Method and device for determining motions of vehicle

Abstract

To provide a method for determining motions of a vehicle (100).SOLUTION: The method includes: reading a first sensor signal (110) for a first inertial sensor (104) of a vehicle (100) through an interface and reading a second sensor signal (112) for a second inertial sensor (106) for the vehicle (100) through an interface; determining a first level signal (122) showing the noise level of the first sensor signal (110) and a second level signal (124) showing the noise level of the second sensor signal (112) by using the first sensor signal (110), the second sensor signal (112), and an activation signal showing activation of the vehicle (100); and determining a motion signal (116) showing motions of the vehicle (100) by using the first sensor signal (110), the second sensor signal (112), the first level signal (122), and the second level signal (124).SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The invention starts from an apparatus or method according to the preamble of the independent claims. The subject of the invention is also a computer program. [Background technology]

[0002] Many applications in the vehicle field, such as for example autonomous driving functions, require the use of reliable sensor signals. Summary of the Invention [Problem to be solved by the invention]

[0003] Disclosure of the Invention Against this background, the approach presented here envisages a method for determining the movement of a vehicle, as well as an apparatus using this method and finally a corresponding computer program according to the independent claims. The measures recited in the dependent claims enable advantageous developments and improvements of the apparatus indicated in the independent claims.

[0004] Sensor signals from the vehicle's sensors may preferably be monitored in light of vehicle activation. [Means for solving the problem]

[0005] The method for determining the motion of a vehicle comprises the following steps: reading a first sensor signal via an interface to a first inertial sensor on the vehicle and reading a second sensor signal via an interface to a second inertial sensor on the vehicle; determining a first level signal representative of a noise level of the first sensor signal and a second level signal representative of a noise level of the second sensor signal using the first sensor signal, the second sensor signal, and an activation signal representative of activation of the vehicle; determining a motion signal representative of motion of the vehicle using the first sensor signal, the second sensor signal, the first level signal, and the second level signal; Includes.

[0006] The vehicle may be a motor vehicle, for example an automated vehicle or a manually controlled vehicle. The inertial sensors may be implemented as, for example, acceleration sensors or angular velocity sensors. The inertial sensors may be of the same or different types. The motion of the vehicle may be understood to mean, for example, the acceleration of the whole vehicle or the rotation of the vehicle around the vehicle axis. The level signals may be used to determine, for example, which of the sensor signals is used for the motion determination and possibly with what weighting. For example, a sensor signal with strong noise may be excluded from the motion determination or may be considered with less weighting compared to a sensor signal with weak noise. The activation state of the vehicle may indicate a state of the vehicle having a predetermined influence on at least one of the sensor signals. For example, the activation state may indicate the absence of motion of the vehicle or driving on a smooth or unpaved road surface.

[0007] The method may include a step of providing an activation signal using at least one of the sensor signals. For this purpose, a suitable evaluation of the sensor signals may be performed, for example comparing the time course of the sensor signal with reference courses assigned to different activations or comparing the level of the sensor signal with thresholds assigned to different activations in order to identify and display a current activation of the vehicle via an activation signal. Additionally or alternatively, one or more other signal sources may also be used for providing the activation signal.

[0008] For example, in the providing step, at least one sensor signal is filtered and then a comparison with at least one threshold value is performed, whereby activation of the vehicle can be identified depending on the result of the comparison, thereby making it possible to reliably identify activation quickly and without significant computational effort.

[0009] For example, an activation can be identified as a vehicle stopping, a vehicle going off-road, or a vehicle going on a highway. Thus, any activation that can be uniquely distinguished based on different signal profiles of the inertial sensors can be used for the approach described herein.

[0010] In the determining step, a first filter for filtering the first sensor signal and a second filter for filtering the second sensor signal can be set using the activation 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 level signal can be determined using the first filtered sensor signal and the second level signal can be determined using the second filtered sensor signal. By filtering, a frequency range of the sensor signal having a particularly strong noise can be selected. Advantageously, the filter range of the filter can be set using the activation, so that a filtering adapted to the situation can be performed.

