 102.

The first device 110 has a processing device 212 and a determination device 214. The processing device 212 is configured to process the sensor signals 140 using a processing rule to produce processed sensor signals 245. The sensor signals 140 represent state data read in by at least one detection device and correlatable with the coefficient of friction. The processed sensor signals 245 represent at least one provisional coefficient of friction with respect to at least one measured subsection of the roadway, the friction value being defined using a linear combination of deterministic functions

Trend function and a random variable is determined, wherein the trend function represents a local trend of state data.

The at least one detection device is the vehicle sensors and / or the data source from FIG

Detecting means 214 is configured to be detected using the

processed sensor signals 245 and a geostatistical process to determine the coefficient of friction for a sought section of the road. The first Device 110 is designed to output the determined friction value in the form of the control signal 150 or to provide it for output.

The second device 120 has a receiving device 222 and a drive device 224. In this case, the receiving device 222 is designed to receive the control signal 150 from the first device 110. Furthermore, the receiving device 222 is designed to output or provide a received control signal 255 to the driver 224. The driver 224 is configured to forward the received control signal 255 to the vehicle function 260 to control the vehicle function 260 using the received control signal 255.

Alternatively, the vehicle function 260 may be directly using the

Control signal 150 can be controlled. Here, the first device 110 may be configured to provide or output a suitable drive signal 150 for the vehicle function 260. In this case, the second device may be omitted.

3 shows a flow chart of a method 300 for determining according to an exemplary embodiment. The method 300 is executable to include a

To determine coefficient of friction for a contact between a tire of a vehicle and a roadway. In this case, the method 300 for determining in conjunction with the system from FIG. 1 or FIG. 2 can be executed. Also, the method 300 for determining using the first device of FIG. 1 or FIG. 2 is executable.

In a step 310 of processing, in the method 300 for determining, sensor signals are processed using a processing rule to produce processed sensor signals. The sensor signals represent state data read by at least one detection device and correlatable with the coefficient of friction. The processed sensor signals represent at least one provisional coefficient of friction with respect to at least one measured subsection of the roadway. Subsequently, in a step 320 of the determination, the coefficient of friction for a searched subsection of the roadway is determined using the processed sensor signals and a geostatistical process. According to one exemplary embodiment, the coefficient of friction for the sought-after section of the roadway is determined in step 320 of the determination using Kriging as a geostatistical process. A relationship between the coefficient of friction for the sought Teilabschnit the road and the at least one provisional coefficient of friction with respect to at least one measured

Teilabschnits the roadway is doing using a

Weighting factor modeled. The weighting factor is determined by Kriging. Furthermore, in particular, a spatial relationship between sub-sections of the roadway is modeled using a semivariogram and / or a cross-variogram using environmental drivers 106 and / or roadriders 108. According to another

In the exemplary embodiment, in step 320 of the determination using the geostatistical process, the coefficient of friction for the sought subsection of the road is interpolated from provisional coefficients of friction with respect to measured road sections and / or preliminary friction values relating to a measured subsection of the roadway are combined to form a coefficient of friction, and / or Further influencing variables are used in the form of the state data for prediction of the coefficient of friction, in particular environmental tripple 106 or road tram 108. Optionally, at least one parameter of the geostatistical process is set or adjusted in step 320 of the determination for each measured subsection of the roadway. In one embodiment, in step 310 of processing, the

Sensor signals using a suitable

Processed signal processing.

The method 300 for determining, according to one embodiment, also comprises a step 330 of reading in the sensor signals from a junction to the at least one detection device. Also, the determining method 300 optionally includes a step 340 of providing the coefficient of friction in the form of a control signal for output to an interface to at least one vehicle.

4 shows a flowchart of a method 400 for controlling according to an exemplary embodiment. The method 400 is executable to perform a

Control vehicle function of a vehicle. In this case, the method 400 for controlling in conjunction with the system from FIG. 1 or FIG. 2 can be executed. Also For example, the method 400 for controlling using the second device of FIGS. 1 and 2 is executable.

In a step 410 of receiving, the method 400 is entered

Receive control signal generated using a coefficient of friction determined by carrying out the method of determining of FIG. 3 or a similar method. In a subsequent step 420 of the drive, the vehicle function is activated using the control signal received in step 410 of receiving.

With reference to the figures described above

Exemplary embodiments are described in summary again in other words and / or presented briefly.

By various estimation methods, for example using sensor signals 140 with regard to vehicle data, environmental attributes,

Road attributes etc., are obtained in step 310 of the processing or by means of the processing means 212 first provisional, spatially distributed coefficients of friction with the respective uncertainty of the measured value at the respective measurement time. Estimation methods typically measure coefficients of friction for a specific location, ie. H. there are no or insufficient data for spaces, which is disadvantageous for generating a map, but according to embodiments it can be achieved that a continuous map is generated from these data. Also, in some areas measuring points can accumulate and these can according to a

Embodiment summarized, which is also advantageous for the creation of a card.

In order to create a spatial map, correlations can be determined according to an exemplary embodiment in order to be able to optimally predict friction coefficients at points between measuring points for the map. In particular, according to this exemplary embodiment, it is possible to take into account further influencing variables in the form of the state data, in particular environmental attributes and road attributes, for optimal interpolation of the coefficient of friction at points between measuring points. Furthermore, according to an exemplary embodiment, it is possible for the users of the coefficient of friction map to recognize an accuracy of the coefficients of friction from such a friction value map in order, if necessary, to carry out further actions Functions such as controlling a vehicle speed in curves based on these data derived.

