DE102007041121B4 - Method and device for processing sensor data for a driver assistance system of a vehicle - Google Patents

Abstract

Method for processing sensor data for a driver assistance system of a vehicle, in which
- a first sensor (1) records a measured value (M1) and transmits it to a processing unit (4), an error value (ΔM1) being assigned to the measured value (M1), characterized in that
- a second sensor (2) records a measured value (M2) for a variable that cannot be measured with the first sensor (1),
- the error value (ΔM1) associated with the measured value (M1) of the first sensor (1) is adjusted using the measured value (M2) of the second sensor (2),
and that the measurement value (M1) of the first sensor (1) is recorded by a third sensor (3) in a different way than with the first sensor (1) if the adjusted error value (ΔM1) of the first sensor (1) exceeds a limit value exceeds.

Description

The present invention relates to a method for processing sensor data for a driver assistance system of a vehicle, in which a first sensor records a measured value and transmits it to a processing unit, with an error value being assigned to the measured value. Furthermore, the invention relates to a device for processing such sensor data with a first sensor for recording a measured value and a processing unit coupled to the first sensor for receiving the measured value, an error value being assigned to the measured value.

Modern motor vehicles have a large number of sensors for various driver assistance systems. For example, the position of the vehicle is recorded using various sensors for a navigation system. Sensors for wheel speeds, yaw rate and lateral acceleration are used to determine the relative movement of the vehicle. Satellite navigation systems such as the Global Positioning System (GPS) and in particular the differential GPS (DGPS) are used to determine the absolute position of the vehicle. Radar, laser and/or ultrasonic sensors are used to detect other road users and obstacles in the area surrounding the vehicle.

• Dirk Stüker: „Heterogene Sensordatenfusion zur robusten Objektverfolgung im automobilen Straßenverkehr“, Dissertation an der Fakultät II - Informatik, Wirtschafts- und Rechtswissenschaften der Carl von Ossietzky Universität (vgl. auch http://docserver.bis.uni-oldenburg.de/publikationen/dissertation/2004/stuhet04/stuhet04.html; Zugriff 12.07.2006).

When processing the data in a driver assistance system, it is known to use a heterogeneous sensor network in which different sensors are used to determine a specific variable in order to be able to compensate for any weaknesses in a sensor with the strengths of a different type of sensor. In order to reduce the error associated with a variable, e.g. B. used a Kalman filter. In this way, both the movement of the vehicle itself and the movement of other road users can be estimated using a number of sensors, for example. The weighting of the information from different sensors is based on statistical uncertainties, which z. B. are supplied by the sensors themselves. Sensor data fusion is described, for example, in the following publication:

• Dirk Stüker: "Heterogeneous sensor data fusion for robust object tracking in automotive traffic", dissertation at Faculty II - Computer Science, Economics and Law at the Carl von Ossietzky University (see also http://docserver.bis.uni-oldenburg.de/publikationen /dissertation/2004/stuhet04/stuhet04.html; accessed 07/12/2006).

From the DE 10 2005 002 719 A1 a method for course prediction in driver assistance systems for motor vehicles is known, in which a course hypothesis is created using information from different information sources by extracting a set of raw data for the course hypothesis from each information source, a representation of the various sets of raw data in a uniform Description system is created and the raw data are merged to form the course hypothesis. Due to the broadened information base, due to the different sources for the raw data, a greater robustness of the course prediction in extraordinary situations is achieved.

From the DE 199 45 124 A1 a method for navigating a vehicle is known in which an actual position of the vehicle is determined using the length and direction of distances covered, a control position is determined at time intervals using external aids and the actual position is reset to the control position , if the distance between the actual position and the control position exceeds an error value. The error value is changed depending on a reliability check. The external aids are formed, for example, by a satellite navigation system, which determines the actual position of the vehicle in a different way than determining the position using the routes covered.

the DE 198 12 426 A1 describes a method for calculating the system variables location, course angle and speed of a vehicle using an independent and an on-board navigation system, the latter having a speed sensor and an acceleration sensor. The system variables determined by the two navigation systems are fed to a filter module, which determines correction values for the system variables and correction values for the parameters of the error models of the on-board navigation system.