[0011] In the determining 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, where 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 well suited for noise evaluation. When determining the quantized value, further properties of the filtered sensor signal can be taken into account, such as, for example, the distance between signal peaks, the average value or the variance. This allows for a refined quantization.

[0012] In the determining step, the first level signal can be compared to a first threshold defined using an activation signal to create a first quality value representative of the quality of the first sensor signal. Correspondingly, the second level signal can be compared to a second threshold defined using an activation signal to create a second quality value representative of the quality of the second sensor signal. The motion signal can finally be determined using the first sensor signal, the second sensor signal, the first quality value and the second quality value. For example, the quantization value assigned to the sensor signal can be compared to a threshold to determine whether the quality of the sensor signal is good or bad. For example, the sensor signals assigned a poor quality value can be excluded from the determination of the motion signal or included in the determination of the motion signal only with a low weighting.

[0013] Additionally or alternatively, in the determining step, the reference value can be determined using the first level signal and the second level signal. The first noise index value can be determined as the difference between the first level signal and the reference value, and the second noise index value can be determined as the difference between the second level signal and the reference value. Here, the motion signal can be determined using the first sensor signal, the second sensor signal, the first noise index value and the second noise index value. For example, the sensor signal assigned a poor noise index value can be excluded from the determination of the motion signal or can be included in the determination of the motion signal only with a low weighting.

[0014] In the determining step, a first error signal indicative of an error state of the first inertial sensor can be determined using the first level signal, and a second error signal indicative of an error state of the second inertial sensor can be determined using the second level signal, which can be used, for example, to deactivate or mark as erroneous the inertial sensor assessed as being erroneous.

[0015] The steps of the method can be implemented in a corresponding manner using multiple sensor signals from multiple inertial sensors, for example using sensor signals from three, four, five or more inertial sensors. Accordingly, in the reading step, at least one further sensor signal can be read via an interface to at least one further inertial sensor of the vehicle, and in the determining step, at least one further level signal representative of the noise level of the at least one further sensor signal can be correspondingly determined using the at least one further sensor signal and the activation signal. The described approach can therefore be adapted to different sensor configurations.

[0016] The method may for example be implemented in software or hardware or in a mixed form of software and hardware, for example in a controller.

[0017] The approach presented herein also provides for an apparatus adapted to implement, drive or execute the steps of the method variants presented herein in a corresponding apparatus, which also allows the problem underlying the present invention to be solved quickly and efficiently.

[0018] For this purpose, the device may comprise at least one computing unit for processing signals or data, at least one memory unit for storing signals or data, at least one interface to sensors or actuators for reading sensor signals from sensors or outputting data or control signals to actuators, and / or at least one communication interface for reading or outputting data embedded in a communication protocol. The computing unit may be, for example, a signal processor, a microcontroller, etc., in which case the memory unit may be a flash memory or a magnetic memory unit. The communication interface may be configured to read or output data wirelessly and / or wired, in which case a communication interface that may read or output wired data may read or output these data, for example electrically or optically, from or to a corresponding data transmission line.

[0019] In the present invention, a device can be understood as an electrical device that processes sensor signals and outputs control and / or data signals therefrom. The device can be equipped with an interface that can be configured on the basis of hardware and / or software. In the case of a hardware-based configuration, the interface can be, for example, part of a so-called system ASIC that includes the various functions of the device. However, it is also possible for the interface to be a single integrated circuit or to consist at least partially of separate components. In the case of a software-based configuration, the interface can be, for example, a software module that is present on a microcontroller next to other software modules.

[0020] In particular, a computer program product or computer program having a program code used to implement, execute and / or drive control the steps of the method according to the above-described embodiments, which may be stored on a machine-readable carrier or storage medium, such as a semiconductor memory, hard disk memory or optical memory, when the program product or program is executed on a computer or device, is also advantageous.

