Vehicle speed prediction method and device, vehicle and storage medium

Abstract

This invention provides a vehicle speed prediction method, apparatus, vehicle, and storage medium, belonging to the field of vehicle speed prediction technology. The method includes: acquiring a first vehicle speed signal from a previous time period and a second vehicle speed signal from the current time period; inputting the first vehicle speed signal into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals; determining at least two weight allocation schemes for the at least two vehicle speed prediction signals based on at least two deviation algorithms, and determining at least two weighted prediction signals corresponding one-to-one with the at least two deviation algorithms based on the at least two weight allocation schemes; determining a secondary weight allocation scheme for the at least two weighted prediction signals; and determining a comprehensive vehicle speed prediction signal based on the secondary weight allocation scheme and the at least two weighted prediction signals. This invention improves vehicle speed prediction accuracy through multi-dimensional weighting.

Description

Technical Field

[0001] This invention relates to the field of vehicle speed prediction technology, specifically to a vehicle speed prediction method, device, vehicle, and storage medium. Background Technology

[0002] To make vehicles more intelligent, it's necessary to predict their potential operating conditions in the near future. Based on these predictions, the ECU (Electronic Control Unit) can proactively activate or deactivate relevant functions, allowing the vehicle to anticipate and respond to potential changes, thus achieving smoother control and more energy-efficient operation. Among all vehicle operating parameters, vehicle speed is a crucial predictive parameter.

[0003] The current method for predicting vehicle speed in vehicles involves using a single speed prediction method to predict the vehicle speed, and then directly using the predicted speed as input to the ECU. The problem with this method is that the accuracy and reliability of a single speed prediction method are not high, leading to inaccurate speed predictions.

[0004] Therefore, there is an urgent need to provide a vehicle speed prediction method, device, vehicle, and storage medium to achieve multi-dimensional vehicle speed prediction in order to improve the accuracy of the predicted vehicle speed. Summary of the Invention

[0005] In view of this, it is necessary to provide a vehicle speed prediction method, device, vehicle, and storage medium to solve the technical problem in the prior art where predicting vehicle speed using a single prediction method results in low accuracy of the predicted vehicle speed.

[0006] On the one hand, in order to solve the above-mentioned technical problems, the present invention provides a vehicle speed prediction method, including:

[0007] Acquire the first vehicle speed signal in the previous time period and the second vehicle speed signal in the current time period;

[0008] The first vehicle speed signal is input into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals.

[0009] At least two weight allocation schemes for the at least two vehicle speed prediction signals are determined based on at least two deviation algorithms, and at least two weighted prediction signals corresponding one-to-one with the at least two deviation algorithms are determined based on the at least two weight allocation schemes.

[0010] A secondary weight allocation scheme for the at least two weighted prediction signals is determined, and a comprehensive vehicle speed prediction signal is determined based on the secondary weight allocation scheme and the at least two weighted prediction signals.

[0011] In one possible implementation, the at least two vehicle speed prediction models include a first vehicle speed prediction model and a second vehicle speed prediction model with different model structures; the at least two vehicle speed prediction signals include a first vehicle speed prediction signal corresponding to the first vehicle speed prediction model and a second vehicle speed prediction signal corresponding to the second vehicle speed prediction model; and the at least two deviation algorithms include a first deviation algorithm and a second deviation algorithm.

[0012] The step of determining at least two weight allocation schemes for the at least two vehicle speed prediction signals based on at least two bias algorithms, and determining at least two weighted prediction signals corresponding one-to-one with the at least two bias algorithms based on the at least two weight allocation schemes, includes:

[0013] The first difference between the first vehicle speed prediction signal and the second vehicle speed signal and the second difference between the second vehicle speed prediction signal and the second vehicle speed signal are determined based on the first deviation algorithm.

[0014] A first weight allocation scheme for the first vehicle speed prediction signal and the second vehicle speed prediction signal is determined based on the first difference and the second difference;

[0015] The third difference between the first vehicle speed prediction signal and the second vehicle speed signal, and the fourth difference between the second vehicle speed prediction signal and the second vehicle speed signal are determined based on the second deviation algorithm.

