Vehicle control method and device, vehicle and storage medium

Abstract

The application discloses a vehicle control method and device, a vehicle and a storage medium. The vehicle control method comprises the following steps: acquiring an error of a target control quantity of a self-vehicle at a current time point and a historical error integral result at a last time point before the current time point; the error is a difference between a target value and an actual value of the target control quantity; the historical error integral result is obtained by weightedly integrating errors of the target control quantity at historical time points; performing weighted integration on the historical error integral result, the error of the target control quantity at the current time point and corresponding weights to obtain a current error integral result of the target control quantity at the current time point; and controlling the self-vehicle based on the current error integral result. According to the method, the error of the target control quantity of the self-vehicle is smaller, and the driving process of the self-vehicle is more stable and safe.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a vehicle control method, apparatus, vehicle, and storage medium. Background Technology

[0002] During autonomous driving, the vehicle can determine the integral result of the control variable error based on the error between the actual value and the target value of the control variable. The control variable can be, for example, the vehicle's speed or acceleration. Then, the vehicle can control the autonomous driving process based on the integral result of the control variable error, so that the error between the actual value and the target value of the control variable is smaller and the autonomous driving process is more stable.

[0003] However, when using this method to control the vehicle, the accuracy of vehicle control is low, resulting in poor stability during vehicle operation. Summary of the Invention

[0004] This application proposes a vehicle control method, device, vehicle, and storage medium to improve the stability of the vehicle during operation.

[0005] In a first aspect, embodiments of this application provide a vehicle control method, the method comprising:

[0006] Obtain the error of the target control quantity of the vehicle at the current time point and the historical error integral result of the previous time point before the current time point; the error is the difference between the target value and the actual value of the target control quantity; the historical error integral result is obtained by weighted integration of the error of the target control quantity at historical time points;

[0007] The current error integral result of the target control quantity is obtained by performing a weighted integral based on the historical error integral result, the error of the target control quantity at the current time point and the corresponding weight;

[0008] Based on the current error integral result, the vehicle is controlled.

[0009] Secondly, embodiments of this application provide a vehicle control device, the device comprising:

[0010] The acquisition module is used to acquire the error of the target control quantity of the vehicle at the current time point and the historical error integral result of the previous time point before the current time point; the error is the difference between the target value and the actual value of the target control quantity; the historical error integral result is obtained by weighted integration of the error of the target control quantity at historical time points;

[0011] The determination module is used to perform weighted integration based on the historical error integral results, the error of the target control quantity at the current time point, and the corresponding weights, to obtain the current error integral result of the target control quantity at the current time point.

[0012] The control module is used to control the vehicle based on the current error integral result.

[0013] Thirdly, embodiments of this application also provide a vehicle, the vehicle including: one or more processors; a memory; one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to perform the method described in the first aspect above.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing processor-executable program code, which, when executed by the processor, causes the processor to perform the above-described method.

[0015] This application provides a vehicle control method, device, vehicle, and storage medium. Since the error of the target control quantity varies at different time points as the vehicle moves, and the impact of these errors on the vehicle's driving process differs (i.e., the importance of the error varies at each time point), this application performs a weighted integration based on the historical error integration results, the error of the target control quantity at the current time point, and the corresponding weights to obtain the current error integration result of the target control quantity at the current time point. This achieves weighted integration of the target control quantity's error at each time point based on its weights, making the current error integration result more accurate. It reduces the possibility of low accuracy in differential integration results when directly integrating the error of the target control quantity at each time point without considering the importance of the error at each time point. Therefore, when controlling the vehicle based on the more accurate current error integration result determined in this application, the control process is more accurate, the error of the target control quantity is smaller, and the vehicle's driving process is more stable and safer.

[0016] Other features and advantages of the embodiments of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the embodiments of this application. The objects and other advantages of the embodiments of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of a vehicle hardware environment applicable to embodiments of this application is shown.

[0019] Figure 2 A flowchart of a vehicle control method according to an embodiment of this application is shown.

[0020] Figure 3 The diagram shows the relationship between the weight of the acceleration error and the historical error integral result of the acceleration in an embodiment of this application.

[0021] Figure 4 It shows Figure 2 The flowchart of step S130 in one embodiment is shown in the corresponding example.

