Method and device for determining movement of vehicle

Abstract

The invention relates to a method for determining a movement of a vehicle (100), comprising: reading in a first sensor signal (110) via an interface connected to a first inertial sensor (104) of the vehicle (100) and reading in a second sensor signal (112) via an interface connected to a second inertial sensor (106) of the vehicle (100); acquiring a first level signal (122) representing a noise level of the first sensor signal (110) and a second level signal (124) representing a noise level of the second sensor signal (112) using the first sensor signal (110), the second sensor signal (112), and an activity signal representing an activity of the vehicle (100); and determining a motion signal (116) representative of a motion of the vehicle (100) using the first sensor signal (110), the second sensor signal (112), the first level signal (122), and the second level signal (124).

Description

Technical Field

[0001] The invention is based on a device or a method of the type according to the independent claims.A computer program is also a subject of the invention. Background Art

[0002] Many applications in the vehicle sector, such as automated driving functions, require the use of reliable sensor signals. Summary of the invention

[0003] Against this background, the solution proposed herein provides a method for determining the movement of a vehicle according to the independent claim, a device for using the method, and finally a corresponding computer program. Advantageous improvements and enhancements of the device given in the independent claim are achieved by the measures listed in the dependent claims.

[0004] Advantageously, sensor signals of vehicle sensors may be monitored taking into account the movement of the vehicle.

[0005] The method for determining the motion of a vehicle comprises the following steps:

[0006] a reading step, wherein a first sensor signal is read in via an interface connected to a first inertial sensor of the vehicle, and a second sensor signal is read in via an interface connected to a second inertial sensor of the vehicle;

[0007] an acquisition step of acquiring a first level signal indicating a noise level of the first sensor signal and a second level signal indicating a noise level of the second sensor signal using the first sensor signal, the second sensor signal, and an activity signal indicating an activity of the vehicle; and

[0008] A determining step in which a motion signal representing the motion of the vehicle is determined using the first sensor signal, the second sensor signal, the first level signal, and the second level signal.

[0009] The vehicle may be a motor vehicle, such as an automatically controlled or manually controlled vehicle. The inertial sensor may be designed, for example, as an acceleration sensor or a rotation speed sensor. The inertial sensor may be designed in the same or different ways. The movement of the vehicle may be understood, for example, as the acceleration of the entire vehicle or the rotation of the vehicle around the vehicle axis. By using the level signal, for example, it may be determined which of the sensor signals are used to determine the movement and, if necessary, with what weight the sensor signals are used to determine the movement. For example, a sensor signal with a lot of noise may be excluded from determining the movement, or the sensor signal with a lot of noise may be considered with a smaller weight than a sensor signal with very little noise. The activity of the vehicle may represent a vehicle state that has a predetermined influence on at least one of the sensor signals. As an example, the activity may represent that the vehicle is not moving or is driving on a flat road or on an unpaved road.

[0010] The method may include the step of using at least one of the sensor signals to provide an activity signal. To this end, the sensor signals may be appropriately evaluated, for example a curve of the sensor signal over time may be compared with a reference curve assigned to different activities, or the level of the sensor signal may be compared with threshold values ​​assigned to different activities, in order to identify the current activity of the vehicle and indicate it by the activity signal. Additionally or alternatively, one or more further signal sources may be used to provide the activity signal.

[0011] For example, at least one sensor signal can be filtered in the providing step and then compared with at least one threshold value. Here, the activity of the vehicle can be identified based on the result of the comparison. This allows the activity to be reliably identified quickly and without requiring a lot of computing effort.

[0012] As an example, the activity can be identified as a stationary state of the vehicle, an off-road driving of the vehicle or a highway driving of the vehicle. Thus, activities that can be clearly distinguished based on different signal curves of the inertial sensor can be used for the solution described herein.

[0013] In the acquisition step, the activity signal can be used to set a first filter for filtering the first sensor signal and a second filter for filtering the second sensor signal. The first sensor signal can be filtered using the first filter to obtain a first filtered sensor signal. The second sensor signal can be filtered using the second filter to obtain a second filtered sensor signal. The first filtered sensor signal can be used to acquire a first level signal, and the second filtered sensor signal can be used to acquire a second level signal. By filtering, a frequency range of the sensor signal with particularly strong noise can be selected. Advantageously, the activity can be used to set the filtering range of the filter, so that filtering adapted to the situation can be performed.

