Vehicle control method and device, vehicle and computer readable storage medium

Abstract

The invention relates to a vehicle control method and device, a vehicle and a computer readable storage medium, and relates to the technical field of control. The control method comprises the steps that the current speed of the vehicle in the running process is obtained; determining a target speed interval to which the current speed belongs from a plurality of continuous speed intervals formed by the running speed of the vehicle; determining a target turning angle of the front wheels of the vehicle according to the target speed interval and a pre-determined corresponding relation between each speed interval and the turning angle of the front wheels of the vehicle; and performing transverse control on the vehicle according to the target turning angle.

Description

Technical Field

[0001] The present disclosure relates to the field of control technology, and in particular to a vehicle control method, a vehicle control device, a vehicle, and a computer-readable storage medium. Background Art

[0002] With the continuous development of unmanned driving technology, rescue vehicles (such as fire trucks) have gradually changed from manual control mode to unmanned control mode. Compared with the application of unmanned driving technology on passenger cars, the application of unmanned driving technology on rescue vehicles (such as unmanned fire trucks) has higher requirements on the accuracy of vehicle lateral control.

[0003] Therefore, how to design a lateral control method with higher lateral control accuracy has become a problem to be solved urgently in this field. Summary of the invention

[0004] In order to solve the above problems, the embodiments of the present disclosure provide the following solutions.

[0005] According to some embodiments of the present disclosure, a vehicle control method is provided, comprising: acquiring a current speed of the vehicle during driving; determining a target speed interval to which the current speed belongs from a plurality of continuous speed intervals formed by the driving speed of the vehicle; determining a target turning angle of the front wheels of the vehicle based on a correspondence between the target speed interval and each speed interval in the plurality of speed intervals and a turning angle of the front wheels of the vehicle; and performing lateral control of the vehicle based on the target turning angle.

[0006] In some embodiments, the turning angles of the front wheels of the vehicle corresponding to speeds within the same speed range are the same.

[0007] In some embodiments, the difference between the upper limit and the lower limit of each speed interval is equal.

[0008] In some embodiments, the driving speed of the vehicle includes a first speed and a second speed, and the directions of the first speed and the second speed are different; the multiple speed intervals include a continuous plurality of first sub-intervals formed by the first speed and a continuous plurality of second sub-intervals formed by the second speed; the corresponding relationship includes a first sub-relationship between each first sub-interval in the multiple first sub-intervals and the turning angle of the front wheels of the vehicle and a second sub-relationship between each second sub-interval in the multiple second sub-intervals and the turning angle of the front wheels of the vehicle.

[0009] In some embodiments, the direction of the first speed is the direction in which the vehicle body moves forward, and the direction of the second speed is the direction opposite to the direction in which the vehicle body moves forward.

[0010] In some embodiments, the corresponding relationship is determined as follows: taking the lateral displacement error and heading angle error of the vehicle as state variables of the model predictive control algorithm of the vehicle, taking the turning angle of the front wheels of the vehicle as the control input of the model predictive control algorithm, determining the objective function of the model predictive control algorithm based on the weighted sum of the state variables and the control input, the lateral displacement error being the lateral distance between the driving path of the vehicle and the target path, and the heading angle error being the difference between the heading angle of the vehicle and the target heading angle; determining the target weights of the state variables and the target weights of the control input corresponding to each speed interval; solving the objective function according to the target weights of the state variables and the target weights of the control input to obtain the target value of the control input as the turning angle of the front wheels of the vehicle corresponding to each speed interval; storing each speed interval and the turning angle of the front wheels of the vehicle corresponding to each speed interval correspondingly to obtain the corresponding relationship.

[0011] In some embodiments, determining the target weight of the state variable and the target weight of the control input corresponding to each speed interval includes: obtaining a first weight of the state variable and a second weight of the control input corresponding to each speed interval; solving the objective function based on the first weight and the second weight to obtain a first value of the control input; determining a first lateral control accuracy of the vehicle according to the first value; and determining the target weight of the state variable and the target weight of the control input according to the first lateral control accuracy of the vehicle, the first weight, and the second weight.

[0012] In some embodiments, determining the target weight of the state variable and the target weight of the control input based on the first lateral control accuracy of the vehicle, the first weight and the second weight includes: in response to the first lateral control accuracy satisfying a specified accuracy, determining the first weight as the target weight of the state variable, and determining the second weight as the target weight of the control input.

[0013] In some embodiments, determining the target weight of the state variable and the target weight of the control input based on the first lateral control accuracy of the vehicle, the first weight and the second weight includes: in response to the first lateral control accuracy not meeting the specified accuracy, performing at least one of a first adjustment operation and a second adjustment operation, and determining the target weight of the state variable and the target weight of the control input based on the result of the at least one adjustment operation, the first weight and the second weight, wherein the first adjustment operation includes adjusting the first weight based on a first association relationship between the vehicle's driving speed and the weight of the state variable to obtain a third weight as a result of the first adjustment operation, and the second adjustment operation includes adjusting the second weight based on a second association relationship between the vehicle's driving speed and the weight of the control input to obtain a fourth weight as a result of the second adjustment operation.

[0014] In some embodiments, the first correlation relationship is an inverse correlation relationship, and the second correlation relationship is a positive correlation relationship.

[0015] In some embodiments, determining the target weight of the state variable and the target weight of the control input based on the result of the at least one adjustment operation, the first weight and the second weight includes: in response to the result of the at least one adjustment operation including the result of the first adjustment operation, solving the objective function according to the third weight and the second weight to obtain a second value of the control input; determining a second lateral control accuracy of the vehicle according to the second value; and in response to the second lateral control accuracy satisfying the specified accuracy, determining the third weight as the target weight of the state variable.

[0016] In some embodiments, determining the target weight of the state variable and the target weight of the control input based on the result of the at least one adjustment operation, the first weight and the second weight includes: in response to the result of the at least one adjustment operation including the result of the second adjustment operation, solving the objective function according to the first weight and the fourth weight to obtain a third value of the control input; determining a third lateral control accuracy of the vehicle according to the third value; and in response to the third lateral control accuracy satisfying the specified accuracy, determining the fourth weight as the target weight of the control input.

[0017] In some embodiments, determining the target weight of the state variable and the target weight of the control input based on the result of the at least one adjustment operation, the first weight and the second weight includes: in response to the result of the at least one adjustment operation including the result of the first adjustment operation and the result of the second adjustment operation, solving the objective function according to the third weight and the fourth weight to obtain a fourth value of the control input; determining a fourth lateral control accuracy of the vehicle according to the fourth value; in response to the fourth lateral control accuracy satisfying the specified accuracy, determining the third weight as the target weight of the state variable, and determining the fourth weight as the target weight of the control input.

[0018] In some embodiments, performing laterally controlling the vehicle according to the target angle includes: determining a steering wheel angle of the vehicle according to the target angle and a steering ratio of the vehicle; and performing laterally controlling the vehicle according to the steering wheel angle.

[0019] In some embodiments, the vehicle is an unmanned fire truck.

[0020] According to some other embodiments of the present disclosure, a control device for a vehicle is provided, comprising: an acquisition module, configured to acquire a current speed of the vehicle during driving; a determination module, configured to determine a target speed interval to which the current speed belongs from a plurality of continuous speed intervals formed by the driving speed of the vehicle; and determining a target turning angle of the front wheels of the vehicle according to a correspondence between the target speed interval and each speed interval in the plurality of speed intervals and the turning angle of the front wheels of the vehicle; and a control module, configured to perform lateral control of the vehicle according to the target turning angle.

[0021] According to some further embodiments of the present disclosure, a control device for a vehicle is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute a control method for the vehicle in any one of the above embodiments based on instructions stored in the memory device.

[0022] According to some further embodiments of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored, and when the instructions are executed by a processor, the vehicle control method in any of the above embodiments is implemented.

[0023] According to some further embodiments of the present disclosure, a computer program product is provided, comprising instructions, which, when executed by a processor, enable the processor to execute the vehicle control method according to any one of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0025] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0026] Figure 1 A flowchart showing some embodiments of the vehicle control method of the present disclosure;

[0027] Figure 2 A flowchart showing some embodiments of a method for determining a lateral control strategy of the present disclosure;

[0028] Figure 3 Show Figure 2 Flowcharts of some embodiments of step 220;

[0029] Figure 4 Flow charts showing other embodiments of the method for determining the lateral control strategy of the present disclosure;

[0030] Figure 5 Schematic diagrams showing some embodiments of simplified models of vehicles of the present disclosure;

[0031] Figure 6 A block diagram showing some embodiments of the control device of the vehicle of the present disclosure;

[0032] Figure 7 A block diagram showing some other embodiments of the vehicle control device of the present disclosure;

[0033] Figure 8 A block diagram showing still some embodiments of the control device of a vehicle disclosed herein. DETAILED DESCRIPTION

[0034] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0035] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0036] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0037] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0038] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0039] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0040] The inventor of the present application has found through research that in the related art, the lateral control method of the vehicle is usually designed based on the premise that the vehicle speed is a constant value or the change range of the vehicle speed is small. However, during the driving process of the vehicle, the speed of the vehicle may change frequently or the change range is large. Therefore, the lateral control method designed based on this premise is difficult to adapt to the speed change of the vehicle, resulting in low lateral control accuracy of the vehicle.

[0041] In view of this, the present disclosure proposes a vehicle control method, which can accurately determine the target turning angle of the front wheels of the vehicle for lateral control of the vehicle according to the speed range to which the current speed of the vehicle during driving belongs, thereby controlling the vehicle based on the target turning angle, which can effectively improve the lateral control accuracy of the vehicle.

[0042] Figure 1 A flow chart showing some embodiments of the vehicle control method of the present disclosure.

[0043] like Figure 1 As shown, for example, the vehicle control method includes step 110, step 120 and step 130. For example, the vehicle control method may be executed by a control device of the vehicle.

[0044] In step 110, the current speed of the vehicle during driving is obtained.

[0045] In some embodiments, the vehicle may include at least one of a passenger car and a rescue vehicle. For example, the vehicle may be an unmanned fire truck.

