Method and apparatus for calibrating vehicle sensors

Abstract

The invention relates to a method for calibrating a vehicle sensor of a motor vehicle, comprising the following steps: acquiring sensor data at a plurality of measuring times during a total measuring time period by means of the vehicle sensor, wherein the total measuring time period is divided into a plurality of partial measuring time periods, wherein the motor vehicle moves relative to an object in the surroundings of the motor vehicle; for each partial measuring time period, calculating an object position of the object based on the acquired sensor data; for each partial measuring time period, calculating a partial measuring time period sinusoidal diagram based on the object position calculated for the partial measuring time period; calculating a total measuring time period sinusoidal diagram by adding the partial measuring time period sinusoidal diagrams and correcting them with the aid of factors associated with the partial measuring time period sinusoidal diagrams; acquiring a direction of the vehicle sensor relative to a driving axis of the motor vehicle based on the total measuring time period sinusoidal diagram; and calibrating the vehicle sensor based on the acquired direction of the vehicle sensor relative to the driving axis of the motor vehicle.

Description

Technical Field

[0001] The invention relates to a method for calibrating a vehicle sensor of a motor vehicle. The invention also relates to a corresponding device for calibrating a vehicle sensor of a motor vehicle. Background Art

[0002] Driver assistance systems access sensor data from vehicle sensors. In order for the driver assistance systems to be usable before leaving the factory, the vehicle sensors must be calibrated. For calibration, the motor vehicle can be moved on a chassis test stand (Fahrwerksstand) with the travel axis measured towards a suitable target. However, the stationary measurement of the travel axis and the calibration formed therefor are time- and cost-intensive. In particular, the determination of the direction in which the sensor reacts to the travel axis of the motor vehicle can become problematic in a production environment, since short stop times must be expected. Therefore, the cost of static methods can increase linearly with each sensor, which can become difficult for multi-sensor systems consisting of a plurality of sensors.

[0003] When dynamically calibrating the sensor with fixed objects by means of trilateration, it can be difficult to monitor the position of the objects relative to one another in order to ensure that the objects do not move. In addition, the tracking of objects is very complex. An "object" is understood to be any point to which coordinates can be assigned based on sensor data, such as radar reflections, etc.

[0004] A method for ascertaining the misalignment of a radar sensor is known from DE 10 2013 209 494 A1, in which a measured angle is compared with an angle calculated from an interval measurement.

[0005] Furthermore, a method and a measuring path for calibrating a distance sensor are known from EP 1 947 473 A2, wherein measuring markers are arranged on the roadway. Summary of the invention

[0006] The invention provides a method for calibrating a vehicle sensor of a motor vehicle having the features according to the invention and a device for calibrating a vehicle sensor of a motor vehicle having the features according to the invention.

[0007] Preferred embodiments are correspondingly preferred embodiments.

[0008] Therefore, according to a first aspect, the present invention relates to a method for calibrating a vehicle sensor of a motor vehicle. The vehicle sensor acquires sensor data at multiple measuring times during a total measuring time period, wherein the motor vehicle moves relative to an object in the surroundings of the motor vehicle. The total measuring time period is divided into multiple partial measuring time periods. For each partial measuring time period, the object position is calculated based on the acquired sensor data. For each partial measuring time period, a partial measuring time period sinogram is calculated based on the object position calculated for the partial measuring time period. The total measuring time period sinogram is calculated by adding the partial measuring time period sinograms and by means of corrections (corrections) of factors associated with the partial measuring time period sinograms. The direction of the vehicle sensor relative to the driving axis of the motor vehicle is acquired based on the total measuring time period sinogram. The vehicle sensor is calibrated based on the acquired direction of the vehicle sensor relative to the driving axis of the motor vehicle.

[0009] According to a second aspect, the invention relates to a device for calibrating a vehicle sensor of a motor vehicle, the device comprising an interface, a computing device and a calibration device. The interface receives sensor data, which are obtained by the vehicle sensor at a plurality of measuring times during a total measuring time period, while the motor vehicle moves relative to an object in the surroundings of the motor vehicle. The computing device divides the total measuring time period into a plurality of partial measuring time periods. The computing device calculates the object position for each partial measuring time period based on the obtained sensor data. The computing device calculates a partial measuring time period sinogram for each partial measuring time period based on the object position calculated for the partial measuring time period. The computing device calculates the total measuring time period sinogram by adding the partial measuring time period sinograms and by correcting the partial measuring time period sinograms with the aid of factors associated with the partial measuring time period sinograms. The computing device also calculates the direction of the vehicle sensor relative to the driving axis of the motor vehicle based on the total measuring time period sinogram. The calibration device calibrates the vehicle sensor based on the obtained direction of the vehicle sensor relative to the driving axis of the motor vehicle.

