Vehicle speed prediction method, prediction device, computer readable storage medium and vehicle

Abstract

The present application provides a vehicle speed prediction method, prediction device, computer-readable storage medium, and vehicle. The method includes: performing linear fitting on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula; calculating the predicted vehicle speed value of the target vehicle at the current moment based on the vehicle speed prediction formula and the target sequence number in the target array at the current moment; determining the predicted vehicle speed value as the current vehicle speed value at the current moment when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold; and determining the current vehicle speed value at the current moment based on at least the target vehicle speed value at the previous target moment adjacent to the current moment when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than the predetermined threshold, thereby solving the problem of a large amount of historical data and a large amount of calculation required for vehicle speed prediction in the prior art.

Description

Technical Field

[0001] The present application relates to the field of vehicles, and more specifically, to a vehicle speed prediction method, a vehicle speed prediction device, a computer-readable storage medium, and a vehicle. Background Art

[0002] During the driving process of the vehicle, if the vehicle speed signal value is lost occasionally, it will cause the PTO (Power Take Off, PTO for short) and other vehicle functions to abnormally exit.

[0003] In order to solve the problem of abnormal vehicle function exit caused by occasional loss of vehicle speed signal value, the vehicle speed prediction method is usually used. However, in the existing technology, vehicle speed prediction usually has the problem of requiring a large amount of historical data and a large amount of calculation. Summary of the Invention

[0004] The main purpose of this application is to provide a vehicle speed prediction method, a vehicle speed prediction device, a computer-readable storage medium and a vehicle, so as to at least solve the problem of a large amount of historical data and a large amount of calculation required for vehicle speed prediction in the prior art.

[0005] To achieve the above-mentioned object, according to one aspect of the present application, a vehicle speed prediction method is provided, comprising: performing linear fitting on target vehicle speed values ​​and target sequence numbers at each target moment in a target array to obtain a vehicle speed prediction formula, wherein the target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments, the target sequence number being the position of each target moment in the target array, and the multiple target moments being stored in the target array in chronological order; calculating a predicted vehicle speed value of a target vehicle at the current moment based on the vehicle speed prediction formula and the target sequence number at the current moment in the target array; determining the predicted vehicle speed value as the current vehicle speed value at the current moment if the difference between the predicted vehicle speed value and the actual vehicle speed value at the current moment is greater than or equal to a predetermined threshold; and determining the current vehicle speed value at the current moment based on at least the target vehicle speed value at the previous target moment adjacent to the current moment if the difference between the predicted vehicle speed value and the actual vehicle speed value at the current moment is less than the predetermined threshold, wherein the actual vehicle speed value is the actual value of the vehicle speed at the current moment collected by a speed sensor.

[0006] Optionally, performing a linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a vehicle speed prediction formula includes: performing a first-order linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant; and obtaining the vehicle speed prediction formula v based at least on the first constant. pre=b0(n·dt), where b0 is the first constant, v pre is the predicted vehicle speed value, n is the target sequence number, t is the predetermined step length, and the predetermined step length is the duration between two adjacent target moments.

[0007] Optionally, performing a linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a vehicle speed prediction formula includes: performing a first-order linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant and a second constant; and obtaining the vehicle speed prediction formula v based on at least the first constant and the second constant. pre =b1+b0(n·dt), where b0 is the first constant, b1 is the second constant, and v pre is the predicted vehicle speed value, n is the target sequence number, t is the predetermined step length, and the predetermined step length is the duration between two adjacent target moments.

[0008] Optionally, performing a first-order linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant and a second constant includes: performing a first-order linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain the first constant and the second constant Among them, x i is the i-th target moment, y i is the target vehicle speed value corresponding to the i-th target time, is the average time of n target times, is the average speed value of n target speed values, n is the total number of target moments, i is an integer and the value of i starts from 1 and goes up to n.

[0009] Optionally, based on the vehicle speed prediction formula and the target sequence number in the target array at the current moment, the predicted vehicle speed value of the target vehicle at the current moment is calculated, including: storing the current moment to the target array according to the first-in-first-out method, and determining the position of the current moment in the target array as the target sequence number in the target array at the current moment; based on the vehicle speed prediction formula and the target sequence number at the current moment, calculating the predicted vehicle speed value corresponding to the current moment.

[0010] Optionally, when the difference between the predicted vehicle speed value and the actual vehicle speed value at the current moment is less than the predetermined threshold value, the current vehicle speed value at the current moment is determined at least based on the target vehicle speed value at the previous target moment adjacent to the current moment, including: determining whether the target vehicle speed value at the previous target moment adjacent to the current moment is the predicted vehicle speed value; when the target vehicle speed value at the previous target moment adjacent to the current moment is the predicted vehicle speed value, and the difference between the predicted vehicle speed value and the actual vehicle speed value at the current moment is within a predetermined range, determining the actual vehicle speed value as the current vehicle speed value at the current moment; when the target vehicle speed value at the previous target moment adjacent to the current moment is not the predicted vehicle speed value, and / or when the difference between the predicted vehicle speed value and the actual vehicle speed value at the current moment is not within the predetermined range, determining the predicted vehicle speed value as the current vehicle speed value at the current moment.

[0011] Optionally, before performing linear fitting on the target vehicle speed value and target serial number at each target moment in the target array to obtain a vehicle speed prediction formula, the vehicle speed prediction method further includes: determining whether the total number of the target moments in the target array reaches a predetermined number; when the total number of the target moments in the target array is less than the predetermined number, determining the actual vehicle speed value as the current vehicle speed value at the current moment; when the total number of the target moments in the target array is greater than or equal to the predetermined number, performing linear fitting on the target vehicle speed value and the target serial number at each target moment.

[0012] According to another aspect of the present application, a vehicle speed prediction device is provided, comprising: a fitting unit for performing linear fitting on the target vehicle speed value and the target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula, wherein the target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments, the target sequence number is the position of each target moment in the target array, and the multiple target moments are stored in the target array in chronological order; a calculation unit for calculating the target vehicle speed at the target moment based on the vehicle speed prediction formula and the target sequence number at the current moment in the target array. The predicted vehicle speed value at the current moment; a first determination unit, used to determine the predicted vehicle speed value as the current vehicle speed value at the current moment when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold value, and to determine the current vehicle speed value at the current moment based on at least the target speed value at the previous target moment adjacent to the current moment when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than the predetermined threshold value, wherein the actual vehicle speed value is the actual value of the vehicle speed at the current moment collected by the speed sensor.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the vehicle speed prediction methods.