[0016] A second weighting scheme for the first vehicle speed prediction signal and the second vehicle speed prediction signal is determined based on the third difference and the fourth difference;

[0017] The first vehicle speed prediction signal and the second vehicle speed prediction signal are weighted according to the first weight allocation scheme to obtain a first weighted prediction signal, and the first vehicle speed prediction signal and the second vehicle speed prediction signal are weighted according to the second weight allocation scheme to obtain a second weighted prediction signal.

[0018] In one possible implementation, the weights in the weight allocation scheme are:

[0019]

[0020] In the formula, The weights of the i-th vehicle speed prediction model; The difference between the vehicle speed prediction signal predicted by the i-th vehicle speed prediction model and the second vehicle speed signal; These are variable coefficients; It is a constant; n This represents the total number of vehicle speed prediction models.

[0021] In one possible implementation, the deviation algorithm is the least squares method, the absolute deviation normalization cumulative method, or the window moving average deviation normalization method.

[0022] In one possible implementation, the at least two weighted prediction signals include a first weighted prediction signal corresponding to the least squares method, a second weighted prediction signal corresponding to the absolute deviation normalization accumulation method, and a third weighted prediction signal corresponding to the window moving average deviation normalization method.

[0023] The step of determining the secondary weight allocation scheme for the at least two weighted prediction signals includes:

[0024] Determine the first prediction difference between the first weighted prediction signal and the second vehicle speed signal, the second prediction difference between the second weighted prediction signal and the second vehicle speed signal, and the third prediction difference between the third weighted prediction signal and the second vehicle speed signal;

[0025] The secondary weight allocation scheme is determined based on the first prediction difference, the second prediction difference, and the third prediction difference.

[0026] In one possible implementation, the vehicle speed prediction model is a least squares curve fitting algorithm, a moving average window algorithm, a long short-term memory network model, or a gated recurrent unit network model.

[0027] In one possible implementation, before determining the secondary weight allocation scheme for the at least two weighted prediction signals, the method further includes:

[0028] The weighted prediction signals are then subjected to Kalman filtering.

[0029] On the other hand, the present invention also provides a vehicle speed prediction device, comprising:

[0030] The vehicle speed signal acquisition unit is used to acquire the first vehicle speed signal in the previous time period and the second vehicle speed signal in the current time period.

[0031] The vehicle speed prediction unit is used to input the first vehicle speed signal into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals.

[0032] The first weight allocation unit is used to determine at least two weight allocation schemes for the at least two vehicle speed prediction signals based on at least two deviation algorithms, and to determine at least two weighted prediction signals that correspond one-to-one with the at least two deviation algorithms based on the at least two weight allocation schemes.

[0033] The second weight allocation unit is used to determine a secondary weight allocation scheme for the at least two weighted prediction signals, and to determine a comprehensive vehicle speed prediction signal based on the secondary weight allocation scheme and the at least two weighted prediction signals.

[0034] On the other hand, the present invention also provides a vehicle including a memory and a processor, wherein,

[0035] The memory is used to store programs;

[0036] The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the vehicle speed prediction method described in any of the above possible implementations.

[0037] On the other hand, the present invention also provides a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the steps in the vehicle speed prediction method described in any of the above possible implementations.

[0038] The beneficial effects of this invention are as follows: The vehicle speed prediction method provided by this invention first inputs a first vehicle speed signal into at least two vehicle speed prediction models. These models then predict the vehicle speed within the current time period, obtaining at least two vehicle speed prediction signals. Next, different deviation algorithms are used to determine the deviation between the predicted vehicle speed signals and the actual second vehicle speed signal. Based on this deviation, the predicted vehicle speed signals from the at least two prediction models are weighted to obtain weighted prediction signals that correspond one-to-one with the deviation algorithms. This weighting process reduces the error caused by a single method in predicting vehicle speed, thus improving the accuracy of the predicted vehicle speed. Furthermore, to avoid problems such as deviation caused by a single deviation algorithm, this invention, after determining the at least two weighted prediction signals, determines a secondary weight allocation scheme for the at least two weighted prediction signals. A second weighting process is then performed on the at least two weighted prediction signals to finally obtain a comprehensive vehicle speed prediction signal, further improving the accuracy of the determined comprehensive vehicle speed prediction signal. In other words, this invention assigns weights to both the prediction model and the deviation algorithm, eliminating the limitations or inaccuracies caused by a single prediction algorithm and a single deviation evaluation method, thus ensuring the accuracy of the obtained comprehensive vehicle speed prediction signal. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic flowchart of an embodiment of the vehicle speed prediction method provided by the present invention;