[0010] Advantages of the present invention:

[0011] The invention enables dynamic calibration, so that the calibration process can be carried out without downtime and without orientation of the axis of travel during travel. Thus, for example, the travel from the factory to the parking lot or to the transport vehicle can be used for calibration. The invention can thus be used to optimize time processes in the factory.

[0012] The calibration of the vehicle sensors according to the invention can also be sufficient without measuring the (radial) speed, yaw rate or other signals. Tracking of objects is also not required. In addition, the method is more accurate in the elevation direction, in particular, compared to static methods.

[0013] Finally, the method is simpler to implement. Even with good results, the method does not rely on a stationary object, which reduces costs because the placement of the object does not need to be constantly monitored.

[0014] By dividing the total measurement time period into a plurality of partial measurement time periods and obtaining the corresponding partial measurement time period sinograms, concise interference effects can be eliminated by applying a correction factor. If no correction is required, the correction factor can optionally also be zero. Possible interference effects include external structures, such as curves, straight lines perpendicular to the direction of motion, etc. In particular, motion effects acting opposite to the main flow direction and spatial structures having an adverse effect on the calculation of the sinogram can be compensated. Therefore, the total measurement time period sinogram basically only contains the main motion flow. The present invention can therefore achieve better determination of the main motion of the object. In particular, short-term effects such as unevenness, braking and acceleration processes of motor vehicles and short steering turns can be compensated. Compensation is particularly advantageous for continuous compensation during driving, because multiple interference factors are multiplied by themselves, which can be continuously filtered out.

[0015] Furthermore, static angular deviations can also be corrected which are caused by surfaces (eg coverings) in front of the radar through which the radar emits its radiation.

[0016] According to another embodiment of the method for calibrating a vehicle sensor, the vehicle sensor comprises at least one radar sensor, a lidar sensor, an ultrasonic sensor, an infrared sensor and / or a camera sensor. The present invention can be used for all sensors that scan the surroundings without additional external information.

[0017] According to a further embodiment of the method for calibrating a vehicle sensor, the speed and yaw rate of the vehicle are also determined in order to carry out an accurate re-filtering of the direction of the vehicle sensor relative to the driving axis of the motor vehicle, ie with respect to the direction error angle in the azimuth and elevation directions.

[0018] According to a further specific embodiment of the method for calibrating a vehicle sensor, the partial measurement time period sinusoidal graph is calculated by means of a Hough transformation.

[0019] According to another embodiment of the method for calibrating a vehicle sensor, the partial measurement period sinogram is normalized for correction by means of a factor associated with the partial measurement period sinogram. Furthermore, a difference sinogram is calculated by multiplication of the normalized partial measurement period sinogram. The difference sinogram can again be normalized. The factor can be calculated in the following way: the normalized difference sinogram is subtracted from the sinogram with the value 1. This corresponds to compensation of "background noise" based on motion and structure. Thus, a flow determination is achieved that is filtered or compensated for interference factors.

[0020] According to another embodiment of the method for calibrating a vehicle sensor, a weighting of the object position is implemented in the creation of the partial measurement time period sinusoidal diagram. Weighting can lead to better results, because quality features of the object position assigned to the object can be taken into account. For weighting, device information of the vehicle sensor can be taken into account, in particular the range of action of the sensor or the angular or interval relationship of the quality of the sensor data determined by the vehicle sensor. The more reliable the data, the higher the weighting of the corresponding information.

[0021] According to another embodiment of the method for calibrating a vehicle sensor, the sinusoidal diagram passes through an intensity maximum value according to the total measurement period. The defined method determines the main direction of the object position in Hough space, in particular by means of a kernel density estimation, wherein the determination of the direction of the vehicle sensor relative to the axis of travel of the motor vehicle includes calculating the angle between the main direction and the axis of travel of the motor vehicle.

[0022] According to a further specific embodiment of the method for calibrating a vehicle sensor, an azimuth angle and an elevation angle are calculated, which extend between the respective main direction and the axis of travel of the motor vehicle.

