Vehicle sharp turn determination method, device, electronic device and storage medium

Abstract

The present application relates to the field of vehicle technology, and in particular to a method, device, electronic device, and storage medium for determining a vehicle sharp turn, wherein the method comprises: collecting vehicle driving time series data within a preset travel range at a preset collection frequency, and calculating a moving average of the vehicle lateral acceleration of the vehicle driving time series data within the preset travel range; performing a first-order difference operation on the moving average to obtain multiple differential results, and calculating the average and standard deviation of the multiple differential results; determining a sharp turn threshold interval based on the average and standard deviation, wherein when the average is within the sharp turn threshold interval, it is determined that the vehicle is not in a sharp turn condition; and when the average is not within the sharp turn threshold interval, it is determined that the vehicle is in a sharp turn condition. Thus, the problem in the related art of determining whether a vehicle is in a dangerous condition based on the GPS speed or the comprehensive lateral force coefficient being higher than a set threshold, which often leads to misjudgment or erroneous judgment, is solved, judgment accuracy is improved, and safety risks are reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, electronic device, and storage medium for determining a sharp turn of a vehicle. Background Art

[0002] Common adverse driving behaviors in daily driving include sudden acceleration, braking, sharp turns, speeding, fatigued driving, and not wearing seatbelts. These behaviors are often a major cause of traffic accidents, and in more serious cases, can result in casualties, with negative consequences for families and society. Therefore, it is crucial to accurately identify and assess driver misconduct in a timely and proactive manner, enabling risk assessment and continuous monitoring. This can significantly reduce the incidence of traffic accidents. However, current vehicle monitoring platforms rely on subjective human expertise to identify drivers. This not only requires high technical and experience expertise from supervisors, but also significantly incurs labor costs, leading to low efficiency and difficulty in ensuring the accuracy of monitoring results. Therefore, it is necessary to leverage artificial intelligence and big data statistical methods to analyze adverse driving behaviors, reduce misjudgments due to human experience, lower the cost of installing relevant monitoring equipment on vehicles, and alleviate the workload of supervisors.

[0003] Related technologies generally make judgments based on the GPS (Global Positioning System) speed measured once per second, or simply rely on historical experience to determine whether the comprehensive lateral force coefficient is higher than a set threshold.

[0004] However, due to problems such as inaccurate GPS or weak signals, misjudgments or delays may occur; or because the comprehensive lateral force coefficients for different journeys and road conditions are often different, a one-size-fits-all result often leads to misjudgments. Summary of the Invention

[0005] The present application provides a method, device, electronic device and storage medium for determining whether a vehicle is in a sharp turn, so as to solve the problem in the related art of determining whether a vehicle is in a dangerous condition based on GPS speed or whether the comprehensive lateral force coefficient is higher than a set threshold, which often leads to misjudgment or erroneous judgment, thereby improving judgment accuracy and reducing safety risks.

[0006] A first embodiment of the present application provides a method for determining a sharp turn of a vehicle, comprising the following steps: collecting vehicle travel time series data within a preset travel range at a preset collection frequency, and calculating a moving average of the vehicle lateral acceleration of the vehicle travel time series data within the preset travel range;

[0007] performing a first-order difference operation on the moving average to obtain a plurality of difference results, and calculating an average value and a standard deviation of the plurality of difference results; and

[0008] A sharp turn threshold interval is determined based on the average value and the standard deviation, wherein when the average value is within the sharp turn threshold interval, it is determined that the vehicle is not in a sharp turn condition; and when the average value is not within the sharp turn threshold interval, it is determined that the vehicle is in a sharp turn condition.

