EP3788400A1

EP3788400A1 - Method and apparatus for identifying a road condition

Info

Publication number: EP3788400A1
Application number: EP19719877.3A
Authority: EP
Inventors: Christian Beer; Timo Koenig; Philipp SAUER; Simon Weissenmayer; Michael Schumann
Original assignee: Robert Bosch GmbH
Current assignee: Robert Bosch GmbH
Priority date: 2018-05-02
Filing date: 2019-04-25
Publication date: 2021-03-10
Also published as: WO2019211169A1; DE102018206739A1

Abstract

The present invention relates to a method for identifying a road condition, wherein a plurality of noise levels (108), each captured by an ultrasonic sensor (104) of a vehicle (100) at a different installation location on the vehicle (100), is read in a reading step, respectively one first speed value (120) corresponding to a first possible road condition and at least respectively one second speed value (120) corresponding to a second possible road condition are assigned to the noise levels (108) in an assignment step using respectively one assigned processing prescription (116) which assigned to the respective detecting ultrasonic sensor (104) on an individual sensor basis, a first dispersion parameter (124) of the first speed values (120) and a second dispersion parameter (124) of the second speed values (120) are ascertained in an ascertainment step, and the road condition is determined using the dispersion parameters (124) in a determination step, wherein the road condition is determined to be the first possible road condition if the first dispersion parameter (124) is smaller than the second dispersion parameter (124), and the road condition is determined to be the second possible road condition if the second dispersion parameter (124) is smaller than the first dispersion parameter.

Description

description

Method and device for detecting a road condition

Field of the invention

The invention relates to a method and a device for detecting a road condition.

State of the art

A road condition, ie whether a road surface of a road is dry, moist or wet, can be detected, for example, by a camera system. Likewise, a rain sensor of a vehicle may be used as it is assumed that the road is wet when it rains.

Disclosure of the invention

Against this background, with the approach presented here, a method for detecting a road condition and a device for detecting a road condition, and finally a corresponding

A computer program product and a machine-readable storage medium according to the independent claims. Advantageous developments and improvements of the approach presented here emerge from the

Description and are described in the dependent claims.

Advantages of the invention Embodiments of the present invention can advantageously make it possible to robustly recognize a road condition by plausibility of measured values.

A method for detecting a road condition is presented, the method comprising the following steps:

Reading in a plurality of noise levels detected at each of the vehicle by an ultrasonic sensor of a vehicle at various installation locations;

Assign one each with a first possible road condition

corresponding first speed value and at least one corresponding to a second possible road condition second

Speed value to the noise levels using each one of the respective detected ultrasonic sensor sensor associated with each individual

Processing rule;

Determining a first scattering parameter of the first velocity values and a second scattering parameter of the second velocity values;

Determining the road condition using the dispersion parameters, wherein the road condition is determined as the first possible road condition when the first dispersion parameter is smaller than the second dispersion parameter, and the road condition is determined as the second possible road condition when the second dispersion parameter is smaller than the first one

Scattering parameter is.

Ideas for embodiments of the present invention may be considered, inter alia, as being based on the thoughts and findings described below.

When a vehicle is running, a driving sound is generated. The driving noise includes at least portions of tire noise of tires of the vehicle on the road and portions of wind noise on a body of the vehicle. The tire noise changes at different

Speeds of the vehicle and in different road conditions, such as dry road, wet road or wet road. The Wind noise changes at different wind speeds on the sensor. The wind speed is essentially dependent on the speed of the vehicle.

The driving sound is recorded at several differently arranged sensors of the vehicle and mapped in a noise level. The sensors may in particular be ultrasonic sensors. The noise level depends, among other things, on an arrangement of the sensor, the

Road conditions and speed respectively

Wind speed. If the driving noise changes, this will result

Sensor-individually different noise levels changed. The change in the noise level in relation to the road condition and the speed can be in a multidimensional associated with the respective sensor

Processing instructions are displayed. A single noise level can thus be assigned using the processing instructions of a sensor several value pairs of a road condition and a speed.

