Method and device for adjusting acquisition frequency of sensor in vehicle driving state

Abstract

The invention relates to the technical field of vehicle driving detection, and discloses a method and device for adjusting the acquisition frequency of a sensor in a vehicle driving state, and the method is beneficial to flexibly adjusting the current acquisition frequency of a target sensor by obtaining a data dynamic adjustment result of the target sensor, facilitates the timely adaptation to a rapidly changing external environment, and improves the detection accuracy. And therefore, the driving system can adjust power output or other key control parameters in time. Meanwhile, the situation that under the condition that the actual working condition of the vehicle is stable, due to the fact that a large amount of similar data is collected, data redundancy is likely to be caused, and the calculation burden of the driving system is increased can be avoided, and therefore the overall efficiency of the driving system is improved. Besides, in combination with a data dynamic adjustment result and by calculating a multi-sensor cooperative detection result, cooperative calculation and information sharing among multiple sensors can be realized, so that the efficiency of the whole system and the effectiveness of data processing are improved, and finally, the driving system can make efficient intelligent response in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle driving detection, and in particular to a method and device for adjusting a sensor acquisition frequency when a vehicle is driving. Background Art

[0002] When a vehicle is driving, it often uses various detection data of the vehicle to perform power output or intelligent control. For example, the power output is adjusted through parameters such as the vehicle's speed, torque, and acceleration. When the external environment changes, it will also affect the vehicle's drive system. For example, when the temperature, slope, and traffic flow change, the normal driving of the vehicle will be affected. Therefore, testing various types of vehicle detection data while the vehicle is driving is conducive to analyzing the driving performance of the vehicle while driving.

[0003] In the related art, data is collected separately at a fixed frequency to analyze the driving state of the vehicle when the vehicle is in motion. This method collects data in a fixed frequency collection mode, which means that no matter how the external environment and vehicle state change, data is collected at a fixed frequency. When the actual working condition of the vehicle is stable, the collection of a large amount of similar data is likely to cause data redundancy and increase the calculation burden of the drive system, thereby affecting the overall efficiency of the drive system. For example, when a vehicle is driving at a constant speed on a flat road, continuously collecting data at a high frequency will not be conducive to efficient analysis of the current state of the vehicle, but will waste computing resources. In complex road conditions or extreme weather conditions, if the fixed frequency collection mode cannot respond to the rapid changes in the external environment in a timely manner, the drive system will not be able to adjust the power output or other key control parameters in time. For example, when it rains suddenly or encounters a change in slope, the system fails to prioritize the processing of relevant data, resulting in insufficient real-time performance of the drive system, which in turn affects the safety and performance of the vehicle. Summary of the invention

[0004] In view of this, the present invention provides a method and device for adjusting the sensor acquisition frequency when the vehicle is in motion, so as to solve the problem that detection data is collected at a fixed frequency, the detection frequency cannot be flexibly adjusted as the actual driving conditions change, which easily affects the overall efficiency and real-time performance of the drive system, and ultimately affects the safety and performance of the vehicle.

[0005] According to a first aspect, an embodiment of the present disclosure provides a method for adjusting a sensor acquisition frequency when a vehicle is in motion, the method comprising:

[0006] Obtain the data detection results of multiple sensors of the vehicle in the current driving state and the current collection frequency of multiple sensors within a preset time;

[0007] Based on the data detection result of the target sensor and the current acquisition frequency of the target sensor, the current data change rate of the target sensor is obtained; wherein the target sensor is any one of the multiple sensors;

[0008] Calculate the dynamic adjustment result of the data of the target sensor based on the current acquisition frequency of the target sensor and the current data change rate of the target sensor;

[0009] Based on the data dynamic adjustment results, the current acquisition frequency of the target sensor is adjusted.

[0010] The disclosed embodiment obtains the data of the target sensor and dynamically adjusts the result, which is beneficial to flexibly adjust the current acquisition frequency of the target sensor, so as to timely adapt to the rapidly changing external environment, thereby enabling the drive system to timely adjust the power output or other key control parameters. At the same time, it can also avoid data redundancy and increase the calculation burden of the drive system due to the collection of a large amount of similar data when the actual working condition of the vehicle is stable, thereby significantly improving the overall efficiency of the drive system.