[0046] In some embodiments, during the driving process of the vehicle, the speed information of the vehicle can be collected in real time by sensors installed on the vehicle to obtain the current speed of the vehicle in real time.

[0047] In step 120 , a target speed interval to which the current speed belongs is determined from a plurality of continuous speed intervals formed by the vehicle's traveling speed.

[0048] It should be understood that each speed interval in the plurality of speed intervals has an interval upper limit and an interval lower limit. "Continuous plurality of speed intervals" means that the interval upper limit of one speed interval in any two adjacent speed intervals in the plurality of speed intervals is equal to the interval lower limit of the other speed interval.

[0049] For example, the minimum value among the multiple section lower limits of the multiple speed sections is the minimum value of the vehicle's running speed, and the maximum value among the multiple section upper limits of the multiple speed sections is the maximum value of the vehicle's running speed.

[0050] It should be understood that the "minimum value", "maximum value" and the "difference value", "target value" and the like mentioned later in the present disclosure all represent the absolute values ​​of the corresponding parameters. The present disclosure does not limit the number of the multiple speed intervals. For example, the number of the multiple speed intervals can be 3, 5 or other numbers.

[0051] In some embodiments, the difference between the upper limit and the lower limit of each speed interval in at least two speed intervals is not equal. For example, the difference between the upper limit and the lower limit of each speed interval in any two speed intervals is not equal. For example, the speed interval formed by the minimum and maximum values ​​of the vehicle's driving speed is divided into a plurality of speed intervals at unequal intervals.

[0052] In some embodiments, the difference between the upper limit and the lower limit of each speed interval in the multiple speed intervals is equal. For example, the speed interval formed by the minimum and maximum values ​​of the vehicle's driving speed is divided into multiple speed intervals at equal intervals.

[0053] For example, the minimum value of the vehicle's driving speed v is 0 m / s, and the maximum value of the vehicle's driving speed v is 100 m / s. The speed interval 0≤v≤100 m / s formed by the vehicle's driving speed v is equally divided into five consecutive speed intervals, namely: 0≤v<20 m / s, 20≤v<40 m / s, 40≤v<60 m / s, 60≤v<80 m / s, 80≤v≤100 m / s.

[0054] This equally spaced division helps to achieve a smooth transition of control strategies between different speed ranges. For example, when the vehicle's speed changes greatly (such as across different speed ranges), the vehicle's control response can be smoother, reducing the problem of vehicle driving instability that may be caused by sudden changes in control strategies.

[0055] In step 130 , a target turning angle of the front wheels of the vehicle is determined according to the target speed intervals and the corresponding relationship between each speed interval and the turning angle of the front wheels of the vehicle.

[0056] In some embodiments, the corresponding relationship between each speed interval and the turning angle of the front wheels of the vehicle (ie, the lateral control strategy) can be predetermined by designing a model predictive control (MPC) algorithm of the vehicle in combination with simulation experiments.

[0057] In this way, since the corresponding relationship between each speed interval and the turning angle of the front wheel of the vehicle is predetermined, during the driving process of the vehicle, it is only necessary to apply the corresponding lateral control strategy according to the speed interval to which the current speed of the vehicle belongs, without occupying computing resources for real-time calculation during the driving process of the vehicle, thereby reducing resource consumption during the driving process of the vehicle and improving the control efficiency of the vehicle. The method for determining this corresponding relationship will be further described in conjunction with some embodiments below.

[0058] In some embodiments, the turning angles of the front wheels of the vehicle (also referred to as front wheel steering angles) corresponding to the speeds in different speed intervals and used for lateral control of the vehicle are different.

[0059] Continuing with the above example, each of the five consecutive speed intervals formed by the vehicle's driving speed v corresponds to a turning angle of the front wheels of the vehicle for lateral control of the vehicle.

[0060] For example, the turning angle of the front wheel corresponding to the speed interval S1 (0≤v<20m / s) is w1, the turning angle of the front wheel corresponding to the speed interval S2 (20≤v<40m / s) is w2, the turning angle of the front wheel corresponding to the speed interval S3 (40≤v<60m / s) is w3, the turning angle of the front wheel corresponding to the speed interval S4 (60≤v<80m / s) is w4, and the turning angle of the front wheel corresponding to the speed interval S5 (80≤v≤100m / s) is w5, among which w1, w2, w3, w4 and w5 are different from each other.

[0061] In step 140 , the vehicle is laterally controlled according to the target turning angle.

[0062] It is understood here that the control of the vehicle includes longitudinal control and lateral control.

[0063] The longitudinal control of a vehicle refers to the control in the direction of the vehicle's travel, which focuses on controlling the vehicle's travel speed, such as the acceleration control and deceleration control of the vehicle. The purpose of the longitudinal control of the vehicle is to enable the vehicle to travel along the current path at the desired speed.

[0064] The lateral control of a vehicle refers to the control perpendicular to the direction of travel of the vehicle, which focuses on controlling the direction of travel of the vehicle, such as the steering control of the vehicle. The purpose of the lateral control of the vehicle is to enable the vehicle to travel along the desired target path (i.e. path tracking).

[0065] In some embodiments, the front wheels of the vehicle can be controlled to rotate according to the target turning angle. That is, the front wheels of the vehicle can be controlled to rotate at the target turning angle to control the vehicle laterally.

[0066] In some embodiments, the steering wheel angle of the vehicle can be determined according to the target angle and the steering ratio of the vehicle, and then the vehicle can be controlled laterally according to the steering wheel angle. That is, the front wheels of the vehicle can be turned at the target angle by controlling the steering wheel of the vehicle to turn at the steering wheel angle, thereby controlling the vehicle laterally.

[0067] In the above embodiment, the speed interval formed by the vehicle's driving speed is divided into a plurality of continuous speed intervals, and a corresponding relationship between each of the plurality of speed intervals and the turning angle of the front wheels of the vehicle for lateral control of the vehicle is determined. During the driving process of the vehicle, the target turning angle of the front wheels of the vehicle for lateral control of the vehicle can be determined according to the target speed interval to which the current speed of the vehicle belongs and the corresponding relationship, and the vehicle can be laterally controlled based on the target turning angle.

[0068] In this way, the different lateral control requirements of the vehicle at different driving speeds are taken into consideration. For example, when the vehicle is driving at a low speed, more precise lateral control is required to achieve accurate path tracking, while when the vehicle is driving at a high speed, more stable lateral control is required to reduce the occurrence of vehicle skidding or loss of control.

[0069] Therefore, the time-varying nature of the vehicle's driving speed is incorporated into the design considerations of the vehicle's lateral control method, that is, for each speed interval of the vehicle, a lateral control strategy for lateral control of the vehicle is designed accordingly (i.e., the corresponding relationship between each speed interval and the turning angle of the front wheels of the vehicle). In this way, during the vehicle's driving, the control strategy that meets the lateral control requirements of the current speed can be flexibly selected according to the change in the vehicle's driving speed, thereby effectively improving the vehicle's lateral control accuracy (i.e., path tracking accuracy) and the stability of the vehicle's driving during the lateral control process.

[0070] Next, in combination with some embodiments, the relevant implementation of the vehicle control method proposed in the present disclosure is further explained by way of example.

[0071] In some embodiments, the speeds in the same speed interval correspond to the same turning angle of the front wheels of the vehicle. In other words, any speed in each speed interval of the plurality of speed intervals corresponds to the same turning angle of the front wheels of the vehicle for lateral control of the vehicle.

[0072] In this way, when the vehicle's driving speed changes slightly (for example, it does not cross the current speed range), there is no need to adjust the vehicle's lateral control strategy, which reduces the frequency of changing the control strategy and reduces the adverse effects on the vehicle's stability caused by frequent changes in the control strategy. This further improves the vehicle's driving stability while improving the vehicle's lateral control accuracy.

[0073] In some embodiments, the vehicle's driving speed includes a first speed and a second speed, the first speed and the second speed are in different directions, and the plurality of speed intervals include a plurality of first sub-intervals formed by the first speed and a plurality of second sub-intervals formed by the second speed.

[0074] The corresponding relationship between each speed interval in the multiple speed intervals and the turning angle of the front wheels of the vehicle includes a first sub-relationship between each first sub-interval in the multiple first sub-intervals and the turning angle of the front wheels of the vehicle and a second sub-relationship between each second sub-interval in the multiple second sub-intervals and the turning angle of the front wheels of the vehicle.

[0075] It should be understood that the number of the plurality of first sub-intervals and the number of the plurality of second sub-intervals may be the same or different, and the present disclosure does not limit this.

[0076] That is, considering the different driving directions of the vehicle, the driving speed of the vehicle may include speeds in multiple different directions. The speed interval formed by the speed in each direction may be divided into multiple continuous sub-intervals. For example, the driving direction of the vehicle may include a direction that is the same as the forward direction of the vehicle body and a direction that is different from the forward direction of the vehicle body. The driving speed of the vehicle may include a speed in a direction that is the same as the forward direction of the vehicle body and a speed in a direction that is different from the forward direction of the vehicle body.

[0077] For example, the speed in each direction may have a maximum value and a minimum value of the speed in that direction. The minimum value among the multiple interval lower limits of the multiple continuous subintervals formed by the speed in each direction is the minimum value of the speed in that direction, and the maximum value among the multiple interval upper limits of the multiple subintervals is the maximum value of the speed in that direction. Accordingly, the corresponding relationship may include a sub-relationship corresponding to the multiple subintervals formed by the speed in each direction.

[0078] For example, in response to the target speed interval being the first sub-interval, the target turning angle may be determined according to the target speed interval and the first sub-relationship; in response to the target speed interval being the second sub-interval, the target turning angle may be determined according to the target speed interval and the second sub-relationship.

[0079] In the above embodiment, the directionality of the vehicle's driving speed is further incorporated into the design considerations of the vehicle's lateral control method, that is, a control strategy for lateral control of the vehicle is designed specifically for different speed directions (i.e., the sub-relationship between each sub-interval and the turning angle of the front wheel of the vehicle). In this way, the magnitude change of the vehicle's driving speed and the direction change of the driving speed can be considered together, so that the control strategy that meets the lateral control requirements of the current speed can be selected more accurately, further improving the lateral control accuracy of the vehicle.