[0021] Examples of the approaches presented herein are illustrated in the drawings and are described in more detail in the following specification. [Brief description of the drawings]

[0022] [Figure 1] 1 is a schematic diagram showing a vehicle equipped with an apparatus according to an embodiment; [Diagram 2] FIG. 1 is a block circuit diagram illustrating a calculation device according to an embodiment. [Diagram 3] FIG. 2 is a block circuit diagram illustrating an activation identification device according to one embodiment. [Figure 4] FIG. 2 is a block circuit diagram illustrating an extraction device according to an embodiment. [Diagram 5] 1 is a depiction showing root-alan variance according to one embodiment. [Figure 6] 1 is a flowchart of a method according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] In the following description of the preferred embodiment of the present invention, the same or similar reference numerals are used for elements which are illustrated in the various figures and which act similarly, in which case repeated descriptions of these elements will not be provided.

[0024] 1 shows a schematic diagram of a vehicle 100 equipped with an apparatus 102 according to an embodiment. The vehicle 100 is exemplarily an automobile. The vehicle 100 has a number of inertial sensors, exemplarily here a first inertial sensor 104, a second inertial sensor 106 and a third inertial sensor 108. Optionally, the inertial sensors 104, 106 and 108 are part of the apparatus 102.

[0025] 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 110. These sensor signals 110, 112, 114 may represent, for example, similar or different accelerations over time of the vehicle 100 captured by the inertial sensors 104, 106, 108.

[0026] The apparatus 102 is configured to determine a motion signal 116 representative of the motion of the vehicle using the sensor signals 110, 112, 114. For 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. Here, the apparatus 102 is configured to take into account noise in the sensor signals 110, 112, 114 when selecting the sensor signals 110, 112, 114 used for determining the motion signal 116.

[0027] For this purpose, the apparatus 102 comprises a calculation device 120 configured to determine a noise level for each of the sensor signals 110, 112, 114. Exemplarily, the calculation device 120 is configured to determine a first level signal 122 representative of the noise level of the first sensor signal 110, a second level signal 124 representative of the noise level of the second sensor signal 112, and a third level signal 126 representative of the noise level of the third sensor signal 114. Here, the calculation device 120 is configured to determine the noise level taking into account activations performed by the vehicle 100 while the sensor signals 110, 112, 114 were generated. Depending on the embodiment, the calculation device 120 is configured to read an activation signal indicative of an activation, for example exclusively using the sensor signals 110, 112, 114 or in combination with further or optionally read signals, or to generate a corresponding activation signal itself.

[0028] The apparatus 102 comprises a determiner 130 configured to determine the motion signal 116 using the sensor signals 110, 112, 114 and the level signals 122, 124, 126. For example, the determiner 130 is configured to use the level signals 122, 124, 126 as an indication for 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 for determining the motion signal 116.

[0029] According to an embodiment, the determination device 130 is configured to compare the first level signal 122 with a first threshold defined using the activation signal to generate a first quality value representative of the quality of the first sensor signal 110, to compare the second level signal 124 with a second threshold defined using the activation signal to generate a second quality value representative of the quality of the second sensor signal 112, and to compare the third level signal 124 with a third threshold defined using the activation signal to generate a third quality value representative of the quality of the third sensor signal 114. In this case, the motion signal 116 is determined using 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. Advantageously, these quality values ​​can be used, for example, to identify sensor signals 110, 112, 114 that are unsuitable for the determination of the motion signal 116 and accordingly not to include them at all or only with a low weighting in the determination of the motion signal 116.

[0030] According to an embodiment, the determiner 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. Furthermore, the determiner 130 is configured to determine a first noise index value as a difference between the first level signal and the reference value, to determine a second noise index value as a difference between the second level signal and the reference value and to determine 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.

