Method and device for detecting motion state of vehicle, storage medium and vehicle

Abstract

The invention discloses a method and device for detecting the motion state of a vehicle, a storage medium and the vehicle, and the method comprises the steps: obtaining the time domain data of the vehicle, the time domain data comprises the first acceleration data of the vehicle in a static state and the current second acceleration data of the vehicle; converting the time domain data into frequency domain data, wherein the frequency domain data comprises first frequency domain data corresponding to the first acceleration data and second frequency domain data corresponding to the second acceleration data; under the condition that the first frequency domain data meets a preset condition, determining the motion state of the vehicle according to the second frequency domain data; and under the condition that the first frequency domain data does not meet the preset condition, the current motion state of the vehicle is determined according to the second acceleration data. According to the scheme, the motion state of the vehicle can be accurately detected, so that a stable and reliable navigation positioning result can be provided in an urban complex environment or a GNSS denial environment.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle detection, and particularly to a method, device, storage medium and vehicle for detecting the motion state of a vehicle. Background Art

[0002] In recent years, with the rapid development of global satellite navigation systems, inertial devices and multi-sensor fusion technologies, the positioning accuracy and robustness of integrated navigation systems in dynamic environments have also been significantly improved. However, in complex urban environments or GNSS-denied environments (such as underground garages), there are errors in the zero bias of inertial devices, which seriously restricts the stability and reliability of integrated navigation systems. Zero velocity constraints can effectively suppress the zero bias drift problem of inertial devices, but this requires accurate detection of the current motion state of the vehicle. At present, traditional stationary detection methods have a large number of false detections for electric vehicles with small vibrations in low-speed straight scenarios, and there is no obvious difference in the statistical values of IMU data for fuel vehicles with large vibrations in stationary and low-speed motion states. Traditional methods have large errors in detecting the stationary state of vehicles and cannot provide stable and reliable navigation and positioning results in complex environments. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, storage medium and vehicle for detecting the motion state of a vehicle, so as to solve the problem in the prior art that the current motion state of the vehicle cannot be accurately detected.

[0004] To achieve the above purpose, the first aspect of the present application provides a method for detecting the motion state of a vehicle, the method including: Obtaining time-domain data of the vehicle, the time-domain data including first acceleration data when the vehicle is in a stationary state and second acceleration data of the vehicle currently; Converting the time-domain data into frequency-domain data, the frequency-domain data including first frequency-domain data corresponding to the first acceleration data and second frequency-domain data corresponding to the second acceleration data; Determining the motion state of the vehicle according to the second frequency-domain data when the first frequency-domain data meets a preset condition; Determining the current motion state of the vehicle according to the second acceleration data when the first frequency-domain data does not meet the preset condition.

[0005] In the embodiments of the present application, the first frequency-domain data includes a plurality of corresponding first power spectral densities and first frequency values, and the method further includes: determining that the first frequency-domain data meets the preset condition when the maximum value among all the first power spectral densities is greater than the power spectral density threshold and the difference between the first frequency value corresponding to the maximum value and the cut-off frequency is less than the frequency difference threshold.

[0006] In an embodiment of the present application, determining the current motion state of the vehicle according to the second acceleration data includes: dividing the second acceleration data into multiple first signal segments through each first time window; determining the range of the y-axis of the accelerometer for each first signal segment; when the ranges of the y-axis of the accelerometers of all first signal segments are less than the stationary detection threshold corresponding to the first time window, determining that the current motion state of the vehicle is a stationary state; when the range of the y-axis of the accelerometer of any first signal segment corresponding to any first time window is greater than the stationary detection threshold corresponding to the any first time window, determining that the current motion state of the vehicle is a driving state.

[0007] In an embodiment of the present application, the method further includes: determining a smooth signal segment in the first acceleration data; dividing the smooth signal segment into multiple second signal segments through each first time window; determining the range of the y-axis of the accelerometer for each second signal segment; determining the first maximum value of the ranges of the y-axis of the accelerometers of all second signal segments corresponding to the first window in all first time windows as the first stationary detection threshold corresponding to the first window; determining the second stationary detection threshold corresponding to the first window according to the average value and standard deviation of the ranges of the y-axis of the accelerometers of all second signal segments corresponding to the second window in all first time windows.

[0008] In an embodiment of the present application, determining a smooth signal segment in the first acceleration data includes: sequentially traversing the first acceleration data through a preset sliding window to divide the first acceleration data into multiple third signal segments; dividing each third signal segment into multiple fourth signal segments through each first time window; dividing the first acceleration data into multiple fifth signal segments through each first time window; determining the second maximum value of the ranges of the y-axis of the accelerometers of all fourth signal segments corresponding to each first time window and the first minimum value of the ranges of the y-axis of the accelerometers of all fifth signal segments corresponding to each first time window; for each third signal segment, when the product of the first minimum value corresponding to each first time window and a preset multiple is greater than the corresponding second maximum value, determining that the third signal segment is a smooth signal segment in the first acceleration data and stopping traversing the first acceleration data.

[0009] In an embodiment of the present application, the second frequency domain data includes multiple corresponding second power spectral densities and second frequency values. Determining the current motion state of the vehicle according to the second frequency domain data includes: when the difference between the second frequency value corresponding to the maximum value in all second power spectral densities and the cut-off frequency is less than the frequency difference threshold, determining that the current motion state of the vehicle is a stationary state; when the difference between the second frequency value corresponding to the maximum value in all second power spectral densities and the cut-off frequency is greater than or equal to the frequency difference threshold, determining that the current motion state of the vehicle is a driving state.

[0010] In an embodiment of the present application, the time-domain data includes the triaxial modulus values of an accelerometer, and the frequency-domain data includes a plurality of corresponding power spectral densities and frequency values. Converting the time-domain data into frequency-domain data includes: performing a Fourier transform on the triaxial modulus values of the accelerometer to obtain a transformation result; obtaining sampling parameters for the triaxial modulus values of the accelerometer; and determining a plurality of corresponding power spectral densities and frequency values according to the sampling parameters and the transformation result.