[0014] In the acquisition step, the first filtered sensor signal can be quantized to obtain a first quantized value. Correspondingly, the second filtered sensor signal can be quantized to obtain a second quantized value, wherein the first level signal can represent the first quantized value and the second level signal can represent the second quantized value. These quantized values ​​are very suitable for evaluating noise. When acquiring the quantized values, other characteristics of the filtered sensor signal can be taken into account, such as the distance between signal peaks, the average value or the variance. Thereby, the quantization can be refined.

[0015] In the determination step, the first level signal can be compared with a first threshold value defined using the activity signal to create a first quality value representing the quality of the first sensor signal. Correspondingly, the second level signal can be compared with a second threshold value defined using the activity signal to create a second quality value representing the quality of the second sensor signal. Finally, the motion signal can be determined using the first sensor signal, the second sensor signal, the first quality value, and the second quality value. As an example, the quantization value assigned to the sensor signal can be compared with the threshold value to determine whether the quality of the sensor signal is good or bad. As an example, a sensor signal assigned a bad quality value can be excluded from determining the motion signal, or can participate in determining the motion signal only with a low weight.

[0016] Additionally or alternatively, in the determining step, the reference value may be determined using the first level signal and the second level signal. A first noise index value as the difference between the first level signal and the reference value and a second noise index value as the difference between the second level signal and the reference value may be determined. Here, the motion signal may be determined using the first sensor signal, the second sensor signal, the first noise index value, and the second noise index value. As an example, a sensor signal assigned a poor noise index value may be excluded from determining the motion signal, or may participate in determining the motion signal only with a low weight.

[0017] In the determining step, a first fault signal indicating a fault state of the first inertial sensor may be determined using the first level signal, and a second fault signal indicating a fault state of the second inertial sensor may be determined using the second level signal. As an example, these fault signals may be used to deactivate the inertial sensor evaluated as faulty, or mark it as faulty.

[0018] The steps of the method can be performed in a corresponding manner using a plurality of sensor signals of a plurality of inertial sensors. As an example, sensor signals of three, four, five or more inertial sensors are used. Accordingly, in the reading step, at least one other sensor signal can be read in via an interface connected to at least one other inertial sensor of the vehicle, and in the acquisition step, at least one other level signal can be acquired using the at least one other sensor signal and the activity signal, which represents the noise level of the at least one other sensor signal. Thus, the described scheme can be adapted to different sensor configurations.

[0019] The method may be implemented, for example, in the form of software or hardware or in the form of a combination of software and hardware, for example in a controller.

[0020] The solution proposed herein also provides an apparatus which is configured to execute, control or implement the steps of the variant of the method proposed herein in a corresponding device. Through these implementation variants of the apparatus form of the present invention, the purpose on which the present invention is based can also be achieved quickly and effectively.

[0021] For this purpose, the device can have at least one computing unit for processing signals or data, at least one storage unit for storing signals or data, at least one interface connected to a sensor or actuator for reading in sensor signals from a sensor or outputting data signals or control signals to an actuator, and / or at least one communication interface for reading in or outputting data embedded in a communication protocol. The computing unit can be, for example, a signal processor, a microcontroller or the like, wherein the storage unit can be a flash memory or a magnetic storage unit. The communication interface can be designed for reading in or outputting data wirelessly and / or by wire, wherein a communication interface capable of reading in or outputting wired data can read in the data from a corresponding data transmission line, for example electrically or optically, or output it to a corresponding data transmission line.

[0022] At present, a device can be understood as an electrical appliance that processes sensor signals and sends control signals and / or data signals accordingly. The device can have an interface, which can be configured in hardware form and / or software form. In the case of a hardware form, the interface can be, for example, part of a so-called system ASIC, which contains various functions of the device. However, the interface can also be a separate integrated circuit or at least partially composed of discrete components. In the case of a software form, the interface can be a software module, which, for example, exists on a microcontroller together with other software modules.

[0023] Also advantageous is a computer program product or a computer program having a program code, which can be stored on a machine-readable carrier or storage medium, such as a semiconductor memory, a hard disk memory or an optical memory, and is used to execute, implement and / or control the steps of the method according to one of the above-described embodiments, in particular when the program product or the program is executed on a computer or a device. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] An embodiment of the solution proposed herein is shown in the drawings and explained in more detail in the following description.