[0031] Optionally, the decision device 130 is configured to perform an error check on each of the sensor signals 110, 112, 114, thereby being able to identify, for example, an error state of one of the inertial sensors 104, 106, 108. For this purpose, the decision device 130 is configured, according to one embodiment, to use the first level signal 122 to determine a first error signal 132 indicative of an error state of the first inertial sensor 104, to use the second level signal 124 to determine a second error signal 134 indicative of an error state of the second inertial sensor 106, and to determine a third error signal 136 indicative of an error state of the third inertial sensor 108. For this purpose, the level signals 122, 124, 126 are, for example, compared with one or more suitable safety thresholds.

[0032] In the illustrated embodiment, three inertial sensors 104, 106, 108 are shown, however the approach described may be used for any number of sensors and may be adapted accordingly.

[0033] The described approach is used to identify and use degradation indicator values ​​in algorithms, for example for systems having redundant inertial sensors, such as inertial sensors 104, 106, 108. High performance inertial measurement units (IMUs) here measure the physical motion of the vehicle 100 in the form of acceleration and angular velocity, and are frequently used in AD applications with high demands on signal reliability and accuracy.

[0034] For example, many applications, such as autonomous driving functions, require the use of reliable sensor signals 110, 112, 114. Therefore, redundant IMU sensor architectures measuring similar physical events (e.g., three angular rate sensors mounted on the same printed circuit board) are typically used to identify sensor failures by monitoring signal deviations between the redundant signals.

[0035] Available redundant signals can be combined (fused) or a selection algorithm can be implemented to select the "best" final signal from all possibilities in terms of maintaining functionality and signal accuracy.

[0036] The redundant sensor set may have different frequency characteristics, and some sensors may, for example, have more noise than others. It is clear that using signals with less noise can improve the accuracy of the final signal (here the motion signal 116).

[0037] Safety thresholds can be established not only for offset or sensitivity, but also for the noise level (or density) of the signal. In this case, it may be required to monitor the noise level of the signal in order to react accordingly in the presence of noise exceeding the established safety threshold. One reaction may be, for example, to set a signal, here one of the sensor signals 110, 112, 114, to invalid when a diagnostic threshold is exceeded.

[0038] The approaches described herein are optionally used in conjunction with software-based monitoring to address noise levels in the sensor signals 110,112,114.

[0039] The accuracy of the final signal, here the motion signal 116, is improved by taking into account the noise level of the sensor signals 110, 112, 114 during selection and / or fusion of the final signal.

[0040] Information about the vehicle's activation or status can be used to identify known situations in which the sensor signals 110, 112, 114 are presumed to have known characteristics. Therefore, simple components that qualify as impairments in the sensor signals 110, 112, 114 can be identified. For example, it is known that the sensor signals 110, 112, 114 should be close to zero when the vehicle 100 is not moving. If high frequency signals are present, they can be considered as contamination (noise).

[0041] A further advantage is that a monitoring mechanism for predicting the noise level in the sensor signals 110, 112, 114 can be implemented.

[0042] Based on the following drawings, an example of a functional block having technical features for realizing the described approach is described, which is based on the use of activation information in a noise discrimination function, monitoring of the noise level to identify possible errors in the sensor, and the use of a noise level indicator value in the selection and / or fusion of the final signal.

[0043] FIG. 2 shows a block diagram of an embodiment of a calculation unit 120 of a device for determining the movement of a vehicle, for example as described with reference to FIG.

[0044] The computing device 120 is configured to determine and provide the level signals 122, 124, 126 using the sensor signals 110, 112, 114. According to the illustrated embodiment, the device, here exemplarily the computing device 120, includes an activation identification device 240 configured to determine and provide an activation signal 244 using at least one of the sensor signals 110, 112, 114 and optionally an external signal 242 to determine the level signals 122, 124, 126. The activation signal 244 indicates a current activation of the vehicle, i.e. an activation at the time when the sensor signal 110, 112, 114 is detected or is detected. For example, the activation signal 244 indicates a stop of the vehicle, a vehicle going off-road, or a vehicle going on a highway as an activation.