Assuming that the sensors involved may have a uniform road condition, there are different variations in the different road conditions

Speed values from the different processing regulations. Since the actual speed of the vehicle is the same for all sensors, there is a very high likelihood of the road condition in which the speed values have the least scatter.

The speed values may also be determined using a

Speed of the vehicle can be assigned. At known

Speed outliers of the speed values can be ignored because the wind speed at the sensors is strongly influenced by the speed of the vehicle.

The speed values may also be determined using a

Wind speed be assigned. The airstream speed depends on the speed of the vehicle and a

Wind speed. The airstream speed can be controlled by a

Fahrtwindsensor be detected. Head wind, side wind and tailwind strongly influence the driving noise at the sensors. By knowing the Moving wind speed and possibly the wind direction, the speed values can be corrected.

Variances of the velocity values can be determined as scattering parameters. The road condition can be determined using the variances. The dispersion or dispersion can be quantified by the variance. Alternatively or additionally, a standard deviation of the speed values can be determined. The smaller the scatter, the smaller the variance or standard deviation.

Each read-in noise level can have at least one additional one corresponding to another possible road condition

Speed value using each of the detected

Ultrasonic sensor are assigned sensor-individually associated processing rule. Another scattering parameter of the others

Speed values can be determined. For several possible

Road conditions can be determined speed values that correspond to the respective noise level. A higher number of possible road conditions makes a recognition grid more dense and the actual road condition can be resolved more finely.

The road condition may be determined as the first possible road condition when the first scattering parameter is less than the second

Scattering parameter and the other scattering parameter is. Of the

Road condition may be determined as the second possible road condition if the second dispersion parameter is smaller than the first dispersion parameter and the other dispersion parameter. The road condition may be determined as the further road condition if the further scattering parameter is less than the first scattering parameter and the second scattering parameter. The possible road condition with the smallest dispersion parameter is, with a high probability, the actual road condition.

Pro noise levels can be one with a dry road condition

corresponding dry speed value, a wet speed value corresponding to a wet road condition, and at least one corresponding to a wet road condition

Wet speed value assigned. At the same noise level, the vehicle can vary in speed at different road conditions drive. On dry roads, the vehicle can travel faster than on wet roads until the same level of noise is detected at the sensor. Similarly, the vehicle may travel faster on wet roads than on wet roads until the same level of noise is detected at the sensor.

The processing instruction can be in one of the respective

Ultrasonic sensor sensor individually assigned noise map to be mapped. The speed values can be read out of the noise characteristic field. The map, for example, be stored as a multi-dimensional table. Intermediate values can be interpolated. Mathematically complex relationships can also be mapped in the noise characteristic field. The noise map may also be represented by a series of superimposed curves. The curves can be determined, for example, by series of measurements on the vehicle or in the laboratory.

The method may, for example, in software or hardware or in a hybrid of software and hardware, for example in a

Be implemented control unit.

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

to implement, control or implement appropriate facilities.

The device may be an electrical device having at least one computing unit for processing signals or data, at least one memory unit for storing signals or data, and at least one interface and / or a communication interface for reading in or outputting data embedded in a communication protocol, be. The arithmetic unit can be, for example, a signal processor, a so-called system ASIC or a microcontroller for processing sensor signals and outputting

Be data signals in response to the sensor signals. The storage unit may be, for example, a flash memory, an EPROM or a magnetic storage unit. The interface can be used as a sensor interface for reading in the sensor signals from a sensor and / or as an actuator interface for

Outputting the data signals and / or control signals to be formed on an actuator. The communication interface can be designed to read in or output the data wirelessly and / or by cable. The Interfaces may also 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.

It should be understood that some of the possible features and advantages of the invention are described herein with reference to different embodiments. A person skilled in the art will recognize that the features of the device and the method are suitably combined, adapted or

can be exchanged to get to further embodiments of the invention.