[0041] Figure 2 For the present invention Figure 1 A schematic diagram of an embodiment of step S102;

[0042] Figure 3 This is a schematic flowchart of an embodiment of determining the secondary weight allocation scheme in step S103 of the present invention;

[0043] Figure 4 This is a schematic diagram of an embodiment of the vehicle speed prediction device provided by the present invention;

[0044] Figure 5 A schematic diagram of an embodiment of the vehicle provided by the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0046] It should be understood that the illustrative drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the invention. It should be understood that the operations in the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed or performed simultaneously. Furthermore, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0047] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0048] This invention provides a vehicle speed prediction method, device, vehicle, and storage medium, which are described below.

[0049] This invention provides a vehicle speed prediction method, such as... Figure 1 As shown, the vehicle speed prediction methods include:

[0050] S101. Obtain the first vehicle speed signal in the previous time period and the second vehicle speed signal in the current time period;

[0051] S102. Input the first vehicle speed signal into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals;

[0052] S103. Determine at least two weight allocation schemes for at least two vehicle speed prediction signals based on at least two deviation algorithms, and determine at least two weighted prediction signals that correspond one-to-one with at least two deviation algorithms based on at least two weight allocation schemes;

[0053] S104. Determine a secondary weighting scheme for at least two weighted prediction signals, and determine a comprehensive vehicle speed prediction signal based on the secondary weighting scheme and at least two weighted prediction signals.

[0054] In step S101, the previous time period and the current time period are consecutive time periods. For example, after the vehicle starts running, the vehicle speed prediction signal within the next 30 seconds is predicted by acquiring the first vehicle speed signal after 45 seconds, thus achieving rolling prediction.

[0055] It should be noted that the vehicle speed prediction model in step S102 can be a traditional vehicle speed prediction model that predicts vehicle speed through curve fitting and moving window, or it can be a relatively novel neural network prediction model.

[0056] Specifically, traditional vehicle speed prediction models include, but are not limited to: Least Square Method curve fitting algorithm and Weighted Moving Average (WMA) algorithm.

[0057] The specific prediction process is as follows: the first vehicle speed signal in the first time period is fitted and weighted shifted to obtain the vehicle speed trend curve, and the vehicle speed in the second time period is predicted based on the vehicle speed trend curve.

[0058] Neural network prediction models include, but are not limited to: Long Short Term Memory (LSTM) network models and Gated Recurrent Unit (GRU) network models.

[0059] The specific prediction process is as follows: the initial neural network model is trained, verified and tested based on the sample set to obtain a fully trained neural network prediction model. The first vehicle speed signal in the first time period is input into the neural network prediction model to obtain the vehicle speed prediction signal in the second time period.

[0060] It should also be noted that the deviation algorithm is the least squares method, the absolute deviation normalization cumulative method, or the window moving average deviation normalization method.

[0061] It should be understood that the number of vehicle speed prediction models and the number of deviation algorithms can be set or adjusted according to the actual application scenario. For example, the higher the accuracy of vehicle speed prediction, the more of both are needed. If the efficiency requirement for vehicle speed prediction is high, then the number of both should be appropriately reduced.