[0014] According to another aspect of the present application, a vehicle is provided, comprising: a vehicle speed prediction device, wherein the vehicle speed prediction device is used to execute any one of the vehicle speed prediction methods.

[0015] Applying the technical solution of the present application, first, a linear fit is performed based on the target speed values ​​and target serial numbers corresponding to each target moment in the target array to obtain a speed prediction formula; then, based on the target serial number in the target array at the current moment and the speed prediction formula obtained by the linear fit, the predicted speed value at the current moment is obtained; finally, based on the relationship between the predicted speed value at the current moment and the actual speed value, the current speed value at the current moment is determined. Specifically, when the difference between the predicted speed value at the current moment and the actual speed value is greater than or equal to a predetermined threshold, the predicted speed value is determined as the current speed value at the current moment; when the difference between the predicted speed value at the current moment and the actual speed value is less than a predetermined threshold, the current speed value at the current moment is determined based on at least the target speed value of the previous target moment adjacent to the current moment. In the present application, since the speed prediction formula is obtained by linear fitting the target speed value and target serial number at each target moment in the target array, it is possible to obtain the speed prediction formula based on a smaller amount of data. At the same time, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value at the current moment is obtained. This makes it possible to relatively simply predict the speed of the target vehicle, thereby solving the problem of large amounts of historical data and large computational complexity required for speed prediction in the prior art. In addition, the present application also determines the current speed value at the current moment based on the relationship between the predicted speed value at the current moment and the actual speed value, thus ensuring that the current speed value determined at the current moment is relatively accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a vehicle speed prediction method provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a vehicle speed prediction method provided in an embodiment of the present application is shown;

[0019] Figure 3 A method for determining the current vehicle speed value at a given moment is shown in an embodiment of the present application;

[0020] Figure 4 A method for determining whether to perform linear fitting on a target array is shown in an embodiment of the present application;

[0021] Figure 5 A schematic flow chart of another vehicle speed prediction method provided in an embodiment of the present application is shown;

[0022] Figure 6 A schematic structural diagram of a vehicle speed prediction device provided according to an embodiment of the present application is shown.

[0023] The above drawings include the following reference numerals:

[0024] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device; 10. Fitting unit; 20. Calculation unit; 30. First determination unit. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] As introduced in the background technology, the existing technology requires a large amount of historical data and a large amount of calculation to predict vehicle speed. In order to solve the above technical problems, the embodiments of the present application provide a vehicle speed prediction method, a vehicle speed prediction device, a computer-readable storage medium and a vehicle.

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1FIG. 1 is a hardware structure diagram of a mobile terminal for a vehicle speed prediction method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0031] The memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the device information display method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned networks include but are not limited to the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] In this embodiment, a vehicle speed prediction method running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] Figure 2Flowchart of the vehicle speed prediction method according to an embodiment of the present application. Figure 2 As shown, the vehicle speed prediction method includes the following steps:

[0034] Step S201: performing a linear fit on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula. The target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments. The target sequence number is the position of each target moment in the target array. The target moments are stored in chronological order in the target array.

[0035] Specifically, the multiple target moments in the target array may be obtained according to the same predetermined step size. For example, starting from the first target moment, that is, the initial target moment, the corresponding target moment is obtained at every predetermined step size.

[0036] Specifically, the target speed value corresponding to each target moment in the above target array can be a predicted speed value (the predicted speed value of the target vehicle) or an actual speed value (the speed value of the target vehicle at the corresponding target moment collected by the speed sensor).

[0037] Specifically, the above vehicle speed prediction formula is an expression for characterizing the mapping relationship between the target sequence number and the target vehicle speed value.

[0038] Step S202, calculating the predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment;

[0039] Specifically, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value of the target vehicle at the current moment is calculated, which ensures that the predicted speed value at the current moment can be obtained relatively simply and conveniently.

[0040] Step S203, when the difference between the above-mentioned predicted vehicle speed value and the actual vehicle speed value at the above-mentioned current moment is greater than or equal to the predetermined threshold value, the above-mentioned predicted vehicle speed value is determined as the current vehicle speed value at the above-mentioned current moment; when the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is less than the above-mentioned predetermined threshold value, the above-mentioned current vehicle speed value at the above-mentioned current moment is determined based on at least the above-mentioned target speed value at the previous above-mentioned target moment at the above-mentioned current moment, wherein the above-mentioned actual vehicle speed value is the actual value of the vehicle speed at the above-mentioned current moment collected by the speed sensor.

[0041] Specifically, the present application determines the current vehicle speed value at the current moment based on the relationship between the predicted vehicle speed value and the actual vehicle speed value at the current moment. That is, the present application identifies abnormal actual vehicle speed values ​​through the characteristics of the target vehicle's speed jump within a certain time period. This makes the vehicle speed prediction method of this scheme relatively simple, convenient and feasible, and ensures that the overall applicability of the vehicle speed prediction method of this application is relatively strong.

[0042] Specifically, the size of the predetermined threshold can be flexibly calibrated according to multiple factors such as the driving condition of the target vehicle, road conditions, and weather conditions.

[0043] In the above-mentioned vehicle speed prediction method, first, a linear fitting is performed based on the target vehicle speed value and target serial number corresponding to each target moment in the target array to obtain a vehicle speed prediction formula; then, based on the target serial number in the target array at the current moment and the vehicle speed prediction formula obtained by linear fitting, the predicted vehicle speed value at the current moment is obtained; finally, based on the relationship between the predicted vehicle speed value at the current moment and the actual vehicle speed value, the current vehicle speed value at the current moment is determined. Specifically, when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold, the predicted vehicle speed value is determined as the current vehicle speed value at the current moment; when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than a predetermined threshold, the current vehicle speed value at the current moment is determined based on at least the target vehicle speed value of the previous target moment adjacent to the current moment. In the present application, since the vehicle speed prediction formula is obtained by linear fitting the target vehicle speed value and target serial number at each target moment in the target array, it is achieved that the vehicle speed prediction formula is obtained based on a smaller amount of data. At the same time, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value at the current moment is obtained. This makes it possible to relatively simply predict the speed of the target vehicle, thereby solving the problem of large amounts of historical data and large computational complexity required for speed prediction in the prior art. In addition, the present application also determines the current speed value at the current moment based on the relationship between the predicted speed value at the current moment and the actual speed value, thus ensuring that the current speed value determined at the current moment is relatively accurate.