Brief description of the drawings

Embodiments of the invention will now be described with reference to the accompanying drawings, in which neither the drawings nor the description are to be construed as limiting the invention.

1 shows a representation of a vehicle with a device according to an embodiment; and

FIG. 2 shows a representation of two noise maps for two different sensors of a vehicle.

The figures are only schematic and not to scale. Like reference numerals in the figures designate the same or equivalent

Characteristics. Embodiments of the invention

1 shows an illustration of a vehicle 100 with a device 102 according to one exemplary embodiment. The device 102 is configured to implement a method according to the approach presented here for recognizing a

Road condition. The vehicle 100 has a plurality of ultrasonic sensors 104 distributed around the vehicle 100. The ultrasonic sensors 104 record echoes 106 of emitted ultrasonic waves and, in addition, noise from an environment of the vehicle 100. An intensity of the sounds is imaged on each ultrasonic sensor 104 in a noise level 108. The noise level 108 is the higher the louder the noise. The noises are responsible for a high amount of driving noise 110 of the vehicle 100.

Depending on where the single ultrasonic sensor 104 is disposed on the vehicle 100 and in which direction it is aligned, the

Driving noise 110 when the vehicle 100 is different. The

Driving noise 110 includes, among other wind noise 112 on the vehicle 100 and tire noise 114 of wheels of the vehicle 100th Die

Driving sounds 110 become louder with increasing speed of the vehicle 100. At the different ultrasonic sensors 104, the driving sounds 110 vary differently as the speed changes.

This also changes the respective noise level 108 of the ultrasonic sensor 104. The wind noise 112 is essentially of one

Wind speed dependent on the place of origin. This will be the

Wind noise 112 strongly influenced by the speed. Also, a wind force and wind direction in an environment of the vehicle 100

affect the wind noise 112. The tire noise 114 is very much dependent on the current road condition. The wetter and rougher the road is, the louder the tire noise 114. In addition, the

Tire noise 114 also depends on the speed.

At front ultrasonic sensors 104, the influence of wind noise 112 is greater than at rear ultrasonic sensors 104. At the rear

Ultrasonic sensors 104, the influence of the tire noise 110 is greater than at the front ultrasonic sensors 104. The device 102 is connected to the ultrasonic sensors 104 and reads in at least two of the noise levels 108 provided. Using a processing rule 116 for each of the ultrasonic sensors 104 used, an associator 118 of the device 102 assigns at least two velocity values 120 to each noise level 108 read. The speed values 120 correspond to the respective one

Noise level 108 and two different possible road conditions. A determination device 122 of the device 102 determines scattering parameters 124 of the velocity values 120. In each case, a scattering parameter 124 for all corresponding to one of the possible road states

Speed values determined. A determination device 126 of the

Device 102 determines that of the possible road conditions as a detected road condition having the smallest dispersion parameter 124. The detected road condition is mapped in road condition information 128 and provided for further use.

FIG. 2 shows a representation of two noise maps 200 for two

different sensors of a vehicle. A noise characteristic map 200 in each case forms one of the processing instructions from FIG. 1. The noise maps 200 are each shown in a diagram that on the abscissa a

Speed in kilometers per hour and on the ordinate the noise level 108 has been presented as a numerical value. Each of the noise maps 200 is defined here by three velocity noise level curves 202, 204, 206. Here, the first velocity noise level curve 202 represents a progression of the noise level 108 over the velocity in a dry state

Road conditions. The second velocity noise level curve 204 represents the progression of the noise level 108 in wet road conditions. The third velocity noise level curve 206 represents the progression of the noise level 108 in wet road conditions. Thus, there are three speed values 120 per value of the noise level 108. A first speed value 120 corresponds to the dry road state, a second speed value 120 corresponds to the wet road

Road condition and a third speed value 120 corresponds to the wet road condition. Conversely, three noise levels 108 each result in a speed value 120.