[0009] Optionally, before calculating the moving average of the lateral acceleration of the vehicle driving time series data within the preset range, the method further includes:

[0010] Extracting the vehicle lateral acceleration, vehicle steering wheel angle, and vehicle speed corresponding to the acquisition moment from the vehicle driving time series data;

[0011] determining whether the vehicle speed is greater than a preset speed, whether the vehicle steering wheel angle is greater than a preset angle, whether the vehicle steering wheel angle is in a direction opposite to the vehicle lateral acceleration, and whether the duration within the preset travel range is greater than a preset duration;

[0012] If yes, then the moving average of the lateral acceleration of the vehicle driving time series data within the preset stroke is calculated; otherwise, the vehicle driving time series data within the preset stroke is discarded.

[0013] Optionally, the moving average of the lateral acceleration of the vehicle driving time series data within the preset range is calculated by the following formula:

[0014] in, is the moving average of the lateral acceleration at time t, for The lateral acceleration at the moment, t is the time, and N is a positive integer.

[0015] Optionally, the sharp turn threshold interval is:

[0016]

[0017] in, is the average value of the lateral acceleration difference, k is the preset value, is the standard deviation of the lateral acceleration difference.

[0018] Optionally, the vehicle driving timing data includes: any one or more of vehicle identification information, vehicle current driving time, vehicle current ignition time, lateral acceleration, vehicle current speed, vehicle current steering wheel angle, and vehicle current brake / throttle status.

[0019] A second embodiment of the present application provides a vehicle sharp turn determination device, comprising:

[0020] a first calculation module, configured to collect vehicle travel time series data within a preset travel range at a preset collection frequency, and calculate a moving average of vehicle lateral acceleration of the vehicle travel time series data within the preset travel range;

[0021] a second calculation module, configured to perform a first-order difference operation on the moving average to obtain a plurality of difference results, and calculate an average value and a standard deviation of the plurality of difference results; and

[0022] A determination module is configured to determine a sharp turn threshold interval based on the average value and the standard deviation, wherein when the average value is within the sharp turn threshold interval, it is determined that the vehicle is not in a sharp turn condition; and when the average value is not within the sharp turn threshold interval, it is determined that the vehicle is in a sharp turn condition.

[0023] Optionally, before calculating the moving average of the lateral acceleration of the vehicle driving time series data within the preset range, the first calculation module further includes:

[0024] Extracting the vehicle lateral acceleration, vehicle steering wheel angle, and vehicle speed corresponding to the acquisition moment from the vehicle driving time series data;

[0025] determining whether the vehicle speed is greater than a preset speed, whether the vehicle steering wheel angle is greater than a preset angle, whether the vehicle steering wheel angle is in a direction opposite to the vehicle lateral acceleration, and whether the duration within the preset travel range is greater than a preset duration;

[0026] If yes, then the moving average of the lateral acceleration of the vehicle driving time series data within the preset stroke is calculated; otherwise, the vehicle driving time series data within the preset stroke is discarded.

[0027] Optionally, the moving average of the lateral acceleration of the vehicle driving time series data within the preset range is calculated by the following formula:

[0028] in, is the moving average of the lateral acceleration at time t, for The lateral acceleration at the moment, t is the time, and N is a positive integer.

[0029] Optionally, the sharp turn threshold interval is:

[0030]

[0031] in, is the average value of the lateral acceleration difference, k is the preset value, is the standard deviation of the lateral acceleration difference.

[0032] Optionally, the vehicle driving timing data includes: any one or more of vehicle identification information, vehicle current driving time, vehicle current ignition time, lateral acceleration, vehicle current speed, vehicle current steering wheel angle, and vehicle current brake / throttle status.

[0033] The third aspect of the present application provides a vehicle, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the vehicle sharp turn determination method as described in the above embodiment.

[0034] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the vehicle sharp turn determination method as described in the above embodiment.