[0011] A second aspect of the present application provides a device for detecting the motion state of a vehicle, including: a memory configured to store instructions; a processor configured to call instructions from the memory and capable of implementing the method for detecting the motion state of a vehicle according to the above when executing the instructions.

[0012] A third aspect of the present application provides a vehicle, including: an inertial sensor for detecting the time-domain data of the vehicle; the device for detecting the motion state of a vehicle according to the above.

[0013] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon for causing a machine to execute the method for detecting the motion state of a vehicle according to the above.

[0014] Through the above technical solutions, adaptive dual-mode integrated navigation is used to detect the stillness of a vehicle, convert the acceleration time-domain data in the still state into frequency-domain data, analyze whether the vibration of the vehicle has periodic characteristics and the magnitude of the vehicle vibration. For electric vehicles with small vibration and weak periodic characteristics, the acceleration time-domain data is used to analyze whether the vehicle is still, and for fuel vehicles with large vibration and obvious periodic characteristics, the frequency-domain data is used to analyze whether the vehicle is still. In this way, the motion state of the vehicle can be accurately detected to perform zero-speed constraint on the vehicle and provide stable and reliable navigation and positioning results in complex urban environments or GNSS-denied environments.

[0015] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings: Figure 1 Schematically shows a flowchart of a method for detecting the motion state of a vehicle according to an embodiment of the present application; Figure 2aSchematically shows a schematic diagram of the tram static detection result using the traditional method according to an embodiment of the present application; Figure 2b Schematically shows a schematic diagram of the tram static detection result using the innovative method according to an embodiment of the present application; Figure 3a Schematically shows a schematic diagram of the oil vehicle static detection result using the traditional method according to an embodiment of the present application; Figure 3b Schematically shows a schematic diagram of the oil vehicle static detection result using the innovative method according to an embodiment of the present application; Figure 4 Schematically shows a structural block diagram of a device for detecting the motion state of a vehicle according to an embodiment of the present application; Figure 5 Schematically shows a structural schematic diagram of a computer device according to an embodiment of the present application. Detailed implementation manners

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the specific implementation manners described herein are only for explaining and illustrating the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0018] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present application, the directional indications are only used to explain the relative positional relationship and motion conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0019] In addition, if there are descriptions such as "first" and "second" involved in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0020] Figure 1The flowchart schematically shows a method for detecting the motion state of a vehicle according to an embodiment of the present application. As Figure 1 shown, an embodiment of the present application provides a method for detecting the motion state of a vehicle, the method comprising: S102, obtaining time-domain data of the vehicle, the time-domain data including first acceleration data of the vehicle in a stationary state and second acceleration data of the vehicle currently.

[0021] S104, converting the time-domain data into frequency-domain data, the frequency-domain data including first frequency-domain data corresponding to the first acceleration data and second frequency-domain data corresponding to the second acceleration data.

[0022] S106, determining whether the first frequency-domain data meets a preset condition? If so, execute S108; if not, execute S110.

[0023] S108, determining the motion state of the vehicle according to the second frequency-domain data.

[0024] S110, determining the current motion state of the vehicle according to the second acceleration data.

[0025] It can be understood that time-domain data refers to a data sequence recorded with time as the horizontal axis and a physical quantity (such as voltage, acceleration, sound intensity, etc.) as the vertical axis. It directly reflects the dynamic characteristics of a signal or phenomenon changing over time and is the most common form of raw data. The time-domain data of a vehicle refers to the physical quantities or state parameters that are continuously collected in real time by sensors or controllers during the operation of the vehicle and change with time. Such data has time as the horizontal axis and directly reflects the dynamic behavior and working state of various components of the vehicle, and is the core data source in fields such as vehicle performance analysis, fault diagnosis, and intelligent driving. In the embodiments of the present application, the time-domain data includes, but is not limited to, the first acceleration data when the vehicle is in a stationary state and the second acceleration data of the vehicle at present. That is, the first acceleration data is the time-domain data of the acceleration when the vehicle is in a stationary state, and the second acceleration data is the time-domain data of the acceleration of the vehicle at present. Specifically, the time-domain data of the vehicle can be detected by an inertial sensor. Inertial sensors (such as accelerometers, gyroscopes, magnetometers) are core devices for detecting the motion state of an object. By collecting data such as acceleration, angular velocity, and magnetic field strength, they provide basic information for attitude estimation, navigation, motion analysis, etc. However, in complex urban environments or GNSS (Global Navigation Satellite System) denied environments, such as underground garages, the zero bias of inertial devices has error accumulation, which severely restricts the stability and reliability of the integrated navigation system. And this problem can be effectively suppressed by zero velocity constraints to solve the zero bias drift problem of inertial devices, but the premise is that the current motion state of the vehicle needs to be accurately detected. That is, it is necessary to detect whether the vehicle is in a stationary state or a driving state. Generally, the vibration of an electric vehicle motor is small, and the vibration of an electric vehicle is mainly medium and high frequency, with weak periodicity and dispersed energy. The vibration frequency periodic characteristics of an oil vehicle are more obvious, especially the engine ignition and mechanical vibration in the low-frequency range show strong periodicity. On the other hand, the vibration of an electric vehicle motor is very small, and the detection data of inertial devices differ greatly between stationary and low-speed motion. For an oil vehicle with a large engine vibration, the jitter size of the detection data of inertial devices at rest is the same as or may be larger than that at low-speed motion. Therefore, the vibration frequency periodic characteristics of the vehicle can be analyzed, and different methods of stationary detection can be carried out for vehicles with different vibration conditions to determine the current motion state of the vehicle.

