Method and apparatus for calculating and characterizing road surface unevenness

Abstract

The present invention relates to a method for calculating and characterizing the road unevenness of a road surface. For this purpose, a number of sensor data are generated by at least one wheel rotation speed sensor and / or at least one acceleration sensor of a vehicle traveling on a road surface, and the road surface unevenness is calculated and characterized by a computing device using the generated sensor data.

Description

[Technical Field]

[0001] The present invention relates to a method and apparatus for calculating and characterizing road surface unevenness. [Background technology]

[0002] Road irregularities, for example in the form of potholes, occur frequently and pose a safety risk for motor vehicles. The magnitude of the safety risk depends mainly on the shape and size of the road irregularities. Motorcyclists are considered a particularly at-risk group. Moreover, road irregularities are also a source of discomfort for motor vehicle drivers and passengers. However, there is a lack of reliable, region-specific data on the presence and type of such road irregularities. The creation of hazard maps is described, for example, in US Pat. No. 5,649,499.

[0003] Sensor data from lidar, radar, or camera sensors can be used to detect, assess, and map road surface irregularities. Such detection and assessment methods can be used to detect road damage, which may include machine learning algorithms that receive image and video data as input.

[0004] However, the sensors used in this case often do not meet the ASIL-D standard (Automotive Safety Integration Level-D), and the percentage of cars equipped with such sensors is very low.

[0005] Furthermore, machine learning algorithms for detecting and assessing potholes are prone to false positive and false negative results, and the algorithms consume significant computing time resources. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] German Patent Application Publication No. 102010055370 Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention provides a method and a device for calculating and characterizing road surface unevenness having the features set out in the independent claims. Preferred embodiments are the subject of the dependent claims. [Means for solving the problem]

[0008] Thus, according to a first aspect, the present invention relates to a method for calculating and characterizing the road surface unevenness of a road surface, for which sensor data are generated by at least one wheel rotation speed sensor and / or at least one acceleration sensor of a vehicle traveling on the road surface, the road surface unevenness being calculated and characterized by a computing device using the generated sensor data, the characterization of the road surface unevenness comprising calculating at least one of the length, width and depth of the road surface unevenness.

[0009] According to a second aspect, the present invention relates to an apparatus for calculating and characterizing a road surface unevenness, the apparatus comprising an interface and a computing device. The interface is configured to receive sensor data generated by at least one wheel rotation speed sensor and / or at least one acceleration sensor of a vehicle traveling on the road surface. The computing device is configured to calculate the road surface unevenness using the generated sensor data. The characterization of the road surface unevenness includes calculating at least one of the length, width and depth of the road surface unevenness. [Effects of the Invention]

[0010] The present invention allows the frequency, and optionally also the severity or extent (e.g., relative depth and length of potholes) of road surface unevenness to be detected and analyzed, contributing to the creation of a comprehensive database of road surface unevenness.

[0011] Modern cars are equipped with multiple sensors whose data is used by embedded systems or vehicle computers for safety and comfort. Wheel rotation speed sensors are among the most frequently used sensors.

[0012] High-frequency wheel rotation speed sensors provide information about the exact condition of the wheels. These sensors are also among the few that meet the ASIL-D standard, making them extremely reliable compared to other sensors.

[0013] Furthermore, wheel rotation speed sensors are very widespread. Moreover, wheel rotation speed sensors are the sensors that are closest to the road surface because they are attached directly to the wheels. Therefore, high reliability is achieved due to the proximity of the sensor to the road surface. In particular, the combination of wheel rotation speed sensors and acceleration sensors at the wheels is advantageous here.

[0014] The vehicle may be a two-wheeler, a three-wheeler, a car, a truck, a motorcycle, etc. The vehicle may also be, for example, an aircraft, for example for detecting runway damage.

[0015] Calculating road surface unevenness may be understood to mean, in particular, that the presence of road surface unevenness is detected, whereas characterizing may be understood to mean that additional properties (beyond mere presence) are calculated.

[0016] Within the scope of the present invention, road surface irregularities may include road damage, such as depressions, recesses or bumps in the road surface, ruts, but may also include unnatural road surface irregularities, such as speed limits, ramps, etc.

