JP7567680B2 - Bias Calculation Device - Google Patents

Description

The disclosure described herein relates to a bias calculation device that calculates a bias value for a gyro sensor.

Patent document 1 discloses an in-vehicle device equipped with a vibration gyroscope and a control circuit. The control circuit executes a processing flow to determine the bias value of the vibration gyroscope.

JP 2004-219228 A

The control circuit shown in Patent Document 1 calculates the difference value of the output of the vibration gyro in a processing flow. The control circuit then determines whether or not the difference value is equal to or less than a threshold value. If the difference value is equal to or less than the threshold value, the control circuit samples the output of the vibration gyro. The control circuit calculates a bias value based on the sampled output of the vibration gyro.

However, the output of the vibration gyro (gyro signal) contains noise. This noise varies depending on the environment of the vibration gyro. Therefore, there is a risk of sampling a gyro signal that is not suitable for calculating the bias value.

The objective of this disclosure is to provide a bias calculation device that can sample a gyro signal that is suitable for calculating a bias value.

A bias calculation device according to one aspect of the present disclosure includes a storage unit (220) that stores a relationship between a threshold value for calculating a bias value included in a gyro signal output from a gyro sensor (160) mounted on a vehicle and a judgment value related to noise included in the gyro signal;
a sampling unit (230) that reads out a threshold value corresponding to the judgment value from a storage unit and samples a gyro signal lower than the read out threshold value;
and a calculation section (230) that calculates a bias value based on the plurality of gyro signals sampled by the sampling section.

In this way, the threshold changes depending on the noise contained in the gyro signal. This makes it possible to sample a gyro signal that is suitable for calculating the bias value.

Note that the reference numbers in parentheses above merely indicate the corresponding relationship to the configurations described in the embodiments described below, and do not limit the technical scope in any way.

FIG. 2 is a block diagram showing an on-board sensor and a bias calculation device. 10 is a schematic diagram showing a relationship between a threshold value stored in a storage unit and a temperature. FIG. 13 is a flowchart showing a bias value calculation process. 13 is a flowchart showing a threshold setting process. 10 is a schematic diagram showing a relationship between a threshold value stored in a storage unit and the number of samplings. FIG. 13 is a flowchart showing a bias value calculation process. 13 is a flowchart showing a threshold setting process. 10 is a schematic diagram showing the relationship between a threshold value, a temperature, and the number of samplings stored in a storage unit; FIG. 13 is a flowchart showing a threshold setting process. 13 is a flowchart showing a modified example of the bias value calculation process.

Below, several embodiments for implementing the present disclosure will be described with reference to the drawings. In each embodiment, parts corresponding to matters described in the preceding embodiment may be given the same reference numerals, and duplicated explanations may be omitted. In cases where only a portion of the configuration is described in each embodiment, the other embodiment described previously may be applied to the remaining parts of the configuration.

It is possible to combine parts that are specifically indicated as being possible in each embodiment. In addition, even if it is not indicated as being possible to combine them, it is also possible to partially combine embodiments, embodiments and variations, and variations. If no particular problems arise with the combination, it is also possible to partially combine embodiments, embodiments and variations, and variations.

First Embodiment
The first embodiment will be described with reference to FIGS.

In the following, the three mutually orthogonal directions are referred to as the x-direction, y-direction, and z-direction. In this embodiment, the x-direction is along the left-right direction of the vehicle. The y-direction is along the forward and backward direction of the vehicle. The z-direction is along the vertical direction of the vehicle. When the vehicle is parked on a horizontal surface, the x-direction and y-direction are each along the horizontal plane. The z-direction is along the vertical direction.

Figure 1 shows an on-board sensor 100 and a computing device 200. The on-board sensor 100 and the computing device 200 are mounted on an electric vehicle such as a hybrid vehicle or an electric vehicle. Such electric vehicles include passenger cars, buses, construction vehicles, and agricultural machinery vehicles. Of course, the on-board sensor 100 and the computing device 200 may also be mounted on a gasoline-powered vehicle. There are no particular limitations on the type of vehicle on which the on-board sensor 100 and the computing device 200 are mounted (adopted).

