Signal processing, motion state estimation methods, devices, vehicle-mounted equipment and media

Abstract

This invention discloses a signal processing, motion state estimation method, apparatus, vehicle-mounted equipment, and medium to solve the problem of the inevitable compromise between hysteresis and root mean square noise values ​​in the prior art. The method includes: acquiring the raw signals detected by each body sensor on the vehicle at the previous time step; the body sensors include different types of sensors, with multiple sensors of each type; estimating the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by at least three of the multiple body sensors of the same type at the previous time step; calculating the signal estimate value of the body sensor at the current time step based on the raw signals and corresponding signal increment values ​​of the body sensors at the previous time step; and fusing the raw signals of the body sensors at the previous time step and the signal estimate value at the current time step to obtain the fused signal value of the body sensors at the current time step.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a signal processing, motion state estimation method, apparatus, vehicle-mounted equipment, and readable storage medium. Background Technology

[0002] The signals from vehicle body sensors are crucial inputs for vehicle control. Furthermore, as vehicle control functions become increasingly complex, higher demands are placed on body sensors, such as in active suspension system control systems. Currently, traditional methods for processing body sensor signals generally involve using low-pass filters, including digital low-pass filters and hardware low-pass filters. Without considering chip computing power, digital low-pass filtering can essentially achieve all the functions of hardware low-pass filtering. Commonly used digital low-pass filters include averaging filters, Butterworth filters, and Chebyshev filters. However, these low-pass filters share a common drawback: the lower the cutoff frequency, the greater the hysteresis; conversely, the higher the cutoff frequency, the greater the root mean square noise value. An unavoidable compromise between hysteresis and noise root mean square values ​​is thus established. Summary of the Invention

[0003] This invention provides a signal processing, motion state estimation method, device, vehicle-mounted equipment, and storage medium for vehicle sensors to solve the problem of inevitable compromise between hysteresis and root mean square noise values ​​in the prior art.

[0004] Firstly, a signal processing method is provided, including:

[0005] Acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0006] Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time.

[0007] Based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment, calculate the estimated signal value of the vehicle body sensor at the current moment;

[0008] The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused together to obtain the fused signal value of the vehicle body sensor at the current moment.

[0009] In one possible implementation, the signal increment value of another vehicle body sensor of the same type per unit time is estimated based on the raw signals detected by at least three of a plurality of identical vehicle body sensors at the previous time step, including the following steps:

[0010] Based on the raw signals detected by any three vehicle body sensors of the same type at the previous time step, estimate the signal increment value of another vehicle body sensor of the same type per unit time.

[0011] In one possible implementation, based on the raw signals detected by any three of the same type of vehicle body sensors at the previous time step, the signal increment value of another vehicle body sensor of the same type per unit time is estimated, including:

[0012] Based on the vehicle body geometry and the original signals detected by any three vehicle body sensors of the same type at the previous moment, the signal increment value of another vehicle body sensor of the same type per unit time.

[0013] In one possible implementation, the estimated signal value of the vehicle body sensor at the current moment is calculated based on the raw signal and corresponding signal increment value of the vehicle body sensor at the previous moment, including:

[0014] The original signal from the vehicle body sensor and the corresponding signal increment value are added together to obtain the signal estimate of the vehicle body sensor at the current moment.

[0015] In one possible implementation, the raw signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment, including:

[0016] Kalman filtering is used to fuse the original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment to obtain the fused signal value of the vehicle body sensor at the current moment.

[0017] Secondly, a motion state estimation method is provided, including:

[0018] Acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0019] Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time step.

[0020] Based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment, calculate the estimated signal value of the vehicle body sensor at the current moment;

[0021] The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment;

[0022] The fused signal values ​​of all vehicle body sensors at the current moment are sent to the vehicle motion estimation module so that the vehicle motion estimation module can estimate the vehicle's motion state.

[0023] Thirdly, a signal processing apparatus is provided, comprising:

[0024] The acquisition module is used to acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0025] The estimation module is used to estimate the signal increment value of another vehicle body sensor of the same type per unit time based on the raw signals detected by at least three of the multiple vehicle body sensors of the same type at the previous time.

[0026] The calculation module is used to calculate the estimated signal value of the vehicle body sensor at the current moment based on the raw signal and corresponding signal increment value of the vehicle body sensor at the previous moment.

[0027] The fusion module is used to fuse the raw signals and corresponding signal estimates from the vehicle body sensors to obtain the fused signal value of the vehicle body sensors at the current moment.

[0028] Fourthly, a motion state estimation device is provided, comprising:

[0029] The acquisition module is used to acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0030] The estimation module is used to estimate the signal increment value of another vehicle body sensor of the same type per unit time based on the raw signals detected by at least three of the multiple vehicle body sensors of the same type at the previous time.