[0044] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] In a specific implementation process, the above step S201 can be implemented by the following steps: performing linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a vehicle speed prediction formula, including: performing a first-order linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant; obtaining the above vehicle speed prediction formula v based at least on the first constant. pre =b0(n·dt), where b0 is the first constant mentioned above, v pre is the predicted speed value, n is the target sequence number, and t is the predetermined step length, which is the duration between two adjacent target moments. In this embodiment, a linear fit is performed on the target speed values ​​and target sequence numbers corresponding to each target moment in the target array to obtain a first constant. This ensures that the first constant can be relatively simply determined, and that the speed prediction formula based on the first constant can be relatively simply determined, further ensuring that the current speed value of the target vehicle can be predicted relatively simply.

[0046] In order to make the vehicle speed prediction formula of the present application applicable to various road scenarios, the above-mentioned step S201 of the present application can be implemented by the following steps: performing linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain the vehicle speed prediction formula, including: performing a first-order linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant and a second constant; obtaining the above-mentioned vehicle speed prediction formula v based on at least the first constant and the second constant. pre =b1+b0(n·dt), where b0 is the first constant, b1 is the second constant, and v pre is the predicted speed value, n is the target sequence number, t is the predetermined step length, and the predetermined step length is the duration between two adjacent target moments. This ensures that the speed prediction formula is more accurate, and further ensures that the current speed value of the target vehicle subsequently determined based on the speed prediction formula is more accurate.

[0047] In order to further ensure that the obtained first constant and the second constant are relatively accurate, and to further ensure that the determined vehicle speed prediction formula is relatively accurate, in a specific implementation process, a first-order linear fitting is performed on the target vehicle speed value at each target moment in the target array and the target sequence number to obtain the first constant and the second constant, including: performing a first-order linear fitting on the target vehicle speed value at each target moment in the target array and the target sequence number to obtain the first constant and the second constant mentioned above Among them, x i is the i-th target moment, y iis the target vehicle speed value corresponding to the i-th target time, is the average time of the n target moments above, is the average speed value of the n target speed values, n is the total number of the target moments, i is an integer and the value of i starts from 1 and goes up to n.

[0048] Specifically, when performing a first-order linear fit on the target speed values ​​and target numbers corresponding to each target moment in the target array, the target number can be used as the X-axis and the target speed value as the Y-axis for first-order linear fit. Of course, linear fit can also be performed using related linear fitting software, such as Matlab, Origin, Python, etc.

[0049] Of course, in actual applications, we are not limited to performing a first-order linear fit on the target vehicle speed values ​​and target serial numbers corresponding to each target moment in the target array. We can also perform a second-order linear fit or a linear fit of other orders. In addition, we can also perform a linear fit on the target vehicle speed values ​​and target serial numbers corresponding to each target moment in the target array using the least squares method.

[0050] Specifically, it is not limited to linear fitting of the target vehicle speed value and target serial number corresponding to each target moment in the target array. Other fitting methods in the prior art can also be used to fit the target vehicle speed value and target serial number corresponding to each target moment in the target array, which will not be described in detail here.

[0051] In some embodiments, the above-mentioned step S202 can be specifically implemented by the following steps: based on the above-mentioned speed prediction formula and the above-mentioned target sequence number in the above-mentioned target array at the above-mentioned current moment, calculating the predicted speed value of the target vehicle at the above-mentioned current moment, including: storing the above-mentioned current moment in the above-mentioned target array according to the first-in-first-out method, and determining the position of the above-mentioned current moment in the above-mentioned target array as the above-mentioned target sequence number in the above-mentioned target array at the above-mentioned current moment; calculating the above-mentioned predicted speed value corresponding to the above-mentioned current moment based on the above-mentioned speed prediction formula and the above-mentioned target sequence number at the above-mentioned current moment. This solution stores the current moment in the target array according to the first-in-first-out method, which ensures that the target moments stored in the target array always maintain a predetermined number, and further ensures that the calculated predicted speed value of the target vehicle is relatively accurate.

[0052] Specifically, in actual application, a linear fit can be performed using a predetermined number of target speed values ​​and corresponding target serial numbers in a target array to obtain a speed prediction formula. The current time is then stored in the target array. Since a first-in, first-out method is used, the last position in the target array stores the current time. The current speed value at the current moment is then determined using the corresponding target serial number in the target array and the speed prediction formula. The current speed value at the current moment is then stored in the target array again, ensuring that the predetermined number of target times are always present in the target array. Finally, the aforementioned steps can be repeated based on the updated target array. Since the speed prediction formula is continuously updated, the current speed value of the target vehicle is accurately determined.

[0053] In some implementations, such as Figure 3 As shown, the above-mentioned step S203 can also be implemented by the following specific steps: when the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is less than the above-mentioned predetermined threshold value, the above-mentioned current vehicle speed value at the above-mentioned current moment is determined based on at least the above-mentioned target vehicle speed value at the above-mentioned target moment adjacent to the above-mentioned current moment, including: determining whether the above-mentioned target vehicle speed value at the above-mentioned target moment adjacent to the above-mentioned current moment is the above-mentioned predicted vehicle speed value; when the above-mentioned target vehicle speed value at the above-mentioned target moment adjacent to the above-mentioned current moment is the above-mentioned predicted vehicle speed value, and the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is within the predetermined range, determining the above-mentioned actual vehicle speed value as the current vehicle speed value at the above-mentioned current moment; when the above-mentioned target vehicle speed value at the above-mentioned target moment adjacent to the above-mentioned current moment is not the above-mentioned predicted vehicle speed value, and / or when the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is not within the predetermined range, determining the above-mentioned predicted vehicle speed value as the current vehicle speed value at the above-mentioned current moment. Specifically, when the target speed value at the previous target moment is the predicted speed value, and the difference between the predicted speed value and the actual speed value at the current moment is within a predetermined range, it indicates that the actual speed value detected by the speed sensor has returned to a reasonable range. Therefore, in this case, the predicted speed value can no longer be used, and the actual speed value can be used instead. Of course, when the target speed value at the previous target moment is not the predicted speed value, and / or the difference between the predicted speed value and the actual speed value is not within a predetermined range, it indicates that the actual speed value detected by the speed sensor has not yet returned to a reasonable range. Therefore, the predicted speed value can continue to be used as the current speed value at the current moment. This ensures that the current speed value of the target vehicle is relatively reasonable, further avoiding abnormal exit of the overall function due to a speed signal failure.