[0023] According to another embodiment of the method for calibrating a vehicle sensor, the total measurement period sinogram includes intensity values ​​ascertained from the sensor data, wherein a histogram is calculated by applying a threshold function to the intensity values. The histogram is correlated with the azimuth angle and / or elevation angle relative to the axis of travel of the motor vehicle. The direction of the vehicle sensor relative to the axis of travel of the motor vehicle is ascertained based on the calculated histogram.

[0024] According to a further specific embodiment of the method for calibrating a vehicle sensor, the direction of the vehicle sensor relative to the axis of travel of the motor vehicle is ascertained by applying a filter function to the histogram.

[0025] According to another embodiment of the method for calibrating a vehicle sensor, the main direction of the object position is determined by applying a filter function to the histogram. The determination of the direction of the vehicle sensor relative to the axis of travel of the motor vehicle includes calculating the angle between the main direction and the axis of travel of the motor vehicle.

[0026] According to another embodiment of the method for calibrating a vehicle sensor, the calculation of the sinogram of a partial measurement time period is implemented in spherical coordinates. This calculation is preferably carried out without calculating the grid in advance. The use of spherical coordinates has the advantage that the position of the object often already exists in the spherical coordinates, so that this position can be used without transformation into a Cartesian coordinate system. Performance can be improved in this way. The expression in the spherical coordinate system allows for higher precision, which can be compared with the precision of the Radon transform, but with significantly better performance in the case of limited resource availability, because compared to the Cartesian expression of the Hough transform, the object position is not reduced to the resolution of the grid size in Cartesian space, but can be given continuously.

[0027] According to a further specific embodiment of the method for calibrating a vehicle sensor, the calculation of an error of the vehicle sensor relative to the direction of a travel axis of the motor vehicle may be carried out by means of an error propagation method.

[0028] According to another specific embodiment of the method for calibrating a vehicle sensor, the calibration of the vehicle sensor can be used permanently during driving, thereby enabling a continuous calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings show:

[0030] Figure 1 : A schematic block diagram of a device for calibrating a vehicle sensor of a motor vehicle according to one embodiment of the present invention;

[0031] Figure 2 : A grid with object positions for determining azimuth directions;

[0032] Figure 3 : A grid with object positions for determining elevation directions;

[0033] Figure 4 : An exemplary total measurement time period sinusoidal diagram;

[0034] Figure 5 : an exemplary histogram; and

[0035] Figure 6 : Flowchart of a method for calibrating a vehicle sensor of a motor vehicle according to one embodiment of the present invention. DETAILED DESCRIPTION

[0036] Figure 1A schematic block diagram of a device 1 for calibrating a vehicle sensor 5 of a motor vehicle is shown. The device 1 comprises an interface 2 coupled to the vehicle sensor 5 so as to receive sensor data from the vehicle sensor. The sensor data is generated based on measurements of the vehicle sensor 5 at a plurality of measuring times. For example, the vehicle sensor 5 can perform measurements at predetermined time intervals. The vehicle sensor 5 can be a sensor for detecting the surroundings, such as a radar sensor, a lidar sensor, an ultrasonic sensor, an infrared sensor or a camera sensor. During the measurement, the motor vehicle moves relative to an object in the surroundings of the motor vehicle. Preferably, this involves a linear relative motion at a constant speed, such as 5 to 25 km / h. For example, the object is fixedly positioned, while the vehicle moves on a flat ground at a constant speed and in a constant direction. In this case, the driving axis corresponds to the longitudinal axis of the motor vehicle. However, in principle, the acceleration or steering movement of the vehicle can also be read by the sensor and taken into account in the calculation.

[0037] The device 1 also includes a computing device 3 coupled to the interface 2 and further analyzes the received sensor data. The computing device 3 can generate a grid with a predetermined grid size in the vehicle coordinate system of the motor vehicle. In other embodiments, for example, the calculation of the grid can also be omitted in the calculation of spherical coordinates.

[0038] The calculation device 3 divides the total measuring time period into a plurality of partial measuring time periods. For example, the total measuring time period can be divided into a plurality of directly successive partial measuring time periods, for example of the same length. Thus, the duration T_L of the total measuring time period (e.g. 800 milliseconds) corresponds to the sum of the durations T_Kn of the partial measuring time periods (e.g. 60 milliseconds), where n=1 ... N, where N is the number of partial measuring time periods:

[0039]

[0040] The computing device 3 calculates an object position for each partial measuring time period on the basis of the sensor data, which object position indicates the position of the object relative to the motor vehicle.