[0025] Figure 1 shows a schematic diagram of a vehicle having an apparatus according to an embodiment;

[0026] Figure 2 A block diagram of an acquisition device according to an embodiment is shown;

[0027] Figure 3 A block diagram of an activity recognition device according to an embodiment is shown;

[0028] Figure 4 shows a block diagram of an extraction device according to an embodiment;

[0029] Figure 5 shows a graphical representation of Root-Allan-Varianz according to an embodiment; and

[0030] Figure 6 A flow chart of a method according to an embodiment is shown. DETAILED DESCRIPTION

[0031] In the following description of advantageous exemplary embodiments of the invention, identical or similar reference numerals are used for elements which are illustrated in different figures and have a similar function, wherein a repeated description of these elements is omitted.

[0032] Figure 1The schematic diagram of a vehicle 100 with a device 102 according to an embodiment is shown. The vehicle 100 is, for example, a motor vehicle. The vehicle 100 has a plurality of inertial sensors, here, for example, a first inertial sensor 104, a second inertial sensor 106 and a third inertial sensor 108. Optionally, the inertial sensors 104, 106, 108 are part of the device 102.

[0033] The first inertial sensor 104 is configured to provide a first sensor signal 110, the second inertial sensor 106 is configured to provide a second sensor signal 112, and the third inertial sensor 108 is configured to provide a third sensor signal 114. The sensor signals 110, 112, 114 represent, for example, the same acceleration or different accelerations of the vehicle 100 detected by the inertial sensors 104, 106, 108 over time.

[0034] The device 102 is configured to determine a motion signal 116 representing the motion of the vehicle by using the sensor signals 110, 112, 114. As an example, the motion signal 116 is determined based on one of the sensor signals 110, 112, 114 or based on a combination of at least two of the sensor signals 110, 112, 114. In this case, the device 102 is configured to take into account noise on the sensor signals 110, 112, 114 when selecting one or more sensor signals 110, 112, 114 for determining the motion signal 116.

[0035] For this purpose, the device 102 has a detection device 120, which is configured to detect the noise level of each of the sensor signals 110, 112, 114. As an example, the detection device 120 is configured to detect a first level signal 122 representing the noise level of the first sensor signal 110, a second level signal 124 representing the noise level of the second sensor signal 112, and a third level signal 126 representing the noise level of the third sensor signal 114. In this case, the detection device 120 is configured to detect the noise level while taking into account the activity performed by the vehicle 100 during the generation of the sensor signals 110, 112, 114. According to an embodiment, the detection device 120 is configured to read in an activity signal indicating an activity or to generate a corresponding activity signal itself, for example, when using only the sensor signals 110, 112, 114 or in combination with another signal, optionally a signal that has been read in.

[0036] The device 102 has a determination device 130, which is configured to determine the motion signal 116 using the sensor signals 110, 112, 114 and the level signals 122, 124, 126. As an example, the determination device 130 is configured to use the level signals 122, 124, 126 as an indicator of noise on the sensor signals 110, 112, 114, or to estimate the quality of the sensor signals 110, 112, 114 based on the level signals 122, 124, 126, and accordingly select a suitable combination of the sensor signals 110, 112, 114 to determine the motion signal 116.

[0037] According to an embodiment, the determining device 130 is configured to: compare the first level signal 122 with a first threshold value defined using the activity signal to create a first quality value representing the quality of the first sensor signal 110; compare the second level signal 124 with a second threshold value defined using the activity signal to create a second quality value representing the quality of the second sensor signal 112; and compare the third level signal 124 with a third threshold value defined using the activity signal to create a third quality value representing the quality of the third sensor signal 114. In this case, the first sensor signal 110, the second sensor signal 112, the third sensor signal 114, the first quality value, the second quality value and the third quality value are used to determine the motion signal 116. Advantageously, the quality value can be used, for example, to identify sensor signals of the sensor signals 110, 112, 114 that are not suitable for determining the motion signal 116, and accordingly not participate at all or only participate with a small weight in determining the motion signal 116.