[0017] The acceleration sensor may be an inertial sensor fixed to the vehicle and not arranged on a moving component, but may also be a wheel-specific acceleration sensor attached to the wheel and operating together with the wheel, and each wheel may be provided with a corresponding wheel-specific acceleration sensor.

[0018] The computing device is preferably located near the data source or sensor device, for example, integrated in the control unit of a brake control system, so that the sensor values can be processed as unfiltered as possible. According to another embodiment of the method for calculating and characterizing road surface unevenness, the wheel rotation speed sensor detects pulses using, for example, Hall sensors, depending on the movement of a pulse wheel arranged on the wheel of the vehicle. The computing device calculates the angular course of the high-frequency wheel rotation speed using the changes in the detected pulses as a function of time, i.e., using the raw signal of the alternating magnetic field (north / south) emitted by the pulse wheel. In this case, the angular course of the wheel rotation speed should be interpreted as the angle-dependent change in the wheel rotation speed. This can be achieved by calculating the time difference between individual pulses. The computing device detects road surface unevenness using the calculated angular course of the wheel rotation speed. Road surface unevenness often results in short-term changes in the wheel rotation speed because the wheels of the vehicle accelerate or decelerate when driving over the road surface unevenness. The same applies when exiting the road surface unevenness. By detecting such changes in wheel rotation speed, the computing device can calculate the road surface unevenness. Compared to the time course of wheel rotation speed, the angular course obtained from pulse changes over a predetermined time period offers a clear advantage in terms of accuracy in detecting small changes in road surface condition. For example, it is possible to calculate the number of pulses within a predetermined time period, for example, a time interval equal to or shorter than 1 ms. Processing the raw sensor signals in the computing device allows for accurate measurement and detection of even the smallest changes in road surface condition.

[0019] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device calculates road surface unevenness when the magnitude of the angular change in wheel rotation speed exceeds a threshold value, which may be dependent on the vehicle speed.

[0020] According to another embodiment of the method for calculating and characterizing road surface unevenness, a computing device calculates a frequency characteristic of the wheel rotation speed using sensor data generated by the wheel rotation speed sensor, and the computing device then calculates the road surface unevenness using the calculated frequency characteristic of the wheel rotation speed. Thus, the road surface unevenness can be calculated when at least one predetermined frequency occurs in the frequency characteristic. The frequency characteristic may be compared to a predetermined frequency pattern to calculate the road surface unevenness.

[0021] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device is further adapted to determine the type and / or state of the road surface unevenness using the sensor data. The type of road surface unevenness may be, for example, a depression, a recess, a bump, a speed limit, a slope, etc. The state of the road surface unevenness may be understood as the spatial extent, for example the depth, width and length of a depression.

[0022] According to another embodiment of the method for calculating and characterizing road surface unevenness, characterizing the road surface unevenness comprises calculating the depth and / or height (e.g., in centimeters) of the road surface unevenness using the amplitude of the wheel rotation speed variations, where the amplitude of the momentary high frequency wheel rotation speed variations corresponds to the depth or height of the road surface unevenness.

[0023] According to another embodiment of the method for calculating and characterizing road surface unevenness, the wheel rotation speed sensor detects pulses depending on the movement of a pulse wheel arranged on the wheel of the vehicle, and in this case characterizing the road surface unevenness includes calculating the length of the road surface unevenness using the number of pulse changes within the time between entering and exiting the road surface unevenness.

[0024] According to another embodiment of the method for calculating and characterizing road surface unevenness, inertial sensors fixed to the vehicle and / or wheel-specific acceleration sensors detect vertical acceleration, and characterizing the road surface unevenness includes calculating the depth or height of the road surface unevenness using the calculated vertical acceleration. In particular, changes in vertical acceleration can be measured, and the calculation of the depth and / or height of the road surface unevenness is performed using the amplitude of the changes in vertical acceleration measured by at least one acceleration sensor. The amplitude of the changes in vertical acceleration corresponds to the depth or height of the road surface unevenness. By comparing the amplitude of the changes in vertical acceleration with one or more threshold values, different depths or heights can be distinguished.