[0031] The calculation module is used to calculate the estimated signal value of the vehicle body sensor at the current moment based on the raw signal and corresponding signal increment value of the vehicle body sensor at the previous moment.

[0032] The fusion module is used to fuse the raw signal from the vehicle body sensor at the previous moment and the signal estimate at the current moment to obtain the fused signal value of the vehicle body sensor at the current moment.

[0033] The transmitting module is used to send the fused signal values ​​of all vehicle body sensors at the current moment to the vehicle motion estimation module, so that the vehicle motion estimation module can estimate the vehicle motion state.

[0034] Fifthly, an in-vehicle device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the steps of any of the signal processing methods of the first aspect described above, or implements the steps of the motion state estimation method of the second aspect described above.

[0035] In a sixth aspect, a readable storage medium is provided, which stores a computer program, characterized in that, when executed by a processor, the computer program implements the steps of any of the signal processing methods described in the first aspect above, or implements the steps of the motion state estimation method mentioned in the second aspect above.

[0036] In one solution provided by the aforementioned signal processing, motion state estimation method, device, vehicle-mounted equipment, and readable storage medium, the original signals detected by each body sensor on the vehicle at the previous moment are acquired. The body sensors include different types of sensors, with multiple sensors of each type. Based on the original signals detected by at least three of the multiple body sensors of the same type at the previous moment, the signal increment value of another body sensor of the same type within a unit time is estimated. Then, based on the original signal of the body sensor at the previous moment and the corresponding signal increment value, the signal estimate value of the body sensor at the current moment is calculated. The original signal of the body sensor at the previous moment and the signal estimate value at the current moment are fused to obtain the fused signal value of the body sensor at the current moment. It can be seen that this invention estimates the signal by adding the measured signal value of the body sensor at the previous moment to the estimated signal increment value within the current unit time, which can effectively reduce the estimation error at the current moment, thereby reducing the error of the sensor signal fusion value. Furthermore, by directly obtaining the signal increment value from the detection of other body sensors, the response is fast, and a good balance is achieved between hysteresis and the root mean square error of noise. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the flowchart of the signal processing and motion state estimation method of the present invention;

[0039] Figure 2 This is a schematic flowchart of a signal processing method according to an embodiment of the present invention;

[0040] Figure 3 This is a flowchart illustrating a motion state estimation method according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of a signal processing device according to an embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram of a motion state estimation device according to an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of the structure of a vehicle-mounted device in one embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Figure 1 In the schematic diagram provided for this invention, various types of sensors, namely vehicle body sensors, are deployed on the vehicle. Multiple of each type of vehicle body sensor can be used, such as... Figure 1 In this system, each type of vehicle body sensor includes sensor 1, sensor 2, sensor 3, and sensor 4. The onboard equipment acquires the raw signals detected by each of the same type of vehicle body sensor at the previous time. Based on the raw signals detected by at least three of the same type of vehicle body sensor at the previous time, it estimates the signal increment value of another vehicle body sensor of the same type per unit time. Then, based on the raw signals and corresponding signal increment values ​​of the vehicle body sensor at the previous time, it calculates the signal estimate value of the vehicle body sensor at the current time. Finally, it fuses the raw signals and corresponding signal estimates of the vehicle body sensor at the previous time to obtain the fused signal value of the vehicle body sensor at the current time. As can be seen, this invention estimates the signal value by adding the measured signal value of the vehicle body sensor from the previous moment to the estimated signal value increment within the current unit time, which can effectively reduce the signal estimation error at the current moment, thereby reducing the error of the fusion value. Moreover, it directly obtains the signal value increment through the detection of other vehicle body sensors, resulting in a fast response and a good balance between hysteresis and noise root mean square error. Correspondingly, it also provides a motion state estimation method. After obtaining the fusion value of each vehicle body sensor based on the above signal processing method, it can be output to the motion state estimation module. Based on the fusion value output by the above vehicle body sensors, the motion state estimation module can obtain more accurate data and respond faster, facilitating the motion state estimation module to make quick and accurate decisions.

[0046] It should be noted that in-vehicle equipment is a processing device integrated into a vehicle. This in-vehicle equipment can refer to the vehicle's overall controller or a separate controller; there is no specific limitation. Additionally, [the following is an appendix] Figure 1 This is merely an illustrative example and does not limit the scope of the invention.

[0047] The following is in conjunction with the appendix Figure 1 The present invention will be described in detail.

[0048] In one embodiment, such as Figure 2 As shown, a signal processing method is provided, including the following steps:

[0049] S10: Acquire the raw signals detected by each body sensor on the vehicle at the previous moment.

