Vehicle rapid acceleration behavior identification method and device and storage medium

Abstract

The invention discloses a vehicle rapid acceleration behavior identification method and device and a storage medium, and relates to the technical field of intelligent traffic. The method comprises the following steps: acquiring vehicle motion state data in real time by using an IMU module, and carrying out filtering processing and fusion processing to obtain vehicle attitude change data; selecting a preset number of adjacent vehicle attitude data from the double linked list; performing rapid acceleration behavior judgment on the attitude data of the adjacent vehicle based on calculation of a state judgment unit to obtain a rapid acceleration behavior judgment result; and if the judgment result of the rapid acceleration behavior is yes, obtaining rapid acceleration event data, and carrying out alarm recording on the rapid acceleration event data based on an alarm mechanism. The technical effects that vehicle data are collected and processed in real time through the IMU sensor, the environmental adaptability is improved, the sensor detection cost and the detection misjudgment rate are reduced, then the vehicle rapid acceleration behavior detection precision is improved, and timely early warning of the rapid acceleration behavior is ensured are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method, device and storage medium for identifying sudden acceleration behavior of a vehicle. Background Art

[0002] With the improvement of vehicle intelligence, the detection and warning of vehicle sudden acceleration has become an important research direction. Traditional methods of sudden acceleration detection mainly rely on sensors such as radar and cameras, but these methods are susceptible to interference in complex environments and are costly. In addition, the reliability of these sensors in low visibility or extreme weather conditions is poor. Therefore, data processing methods based on IMU have gradually attracted attention. IMU has the advantages of small size, low cost, and fast response, and can provide high-frequency acceleration and angular velocity data. Through the reasonable processing of IMU data, the dynamic state and driving behavior of the vehicle can be effectively judged, providing a basis for sudden acceleration detection. Summary of the invention

[0003] This application provides a method, device and storage medium for identifying sudden acceleration behavior of a vehicle, which solves the technical problem in the prior art that the vehicle sudden acceleration behavior detection accuracy is insufficient due to the susceptibility to interference in complex environments and the high cost. The application achieves the technical effect of realizing real-time vehicle data collection and processing through IMU sensors, improving environmental adaptability, reducing sensor detection costs and detection misjudgment rate, thereby improving the detection accuracy of sudden acceleration behavior of vehicles and ensuring timely warning of sudden acceleration behavior.

[0004] In view of the above problems, the present application provides a vehicle rapid acceleration behavior recognition method, device and storage medium.

[0005] In a first aspect of the present application, a method for identifying a vehicle's rapid acceleration behavior is provided, the method comprising:

[0006] The IMU module is used to collect and acquire vehicle motion state data in real time, and the vehicle motion state data includes vehicle acceleration data and vehicle angular velocity data; the vehicle acceleration data and vehicle angular velocity data are filtered and fused by a data processing unit to obtain vehicle posture change data; a bidirectional linked list is used to store the vehicle posture change data, and a preset number of adjacent vehicle posture data are selected from the bidirectional linked list; a sudden acceleration behavior judgment is performed on the adjacent vehicle posture data based on a state judgment unit calculation to obtain a sudden acceleration behavior judgment result; if the sudden acceleration behavior judgment result is yes, sudden acceleration event data is obtained, an alarm mechanism is activated by an alarm and recording unit, and an alarm record is performed on the sudden acceleration event data based on the alarm mechanism.

[0007] A second aspect of the present application provides a vehicle rapid acceleration behavior recognition device, the device comprising:

[0008] Data acquisition module: using the IMU module to collect and acquire vehicle motion state data in real time, the vehicle motion state data includes vehicle acceleration data and vehicle angular velocity data; data processing module: filtering and fusing the vehicle acceleration data and vehicle angular velocity data through the data processing unit to obtain vehicle posture change data; storage and selection module: using a bidirectional linked list to store vehicle posture change data, and selecting a preset number of adjacent vehicle posture data from the bidirectional linked list; sudden acceleration judgment module: based on the state judgment unit calculation, the sudden acceleration behavior judgment of the adjacent vehicle posture data is performed to obtain a sudden acceleration behavior judgment result; alarm recording module: if the sudden acceleration behavior judgment result is yes, the sudden acceleration event data is obtained, the alarm mechanism is activated through the alarm and recording unit, and the sudden acceleration event data is alarm recorded based on the alarm mechanism.