[0025] According to another embodiment of the method for calculating and characterizing road surface unevenness, the characterization of the road surface unevenness is performed using sensor data from at least one wheel rotation speed sensor. The results of the characterization of the road surface unevenness are validated using sensor data from at least one acceleration sensor. The results of the wheel rotation speed sensor are generally very accurate. In particular, the length can be determined more accurately by calculating the number of pulses between entering and exiting the road surface unevenness than by calculating the vehicle speed. However, the data of the at least one acceleration sensor may be used to validate the results using the wheel rotation speed sensor, for example, by performing an independent detection and / or characterization of the road surface unevenness.

[0026] According to another embodiment of the method for calculating and characterizing road surface unevenness, the calculation of the road surface unevenness comprises calculating the position of said road surface unevenness relative to a reference point of the vehicle using calculated cornering and / or individual wheel evaluation (e.g. by wheel-specific acceleration and / or wheel rotation speed sensors), whereby the width of the road surface unevenness can be determined.

[0027] According to another embodiment of the method for calculating and characterizing road surface unevenness, the frequency patterns of wheel rotation speed amplitudes and the number of pulse changes within a predetermined time period, which are generated, for example, during a test drive under predetermined conditions, for various types and / or states of road surface unevenness can be recorded. By comparing the instantaneously calculated frequency patterns or amplitude variations with recorded frequency patterns or thresholds, the type and / or state of road surface unevenness can be calculated.

[0028] According to another embodiment of the method for calculating and characterizing road surface unevenness, the road surface unevenness, for example the depth of a depression, can be calculated by observing the amplitude of the gradient, i.e. the change in wheel rotation speed over time. The larger the amplitude, the deeper the depression. Using a predetermined relationship, for example a look-up table, the depth of the road surface unevenness can be calculated by the change in wheel rotation speed over time. In this case, other parameters, for example the instantaneous speed of the vehicle, can also be taken into account.

[0029] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device further calculates and / or characterizes the road surface unevenness taking into account driving situations and / or driving events, which may be, for example, braking events, acceleration events or steering events, and in which, for example, the instantaneous speed of the vehicle may be taken into account.

[0030] In order to avoid road surface unevenness being detected already due to acceleration or deceleration itself, the threshold for detecting road surface unevenness can be increased, for example, by using driving situations or driving events to reduce false positive detections during strong acceleration or deceleration.

[0031] However, it is also possible to use driving situations or driving events to detect the prediction of road surface unevenness.When a driver detects, for example, a pothole, the driver brakes in a normal manner, so the existence of a braking event can be taken into account for the validity of the detected road surface unevenness.Therefore, for example, the probability of the existence of a predetermined road surface unevenness can be calculated.This probability is increased by the existence of a braking event.

[0032] According to another embodiment of the method for calculating and characterizing road surface unevenness, a computing device calculates and / or characterizes the road surface unevenness using a machine learning model and / or a statistical model that receives input data that is dependent on sensor data.

[0033] The input data may be, for example, the sensor data itself. However, the sensor data may first be processed before it is provided to the machine learning model and / or the statistical model.

[0034] The machine learning model may be pre-trained using training data, and according to one embodiment, the machine learning model may be adapted to calculate and / or characterize road surface unevenness in real time while driving.

[0035] According to another embodiment of the method for calculating and characterizing road surface unevenness, a machine learning model receives as input at least one wheel rotation speed time course and / or wheel rotation speed frequency characteristic. The machine learning model outputs a value corresponding to the likelihood of the presence of road surface unevenness. The machine learning model may be trained to classify different types and / or states of road surface unevenness.

[0036] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device is an external computing device, i.e., located outside the vehicle. For example, the evaluation can be performed in the cloud. In this case, the sensor data can be output to the computing device via an interface of the vehicle.

[0037] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device is an internal computing device, i.e., located in the vehicle. For example, the computing device is a control unit of the vehicle or a vehicle subsystem. For example, the computing device can be a control unit of an anti-lock system of the vehicle.

[0038] According to another embodiment, the calculation and / or characterization of road surface irregularities is implemented at the edge of a computer network (edge computing), where the computer network includes any combination of an electronic control unit, a vehicle computer, a connected control unit, and a cloud. This combination can also provide information about the vehicle's location, which can then be combined with the detected road surface irregularities to create a map.