the DE 101 53 212 A1 relates to a navigation system for determining the course of a vehicle. This navigation system includes a main sensor system for measuring the state variables that describe the vehicle state, an auxiliary sensor system that includes at least one sensor for measuring another state variable, and a navigation core that estimates error-minimized state variables based on the measured state variables. The navigation core includes a vehicle model in order to achieve a higher level of generality and robustness against sensor errors in the system. The vehicle model predicts the status variables of the vehicle based on the measurement data from the main sensor system. The navigation core further includes an error estimator for predicting the estimation errors of the vehicle model and a correction element that uses the predicted state variables len corrected using the predicted estimation errors.

the DE 196 36 425 C1 FIG. 1 shows a method which includes the acquisition of sensor signals using two complementary methods which exploit the available measurement data and the accuracy, respectively. The measurement signals of the movements of an object from a first sensor are used in a simulation and corresponding data is recorded. The movement data are fed into a simulation of the second measurement method for generating simulated measurement signals of the second sensor arrangement. The simulated signals are compared with the actual measurement signals from the second sensor arrangement. The motion simulation is corrected according to the comparison results and its output signals are used as the output data of the navigation process.

In the DE 103 44 120 A1 the reliability of position data for a satellite-based navigation system is determined.

the EP 1 705 458 A1 refers to robust navigation solutions for aircraft and vehicles. A first and a second navigation error signal are generated from estimated navigation data and at least one adaptive model based on a model vector that describes the vehicle dynamics, with the first error signal adapting the estimated navigation data and the second error signal updating the at least one adaptive model.

the EP 0 602 013 A2 shows a stand-alone navigation system in which the location and direction of travel of a vehicle are recorded. A calibration device of an angular rate sensor used in the stand alone navigation system is disclosed. A course value determined can be corrected by means of the calibration device.

The sensor data fusion in driver assistance systems offers the advantage that the error value associated with a measured value can be reduced. However, certain interferences that can not be detected by the sensor itself or by other sensors used for sensor data fusion. These disturbances can result in an error value that is much larger than the error value provided by the sensor, the preset error value, or the error value associated with the sensor data fusion.

It is therefore the object of the present invention to provide a method and a device of the type mentioned at the outset with which the error value associated with a measured value can be better determined.

According to the invention, this object is achieved by a method having the features of claim 1 and a device having the features of claim 12 . Advantageous training and further developments result from the dependent claims.

In the method according to the invention, a second sensor records a measured value for a variable that cannot be measured with the first sensor, and the error value associated with the measured value of the first sensor is adjusted using the measured value of the second sensor.

A variable is measured with the second sensor, which has an influence on the error value of the measured value of the first sensor. Since the variable recorded by the second sensor cannot be measured with the first sensor, the first sensor or the processing unit cannot take this variable into account when determining the error value for the measured value of the first sensor. The method according to the invention is therefore not a matter of reducing the error value of a measured value by using a number of sensors or a larger database. Rather, background knowledge is obtained through information from another source in order to determine the error value of a measured value from another sensor. The second sensor supplies information that is complementary to the first sensor, i. H. Information that cannot be determined by the first sensor itself.

In the context of the present invention, a sensor is understood to be any data source that supplies data relevant to the driver assistance system. The sensor can thus, on the one hand, measure physical variables in or around the vehicle. On the other hand, the sensor can also read data from data carriers or receive it from other data sources.

The method according to the invention is characterized in that the measured value of the first sensor is recorded by a third sensor, which measures the measured value in a different way than the first sensor when the adjusted error value of the first sensor exceeds a limit value. If the adjustment of the error value based on the measured value of the second sensor shows that the error value associated with the measured value of the first sensor is too large, the method according to the invention uses a different type of sensor to record the measured value for the driver assistance system.

The error value of the measured value of the first sensor can take into account the statistical uncertainty in the measurement with the first sensor and/or the detection quality of the first sensor. The detection quality of the first sensor means the probability that a variable, e.g. B. an object is detected.

According to one exemplary embodiment of the method, the first sensor determines the position of the vehicle using satellite navigation, and the second sensor detects objects that impair the reception of the satellite signals for satellite navigation. For example, the signals from the satellites can be completely blocked by a bridge or a tunnel. This can be recognized by the first sensor for the satellite navigation itself. However, when determining the position of the vehicle using satellite navigation after driving through a tunnel or driving under a bridge, there are measurement inaccuracies that cannot be detected by the sensor for the satellite navigation. Furthermore, noise barriers, trucks in the immediate vicinity of the vehicle or similar objects can lead to shadowing of satellite signals or to reflections, which increase the measurement inaccuracy of the sensor for satellite navigation without this being able to be determined by this sensor itself.