[0080] In some embodiments, the direction of the first speed is the direction in which the vehicle body moves forward, and the direction of the second speed is the direction opposite to the direction in which the vehicle body moves forward.

[0081] That is, the driving direction of the vehicle may include a direction in which the vehicle body moves forward (also referred to as a forward driving direction) and a direction opposite to the direction in which the vehicle body moves forward (also referred to as a reverse driving direction).

[0082] For example, a speed interval formed by the maximum and minimum values ​​of a first speed of the vehicle traveling in the forward direction of the vehicle body is divided into a plurality of first sub-intervals, and a first sub-relationship between each of the plurality of first sub-intervals and the turning angle of the front wheel of the vehicle is determined accordingly. A speed interval formed by the maximum and minimum values ​​of a second speed of the vehicle traveling in the direction opposite to the forward direction of the vehicle body is divided into a plurality of second sub-intervals, and a second sub-relationship between each of the plurality of second sub-intervals and the turning angle of the front wheel of the vehicle is determined accordingly.

[0083] For example, the longitudinal speed of the vehicle in the vehicle body coordinate system may be acquired as the vehicle's speed.

[0084] If the collected longitudinal speed of the vehicle is a positive value, it means that the vehicle is traveling in the direction in which the vehicle body is moving, that is, the direction of the speed is the direction in which the vehicle body is moving. In this case, the target speed interval to which the longitudinal speed belongs can be determined from the multiple first sub-intervals, and then the target turning angle of the front wheels of the vehicle for lateral control of the vehicle can be determined according to the target speed interval and the first sub-relationship.

[0085] If the collected longitudinal speed of the vehicle is a negative value, it means that the vehicle is traveling in the opposite direction to the vehicle body (also called reverse driving), that is, the direction of the speed is opposite to the vehicle body. In this case, the target speed interval to which the longitudinal speed belongs can be determined from multiple second sub-intervals, and then the target turning angle of the front wheels of the vehicle for lateral control of the vehicle can be determined based on the target speed interval and the second sub-relationship.

[0086] In some embodiments, at least one of the maximum and minimum values ​​of the speed in different speed directions is different. In other words, the speed intervals of the vehicle in different driving directions may be different.

[0087] For example, the maximum value of the first speed (also called the forward travel speed) of the vehicle traveling in the forward direction of the vehicle body is different from the maximum value of the second speed of the vehicle traveling in the direction opposite to the forward direction of the vehicle body (also called the reverse travel speed). The minimum value of the first speed of the vehicle traveling in the forward direction of the vehicle body is different from the minimum value of the second speed of the vehicle traveling in the direction opposite to the forward direction of the vehicle body. Thus, the speed interval formed by the minimum and maximum values ​​of the first speed is different from the speed interval formed by the minimum and maximum values ​​of the second speed.

[0088] In the above embodiment, during the lateral control process of the vehicle, it is possible to distinguish whether the direction of the vehicle's current speed is in the forward driving direction or the reverse driving direction, so that the vehicle's lateral control strategy can be dynamically adjusted according to the vehicle's driving direction, thereby being able to more accurately select a control strategy that meets the lateral control requirements of the current speed, further improving the vehicle's lateral control accuracy.

[0089] The following describes, in conjunction with some embodiments, a method for determining the corresponding relationship (ie, the lateral control strategy) in step 130 .

[0090] Figure 2 Flowcharts showing some embodiments of the method for determining the lateral control strategy of the present disclosure. Figure 2 As shown, for example, steps 210 to 240 may be performed to obtain a corresponding relationship between each speed interval and the turning angle of the front wheels of the vehicle (ie, the lateral control strategy of the vehicle).

[0091] In step 210, the lateral displacement error and heading angle error of the vehicle are used as state variables of the MPC algorithm of the vehicle, the steering angle of the front wheels of the vehicle is used as the control input of the MPC algorithm, and the objective function of the MPC algorithm is determined based on the weighted sum of the state variables and the control input.

[0092] Here, the lateral displacement error is the lateral distance between the vehicle's driving path (i.e., the current path) and the target path, and the heading angle error is the difference between the vehicle's heading angle (i.e., the current heading angle) and the target heading angle.

[0093] It should be noted that the MPC algorithm is a control algorithm based on a model, which is called a prediction model. The function of the prediction model includes predicting the future output of the system based on its historical state and future input. The mathematical form of the prediction model can include multiple forms. For example, the mathematical form of the prediction model can include state space equations, transfer functions and other mathematical forms.

[0094] The core idea of ​​the MPC algorithm is to optimize the control input by predicting the system's behavior in the future through the system's prediction model, thereby achieving the system's desired behavior. The following article will introduce the process of establishing the prediction model with some examples.

[0095] In the MPC algorithm, state variables are variables that describe the current state of the system and are the basis for predicting the future behavior of the system. Control inputs are variables that describe the future behavior of the system. By adjusting the control inputs, different controls on the future behavior of the system can be achieved. The objective function is the core of optimizing the control input. It combines the current state of the system and the control input in mathematical form to form an optimizable value, which can be used to quantify the control performance of the system under different control inputs. By optimizing and solving the objective function, the optimal control amount of the control input can be obtained.

[0096] In some embodiments, the objective function may be constructed in the form of a linear quadratic function.

[0097] For example, select the lateral displacement error e of the vehicle xf and heading angle error e θf As the state variable X of the MPC algorithm f , that is, X f =[e xf e θf ]. The expected value of the state variable can be expressed as X f,ref =[e xf,ref e θf,ref ], where e xf,ref is the expected value of the lateral displacement error, e θf,ref is the expected value of the heading angle error.

[0098] Based on this, the objective function J of the constructed MPC algorithm f It can be expressed as the following formula (1):

[0099]

[0100] Among them, U f represents the control input, Q f represents the weight of the state variable, R f Represents the weight of the control input.

[0101] It should be noted that in the objective function of the MPC algorithm, the weight of the state variable is used to penalize the deviation between the current value and the expected value of the state variable (i.e., lateral displacement error, heading angle error). The larger the weight of the state variable, the greater the penalty for the lateral displacement error and heading angle error, that is, the smaller the tolerance for the lateral displacement error and heading angle error. This means that the optimization goal of the objective function focuses more on reducing the tracking error between the current path and the target path of the vehicle and improving the lateral control accuracy of the vehicle.

[0102] The weight of the control input is used to penalize the rate of change of the control input (i.e., the turning angle of the front wheels of the vehicle). The greater the weight of the control input, the greater the penalty for the rate of change of the turning angle of the front wheels of the vehicle, that is, the less tolerance for the drastic change in the turning angle of the front wheels. This means that the optimization goal of the objective function focuses on reducing the rate of change of the control input and improving the stability of the vehicle.

[0103] In step 220 , target weights of state variables and target weights of control inputs corresponding to each speed interval are determined.

[0104] It can be understood here that each speed interval in the multiple speed intervals corresponds to a set of target weights (ie, the target weights of the state variables and the target weights of the control inputs).

[0105] In some embodiments, a group of target weights corresponding to different speed intervals are different, and a group of target weights corresponding to the same speed interval are the same.

[0106] In some embodiments, multiple groups of weights may be set for each speed interval. The objective function is solved according to each group of weights to obtain the value of the control input corresponding to the group of weights. The lateral control accuracy of the vehicle corresponding to each group of weights is determined according to the value of the control input corresponding to each group of weights. A group of weights whose lateral control accuracy meets the specified accuracy is determined as the target weight. This will be further described in conjunction with some embodiments below.

[0107] In step 230, the objective function is solved according to the target weight of the state variable corresponding to each speed interval and the target weight of the control input to obtain the target value of the control input as the steering angle of the front wheels of the vehicle corresponding to each speed interval.

[0108] It can be understood here that the target value of the control input and the target value of the state variable can be obtained by solving the objective function based on the target weight of the state variable corresponding to each speed interval and the target weight of the control input. The target value of the control input represents the optimal control amount of the steering angle of the front wheel of the vehicle corresponding to the target weight, and the target value of the state variable represents the theoretical value of the lateral displacement error and the theoretical value of the heading angle error for lateral control of the vehicle based on this optimal control amount.

[0109] In some embodiments, the target weight of the state variable and the target weight of the control input corresponding to each speed interval can be substituted into the objective function, and the objective function can be converted into a quadratic programming problem in combination with the set constraint conditions of the objective function. The quadratic programming problem is then solved by a quadratic programming solver (e.g., an operator splitting quadratic programming solver (OSQP)) to obtain the target value of the control input as the turning angle of the front wheel of the vehicle corresponding to each speed interval.

[0110] In some embodiments, the constraint condition of the objective function can be set according to at least one of the maximum and minimum values ​​of the turning angle of the front wheels of the vehicle and the maximum and minimum values ​​of the change in the turning angle of the front wheels of the vehicle. Such constraint condition means that the steering control operation of the vehicle is within the physically feasible range.

[0111] For example, the constraint conditions of the objective function are set based on the maximum and minimum values ​​of the turning angle of the front wheels of the vehicle and the maximum and minimum values ​​of the amount of change in the turning angle of the front wheels of the vehicle.

[0112] That is, the constraints of the objective function may include the constraints shown in the following formula (2) and formula (3):

[0113] u min (k+t)<<u(k+t)<<u max (k+t) (2)

[0114] Δu min (k+t)<<Δu(k+t)<<Δu max (k+t) (3)

[0115] Wherein, u(k) represents the steering angle of the front wheel of the vehicle in the current state (i.e., the current value of the steering angle of the front wheel), u(k+t) represents the steering angle of the front wheel of the vehicle after time t (i.e., the predicted value of the steering angle of the front wheel), and u min (k+t) and u max (k+t) represent the minimum and maximum turning angles of the front wheels of the vehicle, respectively, Δu min (k+t) and Δu max (k+t) respectively represent the minimum value and the maximum value of the amount of change in the turning angle of the front wheels of the vehicle.