[0009] The third aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the vehicle rapid acceleration behavior recognition method provided in the present application.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The IMU module is used to collect and obtain vehicle motion state data in real time, and the data processing unit performs filtering and fusion processing on the obtained data to obtain vehicle posture change data. A two-way linked list is used to store the vehicle posture change data, and a preset number of adjacent vehicle posture data are selected from the two-way linked list. The adjacent vehicle posture data is judged for sudden acceleration behavior based on the state judgment unit calculation to obtain the sudden acceleration behavior judgment result. If the sudden acceleration behavior judgment result is yes, sudden acceleration event data is obtained, and the alarm mechanism is activated through the alarm and recording unit. The technical solution of alarm recording the sudden acceleration event data based on the alarm mechanism is achieved. The real-time collection and processing of vehicle data is achieved through the IMU sensor, the environmental adaptability is improved, the sensor detection cost and the detection error rate are reduced, and the detection accuracy of the vehicle sudden acceleration behavior is improved, and the sudden acceleration behavior is ensured. The technical effect of timely warning of sudden acceleration behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of a vehicle rapid acceleration behavior recognition method flow chart provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a vehicle rapid acceleration behavior recognition device provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: data acquisition module 11, data processing module 12, storage and selection module 13, sudden acceleration judgment module 14, alarm recording module 15. DETAILED DESCRIPTION

[0016] The present application solves the technical problem in the prior art that it is difficult to evaluate the vehicle deflection and stability of the side fork AGV under different loads by providing a vehicle rapid acceleration behavior recognition method, device and storage medium.

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0018] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0019] Embodiment 1, as Figure 1 As shown, the present application provides a method for identifying a vehicle's rapid acceleration behavior, the method comprising:

[0020] Step S1: using the IMU module to collect and acquire vehicle motion state data in real time, wherein the vehicle motion state data includes vehicle acceleration data and vehicle angular velocity data.

[0021] Furthermore, the step of obtaining the vehicle motion state data also includes:

[0022] According to the IMU module, a three-axis acceleration sensor and a three-axis gyroscope are obtained; offset calibration and temperature compensation are performed on the three-axis acceleration sensor and the three-axis gyroscope to obtain a standard three-axis acceleration sensor and a standard three-axis gyroscope; a data sampling frequency is set, and the standard three-axis acceleration sensor and the standard three-axis gyroscope are used according to the data sampling frequency to acquire vehicle acceleration data and vehicle angular velocity data in real time; based on the vehicle acceleration data and the vehicle angular velocity data, the vehicle motion state data is obtained.

[0023] Specifically, the IMU module is used to collect and acquire vehicle motion state data in real time, wherein the IMU module is used to collect vehicle motion data sensor. First, according to the IMU module, the three-axis acceleration sensor and the three-axis gyroscope integrated in the module are obtained. Before data acquisition, the three-axis acceleration sensor and the three-axis gyroscope are offset calibrated and temperature compensated to reduce the influence of external factors (such as temperature change, sensor drift) on the data, and the corrected standard three-axis acceleration sensor and standard three-axis gyroscope are obtained. Then the data sampling frequency is set, preferably the data sampling frequency is set to 50Hz (i.e., data is collected once every 20ms) to ensure the continuity and real-time nature of the data. By collecting and processing IMU data at a high frequency (every 20ms), it is ensured that the system can respond quickly to vehicle dynamic changes and adapt to complex driving environments. According to the data sampling frequency, the standard three-axis acceleration sensor and the standard three-axis gyroscope are used to collect and acquire vehicle acceleration data ACCx, ACCy, ACCz and vehicle angular velocity data Gyrox, Gyroy, Gyroz in real time. Based on the vehicle acceleration data and vehicle angular velocity data, the vehicle motion state data is obtained by combination. The vehicle motion data is collected and processed by the IMU module, which significantly improves the real-time performance, so that the sudden acceleration detection can more quickly and accurately reflect the dynamic state of the vehicle, and the IMU sensor is not affected by light and weather conditions, and can provide more reliable data.