[0050] During vehicle operation, sensors deployed on the vehicle (referred to as vehicle body sensors in this invention) can detect and acquire sensor signals in real time. For onboard equipment, it can acquire the raw signals detected by each vehicle body sensor at each moment. When processing the signal at the current moment, it can acquire the raw signals detected by each vehicle body sensor at the previous moment. Depending on the application scenario, the types of vehicle body sensors mentioned above include acceleration sensors, speed sensors, position sensors, etc., and each type of vehicle body sensor includes multiple sensors, such as multiple acceleration sensors, multiple speed sensors, and multiple position sensors. Therefore, it is possible to acquire the raw signals detected by each vehicle body sensor of the same type at the previous moment. For example, it is possible to acquire the raw signals detected by each position sensor at the previous moment, and similarly, it is possible to acquire the raw signals detected by each acceleration sensor at the previous moment, and so on. Examples of all types of vehicle body sensors will not be provided here.

[0051] It should be noted that the smaller the period of the raw sensor signal from each vehicle body sensor, the better the performance of this method; that is, the smaller the unit time is defined, the better the performance of the method. The raw signal refers to the signal without any filtering. In some implementations, the signal noise of the raw signal is white noise and follows a normal distribution.

[0052] S20: Based on the raw signals detected by at least three of the multiple identical body sensors at the previous time step, estimate the signal increment value of another identical body sensor per unit time.

[0053] After obtaining the raw signals detected by multiple body sensors of each type at each moment, in order to obtain more accurate values ​​for each body sensor, it is first necessary to estimate the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by at least three body sensors of the same type at the previous moment. In some embodiments, estimating the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by at least three body sensors of the same type at the previous moment means estimating the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by any three body sensors of the same type at the previous moment. In one embodiment, during the estimation process, the raw signals of three suitable body sensors can be selected from at least three body sensors to estimate the signal increment value of another body sensor per unit time.

[0054] In one embodiment, estimating the signal increment of another similar vehicle body sensor per unit time based on the original signals detected by any three of the multiple identical sensors at the previous moment refers to estimating the signal increment of another similar vehicle body sensor per unit time based on the vehicle body geometry and the original signals detected by any three of the multiple identical sensors at the previous moment. It can be understood that the vehicle body is a rigid body. Given the motion states of three points, the installation coordinates of each vehicle body sensor can be obtained based on the vehicle body geometry. The motion state of the fourth point can then be calculated based on the vehicle body geometry transformation relationship. Similarly, given the position / velocity / acceleration increments of the three points per unit time, the position / velocity / acceleration increments of the fourth point per unit time can be obtained accordingly.

[0055] For example, see attached Figure 1As shown, suppose a vehicle's body sensors include sensor 1, sensor 2, sensor 3, and sensor 4. After obtaining the raw signals detected by sensors 1, 2, 3, and 4 at a certain time t1, the signal increment of another sensor of the same type per unit time can be estimated using the raw signals of any three sensors at time t1. For example, the raw signals of sensors 2, 3, and 4 at time t1 can be used to estimate the signal increment of sensor 1 per unit time; the raw signals of sensors 1, 3, and 4 at time t1 can be used to estimate the signal increment of sensor 2 per unit time; the raw signals of sensors 1, 2, and 4 at time t1 can be used to estimate the signal increment of sensor 3 per unit time; and the raw signals of sensors 1, 2, and 3 at time t1 can be used to estimate the signal increment of sensor 4 per unit time.

[0056] It should be noted that in practical applications, a vehicle may include multiple body sensors. Depending on the specific application scenario, the raw signals of the required type of body sensor can be obtained to estimate the signal increment of another body sensor per unit time. There is no specific limitation. For example, taking an acceleration sensor as an example, when obtaining the acceleration increment value of sensor 1, the raw acceleration signals of sensors 2, 3, and 4 at time t1 can be used to estimate the acceleration increment of sensor 1. As another example, to obtain the position increment value, the raw position signals of any three other position sensors at time t1 can be used to estimate the acceleration increment of another position sensor. Taking position as an example, once the positions (x / y / z coordinates) of any three points on the vehicle are known, the position (x / y / z coordinates) of any fourth point on the vehicle is also determined and can be easily obtained through geometric transformation. The processing method for speed and other types of signals is similar.

[0057] After steps S10-S20, the signal increment value corresponding to each vehicle body sensor at each moment can be obtained.

[0058] S30: Calculate the estimated signal value of the vehicle body sensor at the current moment based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment.