[0026] Specifically, time-domain data is the starting point for understanding dynamic systems, directly recording the variation of physical quantities over time. It is widely used in real-time monitoring, anomaly detection, and trend prediction, and is the basic data form in fields such as signal processing and machine learning. Key events and statistical features can be extracted through time-domain analysis. The time-domain data can be further transformed into frequency-domain data to deeply explore the periodic patterns of vehicle vibration frequencies. Frequency-domain data is a data form obtained by transforming a time-domain signal (a physical quantity varying over time) through a mathematical transformation (such as the Fourier transform), with frequency as the horizontal axis and energy or amplitude as the vertical axis. It reveals the distribution patterns of different frequency components in the signal and is the core tool for analyzing phenomena such as periodicity, vibration, and noise. Specifically, the first acceleration data can be converted into corresponding first frequency-domain data, and the second acceleration data can be converted into corresponding second frequency-domain data. In the embodiments of the present application, the frequency-domain data can identify the main frequency of engine vibration and can separate noise from the effective signal. Then, in the stationary state, it is possible to determine whether the vehicle has obvious vibration frequency periodic characteristics based on the first frequency-domain data. Specifically, preset conditions for distinguishing vibration frequency periodic characteristics can be set for the first frequency-domain data. If the first frequency-domain data meets the preset conditions, it can be determined that the vibration frequency periodic characteristics of the vehicle are obvious. Then, using the second frequency-domain data representing the current vibration frequency periodic characteristics of the vehicle, the motion state of a vehicle with large engine vibration can be detected. If the first frequency-domain data does not meet the preset conditions, it can be determined that the vibration frequency periodic characteristics of the vehicle are weak. And vehicles with weak periodic characteristics are usually electric vehicles, which have small vibrations, and there are significant differences in the time-domain data of inertial devices during stationary and low-speed movement. Then, the second acceleration data can be analyzed to determine the current motion state of the vehicle.

[0027] Through the above technical solutions, adaptive dual-mode combined navigation is used to perform stationary detection on the vehicle. The acceleration time-domain data in the stationary state is converted into frequency-domain data to analyze whether the vehicle's vibration has periodic characteristics and the magnitude of the vehicle's vibration. For electric vehicles with small vibrations and weak periodic characteristics, the acceleration time-domain data is used to analyze whether the vehicle is stationary. For fuel vehicles with large vibrations and obvious periodic characteristics, the frequency-domain data is used to analyze whether the vehicle is stationary. In this way, the motion state of the vehicle can be accurately detected to perform zero-speed constraint on the vehicle, providing stable and reliable navigation and positioning results in complex urban environments or GNSS-denied environments.

[0028] In the embodiments of the present application, the time-domain data includes the triaxial modulus of the accelerometer, and the frequency-domain data includes multiple corresponding power spectral densities and frequency values one by one. Converting the time-domain data into frequency-domain data includes: performing a Fourier transform on the triaxial modulus of the accelerometer to obtain a transformation result; obtaining sampling parameters for the triaxial modulus of the accelerometer; and determining multiple corresponding power spectral densities and frequency values one by one according to the sampling parameters and the transformation result.

[0029] It can be understood that the time-domain data of a vehicle can be detected by inertial sensors such as accelerometers. An accelerometer typically measures the acceleration in three orthogonal directions (X, Y, and Z axes), and the modulus value is the modulus of the vector sum of these three components. That is to say, the modulus value of the three axes of the accelerometer is obtained by taking the square root of the sum of the squares of the accelerations of the three axes, which can comprehensively reflect the vibration condition of the vehicle. Specifically, a Fourier transform is performed on the modulus value of the three axes of the accelerometer to obtain the transformation result. And the sampling parameters when the accelerometer collects the modulus value of the three axes of the accelerometer can be obtained. Among them, the sampling parameters include but are not limited to the sampling frequency and the number of sampling points, and the number of sampling points is the length of the signal sampled each time. The power spectral density (PSD) and its corresponding frequency values are calculated according to the sampling parameters and the transformation result. For example, for the first acceleration data, the power spectral density and the corresponding frequency values when the vehicle is in a stationary state can be determined through the foregoing solution. For the second acceleration data, the current power spectral density and the corresponding frequency values of the vehicle can be determined. It can be understood that the core of calculating the power spectral density and the corresponding frequency values according to the modulus value of the three axes of the accelerometer is to analyze and quantify the energy distribution of the acceleration at different frequencies through the frequency-domain data, which can more comprehensively capture the multi-directional vibration characteristics and identify the periodic components.

[0030] Specifically, in the embodiments of the present application, it can be determined whether there is an obvious periodic law in the time-domain data according to the power spectral density value, and the calculation process of the power spectral density is shown in the following formula (1): (1) In the formula, refers to the th modulus value of the three axes of the accelerometer, refers to the fast Fourier transform function, refers to the transformation result after the fast Fourier transform, refers to the number of sampling points, refers to the sampling frequency, is the th power spectral density corresponding to the modulus value of the three axes of the accelerometer, refers to the th frequency value corresponding to the modulus value of the three axes of the accelerometer, is the frequency value and the corresponding power spectral density when the power spectral density is the largest, and k refers to the th serial number of the modulus value of the three axes of the accelerometer. Among them, the value of N can be 100.

[0031] In an embodiment of the present application, the first frequency-domain data includes a plurality of corresponding first power spectral densities and first frequency values. The method further includes: determining that the first frequency-domain data meets a preset condition when the maximum value among all the first power spectral densities is greater than the power spectral density threshold, and the difference between the first frequency value corresponding to the maximum value and the cut-off frequency is less than the frequency difference threshold.

[0032] It can be understood that generally, the vibration of an oil vehicle is concentrated in the low-frequency band with high energy and large vibration, and there are sharp main frequency peaks and harmonics. Therefore, the measurement dynamic range of the in-vehicle inertial sensor of the oil vehicle is relatively small. Thus, the original data (IMU data) of its inertial device can usually be subjected to low-pass filtering. After the IMU data is processed by a low-pass filter, the power spectral density of the data will be concentrated at the low-pass filter cut-off frequency. While for an electric vehicle, the energy is dispersed and the high-frequency proportion is significant. Based on the above characteristics, corresponding preset conditions can be set to determine whether the vehicle is an oil vehicle or an electric vehicle. Specifically, when the maximum value among all the first power spectral densities is greater than the power spectral density threshold, and the difference between the first frequency value corresponding to the maximum value and the cut-off frequency is less than the frequency difference threshold, it is determined that the first frequency-domain data meets the preset condition. Otherwise, the first frequency-domain data is considered not to meet the preset condition. Among them, the power spectral density is normalized during calculation, and the power spectral density threshold is set based on the normalized data. Those skilled in the art can set the frequency difference threshold and the power spectral density threshold according to experience.