In this exemplary embodiment, the second sensor can, for example, read out an electronic geographic map on which objects are recorded which impair the reception of the satellite signals for satellite navigation. The information that such objects are in the vicinity of the vehicle can be used to increase the error value of position determination using satellite navigation.

Furthermore, the second sensor can be an environment sensor such as B. be a camera, a laser and / or a radar sensor. Such environmental sensors can, for. B. detect noise barriers, bridges and trucks in the vicinity of the vehicle.

According to another exemplary embodiment of the method, the first sensor detects the surroundings of the vehicle and the second sensor reads an electronic geographic map. The data from the environment sensor can, for example, provide lane information for a driver assistance system. When entering a tunnel and when exiting a tunnel, however, there are very strong light-dark differences, which impair the image processing of an optical environment sensor such as e.g. B. complicate a camera. If the electronic geographic map shows that the vehicle is directly in front of a tunnel entrance or exit, this information can be used to increase the error value of the surroundings sensor. This can mean, for example, that the lane information is not taken into account in these areas or that other sensors are used for the lane information.

According to a further exemplary embodiment, the first sensor detects the wheel speed of the vehicle and the second sensor is a wet sensor. The moisture sensor can be a rain sensor, for example. Furthermore, a wet or snow-covered roadway can be detected by means of a camera. Such road conditions cause the wheels to slip, increasing the reading of the wheel speed sensor used to determine speed or distance traveled.

According to a further exemplary embodiment, the first sensor is a camera for lane and object detection. In this exemplary embodiment, the second sensor is a sensor that detects the visibility conditions in the direction of travel. The visibility conditions can be determined, for example, by means of an optical visibility measurement using a sensor. Furthermore, a rain sensor can be used to draw conclusions about the visibility conditions in the direction of travel. If poor visibility conditions result from the measured values of the second sensor, the measurement error resulting from the image processing of the image from the camera can be increased.

Furthermore, based on the measured value of the second sensor, the first sensor can be reinitialized. This re-initialization can result in the error value of the readings of the first sensor decreasing in certain cases.

According to a further exemplary embodiment, the first sensor can be a camera which takes pictures of the surroundings of the vehicle. This camera can B. can also be used for lane and object detection. In this exemplary embodiment, too, the second sensor is a sensor that detects the visibility conditions, specifically in the areas in which the camera takes pictures. In this case, however, the second sensor for detecting the visibility conditions is a sensor for detecting the position of the vehicle using satellite navigation, e.g. B. a GPS transmitter. Such a sensor provides information about where the vehicle is located. Furthermore, such a sensor provides the current time of day. This information is used to derive the likelihood of shadows forming in the areas where the camera is capturing images. is a joke tensing e.g. B. very likely when the sun is very low in the sky, the measurement error resulting from the image processing of the image of the camera can be increased.

The device according to the invention has a second sensor for recording a measured value for a variable that cannot be measured with the first sensor. The processing unit is designed in such a way that the error value associated with the measured value of the first sensor can be adjusted using the measured value of the second sensor.

The device is characterized in that the processing unit is coupled to at least one third sensor, with which the measurement value of the first sensor can be recorded in a different way if the adjusted error value of the first sensor exceeds a limit value.

In particular, the error value can take into account the statistical uncertainty in the measurement with the first sensor and/or the detection quality of the first sensor.

In the device, the first sensor can be selected from a satellite navigation system for determining the position, a sensor for detecting the surroundings of the vehicle, a camera for lane and object recognition and a wheel speed sensor or combinations of one or more of these sensors.

In the device, the second sensor can be selected from an environment sensor, a camera, a laser sensor, a radar sensor, an ultrasonic sensor, a sensor for the visibility conditions in the direction of travel, a moisture sensor and a sensor for reading an electronic geographic map or combinations of one or more of these sensors. In this case, the surroundings sensor detects in particular objects which lead to an impairment of the reception of the satellite signals for the satellite navigation when the first sensor determines the position using a satellite navigation system. Furthermore, in this case, objects can be recorded on a geographic map, which lead to an impairment of the reception of the satellite signals for the satellite navigation.