[0038] According to an embodiment, the determination device 130 is configured to determine a reference value using the first level signal 122, the second level signal 124, and the third level signal 126. In addition, the determination device 130 is configured to determine a first noise index value as a difference between the first level signal and the reference value, a second noise index value as a difference between the second level signal and the reference value, and a third noise index value as a difference between the third level signal and the reference value. Finally, the motion signal 116 is determined using the first sensor signal 110, the second sensor signal 112, the third sensor signal 114, the first noise index value, the second noise index value, and the third noise index value.

[0039] Optionally, the determination device 130 is configured to perform a fault check on each of the sensor signals 110, 112, 114, with which, for example, a fault state of one of the inertial sensors 104, 106, 108 can be identified. To this end, the determination device 130 is configured according to an embodiment to determine a first fault signal 132 indicating a fault state of the first inertial sensor 104 using the first level signal 122, and to determine a second fault signal 134 indicating a fault state of the second inertial sensor 106 using the second level signal 124, and to determine a third fault signal 136 indicating a fault state of the third inertial sensor 108. To this end, the level signals 122, 124, 126 are compared, for example, with one or more suitable safety thresholds.

[0040] Although three inertial sensors 104 , 106 , 108 are shown in the illustrated embodiment, the described approach may be used with any number of sensors and adjusted accordingly.

[0041] The described approach is used, for example, to identify and use degradation indicators in algorithms of a system with redundant inertial sensors, such as inertial sensors 104, 106, 108. High-performance inertial measurement units (IMUs) measure the physical movement of vehicle 100 in the form of acceleration and angular velocity and are often used in automated driving applications that place high demands on signal reliability and accuracy.

[0042] Many applications, such as autonomous driving functions, require the use of reliable sensor signals 110, 112, 114. Therefore, redundant IMU sensor architectures that measure the same physical event (e.g., three speed sensors mounted on the same circuit board) are often used to identify sensor failures by monitoring signal deviations between the redundant signals.

[0043] Valid redundant signals are combined (fused), or a selection algorithm may be implemented to choose the "best" final signal from among all alternatives in terms of maintaining functionality and signal accuracy.

[0044] The redundant sensor groups may have different frequency characteristics, for example, some sensors may have more noise than other sensors. Obviously, using a signal with less noise can improve the accuracy of the final signal, here the motion signal 116.

[0045] Safety thresholds may be specified not only for offset or sensitivity, but also for the noise level (or density) of the signal. In these cases, it may also be necessary to monitor the noise level of the signal so that a corresponding response is made when the noise is above the specified safety threshold. The response may be, for example, to set the signal, here one of the sensor signals 110, 112, 114, to invalid when a diagnostic threshold is exceeded.

[0046] The scheme described herein may optionally be used in conjunction with software-based monitoring to respond to noise levels of the sensor signals 110 , 112 , 114 .

[0047] By taking the noise levels of the sensor signals 110 , 112 , 114 into account when selecting and / or fusing the final signal, the accuracy of the final signal, here the motion signal 116 , is improved.

[0048] Information about the activity or condition of the vehicle can be used to identify known situations where the sensor signals 110, 112, 114 are expected to have known behavior. This makes it easier to discern components of the sensor signals 110, 112, 114 that are considered to be impaired. For example, it is known that when the vehicle 100 is not moving, the sensor signals 110, 112, 114 should be close to zero. When high frequency signals are present, these high frequency signals can be considered dirty (noise).

[0049] Another advantage is that a monitoring mechanism may be implemented to predict the noise level in the sensor signals 110 , 112 , 114 .

[0050] Embodiments of functional blocks with technical features for implementing the described solution are described with reference to the following figures. Here, the solution is based on using activity information in a noise identification function, monitoring noise levels to identify possible faults in the sensor, and using noise level indicators in the selection and / or fusion of the final signal.

[0051] Figure 2 For example, according to Figure 1 A block diagram of an exemplary embodiment of an acquisition device 120 of a device for determining a movement of a vehicle is described.

[0052] The acquisition device 120 is configured to acquire and provide level signals 122, 124, 126 using the sensor signals 110, 112, 114. According to the illustrated embodiment, the device, here for example the acquisition device 120, comprises an activity recognition device 240, which is configured to acquire an activity signal 244 using at least one of the sensor signals 110, 112, 114 and optionally using an external signal 242, and to provide the activity signal to acquire the level signals 122, 124, 126. The activity signal 244 indicates the current activity of the vehicle, i.e., the activity when the sensor signals 110, 112, 114 are detected or being detected. As an activity, the activity signal 244 indicates, for example, the stationary state of the vehicle, the off-road driving of the vehicle, or the highway driving of the vehicle.