[0033] Specifically, in an embodiment of the present application, the functional expression for determining that the first frequency-domain data meets the preset condition is shown according to the following formula (2): (2) In the formula, f is the frequency value corresponding to the maximum power spectral density, p refers to the maximum value of the power spectral density, refers to the cut-off frequency of the low-pass filter of the inertial sensor, refers to the frequency difference threshold, is the power spectral density threshold, and the power spectral density is normalized during calculation. If the first frequency-domain data meets the preset condition, it can be determined that the vibration frequency period characteristic of the vehicle is obvious. Then, using the second frequency-domain data characterizing the current vibration frequency period characteristic of the vehicle, the motion state of a vehicle with large engine vibration can be detected. If the first frequency-domain data does not meet the preset condition, it can be determined that the vibration frequency period characteristic of the vehicle is weak. And a vehicle with a weak period characteristic is usually an electric vehicle, and the vibration of an electric vehicle is small, and the time-domain data of the inertial device is quite different when the vehicle is stationary and moving at a low speed. Then, the second acceleration data can be analyzed to determine the current motion state of the vehicle. Among them, the frequency difference threshold can be taken as 0.2, and the power spectral density threshold can be taken as 0.3.

[0034] In an embodiment of the present application, the second frequency-domain data includes a plurality of corresponding second power spectral densities and second frequency values. Determining the current motion state of the vehicle based on the second frequency-domain data includes: when the difference between the second frequency value corresponding to the maximum value among all the second power spectral densities and the cut-off frequency is less than the frequency difference threshold, determining that the current motion state of the vehicle is a stationary state; when the difference between the second frequency value corresponding to the maximum value among all the second power spectral densities and the cut-off frequency is greater than or equal to the frequency difference threshold, determining that the current motion state of the vehicle is a driving state.

[0035] It can be understood that for fuel vehicles with large engine vibrations, the jitter magnitude of IMU data is the same or even greater when the vehicle is stationary compared to low-speed motion. When the engine idles violently when the fuel vehicle is stationary, its vibration will be greater than or equal to the vibration during low-speed driving, resulting in a similar range (maximum value - minimum value). Therefore, the range method cannot identify the stationary state. If the difference between the second frequency value corresponding to the maximum value among all the second power spectral densities in the second frequency-domain data and the cut-off frequency is less than the frequency difference threshold, it indicates that the current motion state of the fuel vehicle is a stationary state. When the difference between the second frequency value corresponding to the maximum value among all the second power spectral densities and the cut-off frequency is greater than or equal to the frequency difference threshold, it is determined that the current motion state of the vehicle is a driving state. For vehicle-mounted integrated navigation, due to its small dynamic range, the raw data of inertial navigation devices is usually low-pass filtered to filter out high-frequency noise information and retain low-frequency signals. In the scenario where the engine vibrates strongly and the vehicle is stationary, its vibration frequency will exceed the cut-off frequency of the low-pass filter. After the IMU data is processed by the low-pass filter, the power spectral density of the data will be concentrated at the cut-off frequency of the filter. Therefore, the cycle frequency can be used to detect the stationary state of vehicles with large engine vibrations. The above solution can accurately detect the stationary state of the vehicle, and is accurate and efficient.

[0036] The above solution not only considers the maximum value of the power spectral density and its corresponding frequency value, but also introduces the cut-off frequency and the frequency difference threshold as the judgment basis. This method can overcome the limitations of the traditional range method in identifying the stationary state of fuel vehicles, especially in the case of violent engine idling, and can still accurately judge whether the vehicle is in a stationary state. In this way, real-time monitoring and precise control of the vehicle's motion state can be achieved, thereby improving the safety and stability of vehicle driving. For example, in an autonomous driving system, accurately judging the motion state of the vehicle is the basis for realizing functions such as automatic navigation and obstacle avoidance. Therefore, the embodiments of the present application have broad application prospects in the fields of vehicle intelligence and automation.

[0037] In an embodiment of the present application, determining the current motion state of the vehicle based on the second acceleration data includes: dividing the second acceleration data into multiple first signal segments through each first time window; determining the range of the y-axis of the accelerometer for each first signal segment; when the ranges of the y-axis of the accelerometer for all first signal segments are less than the stationary detection threshold corresponding to the first time window, determining that the current motion state of the vehicle is a stationary state; when the range of the y-axis of the accelerometer for any first signal segment corresponding to any first time window is greater than the stationary detection threshold corresponding to the any first time window, determining that the current motion state of the vehicle is a driving state.

[0038] It can be understood that the vibration of the electric vehicle motor is very small, and the IMU data detected by the inertial sensor is quite different when the vehicle is stationary and moving at a low speed. Then, the stationary detection method using a hybrid of long and short windows can be used to detect the motion state of the vehicle. Specifically, the first time window can group the data in chronological order and according to a certain time length. Those skilled in the art can preset multiple first time windows with different lengths to divide the collected second acceleration data into multiple first signal segments for analyzing the motion state of the vehicle. The first signal segment is local data of the second acceleration data, and the length of each first signal segment is the length of the first time window for dividing the data. The length of this first time window can be flexibly set according to the actual application scenario and requirements to ensure that it can accurately reflect the motion state of the vehicle. By setting a reasonable time window, the acceleration data can be utilized more effectively, improving the accuracy and reliability of vehicle motion state detection. Specifically, the second acceleration data includes the y-axis acceleration detected by the accelerometer, and the y-axis acceleration reflects the motion state of the vehicle in the longitudinal direction (forward / backward direction). Further, the range of the y-axis of the accelerometer in each first signal segment is statistically calculated, that is, the difference between the maximum value and the minimum value is used as a statistic. When the ranges of the y-axis of the accelerometer for all first signal segments are less than the stationary detection threshold corresponding to the first time window, that is, whether it is a first signal segment divided by a long window or a short window, the range of the y-axis of the accelerometer is less than the corresponding stationary detection threshold, then it can be determined that the current motion state of the vehicle is a stationary state. When the range of the y-axis of the accelerometer for any first signal segment corresponding to any first time window is greater than the stationary detection threshold corresponding to the any first time window, then it can be determined that the current motion state of the vehicle is a driving state. In a specific implementation manner, the first time window can adopt a long window and a short window. The stationary detection using the hybrid of long and short windows uses the range (the difference between the maximum value and the minimum value) of the IMU data as a statistic, and a corresponding stationary detection threshold, that is, the threshold of the range of the y-axis of the accelerometer, is set for each first time window. The long window data is used to detect a stable long-time stationary state, and the short window is used to detect a short-time stationary state, and it supplements the detection time delay phenomenon of the long window, reducing the missed detection situation caused by the long window.