[0059] After obtaining the signal increment value corresponding to each vehicle body sensor at each moment, for each vehicle body sensor, the estimated signal value of the sensor at the current moment is calculated based on the original signal and the corresponding signal increment value of the sensor at the previous moment. In one embodiment, the original signal and the corresponding signal increment value of the vehicle body sensor at the previous moment are added together to obtain the estimated signal value of the vehicle body sensor at the current moment. It should be noted that in some other embodiments, the estimated signal value after addition can also be verified, which is not limited here.

[0060] Continuing with the example above, let t2 be the next time step after t1, and t2 be the current time. Using the raw signals from sensors 2, 3, and 4 at time t1, we estimate the signal increment of sensor 1 per unit time. Then, we add the raw signal of sensor 1 at time t1 to the signal increment of sensor 1 per unit time to obtain the estimated signal value of sensor 1 at time t2. Similarly, using the raw signals from sensors 1, 3, and 4 at time t1, we estimate the signal increment of sensor 2 per unit time. Then, we add the raw signal of sensor 2 at time t1 to the signal increment of sensor 2 per unit time to obtain the estimated signal value of sensor 2. The estimated signal value of sensor #1 at time t2 is obtained by using the original signals of sensors #1, #2, and #4 at time t1 to estimate the signal increment of sensor #3 per unit time. Then, the original signal of sensor #3 at time t1 is added to the original signal increment of sensor #3 per unit time to obtain the estimated signal value of sensor #3 at time t2. Similarly, the estimated signal increment of sensor #4 per unit time is obtained by using the original signals of sensors #1, #2, and #3 at time t1. Then, the original signal of sensor #4 at time t1 is added to the original signal increment of sensor #4 per unit time to obtain the estimated signal value of sensor #4 at time t2. In other words, in the estimation calculation, the measured value of the original signal should be from the previous time step, and the estimated signal increment per unit time should be from the current time step.

[0061] After step S30, the signal estimate of each body sensor on the vehicle at the current moment can be obtained.

[0062] S40: The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment.

[0063] After obtaining the signal estimate of the vehicle body sensor at the current moment, the original signal of the vehicle body sensor at the previous moment and the signal estimate at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment. In one embodiment, Kalman filtering can be used to fuse the original signal of the vehicle body sensor at the previous moment and the signal estimate at the current moment to obtain the fused signal value of the vehicle body sensor at the current moment.

[0064] It should be noted that Kalman filtering is the preferred type of fusion algorithm. When using Kalman filtering, three covariance matrices are involved: the measurement noise covariance matrix R, the process noise covariance matrix Q, and the observation error covariance matrix P. The measurement noise covariance matrix R and the process noise covariance matrix Q can be obtained through experimental measurements; the observation error covariance matrix P can be obtained iteratively, with its initial value set equal to the process noise covariance matrix Q. Furthermore, it should be noted that signal fusion methods include, but are not limited to, Kalman filtering algorithms; other sensor fusion algorithms such as multi-Bayes estimation can also be used. This invention does not limit the specific methods used.

[0065] Continuing with the above example, after obtaining the signal estimate of sensor 1 at time t2, the original signal of sensor 1 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 1 at time t2; after obtaining the signal estimate of sensor 2 at time t2, the original signal of sensor 2 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 2 at time t2; after obtaining the signal estimate of sensor 3 at time t2, the original signal of sensor 3 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 3 at time t2; after obtaining the signal estimate of sensor 4 at time t2, the original signal of sensor 4 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 4 at time t2.

[0066] As can be seen, this invention provides a signal processing method that estimates the signal value increment by adding the measured signal value from the vehicle body sensor at the previous moment to the estimated signal value increment within the current unit time. This effectively reduces the estimation error at the current moment, thereby reducing the error of the fused value. Furthermore, it directly obtains the signal value increment from other vehicle body sensors, resulting in a fast response and a good balance between hysteresis and noise root mean square error. The inventors also found through comparison that, compared with common methods, this invention exhibits less hysteresis when noise root mean square value and error are comparable; less noise root mean square value when hysteresis and error are comparable; and less error when noise root mean square value and hysteresis are comparable.

[0067] After obtaining the fused signal values ​​from each vehicle body sensor, these fused signal values ​​can be incorporated into subsequent vehicle control requirements, depending on the application. In one application scenario, these fused signal values ​​can be used for vehicle motion state estimation; please refer to [further details omitted]. Figure 1 As shown, after obtaining the signal fusion values ​​corresponding to sensors 1, 2, 3, and 4, these signal fusion values ​​can be referenced in the vehicle motion state estimation. Specifically, the signal fusion values ​​corresponding to sensors 1, 2, 3, and 4 are output to the vehicle motion state estimation module so that the vehicle motion state estimation module can perform motion state estimation based on the signal fusion values, as described below.

[0068] In one embodiment, such as Figure 3 As shown, the present invention also provides a motion state estimation method, comprising the following steps:

[0069] S10: Acquire the raw signals detected by each body sensor on the vehicle at the previous moment.