[0053] According to an embodiment, the acquisition device 120 includes an extraction device 246, a quantization device 248, and an evaluation device 250. The extraction device 246 is configured to use the sensor signals 110, 112, 114 and the activity signal 244 to determine a first filtered sensor signal 252, a second filtered sensor signal 254, and a third filtered sensor signal 256 and provide them to the quantization device 248. According to an embodiment, the filtered sensor signals 252, 254, 256 include noisy components of the sensor signals 110, 112, 114. The quantization device 248 is configured to perform quantization and characterization of the noise components of the filtered sensor signals 252, 254, 256. For example, the quantization device 248 is configured to quantize the filtered sensor signals 252, 254, 256, and obtain a first quantization value 258 using the first filtered sensor signal 252, a second quantization value 260 using the second filtered sensor signal 254, and a third quantization value 262 using the third filtered sensor signal 256, and provide them to the evaluation device 250. The evaluation device 250 is configured to obtain the level signals 122, 124, 126 using the quantization values ​​258, 260, 262 and the activity signal 244. For example, the evaluation device 250 is configured to perform a summation over time to obtain the level signals 122, 124, 126, and / or perform an evaluation to obtain the level signals 122, 124, 126 as noise indicators.

[0054] According to an embodiment, the acquisition device 120 is implemented as part of the algorithm. The activity recognition performed in the activity recognition device 240 enables the current situation or activity of the vehicle to be determined, wherein in the ideal case a well-defined reference state is given, such as a state in which the vehicle is stationary. This information is then used in the extraction device 246 representing a block for noise extraction to identify components in the signal that are considered to be noisy. This component of the signal is provided in the form of filtered sensor signals 252, 254, 256. This polluted signal is quantized and these values ​​are passed to the last functional block responsible for evaluation, here the evaluation device 250. According to an embodiment, each of the sensor signals 252, 254, 256 is quantized. The output is a noise indicator in the form of a level signal 122, 124, 126, which is strongly correlated with the noise level of the signal in the time curve.

[0055] According to an embodiment, after filtering in the extraction device 246, the correspondingly obtained contaminated signal with high noise components is processed to estimate the current noise level, as an example, an RMS calculation (quantization) can be performed using the quantization device 248. In addition, other information such as peak-to-peak distance, mean value, Allan root of variance, etc. can be optionally calculated. A programmable time window can be considered, in which all calculations can be performed. The size of this time window controls the type of noise that can be corrected.

[0056] Finally, an evaluation block, for example evaluation device 250 , analyses the RMS value or another statistical value obtained from the quantization block and creates a noise indicator for the signal, here in the form of level signals 122 , 124 , 126 , which can be used when merging and / or selecting the final signal.

[0057] Here, there are two possible solutions for calculating the noise index:

[0058] According to a first embodiment, each RMS value, here each of the quantized values ​​258, 260, 262, is compared with a specific threshold value, which is defined, for example, according to the ID number of the activity, wherein values ​​below the threshold value are considered "good" and values ​​above the threshold value indicate a degraded noise level. The noise indicator can be calculated as the difference between the RMS value and the threshold value, respectively.

[0059] According to a second embodiment, a reference value, for example a median, is obtained from all available RMS values, here from each of the quantized values ​​258, 260, 262. The difference between the respective RMS value and the reference value is calculated and used as a noise indicator.

[0060] As already mentioned, noise indicators as well as indicators of sensitivity, offset, etc. can be used in the selection / fusion of algorithms to give higher weight / priority to signals with less noise.

[0061] The noise metric or even the RMS value can also be compared to a safety threshold to check if the sensor is faulty relative to the expected noise. As an example, each noise metric is compared to a safety threshold. If the difference is greater than the threshold, the counter is incremented. If the difference is less than the threshold, the counter is decremented. If the counter reaches a specific calibratable value, the signal associated with that counter can be marked as an "affected signal" or invalid.

[0062] In order to take into account the temperature dependence of the noise behavior, the sensor temperature can additionally be used. Based on this temperature, the parameterization of functions can be changed, for example, thresholds in activity recognition, passband parameters and above all thresholds for calculating noise indicators.