[0054] Specifically, in actual application, the predetermined range can be flexibly set according to actual conditions. In this application, the size of the predetermined range is not limited.

[0055] In order to further ensure that the subsequent speed prediction formula is more accurate, in the specific implementation process, such as Figure 4 As shown, the vehicle speed prediction method of the present application further includes step S204, step S205, and step S206. Specifically, before performing linear fitting on the target vehicle speed value and target sequence number at each target moment in the target array to obtain the vehicle speed prediction formula, the vehicle speed prediction method further includes: determining whether the total number of the target moments in the target array reaches a predetermined number; if the total number of the target moments in the target array is less than the predetermined number, determining the actual vehicle speed value as the current vehicle speed value at the current moment; if the total number of the target moments in the target array is greater than or equal to the predetermined number, performing linear fitting on the target vehicle speed value and target sequence number at each target moment.

[0056] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the vehicle speed prediction method of the present application will be described in detail below in combination with specific embodiments.

[0057] This embodiment relates to a specific vehicle speed prediction method, such as Figure 5 As shown, the following steps are included:

[0058] Step S1: Collect target times and target speed values ​​at each target time. Specifically, the target speed value at each target time can be a predicted speed value or an actual speed value. The target speed value corresponding to each target time is stored in a target array, and the position of each target time in the target array is determined as the corresponding target sequence number.

[0059] Step S2: Determine whether the total number of target moments in the target array reaches a predetermined number. If the total number of target moments in the target array is less than the predetermined number, the collected actual vehicle speed value is used as the current vehicle speed value at the current moment and stored in the target array. If the total number of target moments in the target array is greater than or equal to the predetermined number, a first-order linear fit is performed on the target speed values ​​and target sequence numbers corresponding to each target moment in the target array to obtain a vehicle speed prediction formula.

[0060] Step S3: Using a first-in, first-out (FIFO) method, the current time is stored in a target array, and the current time's position in the target array is determined as the target sequence number corresponding to the current time. Based on the target sequence number corresponding to the current time and the vehicle speed prediction formula, the vehicle speed prediction value at the current time is calculated.

[0061] Step S4: Determine the relationship between the predicted speed and the actual speed at the current moment. Specifically, if the difference between the predicted speed and the actual speed at the current moment is greater than or equal to a predetermined threshold, the predicted speed is determined as the current speed at the current moment. If the difference between the predicted speed and the actual speed at the current moment is less than the predetermined threshold, the current speed is determined using the target speed at the previous target moment adjacent to the current moment.

[0062] Step S5: Determine the current vehicle speed value at that moment through the target vehicle speed value at the previous target moment adjacent to the current moment. Specifically: when the target vehicle speed value at the previous target moment adjacent to the current moment is a predicted vehicle speed value, and the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is within a predetermined range, determine the actual vehicle speed value as the current vehicle speed value at the current moment; when the target vehicle speed value at the previous target moment adjacent to the current moment is not a predicted vehicle speed value, and / or when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is within a predetermined range, determine the predicted vehicle speed value as the current vehicle speed value at the current moment.

[0063] Step S6: Store the current vehicle speed value at the current moment into the target array.

[0064] Step S7: loop through steps S2 to S6 in sequence.

[0065] The embodiments of the present application also provide a vehicle speed prediction device. It should be noted that the vehicle speed prediction device of the embodiments of the present application can be used to execute the vehicle speed prediction method provided by the embodiments of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the details that have been explained will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0066] The following is an introduction to the vehicle speed prediction device provided in the embodiments of the present application.

[0067] Figure 6 Schematic diagram of the structure of the vehicle speed prediction device according to an embodiment of the present application. Figure 6 As shown, the vehicle speed prediction device includes:

[0068] The fitting unit 10 is configured to perform linear fitting on the target vehicle speed value and target sequence number at each target moment in the target array to obtain a vehicle speed prediction formula, wherein the target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments, and the target sequence number is the position of each target moment in the target array, and the target moments are stored in the target array in chronological order;

[0069] Specifically, the multiple target moments in the target array may be obtained according to the same predetermined step size. For example, starting from the first target moment, that is, the initial target moment, the corresponding target moment is obtained at every predetermined step size.

[0070] Specifically, the target speed value corresponding to each target moment in the above target array can be a predicted speed value (the predicted speed value of the target vehicle) or an actual speed value (the speed value of the target vehicle at the corresponding target moment collected by the speed sensor).

[0071] Specifically, the above vehicle speed prediction formula is an expression for characterizing the mapping relationship between the target sequence number and the target vehicle speed value.

[0072] The calculation unit 20 is used to calculate the predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment;

[0073] Specifically, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value of the target vehicle at the current moment is calculated, which ensures that the predicted speed value at the current moment can be obtained relatively simply and conveniently.

[0074] The first determination unit 30 is configured to determine the predicted vehicle speed value as the current vehicle speed value at the above-mentioned current moment when the difference between the predicted vehicle speed value and the actual vehicle speed value at the above-mentioned current moment is greater than or equal to a predetermined threshold value, and to determine the current vehicle speed value at the above-mentioned current moment based on at least the target vehicle speed value at the previous target moment adjacent to the above-mentioned current moment when the difference between the predicted vehicle speed value and the actual vehicle speed value at the above-mentioned current moment is less than the predetermined threshold value, wherein the actual vehicle speed value is the actual value of the vehicle speed at the above-mentioned current moment collected by the speed sensor.