[0035] Thus, vehicle travel time series data over a certain distance is collected at a certain frequency, and a moving average of the vehicle's lateral acceleration is calculated for this data. A first-order difference operation is performed on the moving average to obtain multiple difference results. The average and standard deviation of these multiple difference results are then calculated, and a sharp turn threshold interval is determined based on the average and standard deviation. When the average falls within the sharp turn threshold interval, the vehicle is determined not to be in a sharp turn; when the average falls outside the sharp turn threshold interval, the vehicle is determined to be in a sharp turn. This solves the problem in related technologies of determining whether a vehicle is in a dangerous condition based on GPS speed or a comprehensive lateral force coefficient exceeding a set threshold, which often leads to misjudgments or erroneous judgments. This improves judgment accuracy and reduces safety risks.

[0036] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0038] Figure 1 This is a flow chart of a method for determining a sharp turn of a vehicle provided in an embodiment of the present application;

[0039] Figure 2 This is a flow chart of a method for determining a sharp turn of a vehicle according to one embodiment of the present application;

[0040] Figure 3 Schematic diagram of test results of a method for determining a sharp turn of a vehicle according to one embodiment of the present application;

[0041] Figure 4 Schematic diagram of test results of a method for determining a sharp turn of a vehicle according to one embodiment of the present application;

[0042] Figure 5 Schematic diagram of a block diagram of a vehicle sharp turn determination device provided according to an embodiment of the present application;

[0043] Figure 6 A schematic structural diagram of a vehicle provided for an embodiment of the application. DETAILED DESCRIPTION

[0044] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0045] The following describes, with reference to the accompanying drawings, a vehicle sharp turn determination method, device, electronic device, and storage medium according to embodiments of the present application. In response to the problem in the related art mentioned in the background art center that the determination of whether a vehicle is in a dangerous condition based on the GPS speed or the integrated lateral force coefficient being above a set threshold often results in misjudgment or erroneous judgment, the present application provides a vehicle sharp turn determination method. In this method, vehicle travel time series data within a certain travel distance is collected at a certain collection frequency, and a moving average of the vehicle lateral acceleration of the vehicle travel time series data within the certain travel distance is calculated. A first-order difference operation is performed on the moving average to obtain multiple difference results, and the average and standard deviation of the multiple difference results are calculated. A sharp turn threshold interval is determined based on the average and standard deviation. When the average is within the sharp turn threshold interval, the vehicle is determined not to be in a sharp turn condition; when the average is not within the sharp turn threshold interval, the vehicle is determined to be in a sharp turn condition. This solves the problem in the related art that the determination of whether a vehicle is in a dangerous condition based on the GPS speed or the integrated lateral force coefficient being above a set threshold often results in misjudgment or erroneous judgment, thereby improving determination accuracy and reducing safety risks.

[0046] Specifically, Figure 1 A flow chart of a method for determining a sharp turn of a vehicle provided in an embodiment of the present application.

[0047] like Figure 1 As shown, the vehicle sharp turn determination method includes the following steps:

[0048] In step S101 , vehicle driving time series data within a preset journey is collected at a preset collection frequency, and a moving average of the vehicle lateral acceleration of the vehicle driving time series data within the preset journey is calculated.

[0049] Optionally, in some embodiments, the vehicle driving timing data includes any one or more of: vehicle identification information, vehicle current driving time, vehicle current ignition time, lateral acceleration, vehicle current speed, vehicle current steering wheel angle, and vehicle current brake / throttle status.

[0050] Optionally, in some embodiments, the moving average of the lateral acceleration of the vehicle driving time series data within the preset range is calculated using the following formula:

[0051] in, is the moving average of the lateral acceleration at time t, for The lateral acceleration at the moment, t is the time, and N is a positive integer.

[0052] The preset collection frequency and preset travel distance can be pre-set by the user, obtained through a limited number of experiments, or obtained through a limited number of computer simulations, and are not specifically limited here. For example, the preset collection frequency can be 1s, that is, uploading vehicle time series data every 1 second. The time series data can be obtained from the vehicle's T-box (Telematics-Box, an intelligent vehicle terminal).