[0063] Figure 3 FIG. 2 shows a block diagram of an activity recognition device 240 according to an embodiment. Figure 2 The activity recognition device 240 is described.

[0064] According to an embodiment, the activity recognition device 240 includes a filtering device 360, a comparing device 362 and an identifying device 364. The filtering device 360 ​​is configured to filter the sensor signals 110, 112, 114 and provide a first filtered signal 366, a second filtered signal 368 and a third filtered signal 370. To this end, the filtering device 360 ​​includes at least one filter, such as a low-pass filter. According to an embodiment, the comparing device 362 is configured to perform a threshold comparison. For example, the comparing device 362 is configured to compare the filtered signals 366, 368, 370 with at least one threshold to obtain and provide a first comparison result 372, a second comparison result 374 and a third comparison result 376. Here, the same threshold can be used for all filtered signals 366, 368, 370, or, for example, a separate threshold can be set for each of the filtered signals 366, 368, 370. The identifying device 364 is configured to use the comparison results 372, 374, 376 to determine the activity signal 244. For this purpose, recognition device 364 is designed, for example, as an activity decision logic and comprises at least one timer 378 , also referred to as a timer.

[0065] Thus, the activity recognition device 240 is used to detect activities and is also referred to as an activity recognition frame. The purpose of this frame is to recognize situations, i.e. reference states, in which the sensor signals 252, 254, 256 have expected, known values ​​and / or behaviors. For example, if the vehicle is waiting at a red light or is in its final position, the acceleration signal and the rotational speed signal are close to zero for at least a few seconds. External information, such as information received from ESP (stationary information) or a navigation system, can also be used to recognize situations or to check the plausibility of the behavior of the signals. As an example, an external signal 242 is used for this purpose.

[0066] Using this information and calibratable thresholds, the actual activity of the vehicle can be classified: e.g. no movement, off-road, highway, unknown. Each category can be assigned an ID number and this information can be passed to subsequent function boxes.

[0067] To this end, Figure 3 The steps of the activity recognition block are shown in FIG. Thus, sensor signals 252, 254, 256 of different channels and types, such as acceleration and angular velocity in all directions, are filtered and then compared with threshold values. With the aid of a timer 378, the length of time a signal is below or above a certain threshold value can be calculated and the necessary conditions can be determined before the classification is updated.

[0068] Figure 4 For example, reference Figure 2 1 is a block diagram of an embodiment of an extraction device 246. The extraction device 246 is configured to use the sensor signals 110, 112, 114 and the activity signal 244 to determine and provide filtered sensor signals 252, 254, 256. Optionally, the sensor signals 110, 112, 114 are redundant signals. Accordingly, the inertial sensors can be redundant sensors.

[0069] According to an embodiment, the extraction device 246 includes a first filter 480, a second filter 482 and a third filter 484, which are each designed as a bandpass filter, for example. The first filter 480 is used to filter the first sensor signal 110 to obtain a first filtered sensor signal 252, the second filter 482 is used to filter the second sensor signal 112 to obtain a second filtered sensor signal 254, and the third filter 484 is used to filter the third sensor signal 114 to obtain a third filtered sensor signal 256.

[0070] Optionally, the extraction device 246 includes a parameter setting device 486, which is configured to use the activity signal 244, for example representing an activity flag, to determine appropriate settings for the filters 480, 482, 484 and provide corresponding setting values ​​488 to the filters 480, 482, 484.

[0071] According to an embodiment, the extraction device 246 is a block for noise extraction, in which (bandpass) filters are used to examine the frequency ranges of the respective sensor signals 252, 254, 256 in which noise or contamination signals occur. The cutoff frequencies of these filters 480, 482, 484 are optionally adjusted as a function of the ID number of the activity, which is provided by the activity signal 244 according to an embodiment.

[0072] Figure 5 A graph of the Allan variance root σ(τ) over an integration time τ according to an embodiment is shown. Thermal noise 590, correlated noise 591, sinusoidal portion 592, electrical flicker 593 and random walk 594 as well as tilted random walk 595 and bias instability 596 are shown.

[0073] Figure 6 A flow chart of a method for determining the motion of a vehicle according to an embodiment is shown. The method can be implemented, for example, using the apparatus described with reference to the previous figures.