[0022] Figure 5 The diagram shows the relationship between the acceleration error and the integral result of the current acceleration error in one embodiment of this application.

[0023] Figure 6 A schematic diagram illustrating the process for determining a current error integration result in an embodiment of this application is shown.

[0024] Figure 7 A schematic diagram of a vehicle control process according to an embodiment of this application is shown.

[0025] Figure 8 A graph showing the change of acceleration over time in an embodiment of this application is shown.

[0026] Figure 9 A structural block diagram of a vehicle control device according to an embodiment of this application is shown. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application 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 application, and not all of them. The components of the embodiments of the present application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without inventive effort are within the scope of protection of the present application.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Reference Figure 1 , Figure 1 A schematic diagram of a vehicle hardware environment applicable to an embodiment of this application is shown. The vehicle 100 includes a driving system 110, which can have multiple built-in autonomous driving functions. The driving system 110 can store electronic maps. The driving system 110 can plan driving routes based on the electronic maps it stores, and can also control the vehicle to drive autonomously based on the planned driving routes.

[0030] The driving system 110 may include a data acquisition device 111, one or more (only one is shown in the figure) processors 112 and memory 113.

[0031] The data acquisition device 111 is used to detect various signals or data of the vehicle. For example, the data acquisition device 111 can collect specific values ​​of the control quantities of the vehicle. For example, the data acquisition device 111 may include a speed sensor or an acceleration sensor.

[0032] The processor 112 may be a microcontroller unit (MCU) with a built-in memory 113 containing a program that can execute the contents of the following embodiments, and the processor 112 can execute the program stored in the memory 113.

[0033] The processor 112 may include one or more processors. The processor 112 uses various interfaces and circuits to connect various parts of the vehicle 100, and performs various functions of the vehicle 10 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 113, and calling data stored in the memory 113.

[0034] Memory 113 may include random access memory (RAM) or read-only memory (ROM). Memory 15 may be used to store instructions, programs, code, code sets, or instruction sets. Memory 15 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described below, etc.

[0035] Please see Figure 2 , Figure 2 A flowchart of a vehicle control method according to an embodiment of this application is shown, for a vehicle, the method comprising:

[0036] S110. Obtain the error of the target control quantity of the vehicle at the current time point and the historical error integral result of the previous time point before the current time point.

[0037] Here, the error is the difference between the target value and the actual value of the target control quantity; the historical error integral result is obtained by weighted integration of the error of the target control quantity at historical time points. In other words, historical time points refer to all time points prior to the current time point of the vehicle.

[0038] In this embodiment, the vehicle can be an electric vehicle or a fuel vehicle, or it can be a sedan, SUV, bus, or truck, etc. The term "self-vehicle" can refer to the vehicle itself.

[0039] Typically, during the driving process, the entire driving process is divided into multiple driving periods of fixed duration. For each current time point, the historical time point refers to the time point in the driving period that is before the current time point.

[0040] For example, during the driving process of a vehicle, every 10 seconds is considered a driving period. In the driving period from 11 to 20 seconds, if the current time point is the 15th second, the historical time points can be the 11th second, the 12th second, the 13th second, and the 14th second.

[0041] Understandably, the interval between any two adjacent time points during the vehicle's driving process can be set based on requirements. For example, the interval between any two adjacent time points could be 1 second as in the previous example. Of course, in order to control the process more accurately and effectively, the interval between any two adjacent time points can be shorter, for example, 0.2 seconds or 0.5 seconds.

[0042] In this embodiment, the target control quantity can be the vehicle's speed and acceleration, etc.

[0043] During the vehicle's operation, the actual values ​​of the target control quantity at each time point can be collected, and the difference between the actual value and the target value at each time point can be determined as the error of the target control quantity at each time point. The target value of the target control quantity refers to the specific value of the target control quantity of the vehicle under ideal conditions. Generally speaking, the target value of the target control quantity is different for different driving scenarios and different time points of the same driving scenario. Driving scenarios include turning scenarios, straight driving scenarios, parking scenarios, and lane changing scenarios.

[0044] For example, the target speed for a vehicle in a turning scenario differs from the target speed for a vehicle in a parking scenario. Similarly, the target speed at the 3rd second of a turning scenario differs from the target speed at the 5th second of a turning scenario.