[0075] Specifically, the present application determines the current vehicle speed value at the current moment based on the relationship between the predicted vehicle speed value and the actual vehicle speed value at the current moment. That is, the present application identifies abnormal actual vehicle speed values ​​through the characteristics of the target vehicle's speed jump within a certain time period. This makes the vehicle speed prediction method of this scheme relatively simple, convenient and feasible, and ensures that the overall applicability of the vehicle speed prediction method of this application is relatively strong.

[0076] Specifically, the size of the predetermined threshold can be flexibly calibrated according to multiple factors such as the driving condition of the target vehicle, road conditions, and weather conditions.

[0077] In the above-mentioned vehicle speed prediction device, the fitting unit is used to perform linear fitting based on the target vehicle speed values ​​and target serial numbers corresponding to each target moment in the target array to obtain a vehicle speed prediction formula; the calculation unit is used to obtain the predicted vehicle speed value at the current moment based on the target serial number in the target array at the current moment and the vehicle speed prediction formula obtained by linear fitting; and the first determination unit is used to determine the current vehicle speed value at the current moment based on the relationship between the predicted vehicle speed value at the current moment and the actual vehicle speed value. Specifically, if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold, the predicted vehicle speed value is determined as the current vehicle speed value at the current moment; if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than the predetermined threshold, the current vehicle speed value at the current moment is determined based on at least the target vehicle speed value of the previous target moment adjacent to the current moment. In the present application, since the vehicle speed prediction formula is obtained by performing linear fitting on the target vehicle speed value and target serial number at each target moment in the target array, it is possible to obtain the vehicle speed prediction formula based on a smaller amount of data. At the same time, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value at the current moment is obtained. This makes it possible to relatively simply predict the speed of the target vehicle, thereby solving the problem of large amounts of historical data and large computational complexity required for speed prediction in the prior art. In addition, the present application also determines the current speed value at the current moment based on the relationship between the predicted speed value at the current moment and the actual speed value, thus ensuring that the current speed value determined at the current moment is relatively accurate.

[0078] In a specific implementation process, the fitting unit includes a first fitting module and a first determination module, wherein the first fitting module is used to perform a first-order linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant; the first determination module is used to obtain the vehicle speed prediction formula v based on at least the first constant. pre =b0(n·dt), where b0 is the first constant mentioned above, v preis the predicted speed value, n is the target sequence number, and t is the predetermined step length, which is the duration between two adjacent target moments. In this embodiment, a linear fit is performed on the target speed values ​​and target sequence numbers corresponding to each target moment in the target array to obtain a first constant. This ensures that the first constant can be relatively simply determined, and that the speed prediction formula can be relatively simply determined based on the first constant, further ensuring that the current speed value of the target vehicle can be predicted relatively simply.

[0079] In order to make the vehicle speed prediction formula of the present application applicable to various road scenarios, the fitting unit includes a second fitting module and a second determination module, wherein the second fitting module is used to perform a first-order linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant and a second constant; the second determination module is used to obtain the vehicle speed prediction formula v based on at least the first constant and the second constant. pre =b1+b0(n·dt), where b0 is the first constant, b1 is the second constant, and v pre is the predicted speed value, n is the target sequence number, t is the predetermined step length, and the predetermined step length is the duration between two adjacent target moments. This ensures that the speed prediction formula is more accurate, and further ensures that the current speed value of the target vehicle subsequently determined based on the speed prediction formula is more accurate.

[0080] In order to further ensure that the obtained first constant and second constant are relatively accurate, and further ensure that the determined vehicle speed prediction formula is relatively accurate, in the specific implementation process, the second fitting module includes a fitting submodule for performing a first-order linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain the first constant and the second constant mentioned above Among them, x i is the i-th target moment, y i is the target vehicle speed value corresponding to the i-th target time, is the average time of the n target moments above, is the average speed value of the n target speed values, n is the total number of the target moments, i is an integer and the value of i starts from 1 and goes up to n.

[0081] Specifically, when performing a first-order linear fit on the target speed values ​​and target numbers corresponding to each target moment in the target array, the target number can be used as the X-axis and the target speed value as the Y-axis for first-order linear fit. Of course, linear fit can also be performed using related linear fitting software, such as Matlab, Origin, Python, etc.

[0082] Of course, in actual applications, we are not limited to performing a first-order linear fit on the target vehicle speed values ​​and target serial numbers corresponding to each target moment in the target array. We can also perform a second-order linear fit or a linear fit of other orders. In addition, we can also perform a linear fit on the target vehicle speed values ​​and target serial numbers corresponding to each target moment in the target array using the least squares method.

[0083] Specifically, it is not limited to linear fitting of the target vehicle speed value and target serial number corresponding to each target moment in the target array. Other fitting methods in the prior art can also be used to fit the target vehicle speed value and target serial number corresponding to each target moment in the target array, which will not be described in detail here.

[0084] In some embodiments, the calculation unit includes a third determination module and a calculation module, wherein the third determination module is used to store the current moment in the target array according to a first-in, first-out method, and determine the position of the current moment in the target array as the target sequence number of the current moment in the target array; and the calculation module is used to calculate the predicted vehicle speed value corresponding to the current moment based on the vehicle speed prediction formula and the target sequence number of the current moment. This solution stores the current moment in the target array according to a first-in, first-out method, which ensures that the target moments stored in the target array always remain at a predetermined number, and further ensures that the calculated predicted vehicle speed value of the target vehicle is relatively accurate.

[0085] Specifically, in actual application, a linear fit can be performed using a predetermined number of target speed values ​​and corresponding target serial numbers in a target array to obtain a speed prediction formula. The current time is then stored in the target array. Since a first-in, first-out method is used, the last position in the target array stores the current time. The current speed value at the current moment is then determined using the corresponding target serial number in the target array and the speed prediction formula. The current speed value at the current moment is then stored in the target array again, ensuring that the predetermined number of target moments are always present in the target array. Finally, the aforementioned steps can be repeated based on the updated target array. Since the speed prediction formula is continuously updated, the current speed value of the target vehicle is accurately determined.