[0039] According to another embodiment of the method for calculating and characterizing road surface unevenness, when a road damage is detected, information is output to a driver of the vehicle via a display device of the vehicle, in particular the information may include the occurrence of the road surface unevenness and / or details regarding the road surface unevenness, such as the type and / or condition of the road surface unevenness.

[0040] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device can compare the sensor data of various wheel speed sensors of various wheels with each other. For example, if a change in wheel speed occurs only in the wheel speed sensor on one side of the vehicle, the computing device can determine that the presence of road surface unevenness has been detected within the corresponding side of the vehicle. The computing device can then detect, for example, a depression in the road surface.

[0041] When a change in wheel rotation speed occurs in the wheel rotation speed sensors on both sides of the vehicle, the computing device can calculate that the road surface unevenness is increasing. The computing device can then detect, for example, a speed limit.

[0042] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device can also take into account the steering angle of the vehicle. The computing device can detect a pothole if it determines that the steering angle exceeds a predetermined threshold when the vehicle is traveling around a curve and only one of the wheel speed sensors of the wheels measures a significant change in wheel speed above the threshold. In this case, it predicts that only one wheel of the vehicle has passed through the pothole based on the steering angle. If the road surface unevenness is widespread, significant changes in wheel speed above the threshold will be measured at multiple wheels.

[0043] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device calculates the length of the road surface unevenness using multiple sensor data. That is, the computing device can detect entry onto the road surface unevenness based on a first change in wheel rotation speed, and can detect exit from the road surface unevenness using a second change in wheel rotation speed. Taking into account the vehicle speed, the computing device can calculate the length of the road surface unevenness. The number of pulse changes between the time of entry onto the road surface unevenness and the time of exit from the road surface unevenness corresponds to a length in, for example, centimeters.

[0044] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device calculates an average wheel rotation speed by averaging the wheel rotation speed over a predetermined time period, and if the deviation of the instantaneous wheel rotation speed from the average wheel rotation speed exceeds a threshold value, the computing device calculates road surface unevenness.

[0045] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device calculates the presence of road surface unevenness using sensor data calculated by at least one inertial sensor. The inertial sensor may include a rotational angular velocity sensor and / or an acceleration sensor. For example, the acceleration sensor may calculate acceleration measurement data along three perpendicular measurement axes.

[0046] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device can calculate the presence of road surface unevenness using, inter alia, vertical acceleration. When a vehicle travels over a road surface unevenness, the vertical acceleration changes rapidly. This allows the computing device to calculate the presence of road surface unevenness when the change in vertical acceleration exceeds a predetermined threshold. Using this change, the computing device can also calculate the type and / or state of the road surface unevenness. The acceleration measurement data can come from an inertial sensor positioned in the center of the vehicle and from individual wheel acceleration sensors.

[0047] According to another embodiment of the method for calculating and characterizing road surface unevenness, the computing device calculates the presence of road surface unevenness by taking into account sensor data from other sensors, such as wheel-specific acceleration sensors, video sensors, lidar sensors, radar sensors, etc. In particular, the computing device can validate the presence of road surface unevenness using additional sensor data. Thus, the type and / or state of road surface unevenness can be calculated by object recognition methods using video data.

[0048] According to another embodiment of the method for calculating and characterizing road surface unevenness, at least one threshold for calculating the road surface unevenness may be configurable, for example, a bidirectional communication interface between the vehicle and the cloud may be provided for this purpose.

[0049] According to another embodiment of the method for calculating and characterizing road surface unevenness, data related to road surface unevenness are collected to create a geographic map. In particular, a road map may describe road surface unevenness and, optionally, the type and / or state of the road surface unevenness. The creation of the geographic map may be performed in the cloud using statistical and / or machine learning based algorithms. The geographic map may be dynamically updated.