[0062] Compared with existing technologies, the vehicle speed prediction method provided in this invention first inputs a first vehicle speed signal into at least two vehicle speed prediction models. These models then predict the vehicle speed within the current time period, obtaining at least two predicted vehicle speed signals. Next, different deviation algorithms are used to determine the deviation between the predicted vehicle speed signals and the actual second vehicle speed signal. Based on this deviation, the predicted vehicle speed signals from the at least two prediction models are weighted to obtain weighted prediction signals that correspond one-to-one with the deviation algorithms. This weighting process reduces the error caused by a single method in predicting vehicle speed, improving the accuracy of the predicted vehicle speed. Furthermore, to avoid problems such as deviation caused by a single deviation algorithm, this invention, after determining the at least two weighted prediction signals, determines a secondary weight allocation scheme for the at least two weighted prediction signals. A second weighting process is then performed on the at least two weighted prediction signals to finally obtain a comprehensive vehicle speed prediction signal, further improving the accuracy of the determined comprehensive vehicle speed prediction signal. In other words, the embodiments of the present invention allocate weights from two dimensions: the prediction model and the deviation algorithm. This eliminates the limitations or inaccuracies caused by a single prediction algorithm and a single deviation evaluation method, and ensures the accuracy of the obtained comprehensive vehicle speed prediction signal.

[0063] For ease of explanation, this embodiment of the invention uses a vehicle speed prediction model, including a first vehicle speed prediction model and a second vehicle speed prediction model with different model structures, and at least two deviation algorithms, including a first deviation algorithm and a second deviation algorithm, as an example for illustration.

[0064] Correspondingly, at least two vehicle speed prediction signals include a first vehicle speed prediction signal corresponding to the first vehicle speed prediction model and a second vehicle speed prediction signal corresponding to the second vehicle speed prediction model; then, as Figure 2 As shown, step S102 includes:

[0065] S201. Determine the first difference between the first vehicle speed prediction signal and the second vehicle speed signal, and the second difference between the second vehicle speed prediction signal and the second vehicle speed signal, based on the first deviation algorithm.

[0066] S202. Determine a first weight allocation scheme for the first vehicle speed prediction signal and the second vehicle speed prediction signal based on the first difference and the second difference;

[0067] S203. Based on the second deviation algorithm, determine the third difference between the first vehicle speed prediction signal and the second vehicle speed signal, and the fourth difference between the second vehicle speed prediction signal and the second vehicle speed signal.

[0068] S204. Determine a second weighting scheme for the first vehicle speed prediction signal and the second vehicle speed prediction signal based on the third difference and the fourth difference;

[0069] S205. The first vehicle speed prediction signal and the second vehicle speed prediction signal are weighted according to the first weight allocation scheme to obtain the first weighted prediction signal, and the first vehicle speed prediction signal and the second vehicle speed prediction signal are weighted according to the second weight allocation scheme to obtain the second weighted prediction signal.

[0070] It should be understood that the allocation principle of the first and second weight allocation schemes is: the greater the deviation, the smaller the weight, so as to ensure that the speed prediction model with smaller deviation has a larger weight, and to ensure the accuracy and precision of the final determined speed prediction signal.

[0071] In a specific embodiment of the present invention, the weights in the weight allocation scheme are:

[0072]

[0073] In the formula, The weights of the i-th vehicle speed prediction model; The difference between the vehicle speed prediction signal predicted by the i-th vehicle speed prediction model and the second vehicle speed signal; These are variable coefficients; It is a constant; n This represents the total number of vehicle speed prediction models.

[0074] It should be noted that when the deviation algorithm is the absolute deviation normalization accumulation method, the difference between the vehicle speed prediction signal predicted by each vehicle speed prediction model and the second vehicle speed signal is the normalized absolute deviation.

[0075] Specifically, the upper limit of vehicle speed is determined based on the vehicle type, the absolute deviation between the vehicle speed prediction signal and the second vehicle speed signal is determined, and the ratio of the absolute deviation to the upper limit of vehicle speed is used as the difference in the above weight calculation formula.

[0076] It should also be noted that when the deviation algorithm is the window moving average deviation normalization method, the average value within the preset time period is first determined, and the initial difference between the vehicle speed prediction signal and the average value is determined. The ratio of the initial difference to the upper limit of vehicle speed is used as the difference in the weight calculation formula.

[0077] In some embodiments of the present invention, the number of deviation algorithms is 3, and the at least two weighted prediction signals in step S103 include a first weighted prediction signal corresponding to the least squares method, a second weighted prediction signal corresponding to the absolute deviation normalization accumulation method, and a third weighted prediction signal corresponding to the window moving average deviation normalization method.