[0086] In some implementations, such as Figure 3 As shown, the first determination unit includes a fourth determination module, a fifth determination module and a sixth determination module, wherein the fourth determination module is used to determine whether the target vehicle speed value at the previous target moment adjacent to the current moment is the predicted vehicle speed value; the fifth determination module is used to determine the actual vehicle speed value as the current vehicle speed value at the current moment when the target vehicle speed value at the previous target moment adjacent to the current moment is the predicted vehicle speed value and the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is within a predetermined range; the sixth determination module is used to determine the predicted vehicle speed value as the current vehicle speed value at the current moment when the target vehicle speed value at the previous target moment adjacent to the current moment is not the predicted vehicle speed value and / or when the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is not within the predetermined range. Specifically, when the target speed value at the previous target moment is the predicted speed value, and the difference between the predicted speed value and the actual speed value at the current moment is within a predetermined range, it indicates that the actual speed value detected by the speed sensor has returned to a reasonable range. Therefore, in this case, the predicted speed value can no longer be used, and the actual speed value can be used instead. Of course, when the target speed value at the previous target moment is not the predicted speed value, and / or the difference between the predicted speed value and the actual speed value is not within a predetermined range, it indicates that the actual speed value detected by the speed sensor has not yet returned to a reasonable range. Therefore, the predicted speed value can continue to be used as the current speed value at the current moment. This ensures that the current speed value of the target vehicle is relatively reasonable, further avoiding abnormal exit of the overall function due to a speed signal failure.

[0087] Specifically, in actual application, the predetermined range can be flexibly set according to actual conditions. In this application, the size of the predetermined range is not limited.

[0088] In order to further ensure that the subsequent speed prediction formula is more accurate, in the specific implementation process, such as Figure 4As shown, the vehicle speed prediction device further includes a second determination unit, a third determination unit and a fourth determination unit, wherein the second determination unit is used to determine whether the total number of the target moments in the target array reaches a predetermined number before performing linear fitting on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a vehicle speed prediction formula; the third determination unit is used to determine the actual vehicle speed value as the current vehicle speed value at the current moment when the total number of the target moments in the target array is less than the predetermined number; and the fourth determination unit is used to perform linear fitting on the target vehicle speed value and the target sequence number at each target moment when the total number of the target moments in the target array is greater than or equal to the predetermined number.

[0089] The vehicle speed prediction device includes a processor and a memory. The fitting unit, calculation unit, and first determination unit are all stored as program units in the memory. The processor executes the program units stored in the memory to implement the corresponding functions. The modules are all located in the same processor; alternatively, the modules can be located in different processors in any combination.

[0090] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be provided, and kernel parameters can be adjusted to solve the problem of large amount of historical data and large amount of calculation required for vehicle speed prediction in the existing technology.

[0091] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0092] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed, the device where the computer-readable storage medium is located is controlled to execute the vehicle speed prediction method.

[0093] Specifically, the vehicle speed prediction method includes:

[0094] Step S201: performing a linear fit on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula. The target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments. The target sequence number is the position of each target moment in the target array. The target moments are stored in chronological order in the target array.

[0095] Specifically, the multiple target moments in the target array may be obtained according to the same predetermined step size. For example, starting from the first target moment, that is, the initial target moment, the corresponding target moment is obtained at every predetermined step size.

[0096] Specifically, the target speed value corresponding to each target moment in the above target array can be a predicted speed value (the predicted speed value of the target vehicle) or an actual speed value (the speed value of the target vehicle at the corresponding target moment collected by the speed sensor).

[0097] Specifically, the above vehicle speed prediction formula is an expression for characterizing the mapping relationship between the target sequence number and the target vehicle speed value.

[0098] Step S202, calculating the predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment;

[0099] Specifically, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value of the target vehicle at the current moment is calculated, which ensures that the predicted speed value at the current moment can be obtained relatively simply and conveniently.

[0100] Step S203, when the difference between the above-mentioned predicted vehicle speed value and the actual vehicle speed value at the above-mentioned current moment is greater than or equal to the predetermined threshold value, the above-mentioned predicted vehicle speed value is determined as the current vehicle speed value at the above-mentioned current moment; when the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is less than the above-mentioned predetermined threshold value, the above-mentioned current vehicle speed value at the above-mentioned current moment is determined based on at least the above-mentioned target speed value at the previous above-mentioned target moment at the above-mentioned current moment, wherein the above-mentioned actual vehicle speed value is the actual value of the vehicle speed at the above-mentioned current moment collected by the speed sensor.

[0101] Specifically, the present application determines the current vehicle speed value at the current moment based on the relationship between the predicted vehicle speed value and the actual vehicle speed value at the current moment. That is, the present application identifies abnormal actual vehicle speed values ​​through the characteristics of the target vehicle's speed jump within a certain time period. This makes the vehicle speed prediction method of this scheme relatively simple, convenient and feasible, and ensures that the overall applicability of the vehicle speed prediction method of this application is relatively strong.

[0102] Specifically, the size of the predetermined threshold can be flexibly calibrated according to multiple factors such as the driving condition of the target vehicle, road conditions, and weather conditions.

[0103] An embodiment of the present invention provides a processor, which is used to run a program, wherein the vehicle speed prediction method is executed when the program is run.

[0104] Specifically, the vehicle speed prediction method includes:

[0105] Step S201: performing a linear fit on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula. The target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments. The target sequence number is the position of each target moment in the target array. The target moments are stored in chronological order in the target array.

[0106] Step S202, calculating the predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment;

[0107] Step S203, when the difference between the above-mentioned predicted vehicle speed value and the actual vehicle speed value at the above-mentioned current moment is greater than or equal to the predetermined threshold value, the above-mentioned predicted vehicle speed value is determined as the current vehicle speed value at the above-mentioned current moment; when the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is less than the above-mentioned predetermined threshold value, the above-mentioned current vehicle speed value at the above-mentioned current moment is determined based on at least the above-mentioned target speed value at the previous above-mentioned target moment at the above-mentioned current moment, wherein the above-mentioned actual vehicle speed value is the actual value of the vehicle speed at the above-mentioned current moment collected by the speed sensor.

[0108] In a typical embodiment of the present application, a vehicle is further provided, which includes a vehicle speed prediction device, wherein the vehicle speed prediction device is configured to execute any one of the above-mentioned vehicle speed prediction methods.