[0050] According to another embodiment of the method for calculating and characterizing road surface unevenness, sensor data from internal or external acceleration sensors can be referenced to detect three-dimensional vibrations. Road surface unevenness can be detected by statistical methods or machine learning models. [Brief explanation of the drawings]

[0051] [Figure 1] 1 is a schematic block diagram of an apparatus for calculating and characterizing road surface unevenness according to an embodiment of the present invention; [Figure 2] 1 is a schematic block diagram of a vehicle equipped with a device according to the invention for calculating and characterizing road surface unevenness; [Figure 3] FIG. 10 is a schematic diagram for explaining the change in wheel rotation speed when passing over an uneven road surface. [Figure 4] 1 is a flowchart of a method for calculating and characterizing road surface unevenness according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0052] In all figures, identical or functionally identical components and devices are labeled with the same reference numerals. The numbering of method steps is used for clarity and generally does not imply a specific temporal sequence. In particular, several method steps may be performed simultaneously.

[0053] Description of the Examples 1 shows a schematic block diagram of an apparatus 1 for calculating and characterizing road surface unevenness. The apparatus 1 includes an interface 2, which is coupled to at least one wheel rotation speed sensor and / or at least one acceleration sensor, for example via a vehicle communication bus. The apparatus 1 may further be connected to various internal sensors of the vehicle's braking system. Additionally, sensors external to the system may be connected, for example via the vehicle communication bus.

[0054] The interface 2 may be a wireless inductive connection to allow coupling with a motor vehicle, so the device 1 may be located inside the vehicle or may be an external device.

[0055] The device 1 further includes a computing device 3 that calculates road surface unevenness using the sensor data received via the interface 2. The computing device 3 may include one or more electronic processors, such as programmable microprocessors, microcontrollers, etc. The device 1 further includes a non-transitory machine-readable memory device 4 for storing the received sensor data. The computing device 3 is able to read from and write to the memory device 4.

[0056] The computing device 3 may include a first unit 31 for data detection, a second unit 32 for pre-processing the sensor data, and a third unit 33 for calculating road surface unevenness. The first to third units 31 to 33 may be configured as separate electronic processors or may be executed by the same electronic processor or by a combination of electronic processors.

[0057] During the data detection phase, the device 1 detects signals from at least one sensor in near real time. The data received by the at least one sensor is provided in raw format, such as rotation speed pulses from a wheel rotation speed sensor. These signals are detected via an interface 2 and written by a first unit 31, for example, to a storage device 4.

[0058] In a pre-processing stage, the raw sensor data is modified and processed by a second unit 32 to compute high frequency wheel rotation speed data.

[0059] During the calculation of the model algorithm, the third unit 33 uses the high frequency wheel rotation data to detect road surface unevenness. The third unit 33 can distinguish between road roughness and depressions and undulations based on, for example, precisely calibrated thresholds of the model. Furthermore, the third unit 33 can detect the type and / or state of the road surface unevenness. In particular, the depth and / or length and / or width of the road surface unevenness are detected and output.

[0060] The information can be output via interface 2, for example to another computing device in the vehicle or to an external cloud.

[0061] Figure 2 shows a schematic block diagram of a motor vehicle 101 equipped with the device 1 for calculating and characterizing road surface unevenness described in Figure 1. Wheel rotation speed sensors 103 are arranged on each of the wheels of the motor vehicle 101, which are hard-wired or alternatively connected via a motor vehicle bus to the device 1 and to a motor vehicle computer 104. In this case, the device 1 may be an electronic control unit of the motor vehicle 101.

[0062] The device 1 uses the information received by the wheel rotation speed sensors 103 to calculate the vehicle speed, distance traveled, slippage, etc. Furthermore, the device 1 calculates the road surface unevenness as described above.

[0063] Optionally, the vehicle computer 104 may be configured to calculate and characterize road surface unevenness.

[0064] Information about road surface irregularities may be transferred via a communication bus of the vehicle 101 to a device 105 for communication with further vehicles or other external devices (V2X devices), which may store and / or transmit the information to a cloud infrastructure 107 via a wireless inductive communication channel 106. The wireless inductive communication channel 206 may include, for example, a mobile radio network, a Wi-Fi interface, a Bluetooth interface, etc.

[0065] The data can then be managed, modified, processed and visualized in the cloud infrastructure 107. The data can for example be further processed to create a geographical map in which information about road unevenness is visualized. Tables or reports about potholes and road unevenness can also be generated.