[0116] The objective function is the objective function J shown in formula (1) f As an example, combined with the constraints shown in formula (2) and formula (3), the objective function J f Converted into a quadratic programming problem. By solving the quadratic programming problem, the target value of the control input can be obtained (ie, the optimal control amount of the turning angle of the front wheels of the vehicle) is taken as the turning angle of the front wheels of the vehicle corresponding to each speed interval.

[0117] In some embodiments, the steering wheel angle corresponding to each speed interval can be determined based on the mapping relationship between the steering wheel angle of the front wheel of the vehicle and the steering wheel angle and the steering wheel angle of the front wheel of the vehicle corresponding to each speed interval. For example, the mapping relationship between the steering wheel angle of the front wheel of the vehicle and the steering wheel angle is f. And the mapping relationship f, get the target value of the steering wheel angle (i.e. the optimal control amount of the steering wheel angle).

[0118] In some embodiments, the mapping relationship between the steering angle of the front wheels of the vehicle and the steering wheel angle can be determined based on the steering ratio of the vehicle.

[0119] In step 240, each speed interval and the turning angle of the front wheels of the vehicle corresponding to each speed interval are stored correspondingly to obtain a corresponding relationship.

[0120] In some embodiments, each speed interval and the turning angle of the front wheels of the vehicle corresponding to each speed interval may be stored in a controller of the vehicle in a one-to-one correspondence.

[0121] In the above embodiment, the lateral displacement error and the heading angle error are used as the state variables of the MPC algorithm, and the turning angle of the front wheel of the vehicle is used as the control input of the MPC algorithm to construct the objective function of the MPC algorithm. Then, the target weight of the corresponding state variable and the target weight of the control input are determined for each speed interval, and the target value of the control input obtained based on the target weight is used as the turning angle of the front wheel of the vehicle corresponding to each speed interval, thereby obtaining the corresponding relationship between each speed interval and the turning angle of the front wheel of the vehicle (i.e., the lateral control strategy).

[0122] In this way, considering that the weights of the state variables and the weights of the control inputs in the objective function have a great influence on the control performance of the MPC algorithm, the lateral control strategies for different speed ranges are determined by determining the corresponding target weights for different speed ranges, so that the vehicle can accurately track the target path at any speed, thereby improving the lateral control accuracy of the vehicle.

[0123] Next, the related implementation of determining the target weight in step 220 is further explained in conjunction with some embodiments.

[0124] Figure 3 Show Figure 2 Flowchart of some embodiments of step 220 in FIG.

[0125] like Figure 3As shown, for example, step 220 may include step 221 , step 222 , step 223 , and step 224 .

[0126] In step 221 , a first weight of a state variable and a second weight of a control input corresponding to each speed interval are obtained.

[0127] In some embodiments, the first weight and the second weight may be a predetermined initial weight of the state variable and an initial weight of the control input, respectively. For example, the initial weight of the state variable and the initial weight of the control input may be predetermined by an algorithm designer based on past design experience.

[0128] In step 222 , the objective function is solved based on the first weight and the second weight to obtain a first value of the control input.

[0129] It should be understood that solving the objective function based on the first weight and the second weight can obtain the first value of the control input and the first value of the state variable. The first value of the control input represents the optimal control amount of the steering angle of the front wheel of the vehicle corresponding to the first weight and the second weight, and the first value of the state variable represents the theoretical value of the lateral displacement error and the theoretical value of the heading angle error for lateral control of the vehicle based on the optimal control amount.

[0130] In some embodiments, the first weight and the second weight can be substituted into the objective function, and the objective function can be converted into a quadratic programming problem in combination with the set constraint conditions of the objective function. The quadratic programming problem is then solved by a quadratic programming solver (e.g., OSQP) to obtain the first value of the state variable. For the relevant implementation, please refer to the description in the relevant embodiment of the aforementioned step 230, which will not be repeated here.

[0131] In step 223 , a first lateral control accuracy of the vehicle is determined according to a first value of the control input.

[0132] In some embodiments, in each speed interval, the vehicle is laterally controlled according to a first value of the control input, and then actual values ​​of the lateral displacement error and the actual values ​​of the heading angle error at multiple different moments during the lateral control process are collected.

[0133] The root mean square (RMS) value of the lateral displacement error is calculated according to the actual values ​​of the lateral displacement error at multiple different moments, and the root mean square value of the heading angle error is calculated according to the actual values ​​of the heading angle error at multiple different moments.

[0134] The first lateral control accuracy of the vehicle is determined according to the root mean square value of the lateral displacement error and the root mean square value of the heading angle error. That is, the root mean square value of the lateral displacement error and the root mean square value of the heading angle error are used as indicators to measure the lateral control accuracy of the vehicle.

[0135] As some examples, the driving state of the vehicle in each speed range can be simulated by simulation experiments. During the simulation experiment, the driving speed of the vehicle is changed in a certain speed range. In the speed range, the vehicle is laterally controlled based on the first value of the control input, and the actual value e of the lateral displacement error at each sampling time i (i=1, 2, 3...N) in N sampling times is obtained. xi and the actual value of the heading angle error e θi , N≥2. Based on this, the root mean square value of the lateral displacement error is calculated RMS value of heading angle error

[0136] In step 224 , a target weight of the state variable and a target weight of the control input are determined according to the first lateral control accuracy of the vehicle, the first weight, and the second weight.

[0137] In some embodiments, whether to use the first weight and the second weight as the target weight of the state variable and the target weight of the control input, respectively, can be determined based on whether the first lateral control accuracy meets the specified accuracy.

[0138] In the above embodiment, in the process of determining the target weight, the lateral control accuracy of the vehicle can be determined by calculating the value of the state variable corresponding to the current weight (i.e., the first weight and the second weight). Then, the target weight of the state variable and the target weight of the control input are determined according to the current weight and the determined lateral control accuracy of the vehicle.

[0139] In this way, the determined lateral control accuracy can accurately reflect the control effect of lateral control of the vehicle based on the optimal control amount corresponding to the current weight, which helps to improve the accuracy of the determined target weight, and then improves the reliability of the lateral control strategy determined based on the target weight, thereby improving the lateral control accuracy of the vehicle.

[0140] In some embodiments, in response to the first lateral control accuracy satisfying a specified accuracy, the first weight is determined as a target weight of the state variable, and the second weight is determined as a target weight of the control input.

[0141] In response to the first lateral control accuracy not meeting the specified accuracy, an adjustment operation may be performed on the first weight and the second weight, and then the target weight of the state variable and the target weight of the control input are determined according to the adjusted first weight and the second weight.

[0142] It can be understood here that the first lateral control accuracy meets the specified accuracy, which means that the lateral displacement error and the heading angle error of the vehicle controlled by the optimal control amount of the steering angle of the front wheels of the vehicle corresponding to the first weight and the second weight are both within the allowable error range, that is, the lateral control requirement is met. The first lateral control accuracy does not meet the specified accuracy, which means that at least one of the lateral displacement error and the heading angle error of the vehicle controlled by the optimal control amount of the steering angle of the front wheels of the vehicle corresponding to the first weight and the second weight is not within the allowable error range, that is, the lateral control requirement is not met.

[0143] Continuing with the above example, the root mean square value of the lateral displacement error and the root mean square value of the heading angle error are used as indicators to measure the lateral control accuracy of the vehicle. The expected value of the root mean square of the lateral displacement error RMS1' and the expected value of the root mean square of the heading angle error RMS2' are used as the specified accuracy.

[0144] If the actual value of the lateral displacement error e xi The calculated root mean square value RMS1 of the lateral displacement error is less than or equal to the expected root mean square value RMS1' of the lateral displacement error and is based on the actual value of the heading angle error e θi If the calculated root mean square value RMS2 of the heading angle error is less than or equal to the expected root mean square value RMS2' of the heading angle error, it means that the first lateral control accuracy meets the specified accuracy.

[0145] If the actual value of the lateral displacement error e xi The calculated root mean square value RMS1 of the lateral displacement error is greater than the expected root mean square value RMS1' of the lateral displacement error and / or the actual value e based on the heading angle error. θi If the calculated root mean square value RMS2 of the heading angle error is greater than the expected root mean square value RMS2' of the heading angle error, it means that the first lateral control accuracy does not meet the specified accuracy.

[0146] In this way, if the lateral control accuracy of the vehicle corresponding to the current weight meets the specified accuracy, it means that the optimal control amount of the front wheel turning angle of the vehicle corresponding to the current weight can effectively meet the lateral control requirements of the vehicle. In this way, the current weight can be used as the target weight to improve the reliability of the lateral control strategy, thereby improving the lateral control accuracy of the vehicle.

[0147] In conjunction with some embodiments, the following exemplarily describes the adjustment operations that can be performed on the first weight and the second weight and the corresponding method of determining the target weight when the first lateral control accuracy does not meet the specified accuracy.

[0148] In some embodiments, in response to the first lateral control accuracy not meeting the specified accuracy, at least one of the first adjustment operation and the second adjustment operation is performed. According to the result of the at least one operation, the first weight and the second weight, the target weight of the state variable and the target weight of the control input are determined.

[0149] In some embodiments, the first adjustment operation may include: adjusting the first weight based on a first correlation between the vehicle's driving speed and the weight of the state variable to obtain a third weight as a result of the first adjustment operation. For example, the first correlation may be a linear correlation or a nonlinear correlation. For example, the first correlation may be a positive correlation or an inverse correlation.

[0150] In this way, in the process of determining the target weight of the state variable, the driving speed is associated with the weight of the state variable, and the weight configuration of the state variables corresponding to different speed ranges is further optimized. This can further effectively improve the adaptability of the lateral control strategy determined based on the target weight to the changes in the vehicle's driving speed, thereby improving the vehicle's lateral control accuracy.

[0151] In some embodiments, in each speed interval, the first correlation between the vehicle's speed and the weight of the state variable is the same. For example, in each speed interval, the first correlation between the vehicle's speed and the weight of the state variable is a positive correlation or an inverse correlation.