[0045] It is easy to understand that at the same point in time in the same driving scenario, the target values ​​will be different for different target control variables. For example, in the 3rd second of a turning scenario, the target value of the vehicle's speed is different from the target value of the vehicle's acceleration.

[0046] In this application, as time progresses, the later a point in time is, the greater the weight of the error at that point in time; conversely, the earlier a point in time is, the smaller the weight of the error at that point in time.

[0047] In some implementations, the sequence number of the current time point (referring to the sequence number of the current time point within its respective travel period) can be multiplied by a specified coefficient to serve as the weight of the error of the target control quantity at that current time point. The specified coefficient can vary depending on the target control quantity; for example, the specified coefficient might be 0.1 for speed, or 0.15 for acceleration.

[0048] In some other implementations, the weight of the target control quantity's error at the current time point can also be determined based on the historical error integral results.

[0049] For example, the historical error integral result can be multiplied by a first preset coefficient, and the resulting product can be used as the weight of the target control quantity's error at the current time point. The first preset coefficient can be, for example, 0.1 or the natural constant e.

[0050] Alternatively, the weight of the target control variable's error at the current time point can be determined based on the absolute value of the historical error integral result. The absolute value of the historical error integral result and the weight of the target control variable's error at the current time point can be positively correlated.

[0051] For example, the historical error integral result can be multiplied by a second preset coefficient that is positive, and the resulting product can be used as the weight of the target control quantity's error at the current time point. The second preset coefficient can be, for example, the natural constant e or 3.

[0052] For example, the weight of the target control quantity's error at the current time point and the absolute value of the integral of the historical error satisfy the following formula:

[0053] a(t) = e |y(t―1)|

[0054] Where t is the current time point, a(t) is the weight of the error of the target control quantity at the current time point t, and |y(t-1)| is the absolute value of the historical error integral result.

[0055] For example, if the target control variable is the vehicle's acceleration, the weight of the error of the target control variable at the current time point t and the absolute value of the integral result of the historical error satisfy a = e | y (t ― 1)| When the weight of the target control quantity's error at the current time point t is related to the historical error integral result, the following is a relationship: Figure 3 As shown.

[0056] Similarly, for each time point, the weight of the target control quantity's error at that time point can be determined in the manner described above. That is, for each historical time point, the weight of the target control quantity's error at each historical time point is determined in the manner described above, and then the weighted integral of the target control quantity's error at each historical time point is performed to obtain the target control quantity's error integral result, which is used as the historical error integral result.

[0057] Therefore, the historical error integral result L(t-1) of the target control quantity satisfies the following formula:

[0058]

[0059] Where u(t-1) is the error of the target control quantity at the previous time point t-1, and a(t-1) is the weight of the error of the target control quantity at the previous time point t-1.

[0060] S120. Based on the historical error integral results, the error of the target control quantity at the current time point, and the corresponding weights, perform weighted integration to obtain the current error integral result of the target control quantity at the current time point.

[0061] In other words, the error of the target control quantity at the current time point can be multiplied by the corresponding weight to obtain the product result, and the product result can be summed with the historical error integral result to obtain the error integral result of the target control quantity at the current time point, which is used as the current error integral result of the target control quantity at the current time point.

[0062] Therefore, the weight of the target control quantity's error at the current time point and the absolute value of the historical error integral result satisfy the aforementioned formula a(t)=e |y(t―1)| At that time, the current error integral result L(t) of the target control quantity satisfies the following formula:

[0063]

[0064] Where y(t-1) is the historical error integral result of the target control quantity at the previous time point t-1, e is the natural constant, and u(t) is the error of the target control quantity at the current time point t.

[0065] S130. Based on the current error integral result, control the vehicle.

[0066] Once the integral result of the current error of the target control quantity at the current time is obtained, the vehicle can be controlled based on the integral result of the current error at the current time.