[0041] The calculation device 3 calculates the object position of the object. The position of the object is detected at different times by the movement of the vehicle relative to the object, so that the "flow" of the object is determined. When a grid is used, each object is included in the grid multiple times, because the corresponding position of the object is determined for each measuring time. The object position can also be called a "location". Therefore, multiple locations are assigned to each object.

[0042] The calculation device 3 calculates a Hough transformation for each partial measuring time period, for example based on a grid with object positions for the partial measuring time periods, i.e. transforms the object positions into a dual space or Hough space. This generates partial measuring time period sinograms S_TKn, i.e. one sinogram per partial measuring time period.

[0043] The calculation device 3 generates the additive sinusoidal graph S_add by adding the sinusoidal graphs S_TKn of the partial measurement time periods, that is, according to the following formula:

[0044]

[0045] Furthermore, the calculation device normalizes the partial measurement time period sinogram S_TKn and thus calculates the normalized partial measurement time period sinogram norm(S_TKn). "Normalization" is understood as follows: the maximum value of each partial measurement time period sinogram is set to the value 1 and the other values ​​are scaled accordingly.

[0046] The calculation means 3 calculates the difference sinogram by multiplication of the normalized sinograms of the partial measurement time periods:

[0047]

[0048] In each partial measurement time period sinogram S_TKn, ie, the instantaneous recording of a short time period, the current interference factors are also depicted. The remaining most concise interference factors and surrounding structures are squared in the difference sinogram. They are therefore maintained with high weighting, whereas other factors are very lightly weighted.

[0049] The calculation means 3 normalize the difference sinogram, multiply the normalized difference sinogram by the addition sinogram S_add and subtract the result from the addition sinogram S_add in order to calculate the compensated sinogram or the total measurement period sinogram S_komp:

[0050] S komp =S add -norm(S diff )·S add =S add ·(1-norm(S diff )).

[0051] The total measuring time period sinogram is thus generated by adding the partial measuring time period sinograms and corrected by means of a factor dependent on the partial measuring time period sinograms, wherein the factor is generated as follows:

[0052] 1-norm(S diff ).

[0053] The main direction is determined by Hough transformation in the Cartesian coordinate system from the total measurement time period sinogram S_komp using the following formula:

[0054] r_n=x·cos(θ_n)+y·sin(θ_n),

[0055] Among them, r_n represents the ordinate of the Hough sine graph point, and θ_n represents the abscissa of the Hough sine graph point. In addition, x and y represent the Cartesian coordinates of the object position.

[0056] When creating the partial measurement time period sinogram, the calculation device 3 can implement a weighting of the object positions included in the grid. The weighting factors can be calculated based on the quality characteristics of the respective object positions. In particular, the weighting factors can be determined based on the sensor characteristics of the vehicle sensor 5 and can be related, for example, to the distance of the object position from the position of the vehicle sensor 5.

[0057] The weighted modified Hough algorithm can have the following form:

[0058]

[0059]

[0060] Alternatively, the computing device 3 can determine the main directions of the object positions included in the grid by means of a kernel density estimation from the total measurement time period sinogram S_komp.

[0061] Instead of calculating in Cartesian coordinates, the calculation device 3 can also calculate the Hough transform in spherical coordinates, that is to say according to the following formula:

[0062] r_n(ξ_n)=d_R·sin(ξ_R–ξ_n),

[0063] Among them, ξ_R represents One of and θ_R. The ordinate and abscissa (r_n, ξ_n) of the Hough map can be calculated by the radial spacing d_R of the object positions and the angle θ_R (azimuth or elevation) of the object positions. According to the calculated (r_n, ξ_n) coordinates of the Hough map, the weights (d_R, θ_R) of the corresponding object positions are added by the Hough sinusoidal map. In this case, any accurate quantization or division of r_n, ξ_n can be performed, which determines the resolution of the sinusoidal map.

[0064] The total measurement time period sinogram S_komp includes intensity values ​​to which the calculation device 3 applies a threshold value function in order to calculate a histogram. The histogram is related to the possible azimuth or elevation angles of the vehicle sensor 5 relative to the direction of the driving axis of the motor vehicle. The calculation device 3 applies a filter function to the histogram in order to determine the main direction of the object positions included in the grid. The angle between the main direction and the driving axis of the motor vehicle corresponds to the actual azimuth or elevation angle of the vehicle sensor 5.