[0070] During vehicle operation, sensors deployed on the vehicle (referred to as vehicle body sensors in this invention) can detect and acquire sensor signals in real time. For onboard equipment, it can acquire the raw signals detected by each vehicle body sensor at each moment. When processing the signal at the current moment, it can acquire the raw signals detected by each vehicle body sensor at the previous moment. Depending on the application scenario, the types of vehicle body sensors mentioned above include acceleration sensors, speed sensors, position sensors, etc., and each type of vehicle body sensor includes multiple sensors, such as multiple acceleration sensors, multiple speed sensors, and multiple position sensors. Therefore, it is possible to acquire the raw signals detected by each vehicle body sensor of the same type at the previous moment. For example, it is possible to acquire the raw signals detected by each position sensor at the previous moment, and similarly, it is possible to acquire the raw signals detected by each acceleration sensor at the previous moment, and so on. Examples of all types of vehicle body sensors will not be provided here.

[0071] It should be noted that the smaller the period of the raw sensor signal from each vehicle body sensor, the better the performance of this method; that is, the smaller the unit time is defined, the better the performance of the method. The raw signal refers to the signal without any filtering. In some implementations, the signal noise of the raw signal is white noise and follows a normal distribution.

[0072] S20: Based on the raw signals detected by at least three of the multiple identical body sensors at the previous time step, estimate the signal increment value of another identical body sensor per unit time.

[0073] After obtaining the raw signals detected by multiple body sensors of each type at each moment, in order to obtain more accurate values ​​for each body sensor, it is first necessary to estimate the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by at least three body sensors of the same type at the previous moment. In some embodiments, estimating the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by at least three body sensors of the same type at the previous moment means estimating the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by any three body sensors of the same type at the previous moment. In one embodiment, during the estimation process, the raw signals of three suitable body sensors can be selected from at least three body sensors to estimate the signal increment value of another body sensor per unit time.

[0074] In one embodiment, estimating the signal increment of another similar vehicle body sensor per unit time based on the original signals detected by any three of the multiple identical sensors at the previous moment means estimating the signal increment of another similar vehicle body sensor per unit time based on the vehicle body geometry and the original signals detected by any three of the multiple identical sensors at the previous moment. It can be understood that the vehicle body is a rigid body. Given the motion states of three points, based on the vehicle body geometry, the installation coordinates of each vehicle body sensor can be obtained first. Then, based on the vehicle body geometry transformation relationship, the motion state of the fourth point can be calculated. Similarly, given the position / velocity / acceleration increments of the three points per unit time, the position / velocity / acceleration increments of the fourth point per unit time can be obtained accordingly.

[0075] For example, suppose a vehicle's body sensors include sensor 1, sensor 2, sensor 3, and sensor 4. After obtaining the raw signals detected by sensors 1, 2, 3, and 4 at a certain time t1, the signal increment of another sensor of the same type per unit time can be estimated using the raw signals of any three sensors at time t1. For instance, the raw signals of sensors 2, 3, and 4 at time t1 can be used to estimate the signal increment of sensor 1 per unit time; the raw signals of sensors 1, 3, and 4 at time t1 can be used to estimate the signal increment of sensor 2 per unit time; the raw signals of sensors 1, 2, and 4 at time t1 can be used to estimate the signal increment of sensor 3 per unit time; and the raw signals of sensors 1, 2, and 3 at time t1 can be used to estimate the signal increment of sensor 4 per unit time.

[0076] It should be noted that in practical applications, a vehicle may include multiple body sensors. Depending on the specific application scenario, the raw signals of the required type of body sensor can be obtained to estimate the signal increment of another body sensor per unit time. There are no specific limitations. For example, taking an acceleration sensor as an example, to obtain the acceleration increment value of sensor 1, the raw acceleration signals of sensors 2, 3, and 4 at time t1 can be used to estimate the acceleration increment of sensor 1. Similarly, to obtain the position increment value, the raw position signals of any three other position sensors at time t1 can be used to estimate the acceleration increment of another position sensor. Taking position as an example, once the positions (x / y / z coordinates) of any three points on the vehicle are known, the position (x / y / z coordinates) of any fourth point on the vehicle is also determined and can be easily obtained through geometric transformation. The processing methods for speed and other types of signals are similar, and will not be illustrated here.

[0077] After steps S10-S20, the signal increment value corresponding to each vehicle body sensor at each moment can be obtained.

[0078] S30: Calculate the estimated signal value of the vehicle body sensor at the current moment based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment.