[0152] In some embodiments, considering that when the vehicle is traveling at a relatively high speed (i.e., when the vehicle is traveling at a high speed), if the lateral control process of the vehicle is relatively sensitive to changes in the lateral displacement error and the heading angle error, then the vehicle will frequently adjust the steering angle of the front wheels. In this case, even if the amplitude of each adjustment is relatively small, it will have an adverse effect on the stability of the vehicle. However, when the vehicle is traveling at a relatively low speed (i.e., when the vehicle is traveling at a low speed), if the lateral control process of the vehicle is relatively slow to changes in the lateral displacement error and the heading angle error, then when the deviation between the vehicle's traveling path and the target path is relatively large, the steering angle of the front wheels will not be adjusted in time. In this case, even if the amplitude of each adjustment is relatively large, it is difficult to improve the lateral control accuracy of the vehicle (i.e., the accuracy of path tracking).

[0153] Therefore, the first association relationship between the vehicle's traveling speed and the weight of the state variable can be set to an inverse correlation relationship.

[0154] This means that as the vehicle's driving speed increases, the weight of the state variable can be reduced (for example, the first weight can be reduced) to increase the vehicle's tolerance to lateral displacement error and heading angle error, reduce the control frequency of the front wheel turning angle, and avoid excessive adjustment of the lateral displacement error and heading angle error when the vehicle is driving at high speed, thereby improving the vehicle's driving stability while improving the vehicle's lateral control accuracy.

[0155] On the contrary, as the vehicle's driving speed decreases, the state variable can be increased (for example, the first weight can be increased) to make the vehicle's lateral control process more sensitive to changes in lateral displacement error and heading angle error, so that the front wheel turning angle can be controlled in a timely manner, effectively improving the vehicle's lateral control accuracy.

[0156] In some embodiments, in response to the result of at least one adjustment operation including the result of the first adjustment operation, the objective function can be solved according to the third weight and the second weight to obtain a second value of the control input. A second lateral control accuracy of the vehicle is determined according to the second value of the control input. In response to the second lateral control accuracy meeting the specified accuracy, the third weight is determined as the target weight of the state variable.

[0157] It should be understood that solving the objective function based on the third weight and the second weight can obtain the second value of the control input and the second value of the state variable. The second value of the control input represents the optimal control amount of the steering angle of the front wheel of the vehicle corresponding to the third weight and the second weight, and the second value of the state variable represents the theoretical value of the lateral displacement error and the theoretical value of the heading angle error for lateral control of the vehicle based on this optimal control amount.

[0158] That is, when the first lateral control accuracy does not meet the specified accuracy, the first adjustment operation can be performed on the first weight to obtain the third weight. Then, whether to determine the third weight as the target weight of the state variable is determined according to whether the corresponding second lateral control accuracy meets the specified accuracy.

[0159] For example, if the second lateral control accuracy meets the specified accuracy (i.e., meets the lateral control requirement), the third weight is determined as the target weight of the state variable. If the second lateral control accuracy does not meet the specified accuracy (i.e., does not meet the lateral control requirement), the first weight can be adjusted based on the first association relationship until the lateral control accuracy corresponding to the adjusted first weight (i.e., the third weight) meets the specified accuracy. That is, the first weight can be adjusted multiple times based on the first association relationship to determine the target weight of the state variable. Here, there is no limit on the number of times the weight of the state variable can be adjusted.

[0160] In some embodiments, the first weight can be adjusted multiple times to obtain multiple third weights. Then, select the third weight corresponding to the second lateral control accuracy that meets the specified accuracy from the multiple third weights as the target weight of the state variable. For example, the adjustment amplitude each time can be the same or different.

[0161] In some embodiments, when the number of the second lateral control accuracies that meet the specified accuracy is multiple, the third weight corresponding to the second lateral control accuracy with the smallest gap from the specified accuracy can be determined as the target weight of the state variable.

[0162] As some examples, during the simulation experiment, the driving speed of the vehicle is changed within a certain speed range. The driving speed vi of the vehicle at each sampling moment i (i = 1, 2, 3... N) among N sampling moments is obtained, where N ≥ 2.

[0163] Assume that the driving speed vi of the vehicle gradually increases with time, that is, v1 < v2 <... < vN. The first correlation relationship between the driving speed of the vehicle and the weight of the state variable is a linear inverse correlation relationship. Taking the weight of the state variable corresponding to the driving speed v1 at the first sampling moment i = 1 as the first weight Q1 and the weight of the corresponding control input as the second weight R1 as an example, as the driving speed of the vehicle increases from v1 to vN, the first weight Q1 is reduced based on the first correlation relationship to determine the third weights Q2, Q3... QN corresponding to the i = 2, 3... N sampling moments, so as to obtain N - 1 third weights.

[0164] Then, according to each third weight and the second weight, the objective function is solved to obtain the second value of the control input corresponding to each third weight. The vehicle is laterally controlled according to the second value of the control input corresponding to each third weight, and the actual values of the lateral displacement error and the actual values of the heading angle error at multiple different moments during the lateral control process are collected.

[0165] Taking the root mean square value of the lateral displacement error and the root mean square value of the heading angle error as the indexes for measuring the lateral control accuracy of the vehicle, based on the actual values of the lateral displacement error and the actual values of the heading angle error at multiple different moments, the root mean square value of the lateral displacement error and the root mean square value of the heading angle error are calculated to determine the second lateral control accuracy corresponding to each third weight.

[0166] Select the third weight corresponding to the second lateral control accuracy that meets the specified accuracy from the N - 1 third weights as the target weight of the state variable.

[0167] The determination method of the second lateral control accuracy can be implemented similarly by referring to the determination method related to the first lateral control accuracy described in the relevant embodiments above. The relevant descriptions can be seen in the relevant embodiments above and will not be elaborated here.

[0168] In the above embodiment, when the first lateral control accuracy does not meet the specified accuracy, the weight of the state variable can be dynamically adjusted based on the first association relationship, and then the weight of the state variable that meets the specified accuracy is used as the target weight of the state variable. In this way, the weight configuration of the state variable corresponding to different speed intervals can be further optimized until the target weight of the state variable that meets the specified accuracy is determined, thereby improving the reliability of the lateral control strategy determined subsequently, thereby improving the lateral control accuracy of the vehicle.

[0169] In some embodiments, when the weight of the state variable is adjusted by using the first correlation between the vehicle's driving speed and the weight of the state variable, if the first correlation is an anti-correlation relationship, the target weight of the state variable corresponding to the low-speed interval in the multiple speed intervals may be greater than the target weight of the state variable corresponding to the high-speed interval in the multiple speed intervals. For example, the multiple speed intervals include speed interval S1 (0≤v<20m / s) and speed interval S2 (20≤v<40m / s). The target weight Q of the state variable corresponding to speed interval S1 is S1 The target weight Q of the state variable corresponding to the speed interval S2 is greater than S2 .

[0170] In other words, by maintaining the consistency of the weight adjustment logic of the state variables corresponding to different speed ranges, unified weight optimization can be performed on the whole, thereby improving the accuracy of the target weights of the state variables corresponding to each speed range, thereby improving the lateral control accuracy of the vehicle.

[0171] In some embodiments, the second adjustment operation may include: adjusting the second weight based on a second association relationship between the vehicle's driving speed and the weight of the control input to obtain a fourth weight as a result of the second adjustment operation. For example, the second association relationship may be a linear correlation relationship or a nonlinear correlation relationship. For example, the second association relationship may be a positive correlation relationship or an inverse correlation relationship.

[0172] In this way, in the process of determining the target weight of the control input, the driving speed is associated with the weight of the control input, and the weight configuration of the control input corresponding to different speed ranges is further optimized. This can further effectively improve the adaptability of the lateral control strategy determined based on the target weight to changes in the vehicle's driving speed, thereby improving the vehicle's lateral control accuracy.

[0173] In some embodiments, in each speed interval, the second association relationship between the vehicle's driving speed and the weight of the control input is the same. For example, in each speed interval, the second association relationship between the vehicle's driving speed and the weight of the control input is a positive correlation or an inverse correlation.

[0174] In some embodiments, when the vehicle is traveling at a high speed (i.e., when the vehicle is traveling at a high speed), if the rate of change of the turning angle of the front wheels during the lateral control of the vehicle is large, it may cause problems such as the vehicle's roll, thereby adversely affecting the stability of the vehicle's travel. However, when the vehicle is traveling at a low speed (i.e., when the vehicle is traveling at a low speed), if the rate of change of the turning angle of the front wheels during the lateral control of the vehicle is small, it may cause the accumulation of tracking errors, thereby resulting in low lateral control accuracy of the vehicle.

[0175] Therefore, the second correlation relationship between the vehicle's running speed and the weight of the control input can be set to a positive correlation relationship.

[0176] This means that as the vehicle's driving speed increases, the weight of the control input can be increased (for example, the second weight can be increased) to reduce the tolerance for the drastic change in the front wheel turning angle and reduce the rate of change of the front wheel turning angle to reduce the occurrence of problems such as vehicle roll, thereby improving the vehicle's driving stability while improving the vehicle's lateral control accuracy.

[0177] On the contrary, as the vehicle's driving speed decreases, the weight of the control input can be reduced (for example, the second weight is reduced) to reduce the accumulation of tracking errors, thereby effectively improving the lateral control accuracy of the vehicle.

[0178] In some embodiments, in response to the result of at least one adjustment operation including the result of the second adjustment operation, the objective function can be solved according to the first weight and the fourth weight to obtain a third value of the control input. A third lateral control accuracy of the vehicle is determined according to the third value of the control input. In response to the third lateral control accuracy meeting the specified accuracy, the fourth weight is determined as the target weight of the control input.

[0179] It should be understood that solving the objective function based on the first weight and the fourth weight can obtain the third value of the control input and the third value of the state variable. The third value of the control input represents the optimal control amount of the steering angle of the front wheel of the vehicle corresponding to the first weight and the fourth weight, and the third value of the state variable represents the theoretical value of the lateral displacement error and the theoretical value of the heading angle error for lateral control of the vehicle based on this optimal control amount.

[0180] That is, when the first lateral control accuracy does not meet the specified accuracy, the second adjustment operation can be performed on the second weight to obtain the fourth weight. Then, whether to determine the fourth weight as the target weight of the control input is determined according to whether the corresponding third lateral control accuracy meets the specified accuracy.