[0045] According to one embodiment, the calculation 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 activation signal 244 to determine and provide a first filtered sensor signal 252, a second filtered sensor signal 254 and a third filtered sensor signal 256 to the quantization device 248. These filtered sensor signals 252, 254, 256 include, according to one embodiment, the noise 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. Exemplarily, the quantizer 248 is configured to quantize the filtered sensor signals 252, 254, 256, determine a first quantized value 258 using the first filtered sensor signal 252, determine a second quantized value 260 using the second filtered sensor signal 254, and determine a third quantized value 262 using the third filtered sensor signal 256, and provide them to the evaluation device 250. The evaluation device 250 is configured to determine the level signals 122, 124, 126 using these quantized values ​​258, 260, 262 and the activation signal 244. For example, the evaluation device 250 is configured to perform summation and / or evaluation over time to determine the level signals 122, 124, 126, and determine the level signals 122, 124, 126 as noise index values.

[0046] According to an embodiment, the calculation device 120 is realized as part of an algorithm. The activation identification implemented in the activation detection device 240 allows the determination of the current state or activation of the vehicle, ideally assuming a precisely defined reference state, for example a stationary vehicle. This information is then used in an extraction device 246, which represents a block for noise extraction, to identify the part of the signal that is considered as noise. This part of the signal is provided in the form of filtered sensor signals 252, 254, 256. This contaminated signal is quantized and these values ​​are forwarded to the last functional block, here the evaluation device 250, which is responsible for the evaluation. According to an embodiment, each of the sensor signals 252, 254, 256 is quantized. The noise index values ​​are output here in the form of level signals 122, 124, 126, which are strongly correlated with the noise level of the signal over time.

[0047] According to one embodiment, after filtering in the extractor 246, the contaminated signals received with each high noise component are processed to estimate the noise level present, for example an RMS calculation can be performed using the quantizer 248 (quantization). Additionally, further information such as peak-to-peak distance, average value, Root-Allan variance, etc. can be calculated optionally. A programmable time window can be considered within which all calculations can be performed. The size of this time window gives control over the type of noise that can be corrected.

[0048] Finally, an evaluation function block, e.g., evaluator 250, analyzes the RMS values ​​or other statistics obtained from the quantization function block and produces a noise index value for the signal, here in the form of level signals 122, 124, 126, which can be used in the merging and / or selection of the final signal.

[0049] There are now two ways to calculate the noise index value.

[0050] According to a first embodiment, each RMS value, here each of the quantized values ​​258, 260, 262, is compared to a specific threshold defined, for example, depending on the ID number of the activation, where values ​​below the threshold are considered "good" and values ​​above the threshold indicate a bad noise level. A noise index value can be calculated as the difference between the RMS value and the threshold, respectively.

[0051] According to a second embodiment, a reference value, for example the median, is determined from all available RMS values, here from each of the quantized values ​​258, 260, 262. The difference between the individual RMS values ​​and the reference value is calculated and used as noise index value.

[0052] As already mentioned, the noise index value can be used together with index values ​​for sensitivity, offset, etc. in algorithm selection / fusion to give higher weight / priority to signals with weak noise. The noise index value, or even the RMS value, can be compared to a safety threshold to check if the sensor is faulty with respect to the estimated noise. For example, each noise index value is compared to a safety threshold. If the difference is greater than the threshold, a counter is incremented. If the difference is less than the threshold, the counter is decremented. When the counter reaches a predefined calibratable value, the signal coupled to this counter can be flagged as a "concerned signal" or invalid.

[0053] In order to take into account the temperature dependence of the noise characteristics, the sensor temperature can additionally be used. Based on the temperature, the parameterization of the functionality can be changed, such as, for example, the thresholds for the activation discrimination, the bandpass parameters, and in particular the thresholds for the calculation of the noise index value.