[0074] In step 601, a sensor signal provided by an inertial sensor is read in. In step 603, a level signal representing a noise level of the corresponding sensor signal is acquired for each of the sensor signals using the sensor signal and an activity signal representing vehicle activity. Step 603 can be implemented, for example, using the acquisition device described above. In step 605, a motion signal representing vehicle motion is determined. For this purpose, the sensor signal and the level signal are used. Step 605 can be implemented, for example, using the determination device described above. Optionally, in step 607, the activity signal is acquired and provided using, for example, the activity recognition device described above.

Claims

1. A method for determining the motion of a vehicle (100), wherein: The method comprises the following steps: A reading step (601), wherein a first sensor signal (110) is read in via an interface connected to a first inertial sensor (104) of the vehicle (100), and a second sensor signal (112) is read in via an interface connected to a second inertial sensor (106) of the vehicle (100); an acquisition step (603), wherein a first level signal (122) indicating a noise level of the first sensor signal (110) and a second level signal (124) indicating a noise level of the second sensor signal (112) are acquired using the first sensor signal (110), the second sensor signal (112) and an activity signal (244) indicating an activity of the vehicle (100); and A determination step (605) in which a motion signal (116) representing the motion of the vehicle (100) is determined using the first sensor signal (110), the second sensor signal (112), the first level signal (122) and the second level signal (124).

2. The method according to claim 1, comprising a providing step (607), wherein at least one of the sensor signals (110, 112) is used to provide the activity signal (244).

3. The method according to claim 2, wherein: In the providing step (607), the at least one sensor signal (110, 112) is filtered and subsequently compared with at least one threshold value, wherein an activity of the vehicle (100) is detected based on a result of the comparison.

4. A method according to any one of the preceding claims, wherein: In the providing step (607), the activity is identified as stationary state of the vehicle (100), off-road driving of the vehicle (100), or highway driving of the vehicle (100).

5. A method according to any one of the preceding claims, wherein: In the acquisition step (603), the activity signal (244) is used to set a first filter (480) for filtering the first sensor signal (110) and a second filter (482) for filtering the second sensor signal (112), the first sensor signal (110) is filtered using the first filter (480) to obtain a first filtered sensor signal (252), the second sensor signal (112) is filtered using the second filter (482) to obtain a second filtered sensor signal (254), and the first level signal (122) is acquired using the first filtered sensor signal (252) and the second level signal (124) is acquired using the second filtered sensor signal (254).

6. The method according to claim 5, wherein: In the acquisition step (603), the first filtered sensor signal (252) is quantized to obtain a first quantization value (258), and the second filtered sensor signal (254) is quantized to obtain a second quantization value (260), wherein the first level signal (122) represents the first quantization value (258), and the second level signal (124) represents the second quantization value (260).

7. A method according to any one of the preceding claims, wherein: In the determination step (605), the first level signal (122) is compared with a first threshold value defined using the activity signal (244) to create a first quality value representing the quality of the first sensor signal (110), and the second level signal (124) is compared with a second threshold value defined using the activity signal (244) to create a second quality value representing the quality of the second sensor signal (112), wherein the motion signal (116) is determined using the first sensor signal (110), the second sensor signal (112), the first quality value and the second quality value.

8. The method according to any one of claims 1 to 6, wherein: In the determining step (605), a reference value is determined using the first level signal (122) and the second level signal (124), a first noise index value is determined as a difference between the first level signal (122) and the reference value, and a second noise index value is determined as a difference between the second level signal (124) and the reference value, wherein the motion signal (116) is determined using the first sensor signal (110), the second sensor signal (112), the first noise index value, and the second noise index value.

9. A method according to any one of the preceding claims, wherein: In the determining step (605), a first fault signal (132) indicating a fault state of the first inertial sensor (104) is determined using the first level signal (122), and a second fault signal (134) indicating a fault state of the second inertial sensor (106) is determined using the second level signal (124).

10. A method according to any one of the preceding claims, wherein: In the reading step (601), other sensor signals (114) are read in through an interface connected to other inertial sensors (108) of the vehicle (100), and in the acquisition step, other level signals (126) representing the noise level of the other sensor signals (114) are acquired using the other sensor signals (114) and the activity signal (244).

11. A device (102) configured to perform and / or control the steps of the method according to any one of the preceding claims in respective units.

12. Computer program arranged to perform and / or control the steps of the method according to any of the preceding claims.

13. A machine-readable storage medium having stored thereon the computer program according to claim 12.

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