[0067] Because the error of the target control quantity varies at different time points as the vehicle moves, and the impact of the error at each time point on the vehicle's driving process differs, meaning the importance of the error at each time point varies, this application performs a weighted integration based on the historical error integration results, the error of the target control quantity at the current time point, and the corresponding weights to obtain the current error integration result of the target control quantity at the current time point. This achieves weighted integration of the error of the target control quantity at each time point based on its weights, making the current error integration result of the target control quantity at the current time point more accurate. This reduces the possibility of low accuracy in the differential integration result when directly integrating the error of the target control quantity at each time point without considering the importance of the error at each time point. Therefore, when controlling the vehicle based on the more accurate current error integration result determined in this application, the control process is more accurate, the error of the target control quantity of the vehicle is smaller, and the driving process of the vehicle is more stable and safer.

[0068] Furthermore, in this embodiment, the weight of the target control quantity's error at the current time is determined based on the historical error integral result at the previous time point before the current time point. This means that the weight of the target control quantity's error is related to the integral result of the target control quantity's error during the vehicle's movement. This makes the weight of the target control quantity's error more accurate, thereby further improving the accuracy of the current error integral result of the target control quantity at the current time, obtained by weighted integration based on the weight of the target control quantity's error at the current time and the historical error integral result. As a result, when controlling the vehicle based on the target control parameters at the current error integral result, the accuracy of the control process is further improved, the error of the vehicle's target control quantity is further reduced, and the vehicle's driving process is more stable and safer.

[0069] In some embodiments, such as Figure 4 As shown, S130 may include:

[0070] S210. Based on the error of the target control quantity at the current time point, determine the adjustment coefficient of the current error integral result.

[0071] In some implementations, the error of the target control quantity at the current time point can be multiplied by a third preset coefficient, and the resulting product can be used as the adjustment coefficient for the current error integral result of the target control quantity at the current time point. The third preset coefficient can be, for example, 0.1 or the natural constant e.

[0072] Optionally, the adjustment coefficient of the current error integral result of the target control quantity at the current time point can be determined based on the absolute value of the error of the target control quantity at the current time point. The absolute value of the error of the target control quantity at the current time point and the adjustment coefficient of the current error integral result of the target control quantity at the current time point can be positively correlated.

[0073] For example, the absolute value of the error of the target control quantity at the current time point can be multiplied by a positive fourth preset coefficient, and the resulting product can be used as the adjustment coefficient for the integral of the current error of the target control quantity at the current time point. The fourth preset coefficient can be, for example, the natural constant e or 3.

[0074] For example, the adjustment coefficient of the current error integral result and the error of the target control quantity at the current time point satisfy the following formula:

[0075] b(t) = e |u(t)|

[0076] Where b(t) is the adjustment coefficient of the current error integral result, and u(t) is the error of the target control quantity at the current time point t.

[0077] The adjustment coefficient of the current error integral result and the absolute value of the error of the target control quantity at the current time point satisfy b(t) = e |u(t)| And when the target control variable is acceleration, the relationship between the adjustment coefficient of the current error integral result and the error of the target control variable at the current time point is as follows: Figure 5 As shown.

[0078] S220. Based on the current error integral result and the adjustment coefficient, the current error cumulative control adjustment of the target control quantity at the current time point is obtained.

[0079] After obtaining the adjustment coefficient of the current error integral result, the current cumulative control adjustment of the target control quantity at the current time point can be determined directly based on the adjustment coefficient of the current error integral result and the current error integral result.

[0080] For example, the product of the current error integral result and the adjustment coefficient is calculated as the current cumulative error control adjustment of the target control quantity at the current time point.

[0081] For example, the quotient of the current error integral result and the adjustment coefficient of the current error integral result can be calculated as the current error cumulative control adjustment of the target control quantity at the current time point.

[0082] S230: Control the vehicle according to the current error accumulation control adjustment amount.

[0083] Once the current cumulative error control adjustment of the target control quantity at the current time point is obtained, the vehicle's movement can be controlled based on the current cumulative error control adjustment of the target control quantity at the current time point.

[0084] In some implementations, the difference between the error of the target control quantity at the current time point and the error of the target control quantity at the previous time point can be calculated as the error change rate of the target control quantity at the current time point. In other implementations, the difference between the error of the target control quantity at the current time point and the error of the target control quantity at the previous time point can be calculated as the error difference. Then, the ratio of this error difference to the time interval between the current time point and the previous time point can be calculated as the error change rate of the target control quantity at the current time point. Finally, the product of the error change rate of the target control quantity at the current time point and the corresponding rate of change coefficient is calculated as the current error change control adjustment amount of the target control quantity at the current time point. The rate of change coefficient can be a value set based on requirements, such as 0.3.