The noise map 200 shown on the left characterizes a front left ultrasonic sensor of the vehicle. Here are the speed noise level Curves 202, 204, 206 only slightly different, since the driving noise is significantly influenced by the wind noise of the wind on the front left ultrasonic sensor.

The noise map 200 shown on the right characterizes a rear inner right ultrasonic sensor of the vehicle. Here are the

Speed noise level curves 202, 204, 206 are very different, since the rear inner right ultrasonic sensor, the driving noise is significantly influenced by the tire noise.

The two noise levels 108 shown here are humid

Road condition has been detected since the respective second speed values 120 have the lowest scattering. The noise levels 108 thus represent a typical pattern for the wet road condition

Speed values 120 with the least scattering can be determined by a

Pattern recognition done.

In other words, FIG. 2 shows the calculation of the road condition and the wind speed with the aid of ultrasonic sensors according to the approach presented here.

The road condition can be determined on the basis of the measured noise level of ultrasound sensors already used in the vehicle for echolocation. Since the wind speed has a great influence on the calculation of the water level, the wind speed can be measured with the help of already existing or additional sensors and, if necessary, to the

Ultrasonic control unit are transmitted.

Through the approach presented here, the measurement of the

Wind speed with the help of existing or additional

Sensors are dispensed with and the water level alone with the help of

Noise levels of the ultrasonic sensor are measured.

The vehicle first calculates, based on the measured noise level, possible airstream speeds under different road conditions at each individual sensor. Subsequently, for every possible

Road condition calculates how large the standard deviation or variance of the calculated wind speeds of all sensors is. Subsequently, will the road condition is calculated or selected for which the standard deviation or variance of the calculated airstream speeds is the smallest.

The method is based on the knowledge that the

Fahrtwindgeschwindigkeit and the wet wiping of the wheels have different effects on the noise level of the sensors at different locations. The noise level of the front sensors is stronger from the

Wind speed characterized, while the noise level of the rear sensors is more influenced by the wet hiss of the wheels. This causes the level curves shown for example in Fig. 2 on the

Wind speed for dry, wet and wet road with the front sensors are not clearly separable superimposed, while the level curves of the rear sensors, especially at high

Fahrtwindgeschwindigkeiten very well separated from each other. Since the sensors react differently to the wind and road conditions by a common evaluation of the sensor signals both

Fahrtwind and road condition are calculated.

Example: The measured noise level at the front left sensor is _{mk 1} = 2 dB over several measured values on average and the noise level at the left inner rear sensor is m _{K 2} = ^ dB. This will become the

wind speeds

B _dry .i ⁼ 82 km / h

kdry, 2 ⁼ 50km / h

kmo, i 88 km / h

kmo, 2 92 km / h

kwet.i = 100 km / h

kwet, 2 = 198km / h

calculated. The mean and standard deviations are calculated according to

TO

kdry ⁼ 06km / h

° dry ⁼ 16 km / h

kmo is 09 km / h

moist 2km./ h l ^ we _t = 149 km / h

u _wet = 49 km / h

calculated. For o _moist with 2km / h the smallest standard deviation is calculated in comparison to o _wet and o _dry , therefore the road condition is recognized as moist.

By means of the above example, the method can be made plausible by a graphical evaluation as follows: With the front sensors, the approximate speed of the wind is measured on the basis of the noise level. Because the

Level curves are so close to each other, the airstream speed can be read regardless of the road condition to 90 km / h. At a travel speed of 90 km / h and a noise level of 4 dB, the wet road level curve is hit in the diagram above. Also on the basis of this somewhat inaccurate graphical solution, it is possible to use the same solution as with the one described above (optimized for ECUs).

Procedure to come.

The wheels on the right and left can be moved through different depths of water. Therefore, it can be expected that the sensors on the right can also measure a different noise level than the sensors on the left. For this reason, the standard deviations for each combination of the

Lane conditions of the right, as well as calculated for the left side and are selected as described above on the basis of the smallest standard deviation as the current road condition for the right and the left side. The road conditions Z can according to the above example therefore not only by {dry, moist, wet} but by their combinations drydry drymoist drywet

Moisturizing Moisturizer Moisturizing

wetdry wetmoist wetwet be characterized.