<In-vehicle sensors>
The on-board sensor 100 generates and outputs a sensor signal related to the state of the electric vehicle. The sensor signal is input to the arithmetic device 200.

The sensor signal includes the sensor value of the object to be detected, noise, and bias value. The noise includes thermal noise and vibration noise. The higher the sensor temperature, the greater the thermal noise. The higher the sensor vibration, the greater the vibration noise. When these noises are removed from the sensor signal, the detection accuracy of the sensor value of the object to be detected is improved.

The bias value is an inherent characteristic (bias) contained in the product used as a sensor. When this bias value is calculated and the sensor signal is corrected with this bias value, the detection accuracy of the sensor value of the detection target is improved.

The on-board sensor 100 includes a GNSS 110, an exterior camera 120, an acceleration sensor 130, and a wheel speed sensor 140. The on-board sensor 100 also includes a temperature sensor 150 and a gyro sensor 160.

GNSS is an abbreviation for Global Navigation Satellite System. In the drawings, the outside camera 120 is written as OCC. The acceleration sensor 130 is written as ACM. The wheel speed sensor 140 is written as WSS. The temperature sensor 150 is written as TS. The gyro sensor 160 is written as GS.

The GNSS 110 detects the position of the electric vehicle. The exterior camera 120 captures an image outside the electric vehicle (exterior image). The acceleration sensor 130 detects the acceleration of the electric vehicle. The wheel speed sensor 140 detects the speed of the wheels of the electric vehicle. The temperature sensor 150 detects the temperature of the electric vehicle. The gyro sensor 160 detects the angular velocity of the electric vehicle. The sensor signals output from these various sensors are input to the calculation device 200.

The temperature sensor 150 is provided near the gyro sensor 160. Therefore, the temperature detected by the temperature sensor 150 is equivalent to the temperature of the gyro sensor 160. In this embodiment, the gyro sensor 160 detects the angular velocity in the z direction.

<Calculation device>
The arithmetic device 200 has an input unit 210, a memory unit 220, a calculation unit 230, and an output unit 240. In the drawings, the input unit 210 is denoted as IS. The memory unit 220 is denoted as MU. The calculation unit 230 is denoted as OP. The output unit 240 is denoted as OS. The arithmetic device 200 corresponds to a bias calculation device.

Various information is input to the input unit 210. This information includes sensor signals output from the on-board sensor 100. This information includes vehicle information output from various ECUs mounted on the electric vehicle.

The storage unit 220 is a non-transient, tangible storage medium that non-temporarily stores data and programs that can be read by a computer or processor. The storage unit 220 has a volatile memory and a non-volatile memory. The storage unit 220 stores various information input to the input unit 210 and the processing results of the calculation unit 230.

The memory unit 220 stores various programs and various reference values for the calculation unit 230 to perform calculations. These reference values have a relationship between a threshold value and a judgment value related to noise contained in the gyro signal, for example. In this embodiment, the judgment value includes the temperature of the gyro sensor 160.

The relationship between the threshold and the temperature of the gyro sensor 160 is shown diagrammatically in FIG. 2. In the drawing, the thresholds are denoted as Th1, Th2, Th3, ... Thn. The temperatures of the gyro sensor 160 are denoted as T1, T2, T3, ... Tn. The threshold becomes larger as the temperature of the gyro sensor 160 becomes higher.

The calculation unit 230 includes a processor. The calculation unit 230 stores various information input to the input unit 210 in the memory unit 220. The calculation unit 230 executes various calculation processes based on the information stored in the memory unit 220.