[0181] For example, if the third lateral control accuracy meets the specified accuracy (i.e., meets the lateral control requirement), the fourth weight is determined as the target weight of the state variable. If the third lateral control accuracy does not meet the specified accuracy (i.e., does not meet the lateral control requirement), the second weight can be continuously adjusted based on the second correlation relationship until the lateral control accuracy corresponding to the adjusted second weight (i.e., the fourth weight) meets the specified accuracy. That is, the second weight can be adjusted multiple times based on the second correlation relationship to determine the target weight of the control input. Here, there is no limit on the number of adjustments to the weight of the control input.

[0182] In some embodiments, the second weight can be adjusted multiple times to obtain multiple fourth weights. Then, the fourth weight corresponding to the third lateral control accuracy that meets the specified accuracy is selected from the multiple fourth weights as the target weight of the control input. For example, the adjustment amplitude each time can be the same or different.

[0183] In some embodiments, when the number of third lateral control accuracies that meet the specified accuracy is multiple, the fourth weight corresponding to the third lateral control accuracy with the smallest gap from the specified accuracy can be determined as the target weight of the state variable.

[0184] As some examples, during the simulation experiment, the driving speed of the vehicle is changed within a certain speed range. The driving speed vi of the vehicle at each sampling moment i (i = 1, 2, 3... N) among N sampling moments is obtained, where N ≥ 2.

[0185] Assume that the driving speed vi of the vehicle gradually increases with time, i.e., v1 < v2 <... < vN. The second correlation relationship between the driving speed of the vehicle and the weight of the control input is a linear positive correlation relationship. Taking the weight of the state variable corresponding to the driving speed v1 at the first sampling moment i = 1 as the first weight Q1 and the weight of the corresponding control input as the second weight R1 as an example, as the vehicle speed increases from v1 to vN, the second weight R1 is increased based on the second correlation relationship to determine the fourth weights R2, R3... RN corresponding to the sampling moments i = 2, 3... N, thereby obtaining N - 1 fourth weights.

[0186] Then, according to each fourth weight and the first weight, the objective function is solved to obtain the third value of the control input corresponding to each fourth weight. The vehicle is laterally controlled according to the third value of the control input corresponding to each fourth weight, and the actual values of the lateral displacement error and the actual values of the heading angle error at multiple different moments during the lateral control process are collected.

[0187] The root mean square value of the lateral displacement error and the root mean square value of the heading angle error are used as indicators to measure the lateral control accuracy of the vehicle. Based on the actual values ​​of the lateral displacement error and the actual values ​​of the heading angle error at multiple different times, the root mean square value of the lateral displacement error and the root mean square value of the heading angle error are calculated to determine the third lateral control accuracy corresponding to each fourth weight.

[0188] A fourth weight corresponding to the third lateral control accuracy that meets the specified accuracy is selected from the N-1 fourth weights as the target weight of the control input.

[0189] The method for determining the third lateral control accuracy can be implemented similarly to the method for determining the first lateral control accuracy described in the previous related embodiments. For related instructions, please refer to the previous related embodiments and will not be repeated here.

[0190] In the above embodiment, when the first lateral control accuracy does not meet the specified accuracy, the weight of the control input can be dynamically adjusted based on the second association relationship, and then the weight of the control input that meets the specified accuracy is used as the target weight of the control input. In this way, the weight configuration of the control input corresponding to different speed intervals can be further optimized until the target weight of the control input that meets the specified accuracy is determined, thereby improving the reliability of the lateral control strategy determined subsequently, thereby improving the lateral control accuracy of the vehicle.

[0191] In some embodiments, when the weight of the control input is adjusted by using the second correlation between the vehicle's driving speed and the weight of the control input, if the second correlation is a positive correlation, the target weight of the control input corresponding to the low-speed interval in the multiple speed intervals may be less than the target weight of the control input corresponding to the high-speed interval in the multiple speed intervals. For example, the multiple speed intervals include speed interval S1 (0≤v<20m / s) and speed interval S2 (20≤v<40m / s). The target weight Q of the control input corresponding to speed interval S1 is S1 The target weight Q of the control input corresponding to the speed interval S2 is less than S2 .

[0192] In other words, by maintaining the consistency of the weight adjustment logic of the control input corresponding to different speed ranges, unified weight optimization can be performed on the whole, thereby improving the accuracy of the target weight of the control input corresponding to each speed range, thereby improving the lateral control accuracy of the vehicle.

[0193] In some embodiments, in response to the result of at least one adjustment operation including the result of the first adjustment operation and the result of the second adjustment operation, the objective function can be solved according to the third weight and the fourth weight to obtain a fourth value of the control input. A fourth lateral control accuracy of the vehicle is determined according to the fourth value of the control input. In response to the fourth lateral control accuracy meeting the specified accuracy, the third weight is determined as the target weight of the state variable, and the fourth weight is determined as the target weight of the control input.

[0194] It should be understood that solving the objective function based on the third weight and the fourth weight can obtain the fourth value of the control input and the fourth value of the state variable. The fourth value of the control input represents the optimal control amount of the steering angle of the front wheel of the vehicle corresponding to the third weight and the fourth weight, and the fourth value of the state variable represents the theoretical value of the lateral displacement error and the theoretical value of the heading angle error for lateral control of the vehicle based on the optimal control amount.

[0195] That is, when the first lateral control accuracy does not meet the specified accuracy, the first adjustment operation can be performed on the first weight to obtain the third weight, and the second adjustment operation can be performed on the second weight to obtain the fourth weight. Then, according to whether the corresponding fourth lateral control accuracy meets the specified accuracy, it is determined whether to determine the third weight and the fourth weight as the target weight of the state variable and the target weight of the control input, respectively.

[0196] For example, if the fourth lateral control accuracy meets the specified accuracy (i.e., meets the lateral control requirement), the third weight is determined as the target weight of the state variable, and the fourth weight is determined as the target weight of the control input. If the fourth lateral control accuracy does not meet the specified accuracy (i.e., does not meet the lateral control requirement), the first weight can be adjusted based on the first association relationship, and the second weight can be adjusted based on the second association relationship until the lateral control accuracy corresponding to the adjustment of the first weight (i.e., the third weight) and the adjustment of the second weight (i.e., the fourth weight) meets the specified accuracy.

[0197] For relevant explanations on the first adjustment operation and the second adjustment operation, reference may be made to the description in the foregoing relevant embodiments, which will not be repeated here.

[0198] In the above embodiment, when the first lateral control accuracy does not meet the specified accuracy, the weight of the state variable can be dynamically adjusted based on the first association relationship, and the weight of the control input can be dynamically adjusted based on the second linkage relationship. In this way, through this dual adjustment mechanism, the target weight of the state variable that meets the specified accuracy and the weight configuration of the control input can be quickly determined, thereby improving the efficiency of the algorithm design on the basis of improving the lateral control accuracy of the vehicle.

[0199] Figure 4 Flowcharts showing other embodiments of the method for determining the lateral control strategy of the present disclosure.

[0200] like Figure 4 As shown, for example, the method for determining the lateral control strategy may include steps 410 to 460. The method for determining the lateral control strategy may be used as Figure 2 The lateral control strategy is determined by a specific implementation method shown.

[0201] In step 410 , a kinematic model of the vehicle is constructed.

[0202] In some embodiments, a kinematic model of the vehicle may be constructed based on a bicycle model.

[0203] It should be noted that the kinematic model of the vehicle constructed based on the bicycle model is based on the following assumptions: the movement of the vehicle is in a two-dimensional plane, and the tires on both sides of the vehicle have the same steering angle and rotation speed at any time, that is, the movement of the two front wheels of the vehicle can be combined into the movement of one front wheel to describe, and the movement of the two rear wheels of the vehicle can be combined into the movement of one rear wheel to describe.

[0204] Below through Figure 5 The schematic diagram of the simplified model of the vehicle shown exemplarily illustrates the process of constructing the kinematic model of the vehicle.

[0205] like Figure 5 As shown, the center point of the front axle of the vehicle is the center point A3 of the front wheels A1 and A2 of the vehicle. The center point of the rear axle of the vehicle is the center point B3 of the rear wheels B1 and B2 of the vehicle.

[0206] The vehicle body coordinate system is constructed with the rear axle center point B3 of the vehicle as the vehicle center. In the vehicle body coordinate system, the kinematic model of the vehicle is shown in the following formulas (4) to (6):

[0207]

[0208]

[0209] Among them, v p represents the longitudinal speed of the vehicle, θ p represents the heading angle of the vehicle, L represents the distance between the center point A3 of the front axle and the center point B3 of the rear axle, x p and p They represent the lateral displacement (also called horizontal displacement) and longitudinal displacement (also called vertical displacement) of the rear axle center point B3 (i.e., the vehicle center), δ f Indicates the turning angle of the vehicle's front wheels.

[0210] In step 420 , a prediction model of the MPC algorithm of the vehicle is constructed based on the kinematic model of the vehicle.

[0211] In some embodiments, the predictive model of the vehicle's MPC algorithm may be constructed in the form of state-space equations.

[0212] Following the above example, the kinematic model of the vehicle in the body coordinate system is converted into the kinematic model in the Frenet coordinate system to obtain the lateral displacement error and heading angle error of the vehicle as shown in the following formulas (7) and (8):

[0213] e xf =-(x p -x r )sinθ r +(y p -y r )cosθ r (7)

[0214] e θf =θ p -θ r (8)

[0215] Among them, e xf and e θf They represent the lateral displacement error and heading angle error of the rear axle center point B3 (i.e., the vehicle center), θ r represents the desired heading angle of the rear axle center point B3 (i.e. the desired heading angle of the vehicle), x r and r They respectively represent the expected lateral displacement (also called the expected horizontal displacement of the vehicle) and the expected longitudinal displacement (also called the expected vertical displacement of the vehicle) of the rear axle center point B3.

[0216] For example, select the lateral displacement error e xf and heading angle error e θf is the state variable in the state space equation, that is, X f =[e xf e θf ], thus obtaining the state space equation as shown in formula (9):

[0217]

[0218] in, k p Indicates the desired curvature value of the rear axle center point B3 of the vehicle.