[0079] After obtaining the signal increment value corresponding to each vehicle body sensor at each moment, for each vehicle body sensor, the estimated signal value of the sensor at the current moment is calculated based on the original signal and the corresponding signal increment value of the sensor at the previous moment. In one embodiment, the original signal and the corresponding signal increment value of the vehicle body sensor at the previous moment are added together to obtain the estimated signal value of the vehicle body sensor at the current moment. It should be noted that in some other embodiments, the estimated signal value after addition can also be verified, which is not limited here.

[0080] Continuing with the example above, let t2 be the next time step after t1, and t2 be the current time. Using the raw signals from sensors 2, 3, and 4 at time t1, we estimate the signal increment of sensor 1 per unit time. Then, we add the raw signal of sensor 1 at time t1 to the signal increment of sensor 1 per unit time to obtain the estimated signal value of sensor 1 at time t2. Similarly, using the raw signals from sensors 1, 3, and 4 at time t1, we estimate the signal increment of sensor 2 per unit time. Then, we add the raw signal of sensor 2 at time t1 to the signal increment of sensor 2 per unit time to obtain the estimated signal value of sensor 2. The estimated signal value of sensor #1 at time t2 is obtained by using the original signals of sensors #1, #2, and #4 at time t1 to estimate the signal increment of sensor #3 per unit time. Then, the original signal of sensor #3 at time t1 is added to the original signal increment of sensor #3 per unit time to obtain the estimated signal value of sensor #3 at time t2. Similarly, the estimated signal increment of sensor #4 per unit time is obtained by using the original signals of sensors #1, #2, and #3 at time t1. Then, the original signal of sensor #4 at time t1 is added to the original signal increment of sensor #4 per unit time to obtain the estimated signal value of sensor #4 at time t2. In other words, in the estimation calculation, the measured value of the original signal should be from the previous time step, and the estimated signal increment per unit time should be from the current time step.

[0081] After step S30, the signal estimate of each body sensor on the vehicle at the current moment can be obtained.

[0082] S40: The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment.

[0083] After obtaining the signal estimate of the vehicle body sensor at the current moment, the original signal of the vehicle body sensor at the previous moment and the signal estimate at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment. In one embodiment, Kalman filtering can be used to fuse the original signal of the vehicle body sensor at the previous moment and the signal estimate at the current moment to obtain the fused signal value of the vehicle body sensor at the current moment.

[0084] It should be noted that Kalman filtering is the preferred type of fusion algorithm. When using Kalman filtering, three covariance matrices are involved: the measurement noise covariance matrix R, the process noise covariance matrix Q, and the observation error covariance matrix P. The measurement noise covariance matrix R and the process noise covariance matrix Q can be obtained through experimental measurements; the observation error covariance matrix P can be obtained iteratively, with its initial value set equal to the process noise covariance matrix Q. Furthermore, it should be noted that signal fusion methods include, but are not limited to, Kalman filtering algorithms; other sensor fusion algorithms such as multi-Bayes estimation can also be used. This invention does not limit the specific methods used.

[0085] Continuing with the above example, after obtaining the signal estimate of sensor 1 at time t2, the original signal of sensor 1 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 1 at time t2; after obtaining the signal estimate of sensor 2 at time t2, the original signal of sensor 2 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 2 at time t2; after obtaining the signal estimate of sensor 3 at time t2, the original signal of sensor 3 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 3 at time t2; after obtaining the signal estimate of sensor 4 at time t2, the original signal of sensor 4 at time t1 and the signal estimate at time t2 are fused to obtain the fused signal value of sensor 4 at time t2.

[0086] S50: Send the fused signal values ​​of all vehicle body sensors at the current moment to the vehicle motion estimation module so that the vehicle motion estimation module can estimate the vehicle motion state.

[0087] After obtaining the fused signal values ​​of all vehicle body sensors at the current moment, these values ​​are sent to the vehicle motion estimation module so that the module can estimate the vehicle's motion state. The motion state estimation process will not be described in detail here.

[0088] As can be seen, the embodiments of the present invention also provide a motion state estimation method, wherein the signal fusion value used for motion state estimation is obtained by the signal processing method of the present invention. Since the hysteresis and noise root mean square error of the signal fusion value are well balanced, the hysteresis is smaller when the noise root mean square value and the error are comparable; the noise root mean square value is smaller when the hysteresis and the error are comparable; and the error is smaller when the noise root mean square value and the hysteresis are comparable, which facilitates the rapid and accurate estimation of the vehicle motion state.

[0089] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0090] In one embodiment, a signal processing apparatus is provided, which corresponds one-to-one with the signal processing methods described in the above embodiments. For example... Figure 4 As shown, the signal processing device includes an acquisition module 101, an estimation module 102, a calculation module 103, and a fusion module 104. Detailed descriptions of each functional module are as follows:

[0091] The acquisition module 101 is used to acquire the original signal detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0092] The estimation module 102 is used to estimate the signal increment value of another vehicle body sensor of the same type per unit time based on the original signals detected by at least three of the multiple vehicle body sensors of the same type at the previous moment.