• 1 zeigt den prinzipiellen Aufbau der erfindungsgemäßen Vorrichtung und die Koppelung dieser Vorrichtung mit anderen Einrichtungen des Fahrzeugs gemäß einem Ausführungsbeispiel der Erfindung und
• 2 veranschaulicht eine Situation, bei welcher ein Ausführungsbeispiel des Verfahrens zum Einsatz kommt.

The invention will now be explained using exemplary embodiments with reference to the drawings.

• 1 shows the basic structure of the device according to the invention and the coupling of this device to other devices of the vehicle according to an embodiment of the invention and
• 2 illustrates a situation in which an embodiment of the method is used.

The device according to the invention comprises a total of three sensors 1, 2 and 3. The first sensor 1 records a measured value M1 and transmits it to a processing unit 4. The first sensor 1 also transmits an error value ΔM1 to the processing device 4. This error value ΔM1 includes the statistical Uncertainty of the measurement and the detection quality of the first sensor 1.

The second sensor 2 records a measured value M2 for a variable that cannot be measured with the first sensor 1 . The second sensor 2 supplies information that is complementary to the first sensor 1, ie information that cannot be determined by the first sensor 1 itself. The same measurement feature is therefore not measured by a number of possibly heterogeneous sensors in order to determine the error in an individual sensor. Instead, the second sensor 2 determines a variable that influences the error value ΔM1 of the measured value M1 of the first sensor 1 .
The measured value M2 of the second sensor 2 is also transmitted to the processing device 4 . The processing device 4 uses the measured value M2 of the second sensor 2 to adapt the error value ΔM1 associated with the measured value M1 of the first sensor 1 . If the adjusted error value ΔM1 exceeds a specific limit value stored in the processing device 4, the processing device 4 no longer uses the first sensor 1 to record the measured value M1, but instead uses a third sensor 3, which determines the measured value M1 in a different way than the first sensor 1.

The processing device 4 transmits the measured value M1 with the ascertained error value ΔM1 to the driver assistance system for further processing. The driver assistance system 5 visually displays the assistance data to the driver by means of the display device 6 .

Several examples for the first and second sensors 1 and 2 are described below:

1. Adjusting the error value of a DGPS sensor

In this case, the first sensor 1 is a satellite navigation system, with which the position of the vehicle is determined. In particular, a differential GPS sensor is used. With this, the position of the vehicle can be determined with high precision in global coordinates. This enables the vehicle to be positioned on a highly accurate map, which can be used to determine the vehicle's lateral and longitudinal position within a lane. GPS satellite reception is interrupted while driving under a bridge. High-precision localization is not possible here. This therefore affects the availability of the sensor. However, the GPS sensor can clearly determine on its own that no position determination is possible due to a lack of satellite signals. The sensor data is therefore reliable because the GPS sensor transmits a correct error value to the processing unit 4 . In this area, the processing device can thus fall back on the third sensor 3 in order to determine the position of the vehicle. In this case, the third sensor 3 can use the wheel speeds, the yaw rate and the lateral acceleration of the vehicle to determine a relative movement.

After crossing the bridge, the DGPS sensor starts recalculating the position of the vehicle. There is a risk here that it settles into an incorrect phase solution and thus supplies erroneous position data. There is therefore a risk that the DGPS sensor, after driving under a bridge, will output uncertainties that do not match the actual position deviation.
A sensor for reading a digital map is provided as the second sensor 2 . All bridges that impair the reception of satellite signals from the satellite navigation system are listed on the digital map. The areas after driving under the bridge are noted on the digital map in such a way that the readings from the DGPS sensor are less reliable in these areas. This situation is in 2 shown. The vehicle 7 drives under a bridge 9 in the right-hand lane 8 . In the digital map, the area 10 on the right-hand lane 8 immediately behind the bridge 9 is marked in such a way that the sensor for reading the digital map can detect that this area 10 leads to a higher error value in the measured value of the DGPS sensor. For measured values of the DGPS sensor that were recorded in this area 10, the processing unit 4 can thus increase the associated error value accordingly. Furthermore, the processing unit 4 can optionally use the third sensor 3 for determining the position, with which the position of the vehicle is determined on the digital camera, for example using landmarks or vehicle data.