[0054] 3 shows a block diagram of an activation identification device 240 according to an embodiment, here for example the activation identification device 240 described with reference to FIG.

[0055] According to an embodiment, the activation identification device 240 comprises a filter device 360, a comparison device 362 and an identification device 364. The filter device 360 ​​is configured to filter the sensor signals 110, 112, 114 and provide a first filter signal 366, a second filter signal 368 and a third filter signal 370. For this purpose, the filter device 360 ​​comprises at least one filter, for example a low-pass filter. The comparison device 362 is configured to perform a threshold comparison, according to an embodiment. For example, the comparison device 362 is configured to compare the filter signals 366, 368, 370 with at least one threshold in order to receive 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 the filter signals 366, 368, 370 or, for example, a single threshold can be provided for each of the filter signals 366, 368, 370. The discriminator 364 is configured to use the comparison results 372, 374, 376 to determine the activation signal 244. For example, the discriminator 364 is implemented as an activation decision logic for this purpose and includes at least one timer 378, also referred to as a timekeeping mechanism.

[0056] Thereby, the activation identification device 240 is used to ask for activation and is also called the activation identification block. The purpose of this block is to identify a situation in which the sensor signals 252, 254, 256 have known values ​​and / or characteristics to be estimated, i.e. a reference state. When the vehicle is waiting, for example, at a red light or in its final position, the acceleration and angular velocity signals should be close to zero for at least a few seconds. External information, such as information received from the ESP (static information) or from the navigation system, can also be used to identify the situation or to validate the characteristics of the signals. For example, an external signal 242 is used for this purpose.

[0057] Based on this information and calibratable thresholds, a classification of the actual activation of the vehicle can be made, e.g., no motion, off-road, highway, unknown, etc. Each classification can be assigned an ID number, and this information can be forwarded to the next functional block.

[0058] For this purpose, the steps of the Activation Identification block are shown in Fig. 3, whereby the sensor signals 252, 254, 256 of different channels and types, e.g. acceleration and angular velocity in all directions, are filtered and then compared to a threshold. A timer 378 can be used to count how long a signal is below or above a certain threshold, establishing the necessary conditions before the classification is updated.

[0059] Fig. 4 shows a block circuit diagram of an embodiment of an extractor 246, for example as described with reference to Fig. 2. This extractor 246 is configured to determine and provide filtered sensor signals 252, 254, 256 using the sensor signals 110, 112, 114 and the activation signal 244. Optionally, the sensor signals 110, 112, 114 are redundant signals. Accordingly, the inertial sensor may represent a redundant sensor.

[0060] According to one embodiment, the extraction device 246 includes for this purpose a first filter 480, a second filter 482 and a third filter 484, each exemplarily implemented as a band-pass filter. The first filter 480 is used to filter the first sensor signal 110 to obtain the first filtered sensor signal 252, the second filter 482 is used to filter the second sensor signal 112 to obtain the second filtered sensor signal 254 and the third filter 484 is used to filter the third sensor signal 114 to obtain the third filtered sensor signal 256.

[0061] Optionally, the extraction unit 246 includes a parameterization unit 486 configured to determine appropriate settings for the filters 480, 482, 484 using the activation signal 244, for example representing an activation identifier, and to provide corresponding setting values ​​488 to the filters 480, 482, 484.

[0062] According to one embodiment, the extractor 246 represents a block for noise extraction in which (band-pass) filters are used to observe the frequency domain of the respective sensor signal 252, 254, 256 in which noise or contaminating signals occur. The cut-off frequencies of these filters 480, 482, 484 are, according to one embodiment, optionally adapted depending on the activation ID number provided via the activation signal 244.

[0063] 5 shows a plot of the Root-Allan variance σ(τ) over an integration time τ, according to one embodiment, showing thermal noise 590, correlated noise 591, sinusoidal sections 592, electrical flicker 593 and random motion 594, as well as distorted random motion 595 and bias instability 596.