The output unit 240 outputs an electrical signal including the results of the calculation processing by the calculation unit 230 to various ECUs mounted on the electric vehicle. The input/output of electrical signals between the calculation device 200 and the various ECUs is indicated by white arrows in FIG. 1.

<Calculation section>
The calculation unit 230 calculates a bias value included in the gyro signal output from the gyro sensor 160. The calculation unit 230 calculates the bias value when no angular velocity is generated in the electric vehicle. The calculation unit 230 includes a sampling unit and a calculation unit.

The position of the electric vehicle, the outside image, the acceleration of the electric vehicle, the angular velocity of the electric vehicle, and the wheel speed of the electric vehicle are input to the calculation unit 230 from the on-board sensor 100. Based on this, the calculation unit 230 determines whether the electric vehicle is moving in a straight line or is stopped.

When the electric vehicle is moving linearly, the calculation unit 230 determines that the electric vehicle is in a state where no angular velocity is generated (non-angular velocity generating state). When the electric vehicle is stopped, the calculation unit 230 determines that the electric vehicle is in a non-angular velocity generating state.

When calculating the bias value, the calculation unit 230 acquires the temperature output from the temperature sensor 150. The calculation unit 230 then reads out the threshold value corresponding to that temperature from the memory unit 220.

The calculation unit 230 compares the read threshold value with the gyro signal. The calculation unit 230 samples the gyro signal that is lower than the threshold value. At the same time, the calculation unit 230 also samples the temperature of the gyro sensor 160 when the gyro signal is output. The calculation unit 230 stores these sampled gyro signals and temperatures in the memory unit 220.

The calculation unit 230 samples the gyro signal at a speed of, for example, about 1 kHz. The values of the multiple gyro signals acquired at such time intervals differ due to noise. Therefore, the calculation unit 230 calculates a moving average value of the multiple gyro signals acquired at a time interval of about 0.1 s. At the same time, the calculation unit 230 calculates a moving average value of the multiple temperatures acquired at a time interval of about 0.1 s. The calculation unit 230 stores these moving average values of the gyro signals and the moving average values of the temperatures in the memory unit 220.

As mentioned above, the gyro signal contains noise. The calculation device 200 has a bandpass filter for removing the noise contained in the gyro signal. The calculation unit 230 receives the gyro signal that has passed through the bandpass filter and the gyro signal that has not passed through the bandpass filter.

The calculation unit 230 compares the gyro signal, from which noise has been removed by passing the gyro signal through the band-pass filter, with a threshold value. Based on the result of the comparison, the calculation unit 230 decides to store in the memory unit 220 the gyro signal that does not pass through the band-pass filter. Note that the calculation unit 230 may also decide to store in the memory unit 220 the gyro signal that has passed through the band-pass filter based on the result of the comparison.

The calculation unit 230 repeats the sampling of the gyro signal and temperature, the calculation of the moving average value, and the storage thereof as described above. After this, the calculation unit 230 calculates the additive average value of the moving average values of the multiple gyro signals stored in the storage unit 220. The calculation unit 230 calculates the bias value contained in the gyro signal based on this additive average value of the gyro signal. The calculation unit 230 may also calculate the additive average value of the moving average values of the multiple temperatures stored in the storage unit 220 and store it in the storage unit 220.

<Bias value calculation process>
Next, the bias value calculation process will be described with reference to Figures 3 and 4. The calculation unit 230 executes the bias value calculation process as a cycle task.

In step S10, the calculation unit 230 determines whether the electric vehicle is in a non-angular velocity generating state. If the electric vehicle is in a non-angular velocity generating state, the calculation unit 230 proceeds to step S20. If the electric vehicle is in a state where an angular velocity is generated (angular velocity generating state), the calculation unit 230 ends the bias value calculation process.