[0039] Specifically, in a specific embodiment, the functional expression of the long window stationary detection model is shown in the following formula (3): (3) In the formula, is the range of the y-axis of the accelerometer within the long window. The y-axis of the accelerometer is the forward axis, refers to the amount of data within the long window, refers to the th y-axis acceleration, and k refers to the serial number of the th y-axis acceleration, is the stationary detection threshold of the long window, , and q refers to the serial number of the th y-axis acceleration. In the long window detection, if the range of the accelerometer meets the requirement of the stationary detection threshold, it indicates that the vehicle is in a stationary state. In a specific embodiment, the long window can collect data within 4 s, where the data within 4 s can include 400 data.

[0040] The functional expression of the short window stationary detection model is shown in the following formula (4): (4) In the formula, is the range of the y-axis of the accelerometer within the short window, refers to the amount of data within the short window, refers to the th y-axis acceleration, is the stationary detection threshold of the short window, , and n refers to the serial number of the th y-axis acceleration. In the short window detection, if the range of the accelerometer meets the requirement of the stationary detection threshold, it indicates that the vehicle is in a stationary state. In a specific embodiment, the short window can collect data within 1 s, where the data within 1 s can include 100 data.

[0041] In the embodiments of the present application, the method further includes: determining a smooth signal segment in the first acceleration data; dividing the smooth signal segment into multiple second signal segments through each first time window; determining the range of the y-axis of the accelerometer for each second signal segment; determining the first maximum value of the ranges of the y-axis of the accelerometer for all the second signal segments corresponding to the first window in all the first time windows as the first stationary detection threshold corresponding to the first window; and determining the second stationary detection threshold corresponding to the first window according to the average value and standard deviation of the ranges of the y-axis of the accelerometer for all the second signal segments corresponding to the second window in all the first time windows.

[0042] It can be understood that the stationary detection threshold can be obtained through calibration. The calibration of the stationary detection threshold needs to be carried out in an open environment when the vehicle is detected to be stationary by the satellite navigation speed. However, there may still be complex disturbance phenomena in the stationary vehicle, such as getting on and off the vehicle, walking inside the vehicle, etc., resulting in inaccurate calibration thresholds. Therefore, during the calibration process, it is necessary to detect the stationarity of the first acceleration data and select the stationary signal segment in the first acceleration data. Since the data lengths detected by different first time windows are different, the corresponding stationary detection thresholds will also vary. Therefore, for different first time windows, the corresponding stationary detection thresholds are calibrated respectively. During the calibration process, the stationary signal segment in the collected first acceleration data can be divided into multiple second signal segments to calibrate the stationary detection threshold. The second signal segment is a local signal in the stationary signal segment, and the length of each second signal segment is the length of the first time window. In a specific embodiment, the first time window can adopt a long window and a short window. The first window can be the long window, and the second window can be the short window, that is, the signal length intercepted by the first window is greater than the signal length intercepted by the second window. Further, determine the range of the y-axis of the accelerometer for each second signal segment. During the calibration process, the long window detects a stable long-term stationary state, and a relatively loose stationary detection threshold can be used for the long window, while the calibration of the stationary detection threshold for the short window is more stringent. Then, the first maximum value of the range of the y-axis of the accelerometer for all the second signal segments obtained by dividing the first window can be determined as the first stationary detection threshold corresponding to the first window. The second stationary detection threshold corresponding to the first window is determined according to the average value and standard deviation of the range of the y-axis of the accelerometer for all the second signal segments corresponding to the second window.

[0043] In the embodiment of the present application, determining the stationary signal segment in the first acceleration data includes: sequentially traversing the first acceleration data through a preset sliding window to divide the first acceleration data into multiple third signal segments; dividing each third signal segment into multiple fourth signal segments through each first time window; dividing the first acceleration data into multiple fifth signal segments through each first time window; determining the second maximum value of the range of the y-axis of the accelerometer for all the fourth signal segments corresponding to each first time window and the first minimum value of the range of the y-axis of the accelerometer for all the fifth signal segments corresponding to each first time window; for each third signal segment, when the product of the first minimum value corresponding to each first time window and a preset multiple is greater than the corresponding second maximum value, determining the third signal segment as the stationary signal segment in the first acceleration data, and stopping traversing the first acceleration data.

