Vehicle lateral control method and device, electronic equipment and readable storage medium

Abstract

The application provides a vehicle lateral control method and device, electronic equipment and readable storage medium, obtains running state parameters of a vehicle at a first time and a plurality of preset steering wheel angle increments of the vehicle at a second time; inputs the running state parameters and each preset steering wheel angle increment into a vehicle kinematics model to obtain a predicted trajectory corresponding to each preset steering wheel angle increment; inputs the running state parameters and each preset steering wheel angle increment into an expected trajectory calculation model to obtain an expected trajectory corresponding to each preset steering wheel angle increment; obtains a deviation degree value between the predicted trajectory and the expected trajectory corresponding to each preset steering wheel angle increment; obtains a target steering wheel angle increment corresponding to a minimum deviation degree value to control the vehicle laterally according to the target steering wheel angle increment. The application improves the control efficiency and control accuracy of vehicle lateral control.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a vehicle lateral control method, device, electronic device, and readable storage medium. Background Technology

[0002] Lateral control refers to controlling the steering wheel angle to enable the vehicle to track and control according to the planned output path, curvature and other information, in order to reduce tracking errors and improve the stability and comfort of vehicle driving.

[0003] In related technologies, lateral control of vehicles can be achieved through kinematic models such as pure pursuit, Stanley, and proportional-integral-derivative (PID), or through dynamic models such as linear quadratic regulator (LQR) and model predictive control (MPC).

[0004] However, lateral control based on kinematic models has low control accuracy and requires extensive parameter tuning, resulting in low processing efficiency. Lateral control based on kinematic models requires collecting dynamic parameters such as the vehicle's center of gravity position, moment of inertia, front and rear wheel steering angles, and tire lateral stiffness. The strong nonlinear characteristics of vehicles make it difficult to accurately and quickly obtain these dynamic parameters, and the computational load of the dynamic model algorithm is large, leading to high control costs. Summary of the Invention

[0005] In view of the above problems, embodiments of the present invention are proposed to provide a vehicle lateral control method, apparatus, electronic device, and readable storage medium that overcomes or at least partially solves the above problems.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, embodiments of this application disclose a vehicle lateral control method, comprising: acquiring vehicle operating state parameters at a first moment and multiple preset steering wheel angle increments at a second moment; the second moment being after the first moment; inputting the operating state parameters and each preset steering wheel angle increment into a vehicle kinematics model to obtain a predicted trajectory corresponding to each preset steering wheel angle increment; and inputting the operating state parameters and each preset steering wheel angle increment into a desired trajectory calculation model to obtain a desired trajectory corresponding to each preset steering wheel angle increment.

[0008] The deviation value between the predicted trajectory and the expected trajectory corresponding to each preset steering wheel angle increment is obtained respectively; the deviation value reflects the difference between the predicted trajectory and the expected trajectory; from the multiple preset steering wheel angle increments, the target steering wheel angle increment corresponding to the minimum deviation value is obtained, so as to perform lateral control on the vehicle according to the target steering wheel angle increment.

[0009] Secondly, this application discloses a vehicle lateral control device, comprising: a first acquisition module, configured to acquire vehicle operating state parameters at a first moment and multiple preset steering wheel angle increments at a second moment; the second moment is located after the first moment; a second acquisition module, configured to input the operating state parameters and each preset steering wheel angle increment into a vehicle kinematics model to obtain a predicted trajectory corresponding to each preset steering wheel angle increment; a third acquisition module, configured to input the operating state parameters and each preset steering wheel angle increment into a desired trajectory calculation model to obtain a desired trajectory corresponding to each preset steering wheel angle increment; a fourth acquisition module, configured to acquire a deviation value between the predicted trajectory and the desired trajectory corresponding to each preset steering wheel angle increment; the deviation value reflects the difference between the predicted trajectory and the desired trajectory; and a fifth acquisition module, configured to acquire a target steering wheel angle increment corresponding to the minimum deviation value from the multiple preset steering wheel angle increments, so as to perform lateral control on the vehicle based on the target steering wheel angle increment.

[0010] Thirdly, embodiments of this application disclose a vehicle including the vehicle lateral control device described in the second aspect, wherein the vehicle lateral control device is used to implement the vehicle lateral control method described in the first aspect.