In step S10, the calculation unit 230 determines whether the electric vehicle is in a non-angular velocity generating state based on the determination of whether the electric vehicle is stopped or in linear motion. The calculation unit 230 first determines whether the electric vehicle is in a stopped state. If the electric vehicle is in a stopped state, the calculation unit 230 stores the stopped state of the electric vehicle in the storage unit 220. After this, the calculation unit 230 proceeds to step S20. If the electric vehicle is not stopped, the calculation unit 230 determines whether the electric vehicle is in linear motion. If the electric vehicle is in a linear motion state, the calculation unit 230 stores the linear motion state of the electric vehicle in the storage unit 220. Then, the calculation unit 230 proceeds to step S20. If the electric vehicle is in a non-linear motion state, the calculation unit 230 ends the bias value calculation process.

When the process proceeds to step S20, the calculation unit 230 executes the threshold setting process shown in FIG. 4.

In step S21 shown in FIG. 4, the calculation unit 230 acquires the temperature output from the temperature sensor 150. The calculation unit 230 then proceeds to step S22.

When the process proceeds to step S22, the calculation unit 230 reads the threshold value for the temperature obtained in step S21 from the memory unit 220. The calculation unit 230 then proceeds to step S23.

When the process proceeds to step S23, the calculation unit 230 decides to use the threshold value read in step S23 for comparison with the gyro signal. The calculation unit 230 then proceeds to step S30 in FIG. 3.

When the process proceeds to step S30, the calculation unit 230 compares the gyro signal with the threshold value. If the gyro signal is lower than the threshold value, the calculation unit 230 proceeds to step S40. If the gyro signal is equal to or greater than the threshold value, the calculation unit 230 ends the bias value calculation process.

When the process proceeds to step S40, the calculation unit 230 calculates a moving average value of multiple gyro signals, including the gyro signal used in the comparison with the threshold value in step S30. The calculation unit 230 then proceeds to step S50.

As described above, the bias value calculation process is a cyclic task. To achieve this, the calculation unit 230 executes step S30 multiple times. As a result, the calculation unit 230 acquires multiple gyro signals that are lower than the threshold value. The calculation unit 230 calculates the moving average value of these multiple gyro signals in step S40.

When the process proceeds to step S50, the calculation unit 230 stores the moving average value of the gyro signal calculated in step S40 in the memory unit 220. The calculation unit 230 then proceeds to step S60.

In addition, in step S40, the calculation unit 230 may calculate a moving average value of the temperature together with the gyro signal that is lower than the threshold value. Then, in step S50, the calculation unit 230 may store the moving average value of the temperature in the memory unit 220.

When the process proceeds to step S60, the calculation unit 230 compares the number of samples of the gyro signal with a predetermined number stored as a reference value in the memory unit 220. If the number of samples is greater than the predetermined number, the calculation unit 230 proceeds to step S70. If the number of samples is equal to or less than the predetermined number, the calculation unit 230 ends the bias value calculation process.

The number of samples may be the number of gyro signals determined to be lower than the threshold in step S30, or may be the number of moving average values based on multiple gyro signals stored in the memory unit 220 in step S50.

When the process proceeds to step S70, the calculation unit 230 calculates a bias value for the gyro sensor 160 based on the moving average value of the multiple gyro signals stored in the storage unit 220. The calculation unit 230 then ends the bias value calculation process. Note that the calculation unit 230 may clear the sampling count after executing step S70.

<Action and effect>
As described above, the storage unit 220 stores the relationship between the threshold value and the judgment value related to the noise included in the gyro signal. The calculation unit 230 reads out the threshold value corresponding to the judgment value from the storage unit 220. The calculation unit 230 then samples the gyro signal lower than the read threshold value. The calculation unit 230 calculates the bias value based on the sampled gyro signal.

This ensures that the gyro signal is sampled appropriately to calculate the bias value.

The judgment value is the temperature of the gyro sensor 160. The higher the temperature of the gyro sensor 160, the larger the threshold value.

When the temperature of the gyro sensor 160 is high, the gyro signal contains a lot of thermal noise. This causes the value of the gyro signal to increase in a manner that raises the base value.