[0053] It should be noted that the original time series data has irregular changes caused by jitter (filter). When the time series data fluctuates greatly due to periodic changes and random fluctuations, it is not easy to show the development trend of the event. Then the moving average method can eliminate the influence of these factors, thereby smoothing or leveling the original sequence and weakening the ups and downs of the original sequence.

[0054] In step S102 , a first-order difference operation is performed on the moving average to obtain a plurality of difference results, and the average value and standard deviation of the plurality of difference results are calculated.

[0055] It should be noted that differencing is a method for transforming time series data, which can be used to eliminate dependence on time series data. Differencing is performed by subtracting the previous observation from the current observation. After performing first-order or multi-order differencing on the time series, the fluctuations of the original time series data can be weakened, that is, the original time series data can be processed to be smooth. After the difference processing, the time series data can be used with various estimation and testing methods that can only be used for stationary data.

[0056] Specifically, the embodiment of the present application can be based on the lateral acceleration after a moving average Perform first-order difference operation, that is, make the difference between the adjacent lateral accelerations before and after, and get in, is the differential result based on the lateral acceleration, i is a positive integer, and then the average value is calculated and standard deviation

[0057] In step S103, a sharp turn threshold interval is determined based on the average value and the standard deviation, wherein when the average value is within the sharp turn threshold interval, it is determined that the vehicle is not in a sharp turn condition; when the average value is not within the sharp turn threshold interval, it is determined that the vehicle is in a sharp turn condition.

[0058] Optionally, in some embodiments, the sharp turn threshold interval is:

[0059]

[0060] in, is the average value of the lateral acceleration difference, k is the preset value, is the standard deviation of the lateral acceleration difference.

[0061] It's important to note that using the "difference in lateral acceleration" to determine turning status, rather than using a specific "lateral acceleration" or "speed" as the threshold for a sharp turn, can reduce the impact of random errors in the data itself. Furthermore, based on the central limit theorem, if the impact of each factor is minimal, the overall effect can be considered to follow a normal distribution, so the difference in lateral acceleration can be assumed to follow a normal distribution. According to the principle of small probability in statistics, a low-probability event is almost impossible to occur in a single experiment. If the event actually occurs in a single experiment, it can only be assumed that the event is not in the population we hypothesized, and therefore our assumption about the population is incorrect. Similarly, considering a sharp turn during normal driving as a low-probability event is based on the 3σ criterion in statistical distribution: the values of a normally distributed data set are almost entirely concentrated within the interval (μ - 3σ, μ + 3σ), with the probability of exceeding this range being less than 0.3%.

[0062] Specifically, based on the average value of the lateral acceleration difference and standard deviation By setting the threshold of the difference value as the critical value for judging sharp turns, if exist (k=1,2,3), then it is judged that there is no sharp turn. If The value of If the value is other than (k=1, 2, 3), it is considered that a sharp turn has occurred.

[0063] Therefore, by performing a differential operation on the data after a moving average of the vehicle's lateral acceleration and setting a threshold through a probability distribution model to identify whether a sharp turn is made, the number of sharp turns can be accurately counted, which is easy to implement and highly accurate.

[0064] Optionally, in some embodiments, before calculating the moving average of the lateral acceleration of the vehicle driving time series data within a preset journey, it also includes: extracting the vehicle lateral acceleration, vehicle steering wheel angle, and vehicle speed corresponding to the collection time from the vehicle driving time series data; judging whether the vehicle speed is greater than the preset speed, and whether the vehicle steering wheel angle is greater than the preset angle, and whether the direction of the vehicle steering wheel angle is opposite to the direction of the vehicle lateral acceleration, and whether the duration within the preset journey is greater than the preset duration; if so, calculating the moving average of the lateral acceleration of the vehicle driving time series data within the preset journey, otherwise, kicking out the vehicle driving time series data within the preset journey.

[0065] The preset speed, preset angle, and preset duration can be pre-set by the user, obtained through a limited number of experiments, or obtained through a limited number of computer simulations, and are not specifically limited here. For example, the preset speed can be 15 km / h, the preset angle can be 60°, and the preset duration can be 5 minutes.