[0109] Among the above-mentioned vehicles, the above-mentioned vehicle includes a vehicle speed prediction device. The vehicle speed prediction device is used to execute any of the above-mentioned vehicle speed prediction methods. In this vehicle speed prediction method, since the target vehicle speed value and the target serial number at each target moment in the target array are linearly fitted to obtain the vehicle speed prediction formula, it is possible to obtain the vehicle speed prediction formula based on a smaller amount of data. At the same time, based on the vehicle speed prediction formula and the target serial number in the target array at the current moment, the predicted vehicle speed value at the current moment is obtained, which makes it possible to predict the speed of the target vehicle in a relatively simple manner, thereby solving the problem of a large amount of historical data and a large amount of calculation required for the prediction of the vehicle speed in the prior art. In addition, the present application also determines the current vehicle speed value at the current moment based on the relationship between the predicted vehicle speed value at the current moment and the actual vehicle speed value, thereby ensuring that the determined current vehicle speed value at the current moment is relatively accurate.

[0110] An embodiment of the present invention provides a device, comprising a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least the following steps are performed:

[0111] Step S201: performing a linear fit on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula. The target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments. The target sequence number is the position of each target moment in the target array. The target moments are stored in chronological order in the target array.

[0112] Step S202, calculating the predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment;

[0113] Step S203, when the difference between the above-mentioned predicted vehicle speed value and the actual vehicle speed value at the above-mentioned current moment is greater than or equal to the predetermined threshold value, the above-mentioned predicted vehicle speed value is determined as the current vehicle speed value at the above-mentioned current moment; when the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is less than the above-mentioned predetermined threshold value, the above-mentioned current vehicle speed value at the above-mentioned current moment is determined based on at least the above-mentioned target speed value at the previous above-mentioned target moment at the above-mentioned current moment, wherein the above-mentioned actual vehicle speed value is the actual value of the vehicle speed at the above-mentioned current moment collected by the speed sensor.

[0114] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0115] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program for initializing at least the following method steps:

[0116] Step S201: performing a linear fit on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula. The target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments. The target sequence number is the position of each target moment in the target array. The target moments are stored in chronological order in the target array.

[0117] Step S202, calculating the predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment;

[0118] Step S203, when the difference between the above-mentioned predicted vehicle speed value and the actual vehicle speed value at the above-mentioned current moment is greater than or equal to the predetermined threshold value, the above-mentioned predicted vehicle speed value is determined as the current vehicle speed value at the above-mentioned current moment; when the difference between the above-mentioned predicted vehicle speed value and the above-mentioned actual vehicle speed value at the above-mentioned current moment is less than the above-mentioned predetermined threshold value, the above-mentioned current vehicle speed value at the above-mentioned current moment is determined based on at least the above-mentioned target speed value at the previous above-mentioned target moment at the above-mentioned current moment, wherein the above-mentioned actual vehicle speed value is the actual value of the vehicle speed at the above-mentioned current moment collected by the speed sensor.

[0119] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. They can be implemented using program code executable by the computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0120] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0125] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0126] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic 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 computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0127] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0128] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0129] 1) In the vehicle speed prediction method of the present application, first, a linear fit is performed based on the target vehicle speed value and target sequence number corresponding to each target moment in the target array to obtain a vehicle speed prediction formula; then, based on the target sequence number in the target array at the current moment and the vehicle speed prediction formula obtained by the linear fit, a predicted vehicle speed value at the current moment is obtained; finally, based on the relationship between the predicted vehicle speed value at the current moment and the actual vehicle speed value, the current vehicle speed value at the current moment is determined. Specifically, if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold, the predicted vehicle speed value is determined as the current vehicle speed value at the current moment; if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than a predetermined threshold, the current vehicle speed value at the current moment is determined based on at least the target vehicle speed value of the previous target moment adjacent to the current moment. In the present application, since the vehicle speed prediction formula is obtained by linear fitting the target vehicle speed value and target sequence number at each target moment in the target array, it is possible to obtain the vehicle speed prediction formula based on a smaller amount of data. At the same time, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value at the current moment is obtained. This makes it possible to relatively simply predict the speed of the target vehicle, thereby solving the problem of large amounts of historical data and large computational complexity required for speed prediction in the prior art. In addition, the present application also determines the current speed value at the current moment based on the relationship between the predicted speed value at the current moment and the actual speed value, thus ensuring that the current speed value determined at the current moment is relatively accurate.

[0130] 2) In the vehicle speed prediction device of the present application, a fitting unit is configured to perform linear fitting based on the target speed values ​​and target serial numbers corresponding to each target moment in the target array to obtain a vehicle speed prediction formula; a calculation unit is configured to obtain a predicted vehicle speed value at the current moment based on the target serial number in the target array at the current moment and the vehicle speed prediction formula obtained by the linear fitting; and a first determination unit is configured to determine the current vehicle speed value at the current moment based on the relationship between the predicted vehicle speed value at the current moment and the actual vehicle speed value. Specifically, if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold, the predicted vehicle speed value is determined as the current vehicle speed value at the current moment; if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than the predetermined threshold, the current vehicle speed value at the current moment is determined based on at least the target vehicle speed value of the previous target moment adjacent to the current moment. In the present application, since the vehicle speed prediction formula is obtained by performing linear fitting on the target speed values ​​and target serial numbers at each target moment in the target array, the vehicle speed prediction formula can be obtained based on a relatively small amount of data. At the same time, based on the speed prediction formula and the target sequence number in the target array at the current moment, the predicted speed value at the current moment is obtained. This makes it possible to relatively simply predict the speed of the target vehicle, thereby solving the problem of large amounts of historical data and large computational complexity required for speed prediction in the prior art. In addition, the present application also determines the current speed value at the current moment based on the relationship between the predicted speed value at the current moment and the actual speed value, thus ensuring that the current speed value determined at the current moment is relatively accurate.

[0131] 3) In the vehicle of the present application, the above-mentioned vehicle includes a vehicle speed prediction device. The vehicle speed prediction device is used to execute any of the above-mentioned vehicle speed prediction methods. In the vehicle speed prediction method, since the target vehicle speed value and the target serial number at each target moment in the target array are linearly fitted to obtain the vehicle speed prediction formula, it is possible to obtain the vehicle speed prediction formula based on a smaller amount of data. At the same time, based on the vehicle speed prediction formula and the target serial number in the target array at the current moment, the predicted vehicle speed value at the current moment is obtained, which makes it possible to predict the speed of the target vehicle in a relatively simple manner, thereby solving the problem of a large amount of historical data and a large amount of calculation required for the prediction of the vehicle speed in the prior art. In addition, the present application also determines the current vehicle speed value at the current moment based on the relationship between the predicted vehicle speed value at the current moment and the actual vehicle speed value, thereby ensuring that the determined current vehicle speed value at the current moment is relatively accurate.