[0093] The calculation module 103 is used to calculate the signal estimate of the vehicle sensor at the current moment based on the original signal and the corresponding signal increment value of the vehicle sensor at the previous moment.

[0094] The fusion module 104 is used to fuse the original signal from the vehicle body sensor at the previous moment and the signal estimate at the current moment to obtain the fused signal value of the vehicle body sensor at the current moment.

[0095] In one embodiment, the estimation module 102 is specifically used to: estimate the signal increment value of another vehicle body sensor of the same type per unit time based on the original signals detected by any three vehicle body sensors of the same type at the previous time.

[0096] In one embodiment, the estimation module 102 is specifically used to: based on the vehicle body geometry and the original signals detected by any three vehicle body sensors of the same type at the previous moment, and the signal increment value of another vehicle body sensor of the same type per unit time.

[0097] In one embodiment, the calculation module 103 is specifically used to: add the original signal of the vehicle body sensor at the previous moment and the corresponding signal increment value to obtain the signal estimate of the vehicle body sensor at the current moment.

[0098] In one embodiment, the fusion module 104 is specifically used to: fuse the original signal of the vehicle body sensor at the previous moment and the signal estimate at the current moment using Kalman filtering to obtain the fused signal value of the vehicle body sensor at the current moment.

[0099] In one embodiment, a motion state estimation device is provided, which corresponds one-to-one with the motion state estimation methods described in the above embodiments. For example... Figure 5 As shown, the signal processing device includes an acquisition module 201, an estimation module 202, a calculation module 203, a fusion module 204, and a transmission module 205. Detailed descriptions of each functional module are as follows:

[0100] The acquisition module 201 is used to acquire the original signal detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0101] The estimation module 202 is used to estimate the signal increment value of another vehicle body sensor of the same type per unit time based on the original signals detected by at least three of the multiple vehicle body sensors of the same type at the previous moment.

[0102] The calculation module 203 is used to calculate the signal estimate of the vehicle body sensor at the current moment based on the original signal and the corresponding signal increment value of the vehicle body sensor at the previous moment.

[0103] The fusion module 204 is used to fuse the original signal from the vehicle body sensor at the previous moment and the signal estimate at the current moment to obtain the fused signal value of the vehicle body sensor at the current moment.

[0104] The transmitting module 205 is used to send the signal fusion values ​​corresponding to all vehicle body sensors at the current moment to the vehicle motion estimation module so that the vehicle motion estimation module can estimate the vehicle motion state.

[0105] For specific limitations regarding the signal processing device or motion state estimation device, please refer to the limitations on the signal processing method or motion state estimation method above, which will not be repeated here. Each module in the aforementioned signal processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the operations corresponding to each module.

[0106] In one embodiment, an in-vehicle device is provided, the internal structure of which can be shown in the following diagram. Figure 6As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external sensors via a network connection. When the computer program is executed by the processor, it implements a signal processing method or a motion state estimation method.

[0107] In one embodiment, an in-vehicle device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0108] Acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0109] Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time.

[0110] Based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment, calculate the estimated signal value of the vehicle body sensor at the current moment;

[0111] The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused together to obtain the fused signal value of the vehicle body sensor at the current moment.

[0112] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0113] Acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0114] Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time.

[0115] Based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment, calculate the estimated signal value of the vehicle body sensor at the current moment;

[0116] The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused together to obtain the fused signal value of the vehicle body sensor at the current moment.

[0117] In one embodiment, an in-vehicle device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0118] Acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0119] Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time.

[0120] Based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment, calculate the estimated signal value of the vehicle body sensor at the current moment;

[0121] The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment;

[0122] The fused signal values ​​of all vehicle body sensors at the current moment are sent to the vehicle motion estimation module so that the vehicle motion estimation module can estimate the vehicle's motion state.

[0123] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0124] Acquire the raw signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor.

[0125] Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time.

[0126] Based on the original signal and corresponding signal increment value of the vehicle body sensor at the previous moment, calculate the estimated signal value of the vehicle body sensor at the current moment;

[0127] The original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment are fused to obtain the fused signal value of the vehicle body sensor at the current moment;

[0128] The fused signal values ​​of all vehicle body sensors at the current moment are sent to the vehicle motion estimation module so that the vehicle motion estimation module can estimate the vehicle's motion state.