Furthermore, the readings of a DGPS receiver are affected by higher objects in the vicinity of the vehicle. This can be, for example, noise barriers or a truck that is in the immediate vicinity of the vehicle. The DGPS correction signals are usually received using GSM modules. In the vicinity of noise barriers, reception of the correction signal is disrupted by the fact that the view of individual satellites is blocked as a result of shadowing by the walls and/or the signal is falsified by reflections on the noise barriers. If there is even an interruption in the reception of the satellite signals, the DGPS sensor starts recalculating the vehicle position after the satellite signals are received again. In this case, as explained above, there is a risk that the DGPS sensor will settle on an incorrect phase solution and therefore the error value for the vehicle position will increase without the DGPS sensor being able to detect this on its own.

In this case, too, the second sensor 2 can be a sensor for reading a digital map on which the positions of the noise protection walls are noted. Depending on the information from the digital map, the error value of the readings of the DGPS sensor can be adjusted accordingly, and furthermore the processing unit 4 can, if necessary, rely more heavily on other methods for determining the position on the digital map.

In order to detect trucks that are in the immediate vicinity of your own vehicle and that, like noise barriers, can interfere with or interrupt the reception of the DGPS correction signal, an environment sensor can be provided as an alternative or in addition as a second sensor 2, which detects a truck immediately to the left or to the right of one's own vehicle. If this environment sensor detects a truck next to the vehicle, the processing unit 4 can adapt the error value of the measured values of the DGPS sensor, i. H. in this case increase.

2. Correction of the measurement error of the data resulting from the processing of a camera image

In this case, a camera with an associated image processing unit is used as the first sensor 1 in order to record the lane and the objects in the surroundings of the vehicle for a driver assistance system. When entering and exiting a tunnel, the camera has to compensate for very large differences in brightness. When entering the tunnel, she has to adapt to the darkness in the tunnel, and when she exits the tunnel, she is initially blinded. For a short period of time when entering and exiting the tunnel, the error that occurs when processing the images from the camera, e.g. B. for the detection of the lane and objects such as other road users occurs, much larger. If necessary, they can Camera images are not used at all to detect the lane and other objects.

In this case, a sensor for reading a digital map is used again as the second sensor 2 . The tunnel entrances and exits are noted on this digital map. For these areas, the processing device 4 can increase the error value for the measured values that result from the camera images or rely on other data sources such as the third sensor 3 for the lane detection or the detection of other objects.

Furthermore, the image processing of the camera images can be impaired by bad weather conditions such as fog, heavy rain or heavy snowfall. Such weather conditions increase the error value of the measured values, which result from the processing of the image data of the camera. For this reason, as an alternative or in addition, a sensor that detects the visibility conditions in the direction of travel can be provided as the second sensor 2 . A visibility measurement can be carried out, for example, by a laser. Furthermore, a rain sensor can be used, which can detect heavy rain, from which conclusions can be drawn about the visibility conditions. If poor visibility conditions are detected in the direction of travel, the processing unit 4 can increase the error value for the measured values that result from the processing of the image data from the camera. If necessary, the camera can no longer be used to determine lane data and object data, but other sensors.

Finally, the image processing of the camera's images can be affected by the formation of shadows. The formation of shadows also increases the error value of the measured values, which result from the processing of the image data of the camera. For this reason, a sensor can be provided as an alternative or in addition as the second sensor 2, which supplies the geographical position and the time of day as information. For this purpose, a satellite navigation system, in particular a GPS sensor, preferably a differential GPS sensor, can be used as the second sensor. In addition to the geographical position, this also provides the time of day. From this information, a probability can be calculated that shadows appear in the area of the camera images, which impair further image processing. If the probability of shadows occurring in the images from the camera is high, the processing unit 4 can increase the error value for the measured values that result from the processing of the image data from the camera.

3. Adjustment of the error value of a wheel speed meter

The position of one's own vehicle on a digital map can also be determined using data that arise in the vehicle itself. For example, the relative movement of the vehicle can be determined using the wheel speeds, the yaw rate and the lateral acceleration. The problem with the wheel speed sensor is that if the wheels slip, the determination of the position is subject to an error. The greater the slip, the greater the error in position determination. In particular, if there is standing water, snow or even ice on the road, a high level of wheel slip is to be expected. For this reason, a wetness sensor is provided as the second sensor 2, which delivers measured values with which conclusions can be drawn about such road conditions. For example, a rain sensor can measure heavy rain, or a camera can record snow or ice on the road. If corresponding road conditions are detected by the wetness sensor, the error value of the measured values of the wheel speed sensor is increased.