Since the clutter values or the bottom echo are dependent on the airstream and on the condition of the road, it is also possible to calculate travel wind speeds on the basis of the clutter values. At low speeds, the bumpiness of the road surface can be measured with the aid of the clutter value. The more pores of the road surface are closed by water the lower the clutter value. At high airspeed speeds, the influence of the road surface in the high noise is less and is largely determined by the wind noise and the wet hiss. In addition, additional sensors or calculations additional wind speeds can be included in the calculation of the standard deviations. For example, the airstream speed can be measured by the speed of rotation of the fan. Likewise, the airstream speed can be calculated using the received wind speed of a weather service and the measured vehicle speed. It is also conceivable to attach ultrasonic sensors with the cone of sound directed upwards on the body in order to measure the noise of the driving wind with a lesser influence of the wet hiding of the road and from there the

To be able to calculate the airstream speed. By adding further sensor values and sensors into the calculation, the accuracy of the

Determination of the road condition and the calculation of the

Fahrtwindgeschwindigkeit be further increased.

Due to measurement errors, the calculation of the airstream speeds using the different signal types of the ultrasonic sensors as well as with the help of other sensors is more or less reliable. Very unreliable measurements and calculations should be considered as few and very reliable measurements and calculations as possible in the calculation of the merged standard deviation. From the mean value m _{k ί of} the noise level of a sensor over several measured values in a row, an average value m _{z ί} = / _z the travel wind speed are calculated. From the mean m _{k ί of} the noise level and its variance can be a variance ^s _{z, ί} = the travel wind speed are calculated. The same applies to the other sensor values, such as clutter or the

Forward speeds, which are determined with the help of other sensors. From the mean _values m _{z ί} and the variances a _zi the

Fahrtwindgeschwindigkeiten can the fused Mitelwerte m _z> / _hX = -

and the merged variances a _{z us} be calculated. The

Calculations of the merged averages and variances will be for all

calculated airstream speeds i and all possible

Lane conditions Z performed. The current road condition will be as above selected from the smallest merged variance from all calculated merged variances. By weighting the

Traveling speeds using standard deviations due to the reliability of the measured values and the airspeed calculation can determine the accuracy of the determination of the road condition and the

Calculation of the airspeed rate can be further improved.

Other vehicles can significantly influence and distort the measured noise levels by their wind noise and the wet hiss of their wheels. If it is detected that another vehicle is within earshot of the sensor, then on the one hand, the calculated airstream speeds A _{z, ί} = fz (ß _{R, i>} d corrected downward, so that the errors are compensated as well as possible and on the other hand the calculated variances ^s _{z, ί} = dz ^ _R .i ^ _R .i ^ _a ) are corrected upwards to the extent that the calculated airstream speeds have only little or no influence in the calculations of the merged values. The values d _B and d _a being affected by the disturbance and are all the greater, the smaller the distance between the noise source and the sensor. The distance to others

Vehicles can preferably be determined at low humidity by means of the echolocation of the ultrasonic sensors or generally with the help of other sensors such as radar, camera or lidar. The value d _a is calculated not only by the distance of interfering objects, but by all known disturbances on the sensor. In the same way, for example, disturbances of one's own vehicle to individual sensors, eg caused by a large steering angle, can also be taken into account. By the corrections of the calculations for the standard deviations and the mean values of the airstream speeds, the calculation of the merged airstream speed and the

Determination of the road condition can be further improved.