To the coefficient of friction using a linear combination

deterministic functions defined trend function and a random variable, where the trend function represents a local trend of state data, the coefficient of friction is modeled as a random variable and consists of two components, a function g (s, t, ...), which models the local trend and a random variable e (s, t) and can be written as follows: β (s) = g (s, ...) + e (s)

Furthermore, in the following, g (s,...) Is used as a linear combination

of the deterministic functions f ₀ , f, ..., f _L , the coefficients a _t € R {0} and the convention / ₀ (s,) = 1.

To a friction value at any point s ₀ or any

For example, to be able to predict a subsection of the roadway, the following weighted average can be used:

c ₀ = [Ui, o ( ^s i -s ₀ ) ... YN, ₀ (S _N -s ₀ ) f fo- [1 / i ( ^s ₀ ) / w ( ^s ₀ )] ^T 1 = [1 ... 1] ^T , with N elements

7i, i (7I, ₂ 0I - 5 ₂ ) ·

G = 7 _2.1 (s ₂

72.2 (0) ·

-7 / v, i ^{(- s} i) YN, I ^(S N - ^S 2)

With m (s_0) as a predicted or to be determined coefficient of friction at the point s_0, p (s_i) as measured / estimated, in particular provisional, coefficient of friction at the point sj and wj, for example as a factor (sum = 1) or

Weighting factor which models a relationship between p (s_i) and m ^" (SO) Thus, for m (5_0), the sum for N relevant measurement points multiplied by the weighting factor for the relationship is calculated here

Weighting factors are derived by spatial correlation.

The spatial relationship can be modeled by a semivariogram:

However, this can be extended to the modeling of other influencing factors to the above-mentioned factors and a so-called "cross-Variogram" between created. In the case where the cross-variogram is between two quantities mq) Z _A (s) = g (s) + e (s)

Z _B (s) - g (s) + e (s) The result is, for example, the following formula, whereby other variables such as a trend can also be modeled here.

The final prediction of the coefficient of friction can be achieved for example by a method such as co-Kriging. However, it would also be conceivable to consider these variables in the trend function and to use, for example, "Universal Kriging".

If an exemplary embodiment comprises a "and / or" link between a first feature and a second feature, then this is to be read so that the embodiment according to one embodiment, both the first feature and the second feature and according to another embodiment either only first feature or only the second feature.

Claims

claims

A method (300) for determining a coefficient of friction for contact between a tire of a vehicle (102) and a roadway, the method (300) comprising the steps of:

Processing (310) sensor signals (140) using processing instructions to produce processed sensor signals (245), wherein the sensor signals (140) of at least one of

Detection device (104, 109) read represent, correlated with the coefficient of friction condition data, wherein the processed sensor signals (245) at least a provisional coefficient of friction with respect to at least one measured portion of the road

represent; and

Determining (320) the coefficient of friction for a sought subsection of the roadway using the processed sensor signals (245) and a geostatistical process, wherein the coefficient of friction under

Using a defined as a linear combination of deterministic functions trend function and a random variable is determined, the trend function represents a local trend of state data.

2. Method (300) according to claim 1, characterized in that in the step of determining (320) the coefficient of friction for the searched section of the carriageway using Kriging and / or co-Kriging is determined as geostatistischem process, wherein a relationship between the Coefficient of friction for the sought subsection of the road and at least one provisional coefficient of friction and / or a combination of different provisional coefficients of friction different

Measuring sources with respect to at least one measured subsection of the roadway using a weighting factor is modeled, wherein the weighting factor is determined by Kriging and / or co-Kriging.

3. Method (300) according to one of the preceding claims, characterized in that in the step of determining (320) a spatial relationship between coefficients of friction of sub-sections of the roadway from different data sources using a semivariogram and / or one with the semivariogram

correlated size and / or a cross-variogram is modeled.

4. Method (300) according to one of the preceding claims,

characterized in that, in the step of determining (320), the coefficient of friction for the searched subsection of the roadway is interpolated from provisional friction coefficients with respect to measured subsections of the roadway and / or friction coefficient correlatable state data using the geostatistical process, and / or wherein in step determining (320) preliminary coefficients of friction with respect to a measured portion of the roadway using the geostatistical process are combined into a coefficient of friction.

5. The method according to claim 4, characterized in that in the step of determining (320) as at least one environmental attribute (106), in particular the pollen aviation density and / or the leaf fall and / or the soil condition and / or the topology used as correlated with the coefficient of friction condition data becomes.

6. The method according to claim 4 or 5, characterized in that in the step of determining (320) as correlated with the coefficient of friction

Condition data at least one road attribute (108), in particular the road surface and / or the road type and / or the traffic density and / or structural features and / or the wear of the road and / or a scattering with salt is used.

7. Method (300) according to one of the preceding claims,

characterized in that, in the step of determining (320), at least one parameter of the geostatistical process is set for each measured subsection of the roadway.

8. Method (300) according to one of the preceding claims,

characterized in that in step (310) of processing the Sensor signals (140) using a suitable

Signal processing method are processed.

9. A method (400) for controlling a vehicle function (260) of a

Vehicle (102), the method (400) comprising the steps of:

Receiving (410) a control signal (150) generated using a coefficient of friction determined by a method (300) according to any one of the preceding claims; and

Driving (420) the vehicle function (260) using the received control signal (255).

Apparatus (110; 120) arranged to execute and / or drive steps of a method (300; 400) according to any one of the preceding claims in respective units.

A computer program adapted to execute and / or control a method (300; 400) according to any one of the preceding claims.

12. A machine-readable storage medium on which the computer program according to claim 9 is stored.