In contrast, as described above, the higher the temperature of the gyro sensor 160, the larger the threshold value. This prevents sampling of gyro signals that are not suitable for calculating the bias value.

Second Embodiment
Next, a second embodiment will be described with reference to FIGS.

In the first embodiment, an example was shown in which the judgment value was the temperature of the gyro sensor 160. In contrast, in this embodiment, the judgment value is the number of samples of the gyro signal. The relationship between the threshold value and the number of samples is stored in the memory unit 220.

The relationship between the threshold and the number of samples is shown diagrammatically in Figure 5. In the figure, the numbers of samples are denoted as S1, S2, S3, ... Sn. As in Figure 2, the thresholds are denoted as Th1, Th2, Th3, ... Thn. The thresholds become larger as the number of samples decreases.

In the first embodiment, an example was shown in which a predetermined number for comparison with the sampling number is stored in the memory unit 220 as a reference value. In contrast to this, in the present embodiment, a plurality of predetermined numbers with different values are stored in the memory unit 220. These can be expressed, for example, as a first predetermined number N1, a second predetermined number N2, a third predetermined number N3, ..., an Nth predetermined number NN. As the number increases, the number of the predetermined numbers increases. The difference between the predetermined numbers of these adjacent numbers may be the same or different.

The calculation unit 230 executes the bias value calculation process shown in FIG. 6. This bias value calculation process differs from the bias value calculation process shown in FIG. 3 in the content of the threshold setting process in step S20.

In step S20 shown in FIG. 6, the calculation unit 230 executes the threshold setting process shown in FIG. 7.

In step S24 shown in FIG. 7, the calculation unit 230 reads the number of samples from the storage unit 220. The calculation unit 230 then proceeds to step S25.

As described above, the bias value calculation process is executed repeatedly. The number of samples changes during the process. This number of samples is stored in the memory unit 220, for example, in step S50.

When the process proceeds to step S25, the calculation unit 230 reads out the threshold value for the number of samples read out in step S24 from the memory unit 220. The calculation unit 230 then proceeds to step S26.

When the process proceeds to step S26, the calculation unit 230 decides to use the threshold value read in step S25 for comparison with the gyro signal. The calculation unit 230 then proceeds to step S30 in FIG. 6.

In the bias value calculation process shown in FIG. 6, unlike the bias value calculation process shown in FIG. 3, after executing step S70, the calculation unit 230 proceeds to step S80.

When the process proceeds to step S80, the calculation unit 230 updates the predetermined number. For example, if the sampling number is greater than the first predetermined number N1, the calculation unit 230 reads out the second predetermined number N2 from the storage unit 220. The calculation unit 230 then decides to use the second predetermined number N2 for comparison with the sampling number. After this, the calculation unit 230 ends the bias value calculation process.

If the non-angular velocity generating state of the electric vehicle continues continuously after such updating, the calculation unit 230 executes the bias value calculation process again. In this case, since the number of samples has increased, a lower threshold value is read from the storage unit 220 in step S20. Then, in step S30, a comparison is made between this lower threshold value and the gyro signal. The bias value is calculated based on the gyro signal that is lower than this threshold value. Then, the predetermined number is updated again.

The increase in the sampling number, the decrease in the threshold value, the update of the bias value, and the update of the predetermined number shown above are performed as long as the non-angular velocity generating state of the electric vehicle continues continuously. Such changes and updates stop, for example, when the electric vehicle changes from a non-angular velocity generating state to an angular velocity generating state. In addition, when the vehicle stops and the power is turned off, the sampling number is cleared. The predetermined number is set to the initial value, the first predetermined number N1.

<Action and effect>
The electric vehicle goes through a transitional period in which it transitions from an angular velocity generating state to a non-angular velocity generating state, and then enters the non-angular velocity generating state. Therefore, when it begins to be determined that the electric vehicle is in the non-angular velocity generating state, in other words, when the calculation unit 230 begins to sample the gyro signal, there is a possibility that the gyro signal contains a large amount of vibration noise caused by the power source of the electric vehicle, etc.