[0024] Step S2: filtering and fusing the vehicle acceleration data and the vehicle angular velocity data through a data processing unit to obtain vehicle posture change data.

[0025] Furthermore, the step of obtaining the vehicle posture change data also includes:

[0026] The vehicle acceleration data and the vehicle angular velocity data are subjected to low-pass filtering by a data processing unit to obtain filtered vehicle acceleration data and filtered vehicle angular velocity data; the filtered vehicle acceleration data and the filtered vehicle angular velocity data are subjected to Kalman filtering to obtain usable filtered vehicle acceleration data and usable vehicle angular velocity data; and the usable filtered vehicle acceleration data and the usable vehicle angular velocity data are subjected to fusion processing by a six-axis fusion algorithm to obtain the vehicle posture change data.

[0027] Specifically, the vehicle acceleration data and the vehicle angular velocity data are filtered and fused by a data processing unit, wherein the data processing unit is used to perform low-pass filtering, Kalman filtering and six-axis fusion algorithm processing on the collected data. First, the vehicle acceleration data and the vehicle angular velocity data are low-pass filtered by the data processing unit to remove high-frequency noise, which can be achieved using a discrete Fourier transform (DFT) or a Butterworth filter. Specifically, a first-order low-pass filter is used for processing, and the formula is: y[n] = α*x[n] + (1-α)*y[n-1], where y[n] is the current output, x[n] is the current input (i.e., the original data), y[n-1] is the previous output, and α is the filter coefficient. The value range of the filter coefficient α is usually between 0.1 and 0.3, so as to balance the smoothness and response speed. A smaller α value will make the filtered data smoother, but the response speed will be slower; a larger α value will make the response speed faster, but α may retain more high-frequency noise, and obtain the filtered vehicle acceleration data and filtered vehicle angular velocity data after filtering. Then, the filtered vehicle acceleration data and the filtered vehicle angular velocity data are processed by Kalman filtering, specifically: initializing the state estimation and covariance matrix, setting the state transfer model and the observation model, using the IMU data for state prediction, and correcting the state estimation in combination with the measured value through the update step to obtain more accurate available filtered vehicle acceleration data and available vehicle angular velocity data. Finally, the available filtered vehicle acceleration data and available vehicle angular velocity data are fused using a six-axis fusion algorithm, the attitude is represented by quaternions, complementary filtering is performed through the measurements of the accelerometer and gyroscope, the quaternions are updated to obtain the direction and position changes of the vehicle, the acceleration and angular velocity data are fused, the real dynamic state of the vehicle is calculated, and the corresponding vehicle attitude change data is obtained. The fused Euler angle can accurately reflect the attitude changes of the vehicle, including pitch angle, yaw angle and roll angle. By combining low-pass filtering, Kalman filtering and the six-axis fusion algorithm, the multi-dimensional data provided by the IMU is fully utilized, and the accuracy and reliability of the data are significantly improved.

[0028] Step S3: using a bidirectional linked list to store vehicle posture change data, and selecting a preset number of adjacent vehicle posture data from the bidirectional linked list.

[0029] Furthermore, the bidirectional linked list of this application also includes the following steps:

[0030] Define a linked list structure and a node structure, wherein the linked list structure includes the number of linked lists and the size of the linked list, and obtain a bidirectional linked list based on the linked list structure and the node structure; determine the time window length according to the data sampling frequency; set the time window size according to data processing requirements; and use a sliding window mechanism to update the time window of the bidirectional linked list based on the time window length and the time window size.