[0078] Then as Figure 3 As shown, step S103, determining the secondary weight allocation scheme for at least two weighted prediction signals, includes:

[0079] S301, Determine the first prediction difference between the first weighted prediction signal and the second vehicle speed signal, the second prediction difference between the second weighted prediction signal and the second vehicle speed signal, and the third prediction difference between the third weighted prediction signal and the second vehicle speed signal;

[0080] S302. Determine a secondary weight allocation scheme based on the first prediction difference, the second prediction difference, and the third prediction difference.

[0081] The principle of the secondary weight allocation scheme in step S302 is: the larger the prediction difference, the smaller its weight.

[0082] It should be noted that the weight calculation formula in the secondary weight allocation scheme is based on the same principle as the weight calculation formula in the aforementioned weight allocation scheme, with the inverse square root ratio of the prediction difference as the numerator and the sum of the inverse square root ratios of multiple prediction differences as the denominator.

[0083] The secondary weight allocation scheme includes the weights corresponding to each deviation algorithm. Therefore, the determination of the comprehensive vehicle speed prediction signal based on the secondary weight allocation scheme and at least two weighted prediction signals in step S104 is as follows: multiply the weights of each weighted prediction signal in the secondary weight allocation scheme with the weighted prediction signal first, and then add them together to obtain the comprehensive vehicle speed prediction signal.

[0084] Because signal distortion and noise pollution can occur during the deviation processing and weighting of vehicle speed prediction signals, in order to avoid this technical problem, in some embodiments of the present invention, the method further includes the following step before step S104:

[0085] Kalman filtering is applied to each weighted prediction signal.

[0086] The embodiments of the present invention can eliminate noise pollution in the bias processing process by performing Kalman filtering on each weighted prediction signal, thereby improving the accuracy of the weighted prediction signal and thus improving the accuracy and precision of the final determined comprehensive vehicle speed prediction signal.

[0087] In a specific embodiment of the present invention, the number of vehicle speed prediction models is 5, and the deviation calculation methods are 3: least squares method, absolute deviation normalization cumulative method, and window moving average deviation normalization method. The specific implementation process of the vehicle speed prediction method proposed in this embodiment is as follows:

[0088] First, all five vehicle speed prediction models use data from segment A to predict the vehicle speed from data in segment B, resulting in five predicted vehicle speed segments, which will be denoted as B1, B2, and B3 respectively. Speed1 B Speed2 B Speed3 B Speed4 B Speed5 The original speed of the vehicle in section B is recorded as B. Speed .

[0089] Secondly, evaluate B using the least squares method. Speed1 B Speed2 B Speed3 B Speed4 B Speed5 With B Speed The deviation is used to obtain a deviation ranking. Rankings are determined based on the magnitude of the deviation, and different weights (B) are assigned to different ranking methods based on the magnitude of the deviation. Weight1 B Weight2 B Weight3 B Weight4 B Weight5 Then, each predicted vehicle speed is multiplied by its corresponding weight value, and the resulting model value is then processed by Kalman filtering to obtain the first weighted prediction signal, Moedel1.

[0090] Then, the second weighted prediction signal Model2 is obtained by the absolute deviation normalization accumulation method, and the third weighted prediction signal Model3 is obtained by the window moving average deviation normalization method.

[0091] Finally, after a secondary weighting of the three weighted prediction signals, the three final model values ​​are obtained. Through weighting, the final comprehensive vehicle speed prediction signal is obtained.

[0092] This multi-dimensional vehicle speed prediction method can minimize the errors caused by single-method predictions, significantly improving prediction accuracy. Actual data verification shows that the average prediction deviation of the vehicle speed prediction method proposed in this embodiment is less than 1%, validating its effectiveness.

[0093] To better implement the vehicle speed prediction method in the embodiments of the present invention, based on the vehicle speed prediction method, the embodiments of the present invention also provide a vehicle speed prediction device, such as... Figure 4 As shown, the vehicle speed prediction device 400 includes:

[0094] The vehicle speed signal acquisition unit 401 is used to acquire the first vehicle speed signal in the previous time period and the second vehicle speed signal in the current time period.