[0011] In some optional embodiments, the multiple sensors include: a first sensor for detecting vehicle operation data, a second sensor for detecting vehicle road condition data, a third sensor for detecting traffic data, a fourth sensor for detecting driving environment data,

[0012] The fifth sensor is used to detect driver operation data.

[0013] In some optional implementations, the dynamic adjustment result of the data of the target sensor is calculated by the following formula;

[0014]

[0015] Among them, f sensor (t) is the current acquisition frequency of the target sensor, ΔC s (t) is the current data change rate of the target sensor, ΔC th is the preset threshold, f high is the first acquisition frequency, f low is the second acquisition frequency.

[0016] In some optional implementations, the method for adjusting the sensor acquisition frequency when the vehicle is in motion further includes:

[0017] Based on the data detection results of multiple sensors and the weight parameters of multiple sensors, the multi-sensor collaborative detection results are calculated;

[0018] Based on the data dynamic adjustment results and multi-sensor collaborative detection results, the data sharing results of multiple sensors are obtained.

[0019] The disclosed embodiment combines the data dynamic adjustment results and calculates the multi-sensor collaborative detection results, which is conducive to realizing collaborative computing and information sharing among multiple sensors, thereby improving the efficiency of the overall system and the effectiveness of data processing, and ultimately enabling the drive system to make efficient and intelligent responses in complex environments.

[0020] In some optional implementations, the multi-sensor collaborative detection result is calculated by the following formula:

[0021]

[0022] Among them, O sync (t) is the multi-sensor collaborative detection parameter, w i is the weight parameter of the i-th sensor, R i (t) is the detection result of the i-th sensor, and n is the total number of sensors.

[0023] In some optional implementations, based on the data dynamic adjustment result and the multi-sensor collaborative detection result, the data sharing results of multiple sensors are obtained, including:

[0024] Based on the data dynamic adjustment result, the current acquisition frequency of the dynamically adjusted target sensor is obtained;

[0025] If the current acquisition frequency of the target sensor after dynamic adjustment remains stable within the preset time, the data detection result of the target sensor after multi-sensor coordination is obtained in the stable state;

[0026] The data detection results of the target sensor after multi-sensor collaboration are used as the data sharing results of multiple sensors.

[0027] According to a second aspect, an embodiment of the present disclosure provides a device for adjusting a sensor acquisition frequency when a vehicle is in motion, the device comprising:

[0028] A first acquisition module is used to acquire data detection results of multiple sensors of the vehicle in the current driving state and current acquisition frequencies of the multiple sensors within a preset time;

[0029] A second acquisition module is used to acquire a current data change rate of the target sensor based on a data detection result of the target sensor and a current acquisition frequency of the target sensor; wherein the target sensor is any one of a plurality of sensors;

[0030] A first calculation module, used for calculating a data dynamic adjustment result of the target sensor based on a current acquisition frequency of the target sensor and a current data change rate of the target sensor;

[0031] The frequency adjustment module is used to adjust the current acquisition frequency of the target sensor based on the dynamic adjustment result of the frequency adjustment module.

[0032] According to a third aspect, an embodiment of the present disclosure provides a computer device, including:

[0033] The memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method of adjusting the sensor acquisition frequency under vehicle driving state in the first aspect or any embodiment of the first aspect by executing the computer instructions.

[0034] According to the fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the method of adjusting the sensor acquisition frequency under the driving state of a vehicle in the first aspect or any embodiment of the first aspect.