[0132] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A vehicle speed prediction method, characterized in that: include: Performing linear fitting on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula, wherein the target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments, the target sequence number being the position of each target moment in the target array, and the target moments being stored in chronological order in the target array; Calculating a predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment; If the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold, the predicted vehicle speed value is determined as the current vehicle speed value at the current moment; if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than the predetermined threshold, the current vehicle speed value at the current moment is determined based on at least a target vehicle speed value at a previous target moment adjacent to the current moment, wherein the actual vehicle speed value is the actual value of the vehicle speed at the current moment collected by the vehicle speed sensor; When the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than the predetermined threshold, determining the current vehicle speed value at the current moment based on at least a target vehicle speed value at a previous target moment adjacent to the current moment includes: Determine whether the target speed value at the previous target moment adjacent to the current moment is the predicted speed value at the previous target moment adjacent to the current moment; if the target speed value at the previous target moment adjacent to the current moment is the predicted speed value at the previous target moment adjacent to the current moment, and the difference between the predicted speed value at the current moment and the actual speed value is within a predetermined range, determine the actual speed value as the current speed value at the current moment; if the target speed value at the previous target moment adjacent to the current moment is not the predicted speed value at the previous target moment adjacent to the current moment, and / or if the difference between the predicted speed value at the current moment and the actual speed value is not within the predetermined range, determine the predicted speed value at the current moment as the current speed value at the current moment.

2. The vehicle speed prediction method according to claim 1, characterized in that: Perform linear fitting on the target speed value and target sequence number at each target time in the target array to obtain the speed prediction formula, including: Performing a first-order linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant; Based on at least the first constant, the vehicle speed prediction formula is obtained ,in, is the first constant, is the predicted vehicle speed value, is the target sequence number, is a predetermined step length, and the predetermined step length is the duration between two adjacent target moments.

3. The vehicle speed prediction method according to claim 1, characterized in that: Perform linear fitting on the target speed value and target sequence number at each target time in the target array to obtain the speed prediction formula, including: Performing a first-order linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant and a second constant; Based on at least the first constant and the second constant, the vehicle speed prediction formula is obtained ,in, is the first constant, is the second constant, is the predicted vehicle speed value, is the target sequence number, is a predetermined step length, and the predetermined step length is the duration between two adjacent target moments.

4. The vehicle speed prediction method according to claim 3, characterized in that: Performing a first-order linear fit on the target vehicle speed value and the target sequence number at each target moment in the target array to obtain a first constant and a second constant includes: Perform a first-order linear fit on the target vehicle speed value and the target sequence number at each target time in the target array to obtain the first constant and the second constant ,in, For the The target moment, For the The target vehicle speed value corresponding to the target time, for The average time of the target time, for The average vehicle speed value of the target vehicle speed values, is the total number of target moments, is an integer and The value starts from 1 and goes up to .

5. The vehicle speed prediction method according to claim 1, characterized in that: Calculating a predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment includes: According to a first-in-first-out method, the current time is stored in the target array, and the position of the current time in the target array is determined as the target sequence number of the current time in the target array; Based on the vehicle speed prediction formula and the target sequence number at the current moment, the predicted vehicle speed value corresponding to the current moment is calculated.

6. The vehicle speed prediction method according to any one of claims 1 to 5, characterized in that: Before performing linear fitting on the target vehicle speed value and the target sequence number at each target time in the target array to obtain the vehicle speed prediction formula, the vehicle speed prediction method further includes: determining whether the total number of the target moments in the target array reaches a predetermined number; When the total number of the target moments in the target array is less than the predetermined number, determining the actual vehicle speed value as the current vehicle speed value at the current moment; When the total number of the target moments in the target array is greater than or equal to the predetermined number, a linear fit is performed on the target vehicle speed value and the target sequence number at each target moment.

7. A vehicle speed prediction device, characterized in that: include: a fitting unit, configured to perform linear fitting on the target vehicle speed value and target sequence number at each target moment in a target array to obtain a vehicle speed prediction formula, wherein the target array stores a predetermined number of target vehicle speed values ​​corresponding to the target moments, the target sequence number being the position of each target moment in the target array, and the target moments being stored in chronological order in the target array; a calculation unit, configured to calculate a predicted speed value of the target vehicle at the current moment based on the speed prediction formula and the target sequence number in the target array at the current moment; a first determining unit, configured to determine the predicted vehicle speed value as the current vehicle speed value at the current moment if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is greater than or equal to a predetermined threshold, and to determine the current vehicle speed value at the current moment based on at least the target vehicle speed value at a previous target moment adjacent to the current moment if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is less than the predetermined threshold, wherein the actual vehicle speed value is the actual value of the vehicle speed at the current moment collected by the vehicle speed sensor; The first determining unit includes a fourth determining module, a fifth determining module, and a sixth determining module, wherein the fourth determining module is used to determine whether the target vehicle speed value at the previous target moment adjacent to the current moment is the predicted vehicle speed value at the previous target moment adjacent to the current moment; the fifth determining module is used to determine the actual vehicle speed value as the current vehicle speed value at the current moment if the target vehicle speed value at the previous target moment adjacent to the current moment is the predicted vehicle speed value at the previous target moment adjacent to the current moment, and the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is within a predetermined range; and the sixth determining module is used to determine the predicted vehicle speed value at the current moment as the current vehicle speed value at the current moment if the target vehicle speed value at the previous target moment adjacent to the current moment is not the predicted vehicle speed value at the previous target moment adjacent to the current moment, and / or if the difference between the predicted vehicle speed value at the current moment and the actual vehicle speed value is not within the predetermined range.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the vehicle speed prediction method according to any one of claims 1 to 6.

9. A vehicle, characterized in that: include: A vehicle speed prediction device, wherein the vehicle speed prediction device is used to execute the vehicle speed prediction method according to any one of claims 1 to 6.

Images