[0065] The device 1 further comprises a calibration device 4 which is coupled to the computing device 3 and which calibrates the vehicle sensor 5 as a function of the orientation of the vehicle sensor 5 ascertained by the computing device 3. For example, the actual orientation of the vehicle sensor 5 can be taken into account in all measured values ​​of the vehicle sensor 5. Furthermore, a calculated orientation error, i.e. a deviation of the calculated orientation of the vehicle sensor 5 from the expected orientation, can be provided.

[0066] Figure 2 For the purpose of illustration, a grid with object positions is shown for determining the azimuth direction. Each measurement point depicted in the grid corresponds to a measurement signal that is assigned by the computing device 3 to the spatial region corresponding to the grid point in the surroundings of the motor vehicle based on the sensor data. The measurement points or object positions are located in main directions parallel to one another for each object. For example, a main direction A2 is marked in the object. The object positions along this main direction A2 correspond to the measurements of the object at different measurement times. In addition, the driving axis A1 of the motor vehicle is depicted. Figure 2 According to the view from above onto the vehicle, the azimuth angle β_AZ is therefore between the main direction A2 and the axis of travel A1 .

[0067] Figure 3 A grid with object positions is shown to determine the elevation direction. Figure 3 This corresponds to the side view, so that the elevation angle β_EL lies between the main direction A3 and the axis of travel A1 .

[0068] Figure 4 The total measurement time period sinogram S_komp is shown for a single object. In this case, a plurality of essentially sinusoidal curves are generated, which intersect at the point B of the highest intensity. For a plurality of objects, further parallel displacement curves are generated, which likewise intersect at the point of the highest intensity for each object. The total measurement time period sinogram shows the projection position P as a function of the angle α, for example the elevation angle or the azimuth angle. Angles α with higher intensity correspond to a greater number of measurement points, which are located on the associated straight line.

[0069] Figure 5An exemplary histogram is shown, which is calculated by applying a threshold function to the intensity values. For each angle α, the intensities, i.e. r_n, are summed at all projection positions P, wherein only the following points are considered: the intensity of this point exceeds a predetermined threshold value. The corresponding histogram produces a maximum value of the summed intensity A for a certain angle α. This value corresponds to the corresponding angle between the main directions A2, A3 and the driving axis A1 of the motor vehicle, i.e., for example, the actual elevation angle or azimuth angle. The summed intensity or total intensity corresponds to the total weighting of the associated angle α and can be calculated with the aid of a filter function. The determination of the angle can be calculated using an angle estimation method, in particular a Kalman filter, an averaging method, etc.

[0070] Figure 6 A flow chart of a method for calibrating vehicle sensor 5 of a motor vehicle according to one specific embodiment of the present invention is shown.

[0071] In a first method step S1, the vehicle sensor 5 transmits sensor data detected at a plurality of measuring times while the motor vehicle moves relative to an object in the surroundings of the motor vehicle. The motor vehicle and the object can in particular move in a straight line relative to each other at a constant speed. The total measuring time period is divided into a plurality of partial measuring time periods.

[0072] In a second method step S2, the object positions are assigned to a grid in the vehicle coordinate system of the motor vehicle for each partial measurement period based on the ascertained sensor data. Each object is assigned multiple times, that is, the corresponding coordinates ("location") are entered into the grid for each measurement time. In other calculations, for example in spherical coordinates, the generation of a grid can be omitted.

[0073] In a third method step S3, a partial measuring time period sinogram is calculated for each partial measuring time period by means of a Hough transform from the object positions calculated for the partial measuring time period. For this purpose, a weighting of the object positions can be implemented. The Hough transform can be calculated in Cartesian coordinates or spherical coordinates.

[0074] In a fourth method step S4, the total measuring time period sinogram is calculated by adding the partial measuring time period sinograms and correcting them by means of factors associated with the partial measuring time period sinograms. The calculation can be performed according to the above formula.

[0075] In a fifth method step S5, the direction of the vehicle sensor 5 relative to the driving axis A1 of the motor vehicle is calculated based on the total measurement time period sinogram. For this purpose, a kernel density estimation can be implemented to determine the main direction. Alternatively, a histogram can be calculated by applying a threshold function to the intensity values ​​of the total measurement time period sinogram, which histogram is related to the azimuth and / or elevation angle relative to the driving axis of the motor vehicle. The direction of the vehicle sensor 5 relative to the driving axis of the motor vehicle is determined based on the calculated histogram. A filter function can be applied to the histogram.