[0035] According to a fifth aspect, an embodiment of the present disclosure provides a computer program product, including computer instructions, which are used to enable a computer to execute the method of adjusting the sensor acquisition frequency under a vehicle driving state in the first aspect or any embodiment of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 is a flow chart of a method for adjusting a sensor acquisition frequency when a vehicle is in motion according to an embodiment of the present invention;

[0038] Figure 2 is another simple block diagram for real-time collection of current vehicle detection data in multiple driving scenarios according to an embodiment of the present invention;

[0039] Figure 3 is a simple structural block diagram of adjusting the sensor acquisition frequency when the vehicle is in motion according to an embodiment of the present invention;

[0040] Figure 4 is a flow chart of another method for adjusting the sensor acquisition frequency when a vehicle is in motion according to an embodiment of the present invention;

[0041] Figure 5is a structural block diagram of a device for adjusting a sensor acquisition frequency when a vehicle is in motion according to an embodiment of the present invention;

[0042] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0044] In this embodiment, a method for adjusting the sensor acquisition frequency when a vehicle is in motion is provided, which can be used in computer devices such as mobile phones, tablet computers, desktop computers, portable notebooks, servers, etc. Figure 1 is a flow chart of a method for adjusting the sensor acquisition frequency in a vehicle driving state according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0045] Step S101, obtaining data detection results of multiple sensors of the vehicle in the current driving state and current acquisition frequencies of the multiple sensors within a preset time.

[0046] Specifically, the current driving state is a state corresponding to the vehicle being in a driving scenario, and the preset time may be a detection time set for turning on a sensor detection mode in the current driving state.

[0047] In a specific example, the multiple sensors include: a first sensor for detecting vehicle operation data, a second sensor for detecting vehicle road condition data, a third sensor for detecting traffic data, a fourth sensor for detecting driving environment data, and a fifth sensor for detecting driver operation data.

[0048] Exemplarily, the first type of sensor includes: an acceleration sensor, a vehicle speed sensor, and a motor speed sensor, wherein the acceleration sensor is used to detect vehicle acceleration, and the speed sensor is used to detect vehicle speed, wherein the acceleration sensor transmits data to the electronic control unit via the CAN bus on the vehicle chassis to ensure that the vehicle speed sensor synchronously collects the vehicle speed. The motor speed sensor monitors the speed of the engine and motor, and reflects it to the data monitoring system in real time, and monitors the current driving force and battery power. The data detection results detected by the above-mentioned first type of sensor belong to vehicle operation data. For example, the acceleration Where v(t) is the vehicle speed and t is the preset time. The motor speed sensor collects the engine and motor speed n(t) and is used to monitor the engine speed and motor speed in real time, reflecting the current driving force F of the vehicle. d (t) and battery charge E b (t), F d (t) = T(t) × r(t), where T(t) is the torque and r(t) is the radius.

[0049] Exemplarily, the second type of sensor includes: a gyroscope sensor, a GPS module, a camera, a laser radar sensor, and a millimeter wave radar sensor, wherein the gyroscope sensor is used to monitor the tilt angle of the vehicle at different slopes, the GPS module is used to record the geographic location information of the vehicle, and in combination with the geographic location information, the road type is determined by a road type determination algorithm, and the camera, the laser radar sensor, and the millimeter wave radar sensor are used to obtain road visual information (slope, marking line, obstacle distance). The data detection results detected by the second type of sensor above belong to vehicle road condition data.

[0050] In a specific example, the road type determination algorithm is expressed by the following formula:

[0051] O r (t) = f(G(x, y, z, t), T road (x,y),I vision (t))

[0052] Among them, O r (t) is the road type judgment operator, G(x, y, z, t) is the geographical location information collected by the GPS module (generally providing the current coordinates of the vehicle), T rood (x, y) is the road type extracted through geographic location information, I vision (t) is the road visual information (slope, marking line, obstacle distance).

[0053] Among them, α i is the weight of the camera or lidar sensor or millimeter wave radar sensor, S i (t) is the data detection result (slope, lane marking, obstacle distance) of the camera, lidar sensor, or millimeter wave radar sensor.

[0054] By integrating the above information, combined with the current road type and the vehicle's inclination angle and slope, the flatness of the road or the slope change can be further determined, which is specifically expressed by the following formula:

[0055]

[0056] Among them, h current The current altitude, h previous is the altitude of the previous moment adjacent to the current moment, d rood is the distance, θ vision (t) is the lane slope change value calculated by the camera based on the slope, θ road (t) is the change value of the road, and w is the weight factor, which is used to balance the impact of visual information on road condition judgment.