[0044] It can be understood that the static detection threshold can be obtained through calibration. The calibration of the static detection threshold needs to be carried out in an open environment when the vehicle is detected to be stationary by the satellite navigation speed. However, there may still be complex perturbation phenomena in the stationary vehicle, such as getting on and off the vehicle, walking inside the vehicle, etc., resulting in inaccurate calibration thresholds. Therefore, during the calibration process, it is necessary to perform a stationarity detection on the first acceleration data and select the stationary signal segment in the first acceleration data. Specifically, the first acceleration data is sequentially traversed through a preset sliding window to divide the first acceleration data into multiple third signal segments. The third signal segment is a local signal of the first acceleration data, and the length of each third signal segment is the length of the preset sliding window. The preset sliding window is a sliding window preset by technicians for detecting the stationary signal segment. The preset sliding window can move on the data sequence of the first acceleration data in chronological order and at a fixed step size, and it can cover continuous subsequences. Further, each third signal segment within the calibration sliding window is divided into multiple fourth signal segments through each first time window, and the first acceleration data is divided into multiple fifth signal segments through each first time window. The fourth signal segment is a local signal of the third signal segment, and the length of each fourth signal segment is the length of the first time window. The implicit condition here is that the preset sliding window is much larger than the length of the first time window, that is, the difference between the length of the preset sliding window and the length of the first time window is greater than or equal to a preset threshold, and this preset threshold can be set according to technical experience. Further, determine the second maximum value among the accelerometer y-axis ranges of all fourth signal segments, and the first minimum value among the accelerometer y-axis ranges of all fifth signal segments. That is, use the first time window to detect the maximum value of the accelerometer y-axis range of the local signal corresponding to the preset sliding window, and use the first time window to detect the minimum value of the accelerometer y-axis range of the global signal. Then, for each third signal segment, when the product of the first minimum value corresponding to each first time window and the preset multiple is greater than the corresponding second maximum value, determine that this third signal segment is the stationary signal segment in the first acceleration data, and stop traversing the first acceleration data. Specifically, the preset multiple can be set to 4.

[0045] Specifically, in a specific embodiment, the static detection thresholds of the above-mentioned long window and short window both need to be obtained through calibration calculations. The calibration needs to be carried out in an open environment when the vehicle is detected to be stationary by the satellite navigation speed. Since there are complex perturbation phenomena during the stationary process of the vehicle, such as getting on and off the vehicle, walking inside the vehicle, etc., resulting in inaccurate calibrated static detection thresholds, it is necessary to perform a stationarity detection on the time-domain data during stationary, and the functional expression of the stationarity detection model is shown in the following formula (5): (5) In the formula, is the maximum value of the range of the y-axis of the accelerometer calculated using a long window within a preset sliding window (local long window maximum), is the range of the y-axis of the b-th accelerometer calculated using a long window within a preset sliding window. R refers to the length of the preset sliding window, generally the data volume of 20 seconds, 2000 sample data, is the minimum value of the range of the y-axis of the accelerometer in the global long window (global long window minimum), representing the minimum value of the range of the y-axis of all long window accelerometers when the vehicle is stationary in an open environment, refers to the maximum value of the range of the y-axis of the accelerometer calculated using a short window within a preset sliding window (local short window maximum), is the range of the y-axis of the d-th accelerometer calculated using a short window within a preset sliding window, is the minimum value of the range of the y-axis of the accelerometer in the global short window, representing the minimum value of the range of the y-axis of all short window accelerometers when the vehicle is stationary in an open environment (global short window minimum), , , c refers to the serial number of the range of the y-axis of the b-th accelerometer in the preset sliding window, and e refers to the serial number of the range of the y-axis of the d-th accelerometer in the preset sliding window.

[0046] By analyzing and processing the data of the long window and the short window for the data in the stationary state, it can provide data support for accurately identifying the stationary state of the vehicle. The long window data analysis can capture the acceleration changes within a relatively long time period, effectively filtering out the short-time noise interference, ensuring that the vehicle is indeed in a stationary state for a relatively long time. The short window data analysis can respond more quickly to sudden changes in acceleration. When the vehicle changes from a stationary state to a moving state, the short window data analysis can quickly capture this change, improving the sensitivity and real-time performance of detection. By combining the data analysis results of the long window and the short window, accurate and reliable detection of the vehicle's motion state can be achieved, providing strong data support for subsequent functions such as navigation and safety warning.

[0047] When the local maximum values simultaneously satisfied by the long window and the short window are less than 4 times the global minimum value, it indicates that the current is in a relatively stable stage and threshold calibration can be performed. During the calibration process, the long window detects a stable long-time stationary state, and a relatively loose stationary detection threshold can be used for the long window, while the stationary detection threshold calibration for the short window is relatively strict. Then, the first maximum value of the range of the y-axis of the accelerometer of all the second signal segments obtained by dividing the first window can be determined as the first stationary detection threshold corresponding to the first window. According to the average value of the range of the y-axis of the accelerometer of all the second signal segments corresponding to the second window plus 3 times the standard deviation, it is used as the second stationary detection threshold corresponding to the first window, and the larger threshold is taken as the final threshold each time it is updated.

[0048] In an embodiment of the present application, the first acceleration data is time-domain data measured when the vehicle is in a stationary state. After being converted into the first frequency-domain data, whether the first frequency-domain data meets a preset condition is used as a judgment basis, and corresponding data is selected according to the judgment result to analyze the motion state of the vehicle. That is to say, the first acceleration data is the basis for selecting which data to analyze the motion state. To ensure the accuracy of the first acceleration data, preferably, when the vehicle is stationary, in an open environment and with a fixed solution of the satellite navigation, using the satellite navigation speed measurement result, when the horizontal speed modulus of the vehicle meets the condition, it means that the vehicle is stationary and the first acceleration data can be collected. Specifically, the processor can obtain GNSS results and inertial sensor data, and perform preprocessing and time synchronization on the data. Among them, the GNSS results include the positioning result and speed measurement result of the vehicle, and the inertial sensor data can be 100Hz raw data. After preprocessing the data, based on PPS timing, the GNSS results and inertial sensor data can be time-synchronized to obtain the first acceleration data with time attributes. Among them, when the horizontal speed modulus of the vehicle is less than or equal to

[0049] m / s, the first acceleration data can be collected.