[0031] Specifically, a bidirectional linked list is used to store vehicle posture change data. First, a linked list structure and a node structure are defined, wherein the linked list structure includes the number of linked lists and the size of the linked list. Preferably, two bidirectional linked lists (or circular buffers) are defined, each of which has a size of N and is used to store the data of ACCy and ACCz, respectively. Each node contains two floating point numbers (ACCy, ACCz) and pointers to the previous and next nodes to support bidirectional traversal. If it is a circular buffer: a circular buffer is used so that when the buffer reaches the maximum size N, the new data will overwrite the old data. This method can effectively manage memory and improve performance. Based on the linked list structure and the node structure, a bidirectional linked list is obtained. According to the data sampling frequency, the time window length is determined. Preferably, the IMU data sampling frequency is set to 50Hz, that is, the acceleration data is collected every 20ms. This means that the length of each time window is 20ms. According to the data processing requirements, the time window size is set, that is, when analyzing the acceleration data, a fixed-size time window N is set to determine how many data are considered in each judgment. The selection of N is usually based on actual needs, for example, 5, 10, or 20 data can be selected. The sliding window mechanism is used to update the time window of the bidirectional linked list based on the time window length and the time window size, that is, the sliding window mechanism is used. When new data is collected each time, the latest ACCy and ACCz values ​​are added to the data set, and the old data is removed. In this way, the latest N data are always kept for judgment, which improves the system's sensitivity to real-time changes. Every time new data arrives, the data in the window will be dynamically updated to ensure that the judgment of the current state is based on the latest data. And according to actual needs, a preset number of neighboring vehicle posture data is selected in the bidirectional linked list, such as selecting 10 latest posture data. The bidirectional linked list is used to store the processed data. The design of the bidirectional linked list makes data storage and management more flexible and efficient, and supports real-time analysis of dynamic data.

[0032] Step S4: Perform a sudden acceleration behavior judgment on the posture data of the neighboring vehicle based on the calculation of the state judgment unit to obtain a sudden acceleration behavior judgment result.

[0033] Furthermore, the step of obtaining the result of the sudden acceleration behavior judgment in this application also includes:

[0034] According to the posture data of the neighboring vehicles, an ACCy value set and an ACCz value set are obtained; based on the state judgment unit, an average change value of the ACCy value set is calculated and obtained; a preset acceleration threshold is obtained, and a maximum value of an uneven road section and a minimum value of an uneven road section are preset; based on the preset acceleration threshold, the preset maximum value of an uneven road section and the minimum value of an uneven road section, a sudden acceleration behavior judgment is performed on the average change value of the ACCy value set and the ACCz value set to obtain a sudden acceleration behavior judgment result.

[0035] Furthermore, the step of obtaining the result of the sudden acceleration behavior judgment in this application also includes:

[0036] Based on the preset acceleration threshold, the average change value of the ACCy value set is compared to determine whether the average change value is greater than the preset acceleration threshold to obtain an acceleration judgment result; based on the preset rough road section maximum value and the preset rough road section minimum value, the ACCz value set is compared to count the number of times the ACCz value set exceeds the preset rough road section maximum value and the preset rough road section minimum value to obtain the number of acceleration out-of-range times; based on the acceleration judgment result and the number of acceleration out-of-range times, a sudden acceleration behavior judgment result is determined.

[0037] Furthermore, the determination of the rapid acceleration behavior judgment result, the steps of this application also include:

[0038] If the acceleration judgment result is no, the sudden acceleration behavior judgment result is that the vehicle did not perform sudden acceleration; if the acceleration judgment result is yes, determine whether the acceleration exceeds the range times greater than the preset exceeding range times, and obtain the exceeding times judgment result; according to the exceeding times judgment result, determine the sudden acceleration behavior judgment result.

[0039] Furthermore, the determination of the rapid acceleration behavior judgment result, the steps of this application also include:

[0040] If the result of the excess number judgment is yes, the result of the sudden acceleration behavior judgment is that the vehicle does not perform sudden acceleration; if the result of the excess number judgment is no, the result of the sudden acceleration behavior judgment is that the vehicle performs sudden acceleration.