[0057] Exemplarily, the third sensor includes: a sensor for detecting traffic flow and a sensor for detecting traffic light data, and the traffic flow is estimated by the following formula: Among them, N vehicles (t) is the unit road area A at the current moment lane The data detection results of the third type of sensor mentioned above belong to traffic data.

[0058] Exemplarily, the fourth type of sensor includes: a temperature sensor, a humidity sensor, a wind direction sensor, and a sunlight intensity sensor. In addition, a friction coefficient sensor for detecting the friction coefficient may also be included. The temperature sensor is used to detect the current ambient temperature T e (t), the humidity sensor is used to detect the current ambient humidity H(t), and the wind direction sensor is used to detect the current ambient wind direction D w (t), the sunlight intensity sensor is used to detect the sunlight intensity S(t) of the current environment. The data detection result detected by the fourth sensor belongs to the driving environment data.

[0059] Exemplarily, the fifth sensor includes: a sensor for detecting the accelerator pedal depth pt h (t) sensor, used to detect the force f th (t) sensor, used to detect the braking frequency f b (t) sensor, used to detect the force f br (t) sensor, representing the driver's operating intention I d (t), calculated by the following formula: d (t) = w1 × p th (t)+w2×f th (t)-w3×f b (t)-w4×f br (t), where w1, w2, w3, and w4 are weight coefficients. The data detected by the fourth sensor described above belongs to the driver's operation data.

[0060] The disclosed embodiments obtain data detection results of the vehicle in the current driving state through the above-mentioned various types of multiple sensors, so as to timely know the current operating status of the vehicle in the current driving state, the current road conditions, the current traffic flow conditions, and the current operation status of the driver.

[0061] Step S102, based on the data detection result of the target sensor and the current acquisition frequency of the target sensor, the current data change rate of the target sensor is obtained; wherein the target sensor is any one of a plurality of sensors.

[0062] Specifically, for example, the target sensor is the first sensor among multiple sensors, and the current acquisition frequency of the first sensor is 5 times every 10 minutes. The current data change rate of the first sensor is obtained based on the 5 data detection results of the first sensor. For example, the first sensor is a specific vehicle speed sensor. The vehicle speed of the first detection is 30Km / h, the vehicle speed of the second detection is 31Km / h, the vehicle speed of the third detection is 30Km / h, the vehicle speed of the fourth detection is 33Km / h, and the vehicle speed of the fifth detection is 32Km / h. The change between the first and second times is 1, the change between the second and third times is 1, the change between the third and fourth times is 3, and the change between the fourth and fifth times is 1. The current data change rate of the target sensor is ΔC s (t) = (1 + 1 + 3 + 1) / 4 = 1.5.

[0063] Step S103: Calculate the dynamic adjustment result of the data of the target sensor based on the current acquisition frequency of the target sensor and the current data change rate of the target sensor.

[0064] Step S104: adjusting the current acquisition frequency of the target sensor based on the data dynamic adjustment result.

[0065] Specifically, since the environment may suddenly change during the driving process of the vehicle, for example, it may suddenly rain or go on a slope or highway. Taking the example of a sudden rainy day, if the current ambient humidity is collected at a fixed frequency through the driving environment sensor, it cannot adapt to the changes in the external rainy environment in time, resulting in the drive system being unable to adjust the power output or other key control parameters in time. If the sensor has been collecting data at a fixed frequency when the actual working condition of the vehicle is stable, the collection of a large amount of similar data may easily cause data redundancy and increase the calculation burden of the drive system, thereby affecting the overall efficiency of the drive system. In addition, when multiple sensors work together at the same time, collecting data at a fixed frequency for all sensors will increase system delays and resource consumption. Therefore, it is necessary to flexibly adjust the current collection frequency of the target sensor in combination with the actual driving situation.

[0066] In some optional implementations, the dynamic adjustment result of the data of the target sensor is calculated by the following formula;

[0067]

[0068] Among them, f sensor (t) is the current acquisition frequency of the target sensor, ΔC s (t) is the current data change rate of the target sensor, ΔC th is the preset threshold, f high is the first acquisition frequency, f low The first acquisition frequency may be a high frequency, the second acquisition frequency may be a low frequency, and the target sensor may be any one of a plurality of sensors.