However, depending on the operation of the electric vehicle, after a transition from an angular velocity generating state to a non-angular velocity generating state, there is a possibility that the non-angular velocity generating state may transition to an angular velocity generating state within a short period of time. If the bias value cannot be calculated or if the reliability of the bias value stored in the memory unit 220 is low, it is necessary to calculate the bias value even within such a short period of time.

Therefore, as shown in this embodiment, the threshold value is set larger as the number of samples decreases. By doing so, even if the time after the transition from the angular velocity generating state to the non-angular velocity generating state is short, the gyro signal for calculating the bias value is more easily sampled.

The calculation unit 230 determines that the electric vehicle is in a non-angular velocity generating state when the electric vehicle is moving linearly or when the electric vehicle is stopped. However, the stability of the non-angular velocity generating state of the electric vehicle differs in these cases. When the electric vehicle is moving linearly, the non-angular velocity generating state of the electric vehicle is unstable compared to when the electric vehicle is stopped. An example of a case where the reliability of the bias value is low is when the bias value is calculated when the electric vehicle is moving linearly.

Furthermore, if the non-angular velocity generating state of the electric vehicle continues for a long period of time after the electric vehicle transitions from an angular velocity generating state to a non-angular velocity generating state, it is expected that the gyro signal will be in a state where vibration noise caused by the power source of the electric vehicle, etc. is less likely to be included. In other words, as the number of samplings increases, it is expected that the gyro signal will be in a state where vibration noise caused by the power source of the electric vehicle, etc. is less likely to be included. In this way, the number of samplings is related to vibration noise to some extent.

In contrast, in this embodiment, the larger the sampling number, the smaller the threshold value. This prevents sampling of gyro signals that contain vibration noise. It also prevents sampling of gyro signals that are not suitable for calculating a bias value.

Note that if the number of samples is small, the calculated bias value may contain vibration noise. This may cause the gyro signal value to be on the higher side.

Therefore, when the bias value is calculated based on a gyro signal in which the sampling number is lower than, for example, a second predetermined number N2, the calculation unit 230 may execute a correction to reduce the bias value. Such a correction is realized, for example, by multiplying the bias value calculated based on the sampled gyro signal by a correction coefficient smaller than 1. This reduces the effect of vibration noise contained in the bias value. The second predetermined number N2 corresponds to the predetermined value.

Third Embodiment
Next, a third embodiment will be described with reference to FIGS.

In the second embodiment, an example was shown in which the judgment value was the number of samples of the gyro signal. In contrast, in this embodiment, the judgment value is the temperature of the gyro sensor 160 and the number of samples. The relationship between the threshold value, the number of samples, and the temperature is stored in the memory unit 220.

The relationship between thresholds, number of samples, and temperature is shown diagrammatically in Figure 8. In the figure, temperatures are denoted as T1, T2, ..., Tn. The number of samples for T1 is denoted as S11, S12, S13 ... S1n, and the thresholds are denoted as Th11, Th12, Th13 ... Th1n. Similarly, the number of samples for T2 is denoted as S21, S22, S23 ... S2n, and the thresholds are denoted as Th21, Th22, Th23 ... Th2n. The number of samples for Tn is denoted as Sn1, Sn2, Sn3 ... Snn, and the thresholds are denoted as Thn1, Thn2, Thn3 ... Thnn.

In this embodiment, the calculation unit 230 executes the bias value calculation process shown in FIG. 6.

However, in step S20, the calculation unit 230 executes the threshold setting process shown in FIG. 9.

In step S21 shown in FIG. 9, the calculation unit 230 acquires the temperature output from the temperature sensor 150. Then, in the next step S24, the calculation unit 230 reads the number of samples from the memory unit 220. Then, the calculation unit 230 proceeds to step S27.