Measurements indicate that road condition determination using clutter values is better at vehicle speeds below 50 km / h than with noise levels, with noise levels better suited for a determination above 50 km / h. For this reason, it may be advantageous for an even more robust determination of the travel wind speed to treat even very high or low vehicle speeds such as a fault and to incorporate them into the value d _a together with further disturbances. Generally, increasing the vehicle speed at low vehicle speeds causes the noise level to increase less than at high vehicle speeds. So it can be changeable but with constant road condition and constant wind

Make the vehicle speed plausible, whether the noise levels or clutter values rise or fall as expected with the vehicle speed. If the increase in one of the sensors does not behave as expected, this is due to a fault in the respective sensor and the value d _a is increased.

For further calculations within the vehicle, it is not only relevant whether it is dry, damp or wet, but also how moist it is or how high the water is on the road. It would be possible very many

Functions to distinguish between something moist, humid, very humid, slightly wet, wet, very wet, 0.1 mm, 0.2 mm 0.3 mm water.

However, this would require a lot of effort to determine the appropriate calculation functions for each state in the driving test and store them in the control unit. It would also take a lot of controller resources. Therefore, each of the states Z can be numbered consecutively and the value S of the road state can be numbered from the variances or

Standard deviations too

Be calculated.

Example: If Z = 1 for dry, Z = 2 for wet and Z = 3 for wet, then the value S of the road condition is calculated to be S = 2.01 according to the above example. So the road is pretty wet and there is a slight tendency towards wet. Analog can also

Road conditions for

1 mm water Z = 4

2 mm water Z = 5

3 mm water Z = 6 can be defined. If S = 5.3 is calculated for the value S of the road condition, then 2.3 mm of water on the road can be assumed. Likewise, the Fahrtwindgeschwindigkeitsberechnung can be optimized. If the road condition is as wet as it is wet, ie if the

Standard deviations o _wet and o _moist assume similar large values, it is then no more or less left to chance whether the

Fahrtwindgeschwindigkeitswert is used for wet or wet road. As a result, the value for the airspeed does not jump back and forth. For this, the airstream speed calculation is added calculated on the basis of all mean values and variances of all road conditions.

The wind speed in the longitudinal direction can be calculated from the mean value f of the selected road condition and the vehicle speed v _v to v _w = fus - ^V v and made available to other control devices in the vehicle. With the help of wind speed, the engine control unit can contribute to the optimization of the operating parameters and thus to an improvement in emissions, or for the detection of a tuned vehicle.

In addition, the wind speed can be provided to other vehicles in the area or to a weather service, whereby these e.g. can optimize their own road condition calculations.

Crosswind also affects the wind speed with which the

Sensors are flown. If the wind comes along with the wind from the left front, the sensors are weaker left front and left stronger behind, against the right front stronger and right behind weaker flow than in calm weather. However, the wet wiping of the wheels on the noise level of the sensors, which affects the calculation of the

Crosswind speed should not disturb. The interference compensation succeeds analogously to the above-described methods, since the sensors, which are better protected from wind, can better recognize the road condition and the sensors, which are particularly affected by the wind, the

Wind speed can determine better. The level curves for dry, wet and wet roads are therefore also different depending on the crosswind. Because of this, for every combination of crosswind {from left, no, from right} and road conditions {dry, wet, wet} analogous as above for the road conditions individual calculation curves are kept: dry inks dry_no dry right

wet jinks wet_kein dampjights

wet inks wet_no wet right

From each of these calculation curve can, as with the

Road conditions are also calculated, the individual wind speeds (in the longitudinal direction) and the combination of road condition and crosswind influence with the lowest standard deviation selected, or values for the road condition S and the side wind speed are calculated as described above.

The rolling and wind noise also influences the noise level. These are also dependent on the tire profile of the steering angle and the acceleration and riot forces. The tire noise depends heavily on the tire model and also on the tire profile. Because the tire noise

Mainly acting on the sensors mounted near the wheel arches is a correction of the calculation of the

Moving wind speeds from the noise levels of these sensors relative to the dependencies of advantage.