[0050] 1. Electric vehicle test: The test data is collected using an electric vehicle. Its motor has a very small vibration frequency when stationary. The combined navigation product uses a satellite navigation module and an inertial navigation device. The sampling frequency of the inertial sensor is 100Hz. A high-precision fiber optic inertial navigation is used as the true value. The vehicle repeats slow straight forward and backward driving under the viaduct to test the stationary detection performance in the extremely complex scenario of low-speed straight driving. Refer to Figure 2a and Figure 2b , Figure 2a are the results of stationary detection of the electric vehicle using the traditional method, Figure 2b are the results of the method for detecting the motion state of the electric vehicle in the embodiment of the present application. Among them, the label "0" represents the driving state, and the label "1" represents the stationary state. The accuracy rates of stationary detection by the two methods are shown in Table 1 below. It can be seen from the figure and the table that the missed detection rates of the electric vehicles by the two methods are all 0, but the false detection rate of the traditional method is extremely high, while the false detection rate of the method in the embodiment of the present application is only 0.49%. Compared with the traditional method, the innovative method in the embodiment of the present application has a significant improvement in the stationary detection accuracy in the case of an electric vehicle with small vibration and a complex low-speed straight driving scenario.

[0051] Table 1 Electric vehicle stationary detection results

[0052] 2. Oil vehicle test: The test data was collected using a diesel vehicle. Its engine vibration frequency is very high. The integrated navigation product still uses the satellite navigation module and inertial navigation devices. The sampling frequency of the inertial sensor is 100 Hz. Using a high-precision fiber optic inertial navigation as the ground truth, the vehicle repeatedly moves forward and backward slowly in a straight line under the viaduct to test the stationary detection performance under large and complex engine vibrations and low-speed straight-line driving. Refer to Figure 3a and Figure 3b , Figure 3a are the results of stationary detection of the oil vehicle using the traditional method, Figure 3b are the results of using the method for detecting the motion state of the oil vehicle in the embodiments of the present application. Among them, the label "0" represents the driving state, and the label "1" represents the stationary state. The accuracy rates of the two methods for stationary detection are shown in Table 2 below. It can be seen that the missed detection rate of the traditional method is extremely low. The missed detection rate of the innovative method in the embodiments of the present application is slightly higher, but the false detection rate of the traditional method reaches 35.83%, and the false detection rate of the innovative method is only 0.18%. Compared with the traditional method, the innovative method has a significant improvement in the accuracy rate of stationary detection in the scenario of large and complex engine vibrations and low-speed straight-line driving.

[0053] Table 2 Results of Stationary Detection of Oil Vehicle

[0054] Figure 1 is a schematic flow chart of a method for detecting the motion state of a vehicle in an embodiment. It should be understood that although Figure 1 the steps in the flow chart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in

[0055] Figure 4 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps. Figure 4 As shown in a memory configured to store instructions; a processor configured to call instructions from the memory and be able to implement the above-mentioned method for detecting the motion state of a vehicle when executing the instructions.

[0056] Specifically, in the embodiments of the present application, the processor may be configured to: Obtain the time-domain data of the vehicle, where the time-domain data includes the first acceleration data of the vehicle in a stationary state and the second acceleration data of the vehicle currently; Convert the time-domain data into frequency-domain data, where the frequency-domain data includes the first frequency-domain data corresponding to the first acceleration data and the second frequency-domain data corresponding to the second acceleration data; When the first frequency-domain data meets the preset conditions, determine the motion state of the vehicle according to the second frequency-domain data; When the first frequency-domain data does not meet the preset conditions, determine the current motion state of the vehicle according to the second acceleration data.

[0057] In the embodiments of the present application, the first frequency-domain data includes a plurality of corresponding first power spectral densities and first frequency values. The method further includes: when the maximum value among all the first power spectral densities is greater than the power spectral density threshold and the difference between the first frequency value corresponding to the maximum value and the cut-off frequency is less than the frequency difference threshold, determine that the first frequency-domain data meets the preset conditions.

[0058] In the embodiments of the present application, determining the current motion state of the vehicle according to the second acceleration data includes: dividing the second acceleration data into a plurality of first signal segments through each first time window; determining the range of the y-axis of the accelerometer for each first signal segment; when the ranges of the y-axis of the accelerometer for all the first signal segments are less than the stationary detection threshold corresponding to the first time window, determine that the current motion state of the vehicle is a stationary state; when the range of the y-axis of the accelerometer for any first signal segment corresponding to any first time window is greater than the stationary detection threshold corresponding to the any first time window, determine that the current motion state of the vehicle is a driving state.

[0059] In the embodiments of the present application, the method further includes: determining the stationary signal segments in the first acceleration data; dividing the stationary signal segments into a plurality of second signal segments through each first time window; determining the range of the y-axis of the accelerometer for each second signal segment; determining the first maximum value of the ranges of the y-axis of the accelerometer for all the second signal segments corresponding to the first window among all the first time windows as the first stationary detection threshold corresponding to the first window; determining the second stationary detection threshold corresponding to the first window according to the average value and standard deviation of the ranges of the y-axis of the accelerometer for all the second signal segments corresponding to the second window among all the first time windows.

[0060] In an embodiment of the present application, determining the stationary signal segment in the first acceleration data includes: sequentially traversing the first acceleration data through a preset sliding window to divide the first acceleration data into multiple third signal segments; dividing each third signal segment into multiple fourth signal segments through each first time window; dividing the first acceleration data into multiple fifth signal segments through each first time window; determining the second maximum value of the range of the y-axis of the accelerometer in all the fourth signal segments corresponding to each first time window and the first minimum value of the range of the y-axis of the accelerometer in all the fifth signal segments corresponding to each first time window; for each third signal segment, when the product of the first minimum value corresponding to each first time window and a preset multiple is greater than the corresponding second maximum value, determining the third signal segment as the stationary signal segment in the first acceleration data, and stopping traversing the first acceleration data.

[0061] In an embodiment of the present application, the second frequency domain data includes multiple corresponding second power spectral densities and second frequency values. Determining the current motion state of the vehicle according to the second frequency domain data includes: when the difference between the second frequency value corresponding to the maximum value in all the second power spectral densities and the cut-off frequency is less than the frequency difference threshold, determining that the current motion state of the vehicle is a stationary state; when the difference between the second frequency value corresponding to the maximum value in all the second power spectral densities and the cut-off frequency is greater than or equal to the frequency difference threshold, determining that the current motion state of the vehicle is a driving state.