[0066] 3 shows a schematic diagram illustrating the change in wheel rotation speed when a vehicle passes over road surface irregularities 302, 303. In this case, the wheel rotation speed sensor calculates the wheel rotation speed of wheel 301 using the incremental encoder principle.

[0067] A sensor element 305 of a wheel rotation speed sensor, such as a Hall sensor anisotropic magnetoresistance (AMR) sensor, giant magnetoresistance (GMR) sensor, etc., is exposed to the changing magnetic field of a rotating encoder 304 attached to the axle of the wheel 301.

[0068] The detected changes in magnetic flux are transmitted as rotation speed pulses to the computing device 1. The computing device 1 measures the time lag between adjacent rotation speed pulses and calculates from this (together with other calibration parameters, e.g., number of pulses per revolution and per wheel size) the instantaneous high-frequency wheel rotation speed.

[0069] When driving into and out of a pothole 302 or a bump 303, sudden, high frequency wheel rotation speed fluctuations occur. This is because the wheel 301 is subjected to a sudden increase 306 in wheel rotation speed when driving into the pothole 302. Conversely, the wheel 301 is subjected to a sudden decrease 307 in rotation speed when driving out of the pothole 302.

[0070] The opposite behavior occurs at a bump 303, i.e. the wheel 301 experiences a sudden drop 308 in wheel rotation speed when going into the bump 303. Conversely, the wheel 301 experiences a sudden increase 309 in rotation speed when coming out of the bump 303.

[0071] The amplitude of the irregularity (wavelet amplitude) is the degree of the depth of the pothole 302 or the height of the road bump 303, and the number of pulses between entering and exiting corresponds to the interval representing the length of the pothole.

[0072] 4 shows a flow chart of a method for calculating and characterizing road surface unevenness, which method can be performed by the above-described device 1. Conversely, this device 1 can be configured to perform the method steps described below.

[0073] In a first method step S1, sensor data are generated by at least one wheel rotation speed sensor 103 and / or at least one acceleration sensor of a motor vehicle 101 traveling on a road surface.

[0074] In a second method step S2, the computing device 3 calculates and characterizes the road surface unevenness using the generated sensor data. For this purpose, the computing device 3 can calculate the time course of the wheel rotation speed. At the onset of road surface unevenness, the computing device 3 can in particular calculate the time change of the wheel rotation speed. If this time change exceeds a threshold value, road surface unevenness is detected.

[0075] The calculation device 3 may also calculate and refer to the frequency characteristics of the wheel rotation speed to calculate the road surface unevenness.

[0076] Acceleration can also be calculated based on sensor data from the acceleration sensor. In particular, vertical acceleration can be calculated. If the change in vertical acceleration exceeds a preset threshold, an uneven road surface is detected.

[0077] The calculation of road surface unevenness is performed by a model algorithm, which may include processing raw sensor data as input, calculating instantaneous high frequency wheel rotation rates and monitoring these wheel rotation rates.

[0078] Furthermore, the computing device 3 can calculate the type and / or state of the road unevenness. Thus, based on a first change in the wheel rotation speed, entering the road unevenness can be detected, and using a second change in the wheel rotation speed, exiting the road unevenness can be detected.

[0079] By calculating the number of pulses in the time between entering and exiting the road unevenness, the length of the road unevenness can be calculated taking into account the vehicle speed.

[0080] Furthermore, for example by calculating the amplitude of the change in wheel rotation speed, the depth of the road surface unevenness can be calculated, which is for example proportional to the amplitude and can be learned by means of calibration.

[0081] Furthermore, the width can be calculated, for example, by detecting whether the road surface irregularity is detected at each wheel or only at certain wheels.

[0082] The road surface unevenness may also be obtained using machine learning models and / or using statistical models.

[0083] Furthermore, information about road surface irregularities can be output to the cloud, which can be used to create a geographical map of road surface irregularities.