Reference List

1

First sensor

2

Second sensor

3

Third sensor

4

processing unit

5

driver assistance system

6

display device

7

vehicle

8th

lane

9

Bridge

10

area behind the bridge

Claims (18)

Method for processing sensor data for a driver assistance system of a vehicle, in which - a first sensor (1) records a measured value (M1) and transmits it to a processing unit (4), with the measured value (M1) being assigned an error value (ΔM1), thereby characterized in that - a second sensor (2) records a measured value (M2) for a variable that cannot be measured with the first sensor (1), - the error value (ΔM1) associated with the measured value (M1) of the first sensor (1). ) is adjusted by means of the measured value (M2) of the second sensor (2), and that the recording of the measured value (M1) of the first sensor (1) by a third sensor (3) in a different way than with the first sensor (1) if the adjusted error value (ΔM1) of the first sensor (1) exceeds a limit value. procedure after claim 1 , characterized in that the error value (ΔM1) takes into account the statistical uncertainty in the measurement with the first sensor (1). Method according to one of the preceding claims, characterized in that the error value (ΔM1) takes into account the detection quality of the first sensor (1). Method according to one of the preceding claims, characterized in that the first sensor (1) determines the position of the vehicle (7) by means of satellite navigation and the second sensor (2) detects objects (9) which impair the reception of the satellite signals for the guide satellite navigation. procedure after claim 4 , characterized in that the second sensor reads an electronic geographic map in which objects are recorded which lead to an impairment of the reception of the satellite signals for satellite navigation. procedure after claim 4 , characterized in that the second sensor (2) is an environment sensor. procedure after claim 4 , characterized in that the second sensor (2) is a camera. procedure after claim 4 , characterized in that the second sensor (2) is a laser and / or radar sensor. Procedure according to one of Claims 1 until 3 , characterized in that the first sensor (1) detects the environment of the vehicle (7) and the second sensor (2) reads an electronic geographic map. Procedure according to one of Claims 1 until 3 , characterized in that the first sensor (1) detects the wheel speed of the vehicle (7) and the second sensor (2) is a wet sensor. Procedure according to one of Claims 1 until 3 , characterized in that the first sensor (1) is a camera for lane and object detection and the second sensor (2) detects the visibility in the direction of travel. Device for processing sensor data for a driver assistance system of a vehicle (7), having - a first sensor (1) for recording a measured value (M1) and - a processing unit (4) coupled to the first sensor (1) for receiving the measured value (M1) , wherein an error value (ΔM1) is assigned to the measured value (M1), characterized in that the device has a second sensor (2) for recording a measured value (M2) for a variable that cannot be measured with the first sensor (1). and - that the processing unit (4) is designed in such a way that the error value (ΔM1) associated with the measured value (M1) of the first sensor (1) can be adapted by means of the measured value (M2) of the second sensor (2), and that at least one third sensor (3) is coupled to the processing unit (4), with which the measurement value (M1) of the first sensor (1) can be recorded in a different way if the adjusted error value (ΔM1) of the first sensor (1) exceeds a limit. device after claim 12 , characterized in that the error value (ΔM1) takes into account the statistical uncertainty in the measurement with the first sensor (1). Device according to one of Claims 12 until 13 , characterized in that the error value (ΔM1) takes into account the detection quality of the first sensor (1). Device according to one of Claims 12 until 14 , characterized in that the first sensor (1) is selected from a satellite navigation system for determining the position of the vehicle (7), a sensor for detecting the surroundings of the vehicle (7), a camera for lane and object detection and a wheel speed sensor or combinations one or more of these sensors. Device according to one of Claims 12 until 15 , characterized in that the second sensor (2) is selected from an environment sensor, a camera, a laser sensor, a radar sensor, an ultrasonic sensor, a sensor for the visibility conditions in the direction of travel, a moisture sensor and a sensor for reading an electronic geographic map or combinations one or more of these sensors. device after Claim 16 , characterized in that the environment sensor detects objects which lead to an impairment of the reception of the satellite signals for satellite navigation. device after Claim 16 , characterized in that objects are recorded in the geographic map, which lead to an impairment of the reception of the satellite signals for satellite navigation.

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