[0129] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0131] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A signal processing method, characterized in that, include: Acquire the raw signals detected by each body sensor on the vehicle at the previous moment, wherein the body sensors include different types of sensors, and each type of body sensor includes multiple sensors; Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time step. Based on the original signal of the vehicle body sensor at the previous moment and the corresponding signal increment value, calculate the estimated signal value of the vehicle body sensor at the current moment; The original signal from the previous moment and the estimated signal value at the current moment of the vehicle body sensor are fused to obtain the fused signal value of the vehicle body sensor at the current moment; The step of estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the original signals detected by at least three of the multiple vehicle body sensors of the same type at the previous moment includes: estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the vehicle body geometry and the original signals detected by any three of the multiple vehicle body sensors of the same type at the previous moment.

2. The signal processing method as described in claim 1, characterized in that, The step of calculating the estimated signal value of the vehicle body sensor at the current moment based on the original signal of the vehicle body sensor at the previous moment and the corresponding signal increment value includes: The original signal of the vehicle body sensor at the previous moment and the corresponding signal increment value are added together to obtain the signal estimate of the vehicle body sensor at the current moment.

3. The signal processing method as described in claim 1, characterized in that, The process of fusing the original signal from the vehicle body sensor at the previous moment and the estimated signal value at the current moment to obtain the fused signal value of the vehicle body sensor at the current moment includes: The Kalman filter is used to fuse the original signal from the previous moment and the estimated signal value at the current moment of the vehicle body sensor to obtain the fused signal value of the vehicle body sensor at the current moment.

4. A motion state estimation method, characterized in that, include: Acquire the raw signals detected by each body sensor on the vehicle at the previous moment, wherein the body sensors include different types of sensors, and each type of body sensor includes multiple sensors; Estimate the signal increment of another vehicle sensor of the same type per unit time based on the raw signals detected by at least three of the multiple identical vehicle sensors at the previous time step. Based on the original signal of the vehicle body sensor at the previous moment and the corresponding signal increment value, calculate the estimated signal value of the vehicle body sensor at the current moment; The original signal from the previous moment and the estimated signal value at the current moment of the vehicle body sensor are fused to obtain the fused signal value of the vehicle body sensor at the current moment; The fused signal values ​​corresponding to all vehicle body sensors at the current moment are sent to the vehicle motion estimation module so that the vehicle motion estimation module can estimate the vehicle motion state. The step of estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the original signals detected by at least three of the multiple vehicle body sensors of the same type at the previous moment includes: estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the vehicle body geometry and the original signals detected by any three of the multiple vehicle body sensors of the same type at the previous moment.

5. A signal processing apparatus, characterized in that, include: The acquisition module is used to acquire the original signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor. The estimation module is used to estimate the signal increment value of another vehicle body sensor of the same type per unit time based on the raw signals detected by at least three of the multiple vehicle body sensors of the same type at the previous time. The calculation module is used to calculate the estimated signal value of the vehicle body sensor at the current moment based on the original signal of the vehicle body sensor at the previous moment and the corresponding signal increment value; The fusion module is used to fuse the original signal from the previous moment and the signal estimate from the current moment of the vehicle body sensor to obtain the fused signal value of the vehicle body sensor at the current moment. The step of estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the original signals detected by at least three of the multiple vehicle body sensors of the same type at the previous moment includes: estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the vehicle body geometry and the original signals detected by any three of the multiple vehicle body sensors of the same type at the previous moment.

6. A motion state estimation device, characterized in that, include: The acquisition module is used to acquire the original signals detected by each body sensor on the vehicle at the previous moment. The body sensors include different types of sensors, and there are multiple of each type of body sensor. The estimation module is used to estimate the signal increment value of another vehicle body sensor of the same type per unit time based on the raw signals detected by at least three of the multiple vehicle body sensors of the same type at the previous time. The calculation module is used to calculate the estimated signal value of the vehicle body sensor at the current moment based on the original signal of the vehicle body sensor at the previous moment and the corresponding signal increment value; The fusion module is used to fuse the original signal from the previous moment and the signal estimate from the current moment of the vehicle body sensor to obtain the fused signal value of the vehicle body sensor at the current moment. The transmitting module is used to send the signal fusion values ​​corresponding to all vehicle body sensors at the current moment to the vehicle motion estimation module, so that the vehicle motion estimation module can estimate the vehicle motion state. The step of estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the original signals detected by at least three of the multiple vehicle body sensors of the same type at the previous moment includes: estimating the signal increment value of another vehicle body sensor of the same type per unit time based on the vehicle body geometry and the original signals detected by any three of the multiple vehicle body sensors of the same type at the previous moment.

7. An in-vehicle device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the signal processing method as described in any one of claims 1 to 3, or the steps of the motion state estimation method as described in claim 4.

8. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the signal processing method as described in any one of claims 1 to 3, or the steps of the motion state estimation method as described in claim 4.

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