[0095] The vehicle speed prediction unit 402 is used to input the first vehicle speed signal into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals.

[0096] The first weight allocation unit 403 is used to determine at least two weight allocation schemes for at least two vehicle speed prediction signals based on at least two deviation algorithms, and to determine at least two weighted prediction signals that correspond one-to-one with at least two deviation algorithms based on at least two weight allocation schemes.

[0097] The second weight allocation unit 404 is used to determine a secondary weight allocation scheme for at least two weighted prediction signals, and to determine a comprehensive vehicle speed prediction signal based on the secondary weight allocation scheme and at least two weighted prediction signals.

[0098] It should be noted that the vehicle speed prediction device 400 provided in the above embodiments can realize the technical solutions described in the above vehicle speed prediction method embodiments. The specific implementation principles or implementation details of each module or unit can be found in the corresponding content of the above vehicle speed prediction method embodiments, which will not be elaborated here.

[0099] like Figure 5 As shown, the present invention also provides a vehicle 500. The vehicle 500 includes a processor 501, a memory 502, and a display 503. Figure 5 Only some components of vehicle 500 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0100] In some embodiments, processor 501 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 502 or process data, such as the vehicle speed prediction method of the present invention.

[0101] In some embodiments of the present invention, processor 501 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 501 may be local or remote. In some embodiments, processor 501 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0102] In some embodiments, memory 502 may be an internal storage unit of vehicle 500, such as hard disk or memory of vehicle 500.

[0103] Furthermore, the memory 502 may include both internal storage units of the vehicle 500 and external storage devices. The memory 502 is used to store application software and various types of data installed on the vehicle 500.

[0104] In some embodiments, display 503 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 503 is used to display information about vehicle 500 and to display a visual user interface. Components 501-503 of vehicle 500 communicate with each other via a system bus.

[0105] In some embodiments of the present invention, when the processor 501 executes the vehicle speed prediction program in the memory 502, the following steps can be implemented:

[0106] Acquire the first vehicle speed signal in the previous time period and the second vehicle speed signal in the current time period;

[0107] The first vehicle speed signal is input into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals.

[0108] At least two weighting schemes are determined based on at least two bias algorithms to identify at least two vehicle speed prediction signals, and at least two weighted prediction signals corresponding one-to-one with the at least two bias algorithms are determined based on the at least two weighting schemes.

[0109] Determine a secondary weighting scheme for at least two weighted prediction signals, and determine a comprehensive vehicle speed prediction signal based on the secondary weighting scheme and the at least two weighted prediction signals.

[0110] It should be understood that when the processor 501 executes the vehicle speed prediction program in the memory 502, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0111] Furthermore, the embodiments of the present invention do not specifically limit the type of vehicle 500 mentioned, and vehicle 500 can be a pure electric vehicle, a fuel vehicle or a hybrid vehicle.

[0112] Accordingly, embodiments of the present invention also provide a computer-readable storage medium for storing a computer-readable program or instruction. When the program or instruction is executed by a processor, it can implement the steps or functions of the vehicle speed prediction method provided in the above-described method embodiments.

[0113] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0114] The present invention provides a detailed description of a vehicle speed prediction method, device, vehicle, and storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A vehicle speed prediction method, characterized in that, include: Acquire the first vehicle speed signal in the previous time period and the second vehicle speed signal in the current time period; The first vehicle speed signal is input into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals. At least two weight allocation schemes for the at least two vehicle speed prediction signals are determined based on at least two deviation algorithms, and at least two weighted prediction signals corresponding one-to-one with the at least two deviation algorithms are determined based on the at least two weight allocation schemes. Determine a secondary weight allocation scheme for the at least two weighted prediction signals, and determine a comprehensive vehicle speed prediction signal based on the secondary weight allocation scheme and the at least two weighted prediction signals; The deviation algorithm is the least squares method, the absolute deviation normalization accumulation method, or the window moving average deviation normalization method; the at least two weighted prediction signals include a first weighted prediction signal corresponding to the least squares method, a second weighted prediction signal corresponding to the absolute deviation normalization accumulation method, and a third weighted prediction signal corresponding to the window moving average deviation normalization method. The step of determining the secondary weight allocation scheme of the at least two weighted prediction signals includes: determining a first prediction difference between the first weighted prediction signal and the second vehicle speed signal, a second prediction difference between the second weighted prediction signal and the second vehicle speed signal, and a third prediction difference between the third weighted prediction signal and the second vehicle speed signal; and determining the secondary weight allocation scheme based on the first prediction difference, the second prediction difference, and the third prediction difference.