[0076] In a sixth method step S6 , vehicle sensor 5 is calibrated as a function of the ascertained orientation of vehicle sensor 5 relative to driving axis A1 of the motor vehicle.

Claims

1. A method for calibrating a vehicle sensor (5) of a motor vehicle, the method comprising the following steps: Sensor data are ascertained (S1) by the vehicle sensor (5) at a plurality of measuring times during a total measuring period, wherein: dividing the total measurement time period into a plurality of partial measurement time periods, in which the motor vehicle moves relative to objects in the surroundings of the motor vehicle; For each partial measuring time period, calculating (S2) an object position of the object based on the ascertained sensor data; for each partial measurement time period, calculating (S3) a partial measurement time period sinogram based on the object position calculated for said partial measurement time period; calculating (S4) a total measuring time period sinusoidal graph by adding the partial measuring time period sinusoidal graphs and correcting them by means of factors associated with the partial measuring time period sinusoidal graphs; Determining (S5) the direction of the vehicle sensor (5) relative to the driving axis (A1) of the motor vehicle based on the total measurement time period sinusoidal diagram; The vehicle sensor (5) is calibrated (S6) as a function of the ascertained direction of the vehicle sensor (5) relative to an axis of travel (A1) of the motor vehicle.

2. The method according to claim 1, wherein: The motor vehicle and objects in the surroundings of the motor vehicle execute a substantially linear relative movement at a constant speed.

3. The method according to claim 1 or 2, wherein: The correction using the factor related to the sinusoidal diagram of the partial measurement time period comprises the following steps: Normalizing the sinusoidal graph of the partial measurement time period; The difference sinogram is calculated by multiplication of the normalized partial measurement time period sinograms.

4. A method according to any one of the preceding claims, wherein: A weighting of the object position is carried out in the adjustment of the partial measuring time period sinusoidal diagram.

5. A method according to any one of the preceding claims, wherein: The main direction (A2, A3) of the object position is determined based on the total measurement time period sinusoidal diagram by a method of determining the intensity maximum, wherein the determination of the direction of the vehicle sensor (5) relative to the driving axis (A1) of the motor vehicle includes calculating the angle between the main direction (A2, A3) and the driving axis (A1) of the motor vehicle.

6. The method according to any one of claims 1 to 4, wherein: The total measurement time period sinogram comprises intensity values ​​ascertained from the sensor data, wherein a histogram is calculated by applying a threshold function to the intensity values, the histogram being related to possible azimuth angles and / or elevation angles relative to an axis of travel (A1) of the motor vehicle, wherein the direction of the vehicle sensor (5) relative to the axis of travel (A1) of the motor vehicle is ascertained based on the calculated histogram.

7. The method according to claim 6, wherein: The direction of the vehicle sensor (5) relative to the driving axis (A1) of the motor vehicle is determined by applying a filter function to the histogram.

8. The method according to claim 7, wherein: The main direction (A2, A3) of the object position is determined by applying the filter function to the histogram, wherein the determination of the direction of the vehicle sensor (5) relative to the driving axis (A1) of the motor vehicle includes calculating the angle between the main direction (A2, A3) and the driving axis (A1) of the motor vehicle.

9. A method according to any one of the preceding claims, wherein: The calculation of the sinogram of the partial measuring time period is carried out in spherical coordinates.

10. A device (1) for calibrating a vehicle sensor (5) of a motor vehicle, the device comprising: An interface (2) for receiving sensor data ascertained by the vehicle sensor (5) at a plurality of measuring times during a total measuring period, wherein: The motor vehicle moves relative to objects in the surroundings of the motor vehicle; A computing device (3), the computing device being configured to: - dividing the total measurement time period into a plurality of partial measurement time periods, - for each partial measuring time period, calculating the object position of the object from the ascertained sensor data, - for each partial measuring time period, calculating a partial measuring time period sinogram from the object position calculated for said partial measuring time period, - calculating the total measuring time period sinogram by adding the partial measuring time period sinograms and correcting them by means of factors dependent on the partial measuring time period sinograms, - calculating the direction of the vehicle sensor (5) relative to the driving axis (A1) of the motor vehicle based on the total measurement time period sinusoidal diagram; A calibration device (4) is designed to calibrate a vehicle sensor (5) as a function of an ascertained orientation of the vehicle sensor (5) relative to an axis of travel (A1) of the motor vehicle.

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