[0066] It is understandable that there are certain criteria for determining whether a vehicle makes a sharp turn. When the vehicle speed is very low, for example, when the vehicle speed is less than 15 km / h, it is unlikely that a sharp turn will occur. At the same time, when the vehicle steering wheel angle is very small, for example, when the steering wheel angle is less than 60°, a sharp turn will not occur. If the small-angle steering wheel angle is included in the vehicle's time series data, it may cause data redundancy or inaccurate judgment results. In addition, the preset time length needs to be greater than a certain preset time length. For example, when the time length within the preset journey is greater than 5 minutes, if the time length within the preset journey is less than 5 minutes, calculating the moving average of the lateral acceleration of the vehicle's driving time series data within the preset journey may cause data redundancy.

[0067] Therefore, before calculating the moving average of the lateral acceleration of the vehicle driving time series data within the preset range, unnecessary time series data can be eliminated to avoid data redundancy and make the calculation result more accurate.

[0068] In order to enable those skilled in the art to further understand the vehicle sharp turn determination method of the present application, it is described in detail below with reference to specific embodiments.

[0069] The workflow of the vehicle sharp turn determination method according to the embodiment of the present application can be as follows: Figure 3 As shown, the following steps are included:

[0070] S201, collect t i The lateral acceleration at time a i , using (t1,a1),(t 2, a2),…,(t n ,a n)express.

[0071] S202,v i ≥15km / h,|swa i |>60°, a i *swa i <0, where v i t i The speed of time, swa i is the steering wheel angle; when making a sharp turn, the speed must be at least 15 km / h, the steering wheel angle must be at least more than 60°, and the steering wheel angle must be opposite to the direction of the lateral acceleration.

[0072] S203, determine t max -t min Is it greater than 5 minutes, where t max -t min Indicates the driving time of a certain trip. If yes, execute S205; otherwise, execute S204.

[0073] S204, remove the time series data of the entire short stroke and jump to S201.

[0074] S205, calculation Calculate the N-period moving average as the correction value of the lateral acceleration.

[0075] S206, calculation Performs first-order difference calculation of a moving average.

[0076] S207, calculation Average value and standard deviation The threshold is and

[0077] S208, judgment or If yes, execute S209; otherwise, jump to execute S206.

[0078] S209, determine t i The moment took a sharp turn.

[0079] The above-mentioned vehicle sharp turn judgment method is used to conduct relevant tests, and the relevant test results are as follows: Figure 3 、 Figure 4 shown.

[0080] According to the vehicle sharp turn determination method proposed in the embodiment of the present application, vehicle travel time series data within a certain travel distance is collected at a certain collection frequency, and the moving average of the vehicle lateral acceleration of the vehicle travel time series data within the certain travel distance is calculated. A first-order difference operation is performed on the moving average to obtain multiple difference results. The average and standard deviation of the multiple difference results are calculated, and a sharp turn threshold range is determined based on the average and standard deviation. When the average is within the sharp turn threshold range, the vehicle is determined not to be in a sharp turn condition; when the average is not within the sharp turn threshold range, the vehicle is determined to be in a sharp turn condition. This solves the problem in the related art of determining whether a vehicle is in a dangerous condition based on whether the GPS speed or the comprehensive lateral force coefficient is higher than a set threshold, which often leads to misjudgment or erroneous judgment, thereby improving judgment accuracy and reducing safety risks.

[0081] Next, the vehicle sharp turn determination device proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0082] Figure 5 4 is a block diagram of a vehicle sharp turn determination device according to an embodiment of the present application.

[0083] like Figure 5 As shown, the vehicle sharp turn determination device 10 includes: a first calculation module 100 , a second calculation module 200 and a determination module 300 .