[0011] Fourthly, embodiments of this application disclose an electronic device, including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0012] Fifthly, embodiments of this application disclose a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0013] In this embodiment, multiple preset steering wheel angle increments are obtained. For each preset steering wheel angle increment, a predicted trajectory of the vehicle is obtained through a vehicle kinematics model, and a desired trajectory of the vehicle is obtained based on a desired trajectory calculation model. Based on the predicted trajectory and the desired trajectory, a deviation value is obtained between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment. The deviation value reflects the magnitude of the difference between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment. A larger deviation value indicates a larger difference between the predicted trajectory and the desired trajectory when the vehicle travels according to the preset steering wheel angle increment, and also indicates that the predicted trajectory is less close to the user's desired vehicle trajectory, meaning the preset steering wheel angle increment is less able to meet the user's driving needs. Conversely, a smaller deviation value indicates a smaller difference between the predicted trajectory and the desired trajectory when the vehicle travels according to the preset steering wheel angle increment, and also indicates that the predicted trajectory is closer to the user's desired vehicle trajectory, meaning the preset steering wheel angle increment is more able to meet the user's driving needs. Therefore, the deviation value is equivalent to the evaluation function value used to evaluate the preset steering wheel angle increment. The target steering wheel angle increment corresponding to the minimum deviation value is obtained, and the vehicle is laterally controlled based on this target steering wheel angle increment, thus improving the control accuracy of vehicle lateral control. Compared to related technologies that simply use kinematic models for vehicle lateral control, this embodiment improves vehicle control accuracy. Compared to related technologies that use dynamic models for vehicle lateral control, this embodiment reduces the amount of data processed, improving processing and control efficiency. This embodiment features high control accuracy and high processing efficiency in vehicle lateral control. Attached Figure Description

[0014] Figure 1 This is a flowchart of the steps of a vehicle lateral control method provided in an embodiment of the present invention;

[0015] Figure 2 This is a flowchart of another vehicle lateral control method provided in an embodiment of the present invention;

[0016] Figure 3 This is a schematic diagram of a vehicle predicted trajectory and desired trajectory provided in an embodiment of the present invention;

[0017] Figure 4 This is a flowchart of the steps of a vehicle lateral control method provided in an embodiment of the present invention;

[0018] Figure 5 This is a flowchart illustrating the steps of a method for obtaining a target understeering coefficient according to an embodiment of the present invention;

[0019] Figure 6 This is a flowchart of the steps of a vehicle lateral control method provided in an embodiment of the invention;

[0020] Figure 7 This is a block diagram of a vehicle lateral control device provided in an embodiment of the present invention;

[0021] Figure 8 This is a block diagram of an electronic device provided in an embodiment of this application;

[0022] Figure 9 This is a block diagram of another electronic device provided in the embodiments of this application. Detailed Implementation

[0023] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0024] Figure 1 A vehicle lateral control method provided in this application embodiment, referring to... Figure 1 The method includes the following steps:

[0025] Step 101: Obtain the vehicle's operating status parameters at the first moment and multiple preset steering wheel angle increments at the second moment.

[0026] The second moment is located after the first moment. Here, the first moment can be the current moment, and the second moment can be the pre-aiming moment.

[0027] Among them, the operating state parameters may include the longitudinal velocity v of the vehicle at the first moment. x And the vehicle's first steering wheel angle δ at the first moment. It may also include the vehicle's yaw rate at the first moment. Longitudinal acceleration a x and lateral acceleration a y .

[0028] For example, vehicle operating status parameters can be obtained through onboard sensors. For instance, the steering wheel angle δ can be obtained through the Electric Power Steering (EPS) system, and the longitudinal speed v can be obtained through the Electronic Stability Program (ESP) system. x yaw rate Longitudinal acceleration a x Lateral acceleration a y .

[0029] Step 102: Input the running state parameters and each preset steering wheel angle increment into the vehicle kinematics model to obtain the predicted trajectory corresponding to each preset steering wheel angle increment.

[0030] For example, the vehicle's operating condition at the first moment is determined based on the operating state parameters, and the corresponding vehicle kinematics model is obtained. For each preset steering wheel angle increment, the operating state parameters and the preset steering wheel angle increment are input into the vehicle kinematics model corresponding to the operating condition to obtain the predicted trajectory corresponding to the preset steering wheel angle increment.

[0031] For example, the vehicle kinematics model includes a yaw angle prediction model for obtaining yaw angle prediction values ​​and a longitudinal displacement calculation model for obtaining lateral displacement prediction values; the predicted trajectory includes yaw angle prediction values ​​and lateral displacement prediction values. Operating state parameters and preset steering wheel angle increments can be input into the yaw angle prediction model to obtain yaw angle prediction values ​​corresponding to the preset steering wheel angle increments; operating state parameters and preset steering wheel angle increments can be input into the lateral displacement prediction model to obtain lateral displacement prediction values ​​corresponding to the preset steering wheel angle increments.

[0032] For example, the operating state parameters at the first moment include the vehicle's first steering wheel angle. Based on the first steering wheel angle and a preset steering wheel angle increment, a second steering wheel angle corresponding to the preset steering wheel angle increment can be obtained. The longitudinal velocity and the second steering wheel angle are input into the yaw angle prediction model to obtain the predicted yaw angle value corresponding to the preset steering wheel angle increment. The longitudinal velocity and the predicted yaw angle value are input into the lateral displacement prediction model to obtain the predicted lateral displacement value.

[0033] Step 103: Input the running status parameters and each preset steering wheel angle increment into the desired trajectory calculation model to obtain the desired trajectory corresponding to each preset steering wheel angle increment.