[0219] The prediction model shown in formula (9) is discretized as shown in formula (10):

[0220]

[0221] Formula (10) is transformed to obtain the discretized state space equation as the prediction model of the MPC algorithm, as shown in formula (11):

[0222] X f (k+1)=(TA f (k)+I)X f (k)+TB f (k)δ f (k)+TC f (k)

[0223] =A df (k)X f (k)+B df (k)δ f (k)+C df (k) (11)

[0224] in,

[0225] Taking the prediction step size and control step size in the MPC algorithm as an example, assuming that the prediction step size N h is 5, the current moment is recorded as k, then the state vector in the future period is [X f (k+1),X f (k+2),…,X f (k+5)]. i Represents the turning angle of the front wheel of the vehicle. The turning angle of the front wheel in the future is [u i (k+1),u i (k+2),…,u i (k+5)].

[0226] In step 430, an objective function of the MPC algorithm is designed based on the prediction model.

[0227] In some embodiments, the objective function of the MPC algorithm is determined based on the weighted sum of the state variables and the control input in the prediction model. For example, the lateral displacement error and the heading angle error of the vehicle are used as the state variables of the prediction model of the MPC algorithm, and the steering angle of the front wheels of the vehicle is used as the control input of the prediction model of the MPC algorithm. The objective function of the MPC algorithm is determined based on the weighted sum of the state variables and the control input.

[0228] In some embodiments, the objective function can be constructed in the form of a linear quadratic function. For example, the constructed objective function J f It can be as shown in the above formula (1).

[0229] Here, the objective function in step 430 may be constructed in a manner similar to that in the foregoing related embodiments. For specific instructions, please refer to the foregoing related embodiments, which will not be repeated here.

[0230] In step 440 , target weights of state variables and target weights of control inputs corresponding to each speed interval are determined.

[0231] In step 450, the objective function is solved according to the target weights of the state variables and the target weights of the control inputs to obtain the target values ​​of the control inputs as the turning angles of the front wheels of the vehicle corresponding to each speed interval.

[0232] In step 460, each speed interval and the turning angle of the front wheels of the vehicle corresponding to each speed interval are stored correspondingly to obtain a corresponding relationship (ie, a lateral control strategy).

[0233] Here, the implementation method of step 440 to step 460 is similar to the implementation method of the aforementioned step 220 to step 240. For specific instructions, please refer to the description in the relevant embodiments of the aforementioned step 220 to step 240, which will not be repeated here.

[0234] The following is a specific example to illustrate the process of obtaining the corresponding relationship of each speed interval.

[0235] The first speed v in the direction of the vehicle's travel speed is x1 and a second speed v in the direction opposite to the direction in which the vehicle body moves forward x2 For example.

[0236] The speed interval formed by the minimum and maximum values ​​of the first speed is equally divided into five consecutive first sub-intervals, and the target weight of the state variable and the target weight of the control input corresponding to each first sub-interval are determined as shown in the following formula (12):

[0237]

[0238] Among them, v min,f and v max,f The first speed v x1 The minimum and maximum values ​​of Q fi is the target weight of the state variable corresponding to the first subinterval of the ith fi is the target weight of the state variable corresponding to the i-th first subinterval.

[0239] Substitute the target weight of the state variable and the target weight of the control input corresponding to each first subinterval into the objective function J shown in formula (1): fIn the above formula, the objective function J is combined with the constraint conditions of the objective function shown in formula (2) and formula (3) to obtain f The quadratic programming problem is converted to the following formula (13) to solve the control input Δu f,i (x):

[0240]

[0241] Among them, Δu f,i (x) represents the objective function J based on the objective weight shown in formula (12) f The control input after conversion into a quadratic programming problem, x represents the objective function J based on the target weight shown in formula (12) f The state variable after conversion to a quadratic programming problem, L f1,i ~L fm,i ,l f1,i ~l fm,i ,S f1,i ~s fm,i ,s f1,i ~s fm,i Represents the weight after conversion.

[0242] Using the quadratic programming solver to solve the quadratic programming problem shown in formula (13), we can obtain the target value of the control input corresponding to the first subinterval of the i-th order: (That is, the turning angle of the front wheel of the vehicle corresponding to each first sub-interval is obtained). The target value of the control input corresponding to the i-th first sub-interval and the i-th first sub-interval is Store accordingly to obtain the first sub-relationship.

[0243] The speed interval formed by the minimum and maximum values ​​of the second speed is equally divided into five consecutive second sub-intervals, and the target weight of the state variable and the target weight of the control input corresponding to each first sub-interval are determined as shown in the following formula (14):

[0244]

[0245] Among them, v min,r and v max,f The second speed v x2 The minimum and maximum values ​​of Q rj is the target weight of the state variable corresponding to the jth second subinterval, R rj is the target weight of the state variable corresponding to the j-th second subinterval.

[0246] Substitute the target weight of the state variable and the target weight of the control input corresponding to each second subinterval into the objective function J shown in formula (1): fIn the above formula, the objective function J is combined with the constraint conditions of the objective function shown in formula (2) and formula (3) to obtain f The quadratic programming problem is converted to the following formula (15) to solve the control input Δu r,j (x):

[0247]

[0248] Among them, Δu r,j (x) represents the objective function J based on the objective weight shown in formula (14) f The control input after conversion into a quadratic programming problem, x represents the objective function J based on the target weight shown in formula (14) f The state variable after conversion to a quadratic programming problem, L r1,j ~L rm,j ,l r1,j ~l rm,j ,S r1,j ~s rm,j ,s r1,j ~s rm,j Represents the weight after conversion.

[0249] Using the quadratic programming solver to solve the quadratic programming problem shown in formula (15), we can obtain the target value of the control input corresponding to the j-th second subinterval: (That is, the turning angle of the front wheel of the vehicle corresponding to each second sub-interval is obtained). The target value of the control input corresponding to the j-th second sub-interval and the j-th second sub-interval is Store accordingly to obtain the second sub-relationship.

[0250] It should be noted that in Figures 1 to 5 In the example, the kinematic model of the vehicle, the objective function of the MPC algorithm, the constraints, the target weights and other related formulas are only exemplary.

[0251] Figure 6 A block diagram showing some embodiments of the control device of the vehicle of the present disclosure.

[0252] like Figure 6 As shown, the vehicle control device 600 includes an acquisition module 601 , a determination module 602 and a control module 603 .

[0253] The acquisition module 601 may be configured to acquire the current speed of the vehicle during driving.

[0254] The determination module 602 can be configured to determine the target speed interval to which the current speed belongs from a plurality of continuous speed intervals formed by the vehicle's driving speed, and determine the target turning angle of the vehicle's front wheels based on the target speed interval and the correspondence between each speed interval and the turning angle of the vehicle's front wheels.

[0255] The control module 603 may be configured to perform lateral control on the vehicle according to the target turning angle.

[0256] In some embodiments, the turning angles of the front wheels of the vehicle corresponding to speeds within the same speed interval are the same.

[0257] In some embodiments, the difference between the upper limit and the lower limit of each speed interval is equal.

[0258] In some embodiments, the driving speed of the vehicle includes a first speed and a second speed, and the directions of the first speed and the second speed are different. The multiple speed intervals include a plurality of continuous first sub-intervals formed by the first speed and a plurality of continuous second sub-intervals formed by the second speed. The predetermined corresponding relationship includes a first sub-relationship between each first sub-interval in the plurality of first sub-intervals and the turning angle of the front wheel of the vehicle and a second sub-relationship between each second sub-interval in the plurality of second sub-intervals and the turning angle of the front wheel of the vehicle.

[0259] In some embodiments, the direction of the first speed is the direction in which the vehicle body moves forward, and the direction of the second speed is the direction opposite to the direction in which the vehicle body moves forward.

[0260] In some embodiments, the determination module 602 can be configured to use the vehicle's lateral displacement error and heading angle error as state variables of the vehicle's model predictive control algorithm, and the vehicle's front wheel turning angle as the control input of the model predictive control algorithm, and determine the objective function of the model predictive control algorithm based on the weighted sum of the state variables and the control input, wherein the lateral displacement error is the lateral distance between the vehicle's driving path and the target path, and the heading angle error is the difference between the vehicle's heading angle and the target heading angle; determine the target weights of the state variables and the target weights of the control input corresponding to each speed interval; solve the objective function based on the target weights of the state variables and the target weights of the control input to obtain the target value of the control input as the turning angle of the vehicle's front wheels corresponding to each speed interval; and store each speed interval and the turning angle of the vehicle's front wheels corresponding to each speed interval correspondingly to obtain a corresponding relationship.

[0261] In some embodiments, the determination module 602 can be configured to obtain a first weight of the state variable and a second weight of the control input corresponding to each speed interval; solve the objective function based on the first weight and the second weight to obtain a first value of the control input; determine a first lateral control accuracy of the vehicle based on the first value; and determine a target weight of the state variable and a target weight of the control input based on the first lateral control accuracy of the vehicle, the first weight, and the second weight.

[0262] In some embodiments, the determination module 602 may be configured to determine the first weight as the target weight of the state variable and the second weight as the target weight of the control input in response to the first lateral control accuracy satisfying the specified accuracy.

[0263] In some embodiments, the determination module 602 may be configured to, in response to the first lateral control accuracy not satisfying the specified accuracy, perform at least one of the first adjustment operation and the second adjustment operation, and determine the target weight of the state variable and the target weight of the control input according to the result of the at least one adjustment operation, the first weight, and the second weight. The first adjustment operation includes adjusting the first weight based on the first association relationship between the driving speed in each speed interval and the weight of the state variable to obtain a third weight as a result of the first adjustment operation. The second adjustment operation includes adjusting the second weight based on the second association relationship between the driving speed in each speed interval and the weight of the control input to obtain a fourth weight as a result of the second adjustment operation.

[0264] In some embodiments, the first correlation relationship is an inverse correlation relationship, and the second correlation relationship is a positive correlation relationship.

[0265] In some embodiments, the determination module 602 can be configured to solve the objective function according to the third weight and the second weight in response to the result of at least one adjustment operation including the result of the first adjustment operation to obtain a second value of the control input; determine a second lateral control accuracy of the vehicle according to the second value; and determine the third weight as the target weight of the state variable in response to the second lateral control accuracy satisfying the specified accuracy.