[0084] The first calculation module 100 is configured to collect vehicle travel time series data within a preset travel range at a preset collection frequency, and calculate a moving average of the vehicle lateral acceleration of the vehicle travel time series data within the preset travel range;

[0085] A second calculation module 200 is configured to perform a first-order difference operation on the moving average to obtain a plurality of difference results, and calculate an average value and a standard deviation of the plurality of difference results; and

[0086] The determination module 300 is configured to determine a sharp turn threshold interval based on the average value and the standard deviation, wherein when the average value is within the sharp turn threshold interval, it is determined that the vehicle is not in a sharp turn condition; and when the average value is not within the sharp turn threshold interval, it is determined that the vehicle is in a sharp turn condition.

[0087] Optionally, before calculating the moving average of the lateral acceleration of the vehicle driving time series data within the preset range, the first calculation module 100 further includes:

[0088] Extracting the vehicle lateral acceleration, vehicle steering wheel angle, and vehicle speed corresponding to the acquisition time from the vehicle driving time series data;

[0089] Determining whether the vehicle speed is greater than a preset speed, whether the vehicle steering wheel angle is greater than a preset angle, whether the vehicle steering wheel angle is opposite to the vehicle lateral acceleration direction, and whether the duration within the preset travel range is greater than a preset duration;

[0090] If yes, the moving average of the lateral acceleration of the vehicle driving time series data within the preset stroke is calculated; otherwise, the vehicle driving time series data within the preset stroke is discarded.

[0091] Optionally, the moving average of the lateral acceleration of the vehicle driving time series data within the preset range is calculated using the following formula:

[0092]

[0093] in, is the moving average of the lateral acceleration at time t, for The lateral acceleration at the moment, t is the time, and N is a positive integer.

[0094] Optionally, the sharp turn threshold interval is:

[0095]

[0096] in, is the average value of the lateral acceleration difference, k is the preset value, is the standard deviation of the lateral acceleration difference.

[0097] Optionally, the vehicle driving timing data includes any one or more of: vehicle identification information, vehicle current driving time, vehicle current ignition time, lateral acceleration, vehicle current speed, vehicle current steering wheel angle, and vehicle current brake / throttle status.

[0098] It should be noted that the above explanation of the embodiment of the vehicle sharp turn determination method is also applicable to the vehicle sharp turn determination device of this embodiment, and will not be repeated here.

[0099] According to the vehicle sharp turn determination device proposed in the embodiment of the present application, vehicle travel time series data within a certain travel distance is collected at a certain collection frequency, and the moving average of the vehicle lateral acceleration of the vehicle travel time series data within the certain travel distance is calculated. A first-order difference operation is performed on the moving average to obtain multiple difference results. The average and standard deviation of the multiple difference results are calculated, and a sharp turn threshold range is determined based on the average and standard deviation. When the average is within the sharp turn threshold range, the vehicle is determined not to be in a sharp turn condition. When the average is not within the sharp turn threshold range, the vehicle is determined to be in a sharp turn condition. This solves the problem in the related art of determining whether a vehicle is in a dangerous condition based on whether the GPS speed or the comprehensive lateral force coefficient is higher than a set threshold, which often leads to misjudgment or erroneous judgment, thereby improving judgment accuracy and reducing safety risks.

[0100] Figure 6 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0101] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0102] When the processor 602 executes the program, the vehicle sharp turn determination method provided in the above embodiment is implemented.

[0103] Furthermore, the vehicle further comprises:

[0104] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0105] The memory 601 is used to store computer programs that can be run on the processor 602 .

[0106] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0107] If the memory 601, processor 602, and communication interface 603 are implemented independently, the communication interface 603, memory 601, and processor 602 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0108] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0109] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0110] This embodiment further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is characterized in that when the program is executed by a processor, the method for determining a sharp turn of a vehicle as described above is implemented.