[0041] Specifically, based on the state judgment unit calculation, the sudden acceleration behavior judgment is performed on the posture data of the neighboring vehicles. First, the selected ACCy value set and ACCz value set are obtained according to the posture data of the neighboring vehicles. Then, the average change value of the ACCy value set is obtained based on the state judgment unit calculation, wherein the state judgment unit is used to perform sudden acceleration state judgment and uneven road section detection according to the processed data. The most recent N ACCy values ​​are selected from the bidirectional linked list, and their average value is calculated: The preset acceleration threshold is set and obtained, and the preset acceleration threshold is the vehicle sudden acceleration judgment range. At the same time, the maximum value of the rough road section and the minimum value of the rough road section are preset. The latest data is stored in a dynamic linked list and real-time calculation is performed. By analyzing the real-time data, the threshold setting can be adjusted according to the actual driving situation, thereby improving the adaptability and flexibility of the system. Based on the preset acceleration threshold, the preset maximum value of the rough road section and the preset minimum value of the rough road section, the average change value of the ACCy value set and the ACCz value set are judged for sudden acceleration behavior, specifically: based on the preset acceleration threshold, the average change value of the ACCy value set is compared, and it is judged whether the average change value is greater than the preset acceleration threshold, and the acceleration judgment result is obtained. When it is detected that the average value of ACCy is greater than the threshold, the sudden acceleration flag is set to 1, and the relevant data is started. Then, based on the preset maximum value of the rough road section and the minimum value of the rough road section, the ACCz value set is compared, and the number of times the ACCz value set exceeds the preset maximum value of the rough road section and the minimum value of the rough road section is counted, and the corresponding acceleration out-of-range number is obtained. Based on the acceleration judgment result and the acceleration over-range times, the sudden acceleration behavior judgment result is determined. If the acceleration judgment result is no, the sudden acceleration behavior judgment result is that the vehicle does not perform sudden acceleration. If the acceleration judgment result is yes, it is determined whether the acceleration over-range times are greater than the preset over-range times, and the over-time judgment result is obtained. According to the over-time judgment result, the sudden acceleration behavior judgment result is determined. If the over-time judgment result is yes, the sudden acceleration behavior judgment result is that the vehicle does not perform sudden acceleration, and it is judged as an uneven road section to avoid misjudgment of sudden acceleration. If the over-time judgment result is no, the sudden acceleration behavior judgment result is that the vehicle performs sudden acceleration. The analysis of ACCz is introduced to distinguish between normal road conditions and uneven roads, reduce misjudgment of sudden acceleration, and improve the reliability of the system. When the average value of ACCy value is less than the threshold value, and the ACCz judgment method confirms that the vehicle is not on an uneven road section, the relevant counter and flag are reset. Intelligent judgment of the environment in which the vehicle is located is realized to avoid misjudgment of sudden acceleration on uneven roads, thereby improving the intelligence level of the system.

[0042] Step S5: If the sudden acceleration behavior judgment result is yes, the sudden acceleration event data is obtained, the alarm mechanism is activated through the alarm and recording unit, and the sudden acceleration event data is alarm-recorded based on the alarm mechanism.

[0043] Specifically, if the result of the sudden acceleration behavior judgment is yes, it indicates that the vehicle is accelerating suddenly, and the sudden acceleration event data associated with it is obtained. The alarm mechanism is activated through the alarm and recording unit, wherein the alarm and recording unit is used to alarm when sudden acceleration or potential sudden acceleration risk is detected, and record relevant data, and the alarm mechanism is: when the sudden acceleration state is activated, a relevant alarm program can be designed. Based on the alarm mechanism, the sudden acceleration event data is recorded, and the sudden acceleration event related data is stored in a non-volatile memory to facilitate subsequent driving behavior analysis and safety assessment. Through real-time monitoring of the dynamic state of the vehicle, sudden acceleration behavior can be quickly identified and an alarm can be issued in a timely manner, thereby improving the driver's vigilance and reducing the possibility of accidents.