[0069] For example, in heavy rain, the friction coefficient of the road affects the driving performance of the vehicle. Therefore, the data priority of the friction coefficient sensor in the fourth sensor used to detect the driving environment data will be increased, usually ΔC s (t)≥ΔC th , according to f sensor (t) = f high , ensuring data collection at a high frequency. When the air humidity has little impact on the vehicle, the data priority of the humidity sensor in the fourth sensor used to detect the driving environment data will be reduced, ΔC s (t)<ΔC th , according to f sensor =f low . Ensure data is collected at a low frequency, thereby saving system resources and avoiding redundant transmission of irrelevant data.

[0070] For another example, when a vehicle is traveling at a constant speed on a flat road, if data is collected at a specific high frequency all the time, data resources will be wasted. At this time, the data priority of the speed sensor in the first sensor used to detect vehicle operation data will be reduced, ΔC s (t)<ΔC th , according to f sensor =f low , ensuring that data is collected at a low frequency, thereby saving system resources and avoiding redundant transmission of irrelevant data.

[0071] In this embodiment, a method for adjusting the sensor acquisition frequency when a vehicle is in motion is provided, which can be used in computer devices such as mobile phones, tablet computers, desktop computers, portable notebooks, servers, etc. Figure 2 is a flow chart of a method for adjusting the sensor acquisition frequency in a vehicle driving state according to an embodiment of the present invention. Figure 2 As shown, the process also includes the following steps:

[0072] Step 201 : Calculate the multi-sensor collaborative detection result based on the data detection results of the multiple sensors and the weight parameters of the multiple sensors.

[0073] In some optional implementations, the multi-sensor collaborative detection result is calculated by the following formula:

[0074]

[0075] Among them, 0 sync (t) is the multi-sensor collaborative detection parameter, w i is the weight parameter of the i-th sensor, R i (t) is the detection result of the i-th sensor, and n is the total number of sensors.

[0076] When the data detection result of a certain sensor is in a stable state during the driving process of the vehicle, the data detection result of the sensor can be shared with other sensors, thereby avoiding repeated collection of interrelated data.

[0077] Step 202: based on the data dynamic adjustment result and the multi-sensor collaborative detection result, the data sharing results of the multiple sensors are obtained.

[0078] In a specific example, the above step S202, based on the data dynamic adjustment result and the multi-sensor collaborative detection result, obtains the data sharing results of multiple sensors, including:

[0079] Step a1, based on the data dynamic adjustment result, obtain the current acquisition frequency of the target sensor after dynamic adjustment.

[0080] Specifically, for example, in heavy rain, the friction coefficient of the road affects the driving performance of the vehicle, so the data priority of the friction coefficient sensor in the fourth sensor for detecting the driving environment data is increased, usually ΔC s (t)≥ΔC th , according to f sensor (t) = f high , ensuring that data is collected at a high frequency. At this time, the data of the target sensor is dynamically adjusted to f sensor (t) = f high , the current acquisition frequency of the target sensor after dynamic adjustment is f high When the air humidity has little effect on the vehicle, the data priority of the humidity sensor in the fourth sensor for detecting the driving environment data will be reduced, ΔC s (t)<ΔC th , according to f sensor =f lowAt this time, the dynamic adjustment result of the target sensor data is f sensor (t) = f low .

[0081] Step a2: if the current acquisition frequency of the target sensor after dynamic adjustment remains stable within a preset time, the data detection result of the target sensor after multi-sensor coordination is obtained in a stable state;

[0082] Step a3, using the data detection result of the target sensor after multi-sensor collaboration as the data sharing result of the multiple sensors.

[0083] Specifically, for example, when the vehicle is driving uphill, road visual information is obtained through the camera, lidar sensor, and millimeter-wave radar sensor in the second sensor. Since the camera, lidar sensor, and millimeter-wave sensor can all detect road visual information, when the current acquisition frequency of the target sensor after dynamic adjustment of the camera remains stable within the preset time, the data detection results of the camera can be used as the data sharing results of multiple sensors.