[0085] Simultaneously, the product of the error of the target control quantity at the current time point and the corresponding error coefficient is calculated, and this product is used as the current error control adjustment amount of the target control quantity at the current time point. The error coefficient can be a value set based on requirements, such as 0.4.

[0086] Finally, the sum of the current cumulative error control adjustment, the current error change control adjustment, and the current error control adjustment at the current time point is calculated as the target error quantity corresponding to the target control quantity.

[0087] Therefore, the calculation process for determining the target error is as follows:

[0088] PID(t) = b(t)·L(t) + K p ·B(t)+K d ·u(t)

[0089] Where PID(t) is the target error of the target control quantity at the current time point t, L(t) is the integral result of the current error of the target control quantity at the current time point, u(t) is the error of the target control quantity at the current time point, B(t) is the rate of change of the error of the target control quantity at the current time point, and K... p K is the rate of change coefficient of the target control quantity.d The error coefficient of the target control quantity.

[0090] After obtaining the target error of the target control quantity at the target time point, the actual value of the target control quantity at the next time point is determined based on the target error and the actual value of the target control quantity at the current time point (for example, calculating the difference between the actual value of the target control quantity at the current time point and the target error). Then, when the next time point is reached, the vehicle is controlled to move based on the actual value of the target control quantity at the next time point.

[0091] For example, the sum of the current cumulative error control adjustment, the current error change control adjustment, and the current error control adjustment at the first time point is calculated. The difference between the actual value of the target control quantity at the first time point and this sum is calculated to obtain the actual value of the target control quantity at the second time point. When the target control quantity is reached at the second time point, the vehicle is controlled to move based on the actual value of the target control quantity at the second time point. As another example, the sum of the current cumulative error control adjustment, the current error change control adjustment, and the current error control adjustment at the fifth time point is calculated. The difference between the actual value of the target control quantity at the fifth time point and this sum is calculated to obtain the actual value of the target control quantity at the sixth time point. When the target control quantity is reached at the sixth time point, the vehicle is controlled to move based on the actual value of the target control quantity at the sixth time point.

[0092] It is understandable that when the target control variable is the speed of the vehicle, the determined actual value includes the specific value of the speed. At this time, the vehicle is controlled according to the actual value of the speed at the next time point so that the vehicle travels at that speed.

[0093] When the target control variable is the acceleration of the vehicle, the determined actual value includes the specific value of the acceleration. At this time, the vehicle is controlled according to the actual value of the acceleration at the next time point so that the vehicle travels at that acceleration.

[0094] When the target control variables are the vehicle's acceleration and speed, the determined actual values ​​include the specific values ​​of acceleration and speed. At this time, the vehicle is controlled according to the actual values ​​of acceleration and speed at the next time point so that the vehicle travels at the specified acceleration and speed.

[0095] The process for determining the current error integral result of the target control quantity is as follows: Figure 6As shown, based on the absolute value of the error of the target control quantity at the current time point, the adjustment coefficient corresponding to the current error integral result is determined. At the same time, based on the historical error integral results before the current time point, the weight of the error of the target control quantity at the current time point is determined. By using the weight of the error of the target control quantity at the current time point, the error of the target control quantity at the current time point and the historical error integral results are weighted and integrated to obtain the current error integral result of the target control quantity at the current time point. Then, the current error integral result of the target control quantity at the current time point is multiplied by the corresponding adjustment coefficient to obtain the current cumulative control adjustment of the target control quantity at the current time point.

[0096] In this embodiment, after obtaining the error integral results at each time point, the current error cumulative control adjustment amount is determined based on the error integral results and the corresponding adjustment coefficients. Since the adjustment coefficients corresponding to the error integral results are determined based on the error of the target control quantity, it means that the adjustment coefficients corresponding to the error integral results are related to the error of the target control quantity during the vehicle's movement. The adjustment coefficients corresponding to the error integral results are no longer constant values, making the adjustment coefficients corresponding to the error integral results match the actual movement state of the vehicle. The adjustment coefficients corresponding to the error integral results of the target control quantity are more accurate, thus making the error cumulative control adjustment amount determined based on the adjustment coefficients corresponding to the error integral results more accurate. Therefore, when controlling the vehicle based on the error cumulative control adjustment amount of the target control parameters, the control process is more accurate, the error of the vehicle's target control quantity is smaller, and the vehicle's driving process is more stable and safer.