[0044] In summary, the embodiments of the present application have at least the following technical effects:

[0045] The IMU module is used to collect and obtain vehicle motion state data in real time, and the data processing unit performs filtering and fusion processing on the obtained data to obtain vehicle posture change data. A two-way linked list is used to store the vehicle posture change data, and a preset number of adjacent vehicle posture data are selected from the two-way linked list. The adjacent vehicle posture data is judged for sudden acceleration behavior based on the state judgment unit calculation to obtain the sudden acceleration behavior judgment result. If the sudden acceleration behavior judgment result is yes, sudden acceleration event data is obtained, and the alarm mechanism is activated through the alarm and recording unit. The technical solution of alarm recording the sudden acceleration event data based on the alarm mechanism is achieved. The real-time collection and processing of vehicle data is achieved through the IMU sensor, the environmental adaptability is improved, the sensor detection cost and the detection error rate are reduced, and the detection accuracy of the vehicle sudden acceleration behavior is improved, and the sudden acceleration behavior is ensured. The technical effect of timely warning of sudden acceleration behavior.

[0046] Embodiment 2, based on the same inventive concept as the vehicle rapid acceleration behavior recognition method in the above embodiment, Figure 2 As shown, the present application provides a vehicle rapid acceleration behavior recognition device, wherein the device comprises:

[0047] Data acquisition module 11: using the IMU module to collect and acquire vehicle motion state data in real time, the vehicle motion state data includes vehicle acceleration data and vehicle angular velocity data; data processing module 12: filtering and fusing the vehicle acceleration data and vehicle angular velocity data through the data processing unit to obtain vehicle posture change data; storage selection module 13: using a bidirectional linked list to store vehicle posture change data, and selecting a preset number of adjacent vehicle posture data from the bidirectional linked list; sudden acceleration judgment module 14: based on the state judgment unit calculation, the sudden acceleration behavior judgment is performed on the adjacent vehicle posture data to obtain a sudden acceleration behavior judgment result; alarm recording module 15: if the sudden acceleration behavior judgment result is yes, the sudden acceleration event data is obtained, the alarm mechanism is activated through the alarm and recording unit, and the sudden acceleration event data is alarm recorded based on the alarm mechanism.

[0048] Furthermore, the device is also used to perform the following method:

[0049] According to the IMU module, a three-axis acceleration sensor and a three-axis gyroscope are obtained; offset calibration and temperature compensation are performed on the three-axis acceleration sensor and the three-axis gyroscope to obtain a standard three-axis acceleration sensor and a standard three-axis gyroscope; a data sampling frequency is set, and the standard three-axis acceleration sensor and the standard three-axis gyroscope are used according to the data sampling frequency to acquire vehicle acceleration data and vehicle angular velocity data in real time; based on the vehicle acceleration data and the vehicle angular velocity data, the vehicle motion state data is obtained.

[0050] Furthermore, the device is also used to perform the following method:

[0051] The vehicle acceleration data and the vehicle angular velocity data are subjected to low-pass filtering by a data processing unit to obtain filtered vehicle acceleration data and filtered vehicle angular velocity data; the filtered vehicle acceleration data and the filtered vehicle angular velocity data are subjected to Kalman filtering to obtain usable filtered vehicle acceleration data and usable vehicle angular velocity data; and the usable filtered vehicle acceleration data and the usable vehicle angular velocity data are subjected to fusion processing by a six-axis fusion algorithm to obtain the vehicle posture change data.

[0052] Furthermore, the device is also used to perform the following method:

[0053] Define a linked list structure and a node structure, wherein the linked list structure includes the number of linked lists and the size of the linked list, and obtain a bidirectional linked list based on the linked list structure and the node structure; determine the time window length according to the data sampling frequency; set the time window size according to data processing requirements; and use a sliding window mechanism to update the time window of the bidirectional linked list based on the time window length and the time window size.

[0054] Furthermore, the device is also used to perform the following method:

[0055] According to the posture data of the neighboring vehicles, an ACCy value set and an ACCz value set are obtained; based on the state judgment unit, an average change value of the ACCy value set is calculated and obtained; a preset acceleration threshold is obtained, and a maximum value of an uneven road section and a minimum value of an uneven road section are preset; based on the preset acceleration threshold, the preset maximum value of an uneven road section and the minimum value of an uneven road section, a sudden acceleration behavior judgment is performed on the average change value of the ACCy value set and the ACCz value set to obtain a sudden acceleration behavior judgment result.