[0084] The disclosed embodiment obtains the data dynamic adjustment result of the target sensor, which is conducive to flexibly adjusting the current acquisition frequency of the target sensor, facilitating timely adaptation to the rapid changes of the external environment, thereby enabling the drive system to adjust the power output or other key control parameters in a timely manner. At the same time, it can also avoid the situation where the actual working condition of the vehicle is stable, and the data redundancy and increase in the calculation burden of the drive system due to the collection of a large amount of similar data are easily caused, thereby significantly improving the overall efficiency of the drive system. In addition, combined with the data dynamic adjustment result, and by calculating the collaborative detection results of multiple sensors, it is conducive to the realization of collaborative calculation and information sharing between multiple sensors, thereby improving the efficiency of the overall system and the effectiveness of data processing, and ultimately enabling the drive system to make efficient intelligent responses in complex environments.

[0085] like Figure 3 As shown, it is a simple block diagram of the vehicle in the embodiment of the present disclosure collecting the current vehicle detection data in real time under multiple driving scenarios. Figure 3 In the current driving state, the vehicle collects data detection results of multiple sensors in real time, mainly including five types of detection data, namely vehicle operation data, vehicle road condition data, traffic data, driving environment data, and driver operation data. These five types of data detection results are pre-processed and optimized. After the optimization process, the data sharing results of multiple sensors are obtained based on the data dynamic adjustment results and multi-sensor collaborative detection results.

[0086] In this embodiment, a method for adjusting the sensor acquisition frequency when a vehicle is in motion is provided, which can be used in computer devices such as mobile phones, tablet computers, desktop computers, portable notebooks, servers, etc. Figure 4 is a flow chart of a method for adjusting the sensor acquisition frequency in a vehicle driving state according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0087] Step S401: pre-processing the data detection results of various sensors.

[0088] In some optional implementations, preprocessing the data detection results of multiple sensors includes:

[0089] Step b1, preprocessing the data detection results of various sensors.

[0090] In a specific example, the preprocessing method includes an outlier processing method and a missing value filling processing method.

[0091] Specifically, because the vehicle may be affected by environmental interference or hardware errors during driving, unreasonable abnormal values ​​may appear in the sensor data detection results. For example, the temperature sensor may have a sudden change in a stable environment. Such abnormal data needs to be identified and filtered.

[0092] In a specific example, the outlier processing method is to use the mean standard deviation method to identify outliers, and the mean standard deviation method is expressed by the following formula:

[0093]

[0094] When Z i (t) exceeds a predetermined threshold (|Z i (t)|>3), the data is considered as an outlier and is processed.

[0095] In a specific example, the missing value filling processing method is to use linear interpolation to fill in the missing values, and the linear interpolation method is expressed by the following formula:

[0096]

[0097] Among them, X i (t prev ) is the normal data point before the missing value, X i (t next ) is the normal data point after the missing value, t prev is the time point before the missing value, t next is the time point corresponding to the missing value, and t is the current time point.

[0098] Step b2: reprocessing the preprocessed data detection results of the various sensors according to the adaptive Kalman filter algorithm.

[0099] In a specific example, the adaptive Kalman filter algorithm is implemented by the following formula:

[0100]

[0101] K(t)=P(t|t-1)H T (HP(t|t-1)H T +R(t)) -1

[0102]

[0103] in, is the state estimation matrix of the i-th sensor at time t-1, F is the state transfer matrix of the i-th sensor at time t-1, Bu(t) is the control input matrix at time t, K(t) is the Kalman gain matrix at time t, P is the covariance matrix of the i-th sensor at time t-1, H T is the observation matrix, R(t) is the observation noise covariance matrix, is the state estimation matrix of the i-th sensor at time t, X i (t) is the prediction result of the i-th sensor at time t.