[0062] In an embodiment of the present application, the time domain data includes the triaxial modulus values of the accelerometer, and the frequency domain data includes multiple corresponding power spectral densities and frequency values. Converting the time domain data into the frequency domain data includes: performing a Fourier transform on the triaxial modulus values of the accelerometer to obtain a transformation result; obtaining the sampling parameters for the triaxial modulus values of the accelerometer; determining multiple corresponding power spectral densities and frequency values according to the sampling parameters and the transformation result.

[0063] An embodiment of the present application further provides a vehicle, which may include: An inertial sensor for detecting the time domain data of the vehicle; The device for detecting the motion state of the vehicle according to the above.

[0064] An embodiment of the present application further provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause the machine to execute the above method for detecting the motion state of the vehicle.

[0065] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 5As shown in the figure. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data for detecting the vehicle motion state. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it implements a method for detecting the vehicle motion state.

[0066] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0067] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0068] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0069] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes Figure 1 one or more of the processes and / or blocks Figure 1 one or more of the blocks

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to generate a computer-implemented process, thereby providing steps for implementing the functions specified in one or more of the processes Figure 1 one or more of the processes and / or blocks Figure 1 one or more of the blocks

[0071] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0072] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0073] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0074] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0075] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for detecting the motion state of a vehicle, characterized in that, The method includes: Obtaining time-domain data of the vehicle, where the time-domain data includes first acceleration data when the vehicle is in a stationary state and second acceleration data of the vehicle currently; Converting the time-domain data into frequency-domain data, where the frequency-domain data includes first frequency-domain data corresponding to the first acceleration data and second frequency-domain data corresponding to the second acceleration data; When the first frequency-domain data meets a preset condition, determining the motion state of the vehicle according to the second frequency-domain data; When the first frequency-domain data does not meet the preset condition, determining the current motion state of the vehicle according to the second acceleration data.

2. The method for detecting the motion state of a vehicle according to claim 1, wherein, The first frequency-domain data includes a plurality of corresponding first power spectral densities and first frequency values, and the method further includes: When the maximum value among all the first power spectral densities is greater than a power spectral density threshold and the difference between the first frequency value corresponding to the maximum value and the cut-off frequency is less than a frequency difference threshold, determining that the first frequency-domain data meets the preset condition.

3. The method for detecting the motion state of a vehicle according to claim 1, characterized in that, Determining the current motion state of the vehicle according to the second acceleration data includes: Dividing the second acceleration data into a plurality of first signal segments through each first time window; Determining the range of the y-axis of the accelerometer for each first signal segment; When the ranges of the y-axis of the accelerometer for all the first signal segments are less than the stationary detection threshold corresponding to the first time window, determining that the current motion state of the vehicle is a stationary state; When the range of the y-axis of the accelerometer for any first signal segment corresponding to any first time window is greater than the stationary detection threshold corresponding to the any first time window, determining that the current motion state of the vehicle is a driving state.

4. The method for detecting the motion state of a vehicle according to claim 3, wherein, The method further includes: Determining a stationary signal segment in the first acceleration data; Dividing the stationary signal segment into a plurality of second signal segments through each first time window; Determining the range of the y-axis of the accelerometer for each second signal segment; Determining the first maximum value of the ranges of the y-axis of the accelerometer for all the second signal segments corresponding to the first window among all the first time windows as the first stationary detection threshold corresponding to the first window; Determining the second stationary detection threshold corresponding to the first window according to the average value and standard deviation of the ranges of the y-axis of the accelerometer for all the second signal segments corresponding to the second window among all the first time windows.

5. The method for detecting the motion state of a vehicle according to claim 4, characterized in that, Determining the stationary signal segment in the first acceleration data includes: Sequentially traversing the first acceleration data through a preset sliding window to divide the first acceleration data into a plurality of third signal segments; Dividing each third signal segment into a plurality of fourth signal segments through each first time window; Dividing the first acceleration data into a plurality of fifth signal segments through each first time window; Determining the second maximum value of the ranges of the y-axis of the accelerometer for all the fourth signal segments corresponding to each first time window and the first minimum value of the ranges of the y-axis of the accelerometer for all the fifth signal segments corresponding to each first time window; For each third signal segment, when the product of the first minimum value corresponding to each first time window is greater than the corresponding second maximum value, determine that the third signal segment is a stable signal segment in the first acceleration data, and stop traversing the first acceleration data.

6. The method for detecting the motion state of a vehicle according to claim 1, wherein, The second frequency domain data includes a plurality of corresponding second power spectral densities and second frequency values. Determining the current motion state of the vehicle according to the second frequency domain data includes: When the difference between the second frequency value corresponding to the maximum value among all the second power spectral densities and the cut-off frequency is less than the frequency difference threshold, determine that the current motion state of the vehicle is a stationary state; When the difference between the second frequency value corresponding to the maximum value among all the second power spectral densities and the cut-off frequency is greater than or equal to the frequency difference threshold, determine that the current motion state of the vehicle is a driving state.

7. The method for detecting the motion state of a vehicle according to claim 1, characterized in that, The time domain data includes the triaxial modulus values of the accelerometer, and the frequency domain data includes a plurality of corresponding power spectral densities and frequency values. The conversion of the time domain data into the frequency domain data includes: Performing a Fourier transform on the triaxial modulus values of the accelerometer to obtain a transformation result; Obtaining sampling parameters for the triaxial modulus values of the accelerometer; Determining the plurality of corresponding power spectral densities and frequency values according to the sampling parameters and the transformation result.

8. A device for detecting the motion state of a vehicle, characterized in that, Comprising: A memory configured to store instructions; A processor configured to call the instructions from the memory and capable of implementing the method for detecting the motion state of a vehicle according to any one of claims 1 to 7 when executing the instructions.

9. A vehicle, characterized in that, Comprising: An inertial sensor for detecting the time domain data of the vehicle; The device for detecting the motion state of a vehicle according to claim 8.

10. A machine-readable storage medium, characterized in that, Instructions are stored on the machine-readable storage medium, and the instructions are used to cause the machine to execute the method for detecting the motion state of a vehicle according to any one of claims 1 to 7.

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