[0266] In some embodiments, the determination module 602 can be configured to solve the objective function according to the first weight and the fourth weight in response to the result of at least one adjustment operation including the result of the second adjustment operation to obtain a third value of the control input; determine a third lateral control accuracy of the vehicle according to the third value; and determine the fourth weight as the target weight of the control input in response to the third lateral control accuracy satisfying the specified accuracy.

[0267] In some embodiments, the determination module 602 can be configured to solve the objective function according to the third weight and the fourth weight in response to the result of at least one adjustment operation including the result of the first adjustment operation and the result of the second adjustment operation to obtain a fourth value of the control input; determine a fourth lateral control accuracy of the vehicle according to the fourth value; in response to the fourth lateral control accuracy meeting the specified accuracy, determine the third weight as the target weight of the state variable, and determine the fourth weight as the target weight of the control input.

[0268] In some embodiments, the control module 603 may be configured to determine a steering wheel angle of the vehicle based on a target angle and a steering ratio of the vehicle; and perform lateral control of the vehicle based on the steering wheel angle.

[0269] In some embodiments, the vehicle is an unmanned fire truck.

[0270] Figure 7 A block diagram showing some other embodiments of the vehicle control device of the present disclosure.

[0271] like Figure 7 As shown, the vehicle control device 700 of this embodiment includes: a memory 701 and a processor 702 coupled to the memory 701, and the processor 702 is configured to execute the vehicle control method in any one of the embodiments of the present disclosure based on the instructions stored in the memory 701.

[0272] The memory 701 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, a database, and other programs.

[0273] Figure 8 A block diagram showing still some embodiments of the control device of a vehicle disclosed herein.

[0274] like Figure 8 As shown, the vehicle control device 800 of this embodiment includes: a memory 801 and a processor 802 coupled to the memory 801 , and the processor 802 is configured to execute the vehicle control method in any one of the aforementioned embodiments based on the instructions stored in the memory 801 .

[0275] The memory 801 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, and other programs.

[0276] The vehicle control device 800 may also include an input / output interface 803, a network interface 804, a storage interface 805, etc. These interfaces 803, 804, 805, the memory 801, and the processor 802 may be connected, for example, via a bus 806. The input / output interface 803 provides a connection interface for input / output devices such as a display, a mouse, a keyboard, a touch screen, a microphone, and a speaker. The network interface 804 provides a connection interface for various networked devices. The storage interface 805 provides a connection interface for external storage devices such as an SD card and a USB flash drive.

[0277] The embodiments of the present disclosure also provide a vehicle, comprising the vehicle control device in any one of the above embodiments (eg, the vehicle control device 600 / 700 / 800).

[0278] The embodiment of the present disclosure further provides a computer-readable storage medium, including computer program instructions, which, when executed by a processor, implement the vehicle control method of any one of the above embodiments.

[0279] The embodiments of the present disclosure also provide a computer program product, including a computer program, which implements the vehicle control method of any one of the above embodiments when executed by a processor.

[0280] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0281] So far, the control technology scheme of the vehicle according to the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical scheme disclosed here.

[0282] The method and system of the present disclosure may be implemented in many ways. For example, the method and system of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0283] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A vehicle control method, comprising: Get the current speed of the vehicle during driving; Determining a target speed interval to which the current speed belongs from a plurality of continuous speed intervals formed by the driving speed of the vehicle; determining a target turning angle of the front wheels of the vehicle according to the target speed interval and a correspondence between each speed interval in the plurality of speed intervals and the turning angle of the front wheels of the vehicle; The vehicle is laterally controlled according to the target turning angle.

2. The control method according to claim 1, wherein: The turning angles of the front wheels of the vehicle corresponding to the speeds in the same speed range are the same.

3. The control method according to claim 1, wherein: The difference between the upper limit and the lower limit of each speed interval is equal.

4. The control method according to claim 1, wherein: The driving speed of the vehicle includes a first speed and a second speed, and the first speed and the second speed are in different directions; The plurality of speed intervals include a plurality of continuous first sub-intervals formed by the first speed and a plurality of continuous second sub-intervals formed by the second speed; The corresponding relationship includes a first sub-relationship between each first sub-interval of the plurality of first sub-intervals and the turning angle of the front wheels of the vehicle and a second sub-relationship between each second sub-interval of the plurality of second sub-intervals and the turning angle of the front wheels of the vehicle.

5. The control method according to claim 4, wherein: The direction of the first speed is the direction in which the vehicle body moves forward, and the direction of the second speed is the direction opposite to the direction in which the vehicle body moves forward.

6. The control method according to claim 1, wherein: The corresponding relationship is determined as follows: Using the lateral displacement error and the heading angle error of the vehicle as state variables of the model predictive control algorithm of the vehicle, using the steering angle of the front wheels of the vehicle as the control input of the model predictive control algorithm, and determining the objective function of the model predictive control algorithm based on the weighted sum of the state variables and the control input, the lateral displacement error is the lateral distance between the driving path of the vehicle and the target path, and the heading angle error is the difference between the heading angle of the vehicle and the target heading angle; Determining a target weight of the state variable and a target weight of the control input corresponding to each speed interval; Solving the objective function according to the target weight of the state variable and the target weight of the control input to obtain the target value of the control input as the turning angle of the front wheel of the vehicle corresponding to each speed interval; Each of the speed intervals and the turning angle of the front wheels of the vehicle corresponding to each of the speed intervals are stored in correspondence to obtain the corresponding relationship.

7. The control method according to claim 6, wherein: Determining the target weight of the state variable and the target weight of the control input corresponding to each speed interval includes: Acquire a first weight of the state variable and a second weight of the control input corresponding to each speed interval; Solving the objective function based on the first weight and the second weight to obtain a first value of the control input; determining a first lateral control accuracy of the vehicle according to the first value; A target weight of the state variable and a target weight of the control input are determined according to a first lateral control accuracy of the vehicle, the first weight, and the second weight.

8. The control method according to claim 7, wherein: The determining the target weight of the state variable and the target weight of the control input according to the first lateral control accuracy of the vehicle, the first weight, and the second weight comprises: In response to the first lateral control accuracy satisfying a specified accuracy, the first weight is determined as a target weight of the state variable, and the second weight is determined as a target weight of the control input.

9. The control method according to claim 7, wherein: Determining the target weight of the state variable and the target weight of the control input according to the first lateral control accuracy of the vehicle, the first weight, and the second weight comprises: In response to the first lateral control accuracy not satisfying a specified accuracy, performing at least one of a first adjustment operation and a second adjustment operation, determining a target weight of the state variable and a target weight of the control input according to a result of the at least one adjustment operation, the first weight, and the second weight, The first adjustment operation includes adjusting the first weight based on a first association relationship between the vehicle's travel speed and the weight of the state variable to obtain a third weight as a result of the first adjustment operation. The second adjustment operation includes adjusting the second weight based on a second association relationship between the driving speed of the vehicle and the weight of the control input to obtain a fourth weight as a result of the second adjustment operation.

10. The control method according to claim 9, wherein: The first correlation relationship is an inverse correlation relationship, and the second correlation relationship is a positive correlation relationship.

11. The control method according to claim 9, wherein: The determining the target weight of the state variable and the target weight of the control input according to the result of the at least one adjustment operation, the first weight, and the second weight comprises: In response to a result of the at least one adjustment operation including a result of the first adjustment operation, solving the objective function according to the third weight and the second weight to obtain a second value of the control input; determining a second lateral control accuracy of the vehicle according to the second value; In response to the second lateral control accuracy satisfying the designated accuracy, the third weight is determined as a target weight of the state variable.

12. The control method according to claim 9, wherein: The determining the target weight of the state variable and the target weight of the control input according to the result of the at least one adjustment operation, the first weight, and the second weight comprises: In response to a result of the at least one adjustment operation including a result of the second adjustment operation, solving the objective function according to the first weight and the fourth weight to obtain a third value of the control input; determining a third lateral control accuracy of the vehicle according to the third value; In response to the third lateral control accuracy satisfying the designated accuracy, the fourth weight is determined as a target weight of the control input.

13. The control method according to claim 9, wherein: The determining the target weight of the state variable and the target weight of the control input according to the result of the at least one adjustment operation, the first weight, and the second weight comprises: In response to a result of the at least one adjustment operation including a result of the first adjustment operation and a result of the second adjustment operation, solving the objective function according to the third weight and the fourth weight to obtain a fourth value of the control input; determining a fourth lateral control accuracy of the vehicle according to the fourth value; In response to the fourth lateral control accuracy satisfying the designated accuracy, the third weight is determined as a target weight of the state variable, and the fourth weight is determined as a target weight of the control input.

14. The control method according to any one of claims 1 to 13, wherein: According to the target turning angle, the lateral control of the vehicle comprises: determining a steering wheel angle of the vehicle according to the target angle and a steering ratio of the vehicle; The vehicle is laterally controlled according to the steering wheel angle.

15. The control method according to any one of claims 1 to 13, wherein: The vehicle is an unmanned fire truck.

16. A vehicle control device, comprising: An acquisition module is configured to acquire the current speed of the vehicle during driving; a determination module configured to determine a target speed interval to which the current speed belongs from a plurality of continuous speed intervals formed by the driving speed of the vehicle; and determining a target turning angle of the front wheels of the vehicle according to the target speed interval and the corresponding relationship between each speed interval in the plurality of speed intervals and the turning angle of the front wheels of the vehicle; The control module is configured to perform lateral control on the vehicle according to the target turning angle.

17. A vehicle control device, comprising: Memory; and A processor coupled to the memory, wherein the processor is configured to execute the control method according to any one of claims 1 to 15 based on instructions stored in the memory.

18. A vehicle comprising: A control device as claimed in claim 16 or 17.

19. A computer-readable storage medium having computer instructions stored thereon, wherein the instructions, when executed by a processor, implement the control method according to any one of claims 1 to 15.

20. A computer program product comprising instructions, which, when executed by a processor, cause the processor to perform the control method according to any one of claims 1 to 15.

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