[0097] Secondly, the adjustment coefficient corresponding to the error integral result is related to the error of the target control quantity during the vehicle's movement, ensuring that the target error determined based on the weighted integral error cumulative control adjustment quantity matches the vehicle's driving process: the larger the historical error integral result, the larger the value of the error multiplied by the weight, the larger the current error integral result, the larger the error cumulative control adjustment quantity, and the larger the determined target error quantity, thus allowing for a larger adjustment range based on the target error quantity. Conversely, the smaller the historical error integral result, the smaller the error multiplied by the weight, the smaller the current error integral result, the smaller the error cumulative control adjustment quantity, and the smaller the determined target error quantity, thus allowing for a larger adjustment range based on the target error quantity. Consequently, the determined target error quantity is more accurate, and when controlling the vehicle based on the target error quantity, the control process is more accurate, the error of the vehicle's target control quantity is smaller, and the vehicle's driving process is more stable and safer.

[0098] In some examples, when the target control variable is the vehicle's acceleration, the vehicle control process is as follows: Figure 7 As shown.

[0099] First, for the current time point, determine the actual value and target value of the vehicle's acceleration. Based on the actual value and target value of the acceleration, determine the acceleration error. Then, through the historical error integration results of the acceleration, determine the weight of the acceleration error at the current time point. Finally, perform weighted integration based on the weight of the acceleration error at the current time point and the historical error integration results of the acceleration to obtain the current error integration result of the acceleration.

[0100] Then, based on the acceleration error at the current time point, the adjustment coefficient of the current error integral result of the acceleration is determined. Then, the current error integral result of the acceleration is multiplied by the corresponding adjustment coefficient to obtain the current error cumulative control adjustment. The current error change control adjustment and the current error control adjustment of the target control quantity at the current time point are obtained. The sum of the current error cumulative control adjustment, the current error change control adjustment, and the current error control adjustment of the target control quantity at the current time point is calculated to obtain the target error quantity for the acceleration.

[0101] Then, based on the target error of acceleration, the actual value of the vehicle's acceleration at the next time point is determined, and at the next time point, the vehicle's movement is controlled according to the actual value of the vehicle's acceleration at the next time point.

[0102] like Figure 8 As shown, 801 represents the curve of acceleration value changing with time when a fixed adjustment coefficient is set for the integral result of acceleration error, and the acceleration error at each time point is directly accumulated to obtain the integral result of acceleration error; 801 also represents the curve of acceleration value changing with time when a varying adjustment coefficient is set for the integral result of acceleration error, and the acceleration error at each time point is weighted and integrated to determine the integral result of acceleration error, according to the method of this application. Figure 8 As can be seen, curve 801 fluctuates more and more frequently, which means that the acceleration changes frequently during the vehicle's driving process and the acceleration stability is poor. In contrast, curve 802 fluctuates less and has a lower frequency of fluctuation, which means that the acceleration changes less during the vehicle's driving process and the acceleration stability is good. In other words, according to the method of this application, the acceleration changes stably during the vehicle's driving process, and the vehicle's driving process is more stable.

[0103] See appendix Figure 9 , Figure 9 This illustration shows a structural block diagram of a vehicle control device according to one embodiment of this application. For use in a vehicle, the device 800 includes:

[0104] The acquisition module 810 is used to acquire the error of the target control quantity of the vehicle at the current time point and the historical error integral result at the previous time point before the current time point; the error is the difference between the target value and the actual value of the target control quantity; the historical error integral result is obtained by weighted integration of the error of the target control quantity at historical time points.

[0105] The determination module 820 is used to perform weighted integration based on the historical error integral results, the error of the target control quantity at the current time point, and the corresponding weights, to obtain the current error integral result of the target control quantity at the current time point.

[0106] The control module 830 is used to control the vehicle based on the current error integral result.