[0084] The calculation of the road surface unevenness can be performed inside the vehicle, for example by calculation in a control unit of the anti-lock system of the motor vehicle 101. However, the calculation of the road surface unevenness can also be performed at least partly outside the motor vehicle 101, for example in the cloud. [Explanation of symbols]

[0085] 1 Device for calculating and characterizing road surface unevenness, electronic control unit 2. Interface 3 Computing device 4 Storage device 31 First Unit 32 Second Unit 33 Third Unit 101 Automobiles 103 Wheel rotation speed sensor 104 Automotive Computer 105 Devices for communicating with external devices 106 Interfaces, communication channels 107 Cloud Infrastructure 301 Wheels 302 Uneven road surface, potholes 303 Road surface unevenness, road surface bumps 304 Encoder 305 Sensor Element 306 rise 307 Decline 308 Decline 309 rise S1 Method Step

Claims

1. 1. A method for calculating and characterizing road surface unevenness of a road surface, comprising the steps of: A step (S1) of generating sensor data by at least one wheel rotation speed sensor (103) and / or at least one acceleration sensor of a vehicle (101) moving on a road surface; and (S2) calculating and characterizing road surface unevenness by a computing device (3) using the generated sensor data, Calculating and characterizing road surface unevenness of a road surface, wherein characterizing the road surface unevenness includes calculating at least one of a length, a width, and a depth of the road surface unevenness; the wheel rotation speed sensor (103) detects pulses depending on the movement of a pulse wheel arranged on a wheel of the vehicle (101), and the characterization of the road surface unevenness includes calculating the length of the road surface unevenness using the number of pulse changes within the time between entering and exiting the road surface unevenness, A method for determining the road surface unevenness as a depression in the road surface if the wheel rotation speed increases when entering and decreases when exiting, and for determining the road surface unevenness as a bump in the road surface if the wheel rotation speed decreases when entering and increases when exiting.

2. A method as described in claim 1, wherein the calculation device (3) calculates the frequency characteristics of the wheel rotation speed using the sensor data generated by the wheel rotation speed sensor (103), and the calculation device (3) calculates the road surface unevenness using the calculated frequency characteristics of the wheel rotation speed.

3. The method of claim 1, wherein the computing device (3) further determines the type and / or state of road surface unevenness using the sensor data to characterize the road surface unevenness.

4. A method as described in claim 1, wherein characterizing the road surface unevenness includes calculating the depth and / or height of the road surface unevenness using the amplitude of the change in wheel rotation speed and / or the amplitude of the change in vertical acceleration measured by at least one acceleration sensor.

5. A method as described in claim 1, wherein the characterization of the road surface unevenness is performed using sensor data from at least one wheel rotation speed sensor (103), and the results of the characterization of the road surface unevenness are validated using sensor data from at least one acceleration sensor.

6. The method of claim 1, wherein the computing device (3) further calculates and / or characterizes road surface unevenness taking into account braking events, acceleration events, steering events and speed of the vehicle (101).

7. The method described in claim 1, wherein the computing device (3) calculates and / or characterizes road surface unevenness using a machine learning model and / or a statistical model that receives input data that is dependent on the sensor data.

8. The computing device (3) is an external computing device (3) relative to the vehicle (101), 2. The method according to claim 1, further comprising outputting the sensor data to the computing device (3) via an interface (106) of the vehicle (101).

9. The method described in claim 1, wherein the computing device (3) is a control unit of an anti-lock system of the motor vehicle (101).

10. An apparatus (1) for calculating and characterizing the road surface unevenness of a road surface, comprising: an interface (2) configured to receive sensor data generated by at least one wheel rotation speed sensor (103) and / or at least one acceleration sensor of a vehicle (101) moving on a road surface; a computing device (3) configured to calculate and characterize road surface unevenness using the generated sensor data, characterizing the road surface unevenness includes calculating at least one of a length, a width, and a depth of the road surface unevenness; the wheel rotation speed sensor (103) detects pulses depending on the movement of a pulse wheel arranged on a wheel of the vehicle (101), and the characterization of the road surface unevenness includes calculating the length of the road surface unevenness using the number of pulse changes within the time between entering and exiting the road surface unevenness, an apparatus for calculating and characterizing the road surface unevenness, wherein the apparatus determines the road surface unevenness as a depression if the wheel rotation speed is increased when entering and decreased when exiting, and determines the road surface unevenness as a bump if the wheel rotation speed is decreased when entering and increased when exiting.

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