When the process proceeds to step S27, the calculation unit 230 reads out from the memory unit 220 the threshold value for the temperature acquired in step S21 and the number of samples read out in step S24. The calculation unit 230 then proceeds to step S28.

When the process proceeds to step S28, the calculation unit 230 decides to use the threshold value read in step S27 for comparison with the gyro signal.

The arithmetic device 200 described in this embodiment includes components equivalent to those of the arithmetic device 200 described in the embodiments described thus far. Therefore, it goes without saying that the arithmetic device 200 of this embodiment provides the same effects as the arithmetic device 200 described in the embodiments described thus far. Therefore, the description thereof will be omitted.

(First Modification)
In the first embodiment, an example has been shown in which the calculation unit 230 executes step S60 after step S50. In step S60, the calculation unit 230 compares the number of samplings of the gyro signal with a predetermined number.

In response to this, for example, as shown in FIG. 10, the calculation unit 230 may execute step S90 after step S50. In step S90, the calculation unit 230 compares the number of samples of the gyro signal at the equivalent temperature with a predetermined number. Then, in step S70, the calculation unit 230 calculates a bias value based on the gyro signal that is at the equivalent temperature and is lower than the threshold value.

This reduces the variation in thermal noise contained in each of the multiple gyro signals used to calculate the bias value. As a result, the degree of degradation of the bias value calculation is reduced.

(Second Modification)
In each embodiment, an example has been shown in which the gyro sensor 160 detects the angular velocity in the z direction. However, the gyro sensor 160 may detect the angular velocity in each of the x direction and the y direction in addition to the z direction. The calculation unit 230 may compare the gyro signals indicating the angular velocities in these three directions with a threshold value, and when each of these three types of gyro signals is lower than the threshold value, a bias value of the gyro signal in the z direction may be obtained. Note that the three threshold values used for comparison with the three types of gyro signals may be the same or different.

Although the present disclosure has been described with reference to the embodiment, it is understood that the present disclosure is not limited to the embodiment or structure. The present disclosure also encompasses various modifications and modifications within the scope of equivalents. In addition, while various combinations and forms are shown in the present disclosure, other combinations and forms including only one element, more, or less are also within the scope and concept of the present disclosure.

100: vehicle-mounted sensor, 160: gyro sensor, 200: computing device, 220: memory unit, 230: computing unit

Claims (7)

a storage unit (220) storing a relationship between a threshold value for calculating a bias value included in a gyro signal output from a gyro sensor (160) mounted on a vehicle and a judgment value related to noise included in the gyro signal;
a sampling unit (230) that reads out the threshold value corresponding to the judgment value from the storage unit and samples the gyro signal that is lower than the read threshold value;
a calculation unit (230) that calculates the bias value based on the plurality of gyro signals sampled by the sampling unit. The bias calculation device according to claim 1, wherein the judgment value includes at least one of the temperature of the gyro sensor and the number of samples of the gyro signal. The determination value includes the temperature of the gyro sensor,
The bias calculation device according to claim 2 , wherein the threshold value is larger as the temperature of the gyro sensor increases. The bias calculation device according to claim 2 or 3, wherein the sampling unit samples the gyro signal that is lower than the threshold value when the vehicle is in a non-angular velocity generating state. The determination value includes the number of samples of the gyro sensor,
The bias calculation device according to claim 4 , wherein the threshold value is larger as the number of samples of the gyro sensor is smaller. The bias calculation device according to claim 5, wherein the calculation unit performs a correction to reduce the bias value calculated based on the multiple gyro signals sampled by the sampling unit when the sampling number is lower than a predetermined value. The bias calculation device according to any one of claims 1 to 6, wherein the calculation unit calculates the bias value based on a plurality of gyro signals sampled by the sampling unit when the temperature of the gyro sensor is equal.

Images