[0111] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0112] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0113] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0114] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

Claims

1. A method for determining a sharp turn of a vehicle, characterized in that: The following steps are involved: collecting vehicle travel time series data within a preset journey at a preset collection frequency, and calculating a moving average of the vehicle lateral acceleration of the vehicle travel time series data within the preset journey; Performing a first-order difference operation on the moving average to obtain a plurality of difference results, and calculating an average value and a standard deviation of the plurality of difference results; as well as A sharp turn threshold interval is determined based on the average value and the standard deviation of the multiple difference results, wherein when the average value of the multiple difference results is within the sharp turn threshold interval, it is determined that the vehicle is not in a sharp turn condition; and when the average value of the multiple difference results is not within the sharp turn threshold interval, it is determined that the vehicle is in a sharp turn condition.

2. The method according to claim 1, characterized in that Before calculating the moving average of the lateral acceleration of the vehicle driving time series data within the preset range, the method further includes: Extracting the vehicle lateral acceleration, vehicle steering wheel angle, and vehicle speed corresponding to the acquisition moment from the vehicle driving time series data; determining whether the vehicle speed is greater than a preset speed, whether the vehicle steering wheel angle is greater than a preset angle, whether the vehicle steering wheel angle is in a direction opposite to the vehicle lateral acceleration, and whether the duration within the preset travel range is greater than a preset duration; If yes, then the moving average of the lateral acceleration of the vehicle driving time series data within the preset stroke is calculated; otherwise, the vehicle driving time series data within the preset stroke is discarded.

3. The method according to claim 2, characterized in that The moving average of the lateral acceleration of the vehicle driving time series data within the preset range is calculated using the following formula: in, is the moving average of the lateral acceleration at time t, for The lateral acceleration at the moment, t is the time, and N is a positive integer.

4. The method according to claim 3, characterized in that The sharp turn threshold interval is: in, is the average value of the lateral acceleration difference, k is the preset value, is the standard deviation of the lateral acceleration difference.

5. The method according to any one of claims 1 to 4, characterized in that The vehicle driving timing data includes: any one or more of vehicle identification information, vehicle current driving time, vehicle current ignition time, lateral acceleration, vehicle current speed, vehicle current steering wheel angle, and vehicle current brake / throttle status.

6. A vehicle sharp turn determination device, characterized in that: include: a first calculation module, configured to collect vehicle travel time series data within a preset travel range at a preset collection frequency, and calculate a moving average of vehicle lateral acceleration of the vehicle travel time series data within the preset travel range; a second calculation module, configured to perform a first-order difference operation on the moving average to obtain a plurality of difference results, and calculate an average value and a standard deviation of the plurality of difference results; as well as A determination module is configured to determine a sharp turn threshold interval based on an average value and a standard deviation of the plurality of difference results, wherein when the average value of the plurality of difference results is within the sharp turn threshold interval, it is determined that the vehicle is not in a sharp turn condition; and when the average value of the plurality of difference results is not within the sharp turn threshold interval, it is determined that the vehicle is in a sharp turn condition.

7. The device according to claim 6, characterized in that Before calculating the moving average of the lateral acceleration of the vehicle driving time series data within the preset range, the first calculation module further includes: Extracting the vehicle lateral acceleration, vehicle steering wheel angle, and vehicle speed corresponding to the acquisition moment from the vehicle driving time series data; determining whether the vehicle speed is greater than a preset speed, whether the vehicle steering wheel angle is greater than a preset angle, whether the vehicle steering wheel angle is in a direction opposite to the vehicle lateral acceleration, and whether the duration within the preset travel range is greater than a preset duration; If yes, then the moving average of the lateral acceleration of the vehicle driving time series data within the preset stroke is calculated; otherwise, the vehicle driving time series data within the preset stroke is discarded.

8. The device according to claim 7, characterized in that The moving average of the lateral acceleration of the vehicle driving time series data within the preset range is calculated using the following formula: in, is the moving average of the lateral acceleration at time t, for The lateral acceleration at the moment, t is the time, and N is a positive integer.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle sharp turn determination method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle sharp turn determination method according to any one of claims 1 to 5.

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