Raindrops and spray drops from vehicles in front have just as much influence on the sensors pointing forwards. The drops or the influence on the noise level of the sensors can be determined. Alternatively, the influence can also be based on the windshield wiper frequency or the

Return value of the optical rain sensor can be determined. Based on this influence, the calculation of the airstream speeds is corrected from the noise levels of the forward sensors.

Not only the road condition, but also the wind speeds in the longitudinal and transverse direction can be calculated alone with the help of ultrasonic sensors and robust against disturbances and changes in the road condition. Because the wind speed in the transverse direction robust opposite

This information can be used excellently by the side wind assistants of the ESP and lead to a more stable handling in gusty crosswinds. The approach presented here can save costs for possibly necessary additional sensors (wind sensor). Furthermore, costs for integration into vehicles or for the definition or adaptation of the

Interfaces are saved (with existing wind sensor signal). The

Water level calculation becomes more accurate and robust. The ultrasonic control unit sets the values for the road condition and the wind speeds on the CAN bus.

Finally, it should be noted that terms such as "comprising," "comprising," etc., do not exclude other elements or steps, and terms such as "a" or "an" do not exclude a multitude. Reference signs in the claims are not to be considered as limiting.

Claims

claims

A method of detecting a road condition, the method comprising the steps of:

Reading in a plurality of noise levels (108) detected by each one of the ultrasonic sensors (104) of a vehicle (100) at different locations on the vehicle (100);

Assign one each with a first possible road condition

corresponding first speed value (120) and at least one second speed value (120) corresponding to a second possible road condition to the noise levels (108) using one respective processing rule (116) individually assigned to each respective detecting ultrasonic sensor (104);

Determining a first dispersion parameter (124) of the first one

Velocity values (120) and a second scattering parameter (124) of the second velocity values (120);

Determining the road condition using the

Scattering parameter (124), wherein the road condition is determined to be the first possible road condition when the first dispersion parameter (124) is less than the second dispersion parameter (124), and the road condition is determined to be the second possible road condition when the second dispersion parameter (124 ) is smaller than the first scattering parameter.

2. The method according to claim 1, wherein in the step of associating the

Speed values are further assigned using a speed of the vehicle (100).

A method according to any one of the preceding claims, wherein, in the associating step, the speed values (120) are further assigned using a traveling speed.

4. Method according to one of the preceding claims, wherein in the step of determining as variance parameter (124) variances of the

Speed values (120) are determined, wherein in the step of

Determining the road condition is determined using the variances.

5. The method according to claim 1, wherein in the step of allocating per each noise level read in, at least one further additional speed value corresponding to a further possible road state is assigned sensor-individually using the respective ultrasound sensor

Processing rule (116) is assigned, wherein in the step of

Determining another scatter parameter (124) of the others

Speed values (120) is determined.

6. The method according to claim 5, wherein in the step of determining the

Road condition is determined as the first possible road condition when the first scattering parameter (124) is less than the second

Scattering parameter (124) and the further scattering parameter (124), the road condition is determined as the second possible road condition when the second scattering parameter (124) is smaller than the first one

Scattering parameter (124) and the further scattering parameter (124), and the road condition is determined as the further road condition when the further scattering parameter (124) is smaller than the first one

Scattering parameter (124) and the second scattering parameter (124).

7. The method according to any one of claims 5 to 6, wherein in the step of

Associating per noise level (108) a dry speed value corresponding to a dry road condition, a wet speed value corresponding to a wet road condition, and at least one wet speed value corresponding to a wet road condition are assigned.

8. The method according to any one of the preceding claims, wherein in the step of determining the processing instruction (116) in a the respective detecting ultrasonic sensor (104) sensor associated with each individual

Noise map (200) is shown, with the speed values (120) being read from the noise map (200).

9. Device (102), wherein the device (102) is adapted to the method according to one of the preceding claims in corresponding

Execute, implement and / or control facilities.

A computer program product adapted to execute, implement and / or control the method of any one of claims 1 to 8.

11. Machine-readable storage medium on which the

Computer program product according to claim 10 is stored.