2. The vehicle speed prediction method according to claim 1, characterized in that, The at least two vehicle speed prediction models include a first vehicle speed prediction model and a second vehicle speed prediction model with different model structures; the at least two vehicle speed prediction signals include a first vehicle speed prediction signal corresponding to the first vehicle speed prediction model and a second vehicle speed prediction signal corresponding to the second vehicle speed prediction model; the at least two deviation algorithms include a first deviation algorithm and a second deviation algorithm. The step of determining at least two weight allocation schemes for the at least two vehicle speed prediction signals based on at least two bias algorithms, and determining at least two weighted prediction signals corresponding one-to-one with the at least two bias algorithms based on the at least two weight allocation schemes, includes: The first difference between the first vehicle speed prediction signal and the second vehicle speed signal and the second difference between the second vehicle speed prediction signal and the second vehicle speed signal are determined based on the first deviation algorithm. A first weight allocation scheme for the first vehicle speed prediction signal and the second vehicle speed prediction signal is determined based on the first difference and the second difference; The third difference between the first vehicle speed prediction signal and the second vehicle speed signal, and the fourth difference between the second vehicle speed prediction signal and the second vehicle speed signal are determined based on the second deviation algorithm. A second weighting scheme for the first vehicle speed prediction signal and the second vehicle speed prediction signal is determined based on the third difference and the fourth difference; The first vehicle speed prediction signal and the second vehicle speed prediction signal are weighted according to the first weight allocation scheme to obtain a first weighted prediction signal, and the first vehicle speed prediction signal and the second vehicle speed prediction signal are weighted according to the second weight allocation scheme to obtain a second weighted prediction signal.

3. The vehicle speed prediction method according to claim 1, characterized in that, The weights in the weight allocation scheme are: In the formula, The weights of the i-th vehicle speed prediction model; The difference between the vehicle speed prediction signal predicted by the i-th vehicle speed prediction model and the second vehicle speed signal; These are variable coefficients; It is a constant; n This represents the total number of vehicle speed prediction models.

4. The vehicle speed prediction method according to claim 1, characterized in that, The vehicle speed prediction model is a least squares curve fitting algorithm, a moving average window algorithm, a long short-term memory network model, or a gated recurrent unit network model.

5. The vehicle speed prediction method according to claim 1, characterized in that, Before determining the secondary weight allocation scheme for the at least two weighted prediction signals, the method further includes: The weighted prediction signals are then subjected to Kalman filtering.

6. A vehicle speed prediction device, characterized in that, The device, applicable to the vehicle speed prediction method according to any one of claims 1-5, comprises: The vehicle speed signal acquisition unit is used to acquire the first vehicle speed signal in the previous time period and the second vehicle speed signal in the current time period. The vehicle speed prediction unit is used to input the first vehicle speed signal into at least two vehicle speed prediction models to obtain at least two vehicle speed prediction signals. The first weight allocation unit is used to determine at least two weight allocation schemes for the at least two vehicle speed prediction signals based on at least two deviation algorithms, and to determine at least two weighted prediction signals that correspond one-to-one with the at least two deviation algorithms based on the at least two weight allocation schemes. The second weight allocation unit is used to determine a secondary weight allocation scheme for the at least two weighted prediction signals, and to determine a comprehensive vehicle speed prediction signal based on the secondary weight allocation scheme and the at least two weighted prediction signals.

7. A vehicle, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the vehicle speed prediction method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the vehicle speed prediction method according to any one of claims 1 to 5.

Images