[0107] Optionally, the determining module 820 is also used to determine the weight of the error of the target control quantity at the current time point based on the historical error integration results.

[0108] Optionally, the determining module 820 is also used to determine the weight of the error of the target control quantity at the current time point based on the absolute value of the historical error integral result.

[0109] Optionally, the control module 830 is further configured to determine the adjustment coefficient of the current error integral result based on the error of the target control quantity at the current time point; obtain the current error cumulative control adjustment amount of the target control quantity at the current time point based on the current error integral result and the adjustment coefficient; and control the vehicle according to the current error cumulative control adjustment amount.

[0110] Optionally, the control module 830 is also used to calculate the product of the current error integral result and the adjustment coefficient, as the current error cumulative control adjustment of the target control quantity at the current time point.

[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0112] Furthermore, the functions in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module.

[0113] On the other hand, this application also provides a computer-readable storage medium storing program code that can be called by a processor to execute the methods described in the above method embodiments.

[0114] Computer-readable storage media can be electronic storage devices such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or a cluster of ROMs. Optionally, computer-readable storage media include non-volatile computer-readable storage media. The computer-readable storage media has storage space for program code that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in a suitable form.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A vehicle control method, characterized in that, The method includes: The error of the target control quantity of the vehicle at the current time point and the historical error integral result at the previous time point before the current time point are obtained; the error is the difference between the target value and the actual value of the target control quantity; the historical error integral result is obtained by weighted integration of the error of the target control quantity at historical time points. Based on the historical error integral result, the error of the target control quantity at the current time point, and the corresponding weights, a weighted integral is performed to obtain the current error integral result of the target control quantity at the current time point. Based on the current error integral result, the vehicle is controlled.

2. The method according to claim 1, characterized in that, Before the step of performing a weighted integral based on the historical error integral result, the error of the target control quantity at the current time point, and the corresponding weights to obtain the current error integral result of the target control quantity at the current time point, the method further includes: Based on the historical error integral results, the weight of the error of the target control quantity at the current time point is determined.

3. The method according to claim 2, characterized in that, The step of determining the weight of the error of the target control quantity at the current time point based on the historical error integral result includes: The weight of the error of the target control quantity at the current time point is determined based on the absolute value of the historical error integral result.

4. The method according to claim 3, characterized in that, The weight of the error of the target control quantity at the current time point and the absolute value of the integral result of the historical error satisfy the following formula: a(t)=e |y(t―1)| Where t is the current time point, a(t) is the weight of the error of the target control quantity at the current time point, and |y(t-1)| is the absolute value of the historical error integral result.

5. The method according to claim 1, characterized in that, The control of the vehicle based on the current error integral result includes: Based on the error of the target control quantity at the current time point, determine the adjustment coefficient of the current error integral result; Based on the current error integral result and the adjustment coefficient, the current cumulative error control adjustment of the target control quantity at the current time point is obtained; The vehicle is controlled based on the current error accumulation control adjustment amount.

6. The method according to claim 5, characterized in that, The adjustment coefficient of the current error integral result and the error of the target control quantity at the current time point satisfy the following formula: b(t)=e |u(t)| Where b(t) is the adjustment coefficient of the current error integral result, and u(t) is the error of the target control quantity at the current time point.

7. The method according to claim 5, characterized in that, The step of obtaining the current cumulative error control adjustment of the target control quantity at the current time point based on the current error integral result and the adjustment coefficient includes: The product of the current error integral result and the adjustment coefficient is calculated as the current cumulative error control adjustment of the target control quantity at the current time point.

8. A vehicle control device, characterized in that, The device includes: The acquisition module is used to acquire the error of the target control quantity of the vehicle at the current time point and the historical error integral result of the previous time point before the current time point; the error is the difference between the target value and the actual value of the target control quantity; the historical error integral result is obtained by weighted integration of the error of the target control quantity at historical time points. The determination module is used to perform a weighted integration based on the historical error integration result, the error of the target control quantity at the current time point, and the corresponding weight, to obtain the current error integration result of the target control quantity at the current time point; The control module is used to control the vehicle based on the current error integral result.

9. A vehicle, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores processor-executable program code, which, when executed by the processor, causes the processor to perform the method according to any one of claims 1-7.

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