[0064] 6 shows a flow chart of a method for determining the motion of a vehicle according to an embodiment, which can be implemented, for example, using an apparatus as described on the basis of the previous figures.

[0065] In step 601, the sensor signals provided by the inertial sensors are read. In step 603, a level signal is determined for each of the sensor signals, which is representative of the noise level of the respective sensor signal, using the sensor signals and an activation signal representative of the activation of the vehicle. This step 603 can be performed, for example, using a calculation device as described above. In step 605, a motion signal representative of the motion of the vehicle is determined. For this purpose, the sensor signals and the level signal are used. Step 603 can be performed, for example, using a determination device as described above. Optionally, in step 607, an activation signal is determined and provided, for example using an activation identification device as described above.

Claims

1. A method for determining motion of a vehicle (100), comprising: The method comprises the following steps: - reading (601) a first sensor signal (110) via an interface to a first inertial sensor (104) of the vehicle (100) and a second sensor signal (112) via an interface to a second inertial sensor (106) of the vehicle (100); determining (603) a first level signal (122) representative of a noise level of the first sensor signal (110) and a second level signal (124) representative of a noise level of the second sensor signal (112) using the first sensor signal (110), the second sensor signal (112) and an activation signal (244) representative of an activation of the vehicle (100); determining (605) 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); A method comprising:

2. The method of claim 1, comprising the step of using at least one of the sensor signals to provide the activation signal.

3. 3. The method of claim 2, wherein in the providing step (607), at least one of the sensor signals (110, 112) is filtered and then a comparison with at least one threshold value is performed, and activation of the vehicle (100) is identified depending on a result of the comparison.

4. 4. The method of claim 1, wherein in the providing step (607), the activation is identified as a stop of the vehicle (100), an off-road driving of the vehicle (100), or a highway driving of the vehicle (100).

5. 5. The method of claim 1, wherein in the determining step, a first filter for filtering the first sensor signal and a second filter for filtering the second sensor signal are set using the activation signal, the first sensor signal is filtered using the first filter to obtain a first filtered sensor signal, the second sensor signal is filtered using the second filter to obtain a second filtered sensor signal, the first level signal is determined using the first filtered sensor signal, and the second level signal is determined using the second filtered sensor signal.

6. 6. The method of claim 5, wherein in the determining step (603), the first filtered sensor signal (252) is quantized to obtain a first quantized value (258) and the second filtered sensor signal (254) is quantized to obtain a second quantized value (260), the first level signal (122) representing the first quantized value (258) and the second level signal (124) representing the second quantized value (260).

7. 7. The method of claim 1, wherein in the determining step (605), the first level signal (122) is compared to a first threshold defined using the activation signal (244) to create a first quality value representative of a quality of the first sensor signal (110), the second level signal (124) is compared to a second threshold defined using the activation signal (244) to create a second quality value representative of a quality of the second sensor signal (112), and 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. 7. The method according to claim 1, 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, a second noise index value is determined as a difference between the second level signal (124) and the reference value, and 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. 9. The method of claim 1, wherein in the determining step (605), the first level signal (122) is used to determine a first error signal (132) indicative of an error condition of the first inertial sensor (104) and the second level signal (124) is used to determine a second error signal (134) indicative of an error condition of the second inertial sensor (106).

10. 10. The method according to claim 1, wherein in the reading step (601) a further sensor signal (114) is read via an interface to a further inertial sensor (108) of the vehicle (100) and in the determining step (603) a further level signal (126) representative of a noise level of the further sensor signal (114) is determined using the further sensor signal (114) and the activation signal (244).

11. An apparatus (102) configured to perform and / or drive the steps of the method according to any one of the claims 1 to 10 in corresponding units.

12. A computer program arranged to carry out and / or drive the steps of the method according to any one of claims 1 to 10.

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

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