[0104] Specifically, in the above formula, the state estimation matrix at the next moment is predicted by the state transfer matrix F and the control input matrix Bu(t), and further based on the error covariance matrix P and the observation matrix H T , observe the noise covariance matrix R(t), calculate the Kalman gain K(t), and use the Kalman gain K(t) to update the current state estimation matrix To make it closer to the actual observation value, in the disclosed embodiment, the Kalman gain is adjusted in real time through the adaptive Kalman filter algorithm to achieve dynamic adjustment of the sensor's data detection results. The error covariance matrix is ​​used to estimate the difference between the current observation value and the predicted value, and to correct the sensor's data detection results based on the difference, so that the updated estimate is closer to the actual data, and finally achieves a data optimization prediction effect. For example, when the sensor cry noise is small or the data is relatively stable, the Kalman gain K(t) will decrease, reducing the current amplitude of the sensor to adapt to the current changes; for example, when the noise is large or the data fluctuates significantly, the Kalman gain K(t) will increase, reducing the current amplitude of the sensor to adapt to the current changes.

[0105] Step S402, obtaining acceleration and velocity from data detection results of various sensors.

[0106] Step S403: Create physical constraints of acceleration and velocity using the following formula.

[0107] v min ≤v(t)≤v max

[0108] a min ≤a(t)≤a max

[0109] Step S404: Based on the physical constraints of acceleration and velocity, a preset optimization algorithm is expressed by the following formula.

[0110]

[0111] Among them, V min is the minimum speed value, V max is the first speed threshold, V max is the second speed threshold, v(t) is the speed at time t, a min is the first acceleration threshold, a max The second acceleration threshold, v(t) is the acceleration at time t;

[0112] Step S405: optimizing the filtered data detection results of the various sensors according to a preset optimization algorithm.

[0113] Specifically, since the speed and acceleration of a vehicle are directly related to its dynamic performance during driving, physical constraints of acceleration and speed are created. The preset optimization algorithm ensures that the filtered data is more consistent with the actual operation of the vehicle, thereby improving the accuracy of the data detection results of multiple sensors.

[0114] In this embodiment, a device for adjusting the sensor acquisition frequency when the vehicle is in motion is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0115] This embodiment provides a device for adjusting the sensor acquisition frequency when the vehicle is in motion. Figure 5 As shown, including:

[0116] The first acquisition module 501 is used to acquire the data detection results of multiple sensors of the vehicle in the current driving state and the current acquisition frequency of the multiple sensors within a preset time;

[0117] The second acquisition module 502 is used to acquire the current data change rate of the target sensor based on the data detection result of the target sensor and the current acquisition frequency of the target sensor; wherein the target sensor is any one of a plurality of sensors;

[0118] A first calculation module 503, configured to calculate a data dynamic adjustment result of the target sensor based on a current acquisition frequency of the target sensor and a current data change rate of the target sensor;

[0119] The frequency adjustment module 504 is used for the frequency adjustment module to adjust the current acquisition frequency of the target sensor based on the dynamic adjustment result of the data.

[0120] In some optional embodiments, the multiple sensors include: a first sensor for detecting vehicle operation data, a second sensor for detecting vehicle road condition data, a third sensor for detecting traffic data, a fourth sensor for detecting driving environment data, and a fifth sensor for detecting driver operation data.

[0121] In some optional implementations, the dynamic adjustment result of the data of the target sensor is calculated by the following formula;

[0122]

[0123] Among them, f sensor (t) is the current acquisition frequency of the target sensor, ΔC s (t) is the current data change rate of the target sensor, ΔC th is the preset threshold, f high is the first acquisition frequency, f low is the second acquisition frequency.

[0124] In some optional implementations, the device for adjusting the sensor acquisition frequency when the vehicle is in motion further includes:

[0125] The second calculation module is used to calculate the multi-sensor collaborative detection result based on the data detection results of the multiple sensors and the weight parameters of the multiple sensors.

[0126] The third acquisition module is used to acquire data sharing results of multiple sensors based on the data dynamic adjustment results and the multi-sensor collaborative detection results.

[0127] In some optional implementations, the multi-sensor collaborative detection result is calculated by the following formula:

[0128]

[0129] Among them, O sync (t) is the multi-sensor collaborative detection parameter, w iis the weight parameter of the i-th sensor, R i (t) is the detection result of the i-th sensor, and n is the total number of sensors.