[0056] Furthermore, the device is also used to perform the following method:

[0057] Based on the preset acceleration threshold, the average change value of the ACCy value set is compared to determine whether the average change value is greater than the preset acceleration threshold to obtain an acceleration judgment result; based on the preset rough road section maximum value and the preset rough road section minimum value, the ACCz value set is compared to count the number of times the ACCz value set exceeds the preset rough road section maximum value and the preset rough road section minimum value to obtain the number of acceleration out-of-range times; based on the acceleration judgment result and the number of acceleration out-of-range times, a sudden acceleration behavior judgment result is determined.

[0058] Furthermore, the device is also used to perform the following method:

[0059] If the acceleration judgment result is no, the sudden acceleration behavior judgment result is that the vehicle did not perform sudden acceleration; if the acceleration judgment result is yes, determine whether the acceleration exceeds the range times greater than the preset exceeding range times, and obtain the exceeding times judgment result; according to the exceeding times judgment result, determine the sudden acceleration behavior judgment result.

[0060] Furthermore, the device is also used to perform the following method:

[0061] If the result of the excess number judgment is yes, the result of the sudden acceleration behavior judgment is that the vehicle does not perform sudden acceleration; if the result of the excess number judgment is no, the result of the sudden acceleration behavior judgment is that the vehicle performs sudden acceleration.

[0062] Embodiment 3, based on the same inventive concept as the vehicle rapid acceleration behavior recognition method in the above embodiment, this embodiment provides a computer-readable storage medium, which can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the vehicle rapid acceleration behavior recognition method in the embodiment of the present application. The processor executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory, that is, realizing the above-mentioned vehicle rapid acceleration behavior recognition method.

[0063] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0065] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for identifying vehicle rapid acceleration behavior, characterized in that: The method comprises: The IMU module is used to collect and obtain vehicle motion state data in real time, wherein the vehicle motion state data includes vehicle acceleration data and vehicle angular velocity data; The vehicle acceleration data and the vehicle angular velocity data are filtered and fused by a data processing unit to obtain vehicle posture change data; A bidirectional linked list is used to store vehicle posture change data, and a preset number of adjacent vehicle posture data are selected from the bidirectional linked list; Performing a sudden acceleration behavior judgment on the posture data of the neighboring vehicle based on the calculation of the state judgment unit to obtain a sudden acceleration behavior judgment result; If the sudden acceleration behavior is determined to be yes, sudden acceleration event data is obtained, an alarm mechanism is activated through an alarm and recording unit, and an alarm record is performed on the sudden acceleration event data based on the alarm mechanism.

2. The method for identifying vehicle rapid acceleration behavior according to claim 1, characterized in that: The obtaining of vehicle motion state data comprises: According to the IMU module, a three-axis acceleration sensor and a three-axis gyroscope are obtained; Performing offset calibration and temperature compensation on the three-axis acceleration sensor and the three-axis gyroscope to obtain a standard three-axis acceleration sensor and a standard three-axis gyroscope; Setting a data sampling frequency, and using the standard three-axis acceleration sensor and the standard three-axis gyroscope according to the data sampling frequency to collect and obtain vehicle acceleration data and vehicle angular velocity data in real time; The vehicle motion state data is acquired based on the vehicle acceleration data and the vehicle angular velocity data.

3. The vehicle rapid acceleration behavior recognition method according to claim 1, characterized in that: The obtaining of vehicle posture change data includes: Performing low-pass filtering on the vehicle acceleration data and the vehicle angular velocity data by a data processing unit to obtain filtered vehicle acceleration data and filtered vehicle angular velocity data; Performing Kalman filtering on the filtered vehicle acceleration data and the filtered vehicle angular velocity data to obtain usable filtered vehicle acceleration data and usable vehicle angular velocity data; The available filtered vehicle acceleration data and the available vehicle angular velocity data are fused using a six-axis fusion algorithm to obtain the vehicle posture change data.