[0130] In some optional implementations, the third acquisition module includes:

[0131] A first acquisition submodule is used to acquire the current acquisition frequency of the target sensor after dynamic adjustment based on the data dynamic adjustment result;

[0132] The second acquisition submodule is used to acquire the data detection result of the target sensor after multi-sensor coordination in a stable state if the current acquisition frequency of the target sensor after dynamic adjustment remains stable within a preset time;

[0133] The data determination module is used to use the data detection results of the target sensor after multi-sensor collaboration as the data sharing results of multiple sensors.

[0134] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0135] The device for adjusting the sensor acquisition frequency when the vehicle is in motion in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0136] An embodiment of the present invention further provides a computer device having the above-mentioned device for adjusting the sensor acquisition frequency when the vehicle is in motion.

[0137] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0138] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0139] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0140] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0141] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0142] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0143] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0144] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0145] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for adjusting the sensor acquisition frequency when a vehicle is in motion, characterized in that: The method comprises: Obtain the data detection results of multiple sensors of the vehicle in the current driving state and the current collection frequency of multiple sensors within a preset time; Based on the data detection result of the target sensor and the current acquisition frequency of the target sensor, the current data change rate of the target sensor is acquired; wherein the target sensor is any one of a plurality of sensors; Calculating a dynamic adjustment result of the data of the target sensor based on a current acquisition frequency of the target sensor and a current data change rate of the target sensor; Based on the data dynamic adjustment result, the current acquisition frequency of the target sensor is adjusted.

2. The method according to claim 1, characterized in that The multiple sensors include: a first sensor for detecting vehicle operation data, a second sensor for detecting vehicle road condition data, a third sensor for detecting traffic data, a fourth sensor for detecting driving environment data, and a fifth sensor for detecting driver operation data.

3. The method according to claim 1 or 2, characterized in that: The dynamic adjustment result of the data of the target sensor is calculated by the following formula; Among them, f sensor (t) is the current acquisition frequency of the target sensor, ΔC s (t) is the current data change rate of the target sensor, ΔC th is the preset threshold, f high is the first acquisition frequency, f low is the second acquisition frequency.

4. The method according to claim 1 or 2, characterized in that: Also includes: Based on the data detection results of multiple sensors and the weight parameters of multiple sensors, the multi-sensor collaborative detection results are calculated; Based on the data dynamic adjustment result and the multi-sensor collaborative detection result, the data sharing results of the multiple sensors are obtained.

5. The method according to claim 4, characterized in that The multi-sensor collaborative detection results are calculated by the following formula; Among them, O sync (t) is the multi-sensor collaborative detection parameter, w i is the weight parameter of the i-th sensor, R i (t) is the detection result of the i-th sensor, and n is the total number of sensors.

6. The method according to claim 4, characterized in that Based on the data dynamic adjustment result and the multi-sensor collaborative detection result, obtaining the data sharing results of the multiple sensors includes: Based on the data dynamic adjustment result, obtaining the current acquisition frequency of the target sensor after dynamic adjustment; If the current acquisition frequency of the target sensor after dynamic adjustment remains stable within a preset time, the data detection result of the target sensor after multi-sensor collaboration is obtained in the stable state; The data detection result of the target sensor after multi-sensor collaboration is used as the data sharing result of the multiple sensors.

7. A device for adjusting the sensor acquisition frequency when a vehicle is in motion, characterized in that: The device comprises: A first acquisition module is used to acquire data detection results of multiple sensors of the vehicle in the current driving state and current acquisition frequencies of the multiple sensors within a preset time; a second acquisition module, configured to acquire a current data change rate of the target sensor based on a data detection result of the target sensor and a current acquisition frequency of the target sensor; wherein the target sensor is any one of a plurality of sensors; A first calculation module, configured to calculate a dynamic adjustment result of the data of the target sensor based on a current acquisition frequency of the target sensor and a current data change rate of the target sensor; The frequency adjustment module is used to adjust the current acquisition frequency of the target sensor based on the dynamic adjustment result of the data.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for adjusting the sensor acquisition frequency when the vehicle is in motion as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for adjusting the sensor acquisition frequency under a vehicle driving state according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to cause a computer to execute the method for adjusting the sensor acquisition frequency under the driving state of the vehicle according to any one of claims 1 to 6.

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