4. The method for identifying vehicle rapid acceleration behavior according to claim 2, characterized in that: The bidirectional linked list comprises: Define a linked list structure and a node structure, wherein the linked list structure includes the number of linked lists and the size of the linked lists, and obtain a bidirectional linked list based on the linked list structure and the node structure; Determine the time window length according to the data sampling frequency; Set the time window size according to data processing requirements; A sliding window mechanism is used to update the time window of the bidirectional linked list based on the time window length and the time window size.

5. The method for identifying vehicle rapid acceleration behavior according to claim 1, characterized in that: The obtaining of the rapid acceleration behavior judgment result includes: According to the neighboring vehicle posture data, obtaining an ACCy value set and an ACCz value set; Obtaining an average change value of the ACCy value set based on calculation by the state judgment unit; Obtain a preset acceleration threshold, and preset a maximum value for an uneven road section and a minimum value for an uneven road section; Based on the preset acceleration threshold, the preset maximum value of the rough road section and the minimum value of the rough road section, a sudden acceleration behavior judgment is performed on the average change value of the ACCy value set and the ACCz value set to obtain a sudden acceleration behavior judgment result.

6. The method for identifying vehicle rapid acceleration behavior according to claim 5, characterized in that: The obtaining of the rapid acceleration behavior judgment result includes: Comparing the average change value of the ACCy value set based on the preset acceleration threshold, determining whether the average change value is greater than the preset acceleration threshold, and obtaining an acceleration determination result; Based on the preset maximum value of the rough road section and the minimum value of the rough road section, the ACCz value set is compared, and the number of times the ACCz value set exceeds the preset maximum value of the rough road section and the minimum value of the rough road section is counted to obtain the number of acceleration out-of-range times; Based on the acceleration judgment result and the number of acceleration exceeding the range, a sudden acceleration behavior judgment result is determined.

7. The method for identifying vehicle rapid acceleration behavior according to claim 6, characterized in that: The determining of the rapid acceleration behavior judgment result includes: If the acceleration judgment result is no, then the sudden acceleration behavior judgment result is that the vehicle does not perform sudden acceleration; If the acceleration judgment result is yes, determine whether the acceleration out-of-range times is greater than a preset out-of-range times, and obtain an out-of-range times judgment result; The sudden acceleration behavior judgment result is determined according to the excess number judgment result.

8. The method for identifying vehicle rapid acceleration behavior according to claim 7, characterized in that: The determining of the rapid acceleration behavior judgment result includes: If the result of the excessive number of times is yes, the result of the sudden acceleration behavior is that the vehicle does not perform sudden acceleration; If the result of the excess number judgment is no, the result of the sudden acceleration behavior judgment is that the vehicle is performing sudden acceleration.

9. A vehicle rapid acceleration behavior recognition device, characterized in that: The device is used to implement the vehicle rapid acceleration behavior recognition method according to any one of claims 1 to 8, comprising: Data acquisition module: using the IMU module to acquire vehicle motion state data in real time, the vehicle motion state data including vehicle acceleration data and vehicle angular velocity data; Data processing module: filtering and fusing the vehicle acceleration data and vehicle angular velocity data through a data processing unit to obtain vehicle posture change data; Storage and selection module: using a bidirectional linked list to store vehicle posture change data, and selecting a preset number of adjacent vehicle posture data from the bidirectional linked list; A sudden acceleration judgment module: based on the calculation of the state judgment unit, a sudden acceleration behavior judgment is performed on the posture data of the neighboring vehicle to obtain a sudden acceleration behavior judgment result; Alarm recording module: if the judgment result of the sudden acceleration behavior is yes, the sudden acceleration event data is obtained, the alarm mechanism is activated through the alarm and recording unit, and the sudden acceleration event data is alarm-recorded based on the alarm mechanism.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle rapid acceleration behavior recognition method as described in any one of claims 1 to 8 is implemented.

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