[0034] For example, the expected trajectory prediction model includes a yaw angle calculation model for calculating the expected yaw angle, and a lateral displacement calculation model for calculating the expected lateral displacement; the expected trajectory includes the expected yaw angle and the expected lateral displacement. The lateral displacement calculation model is a polynomial equation with longitudinal displacement as the variable; the yaw angle calculation module can be an arctangent function with the first derivative of the expected lateral displacement as the variable.

[0035] For example, the operating state parameters at the first moment include the vehicle's first steering wheel angle. Based on the first steering wheel angle and a preset steering wheel angle increment, the second steering wheel angle corresponding to the preset steering wheel angle increment can be obtained. Based on the longitudinal velocity, and the first and second moments, the expected longitudinal displacement corresponding to the preset steering wheel angle increment is obtained. The longitudinal displacement is input into the lateral displacement calculation model to obtain the expected lateral displacement corresponding to the preset steering wheel angle increment. For example, the arctangent of the first derivative of the expected lateral displacement is calculated, and this arctangent is determined as the expected yaw angle.

[0036] Step 104: Obtain the deviation value between the predicted trajectory and the expected trajectory corresponding to each preset steering wheel angle increment.

[0037] The deviation value reflects the magnitude of the difference between the predicted trajectory and the expected trajectory. Furthermore, the deviation value is positively correlated with the difference between the predicted trajectory and the expected trajectory.

[0038] For example, the predicted trajectory includes the predicted lateral displacement, the predicted yaw angle, and a preset steering wheel angle increment. The desired trajectory includes the expected lateral displacement, the expected yaw angle, and a preset steering wheel angle increment.

[0039] For example, we can obtain the first difference between the predicted yaw angle and the expected yaw angle, as well as the first square of the first difference; the first square reflects the magnitude of the lateral error. We can also obtain the second difference between the predicted lateral displacement and the expected trajectory, as well as the second square of the second difference; the second square reflects the magnitude of the heading error. Finally, we can obtain the third square of the preset steering wheel angle increment; the third square reflects the level of comfort. Finally, we can perform a weighted sum of the first, second, and third square values ​​to obtain the deviation between the predicted trajectory and the expected trajectory.

[0040] Step 105: Obtain the target steering wheel angle increment corresponding to the minimum deviation value from multiple preset steering wheel angle increments, so as to perform lateral control of the vehicle based on the target steering wheel angle increment.

[0041] Specifically, according to steps 101 to 104, multiple deviation values ​​can be obtained, each corresponding to a preset steering wheel angle increment. A binary search method can be used to obtain the minimum deviation value from these multiple values, and this minimum deviation value is then determined as the target steering wheel angle increment.

[0042] For example, as the vehicle travels from a first moment (the current moment) to a second moment (the preview moment), the steering wheel angle of the vehicle at the first moment is adjusted so that the vehicle travels based on the adjusted steering wheel angle at the second moment, thereby achieving lateral control of the vehicle. The adjustment range is equal to the target steering wheel angle increment.

[0043] In related technologies, lateral control algorithms based on kinematic models can be used to control vehicles laterally. For example, pure pursuit algorithms, Stanley algorithms, or proportional-integral-derivative (PID) lateral control algorithms can be used to control vehicles laterally. However, these methods are only suitable for low-speed and low-curvature conditions. In addition, although these algorithms require low computational resources, the control accuracy for lateral vehicle control is not high, and parameter tuning is relatively complex, time-consuming, and labor-intensive. In related technologies, lateral control algorithms based on dynamic models can also be used to control vehicles laterally. For example, algorithms such as Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) can be used for lateral control of vehicles. Although these control algorithms have high control accuracy, they rely on vehicle dynamic parameters such as the vehicle's center of gravity position, moment of inertia, front and rear wheel steering angles, and tire lateral stiffness. The strong nonlinear characteristics of vehicles make it difficult to obtain these parameters, and these parameters will change continuously with changes in operating conditions and time. This characteristic further increases the difficulty of obtaining these parameters. In addition, algorithms based on dynamic models require a large amount of computation, which will lead to a significant increase in controller costs.

[0044] In this embodiment, multiple preset steering wheel angle increments are obtained. For each preset steering wheel angle increment, a predicted trajectory of the vehicle is obtained through a vehicle kinematics model, and a desired trajectory of the vehicle is obtained based on a desired trajectory calculation model. Based on the predicted trajectory and the desired trajectory, a deviation value is obtained between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment. The deviation value reflects the magnitude of the difference between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment. A larger deviation value indicates a larger difference between the predicted trajectory and the desired trajectory when the vehicle travels according to the preset steering wheel angle increment, and also indicates that the predicted trajectory is less close to the user's desired vehicle trajectory, meaning the preset steering wheel angle increment is less able to meet the user's driving needs. Conversely, a smaller deviation value indicates a smaller difference between the predicted trajectory and the desired trajectory when the vehicle travels according to the preset steering wheel angle increment, and also indicates that the predicted trajectory is closer to the user's desired vehicle trajectory, meaning the preset steering wheel angle increment is more able to meet the user's driving needs. Therefore, the deviation value is equivalent to the evaluation function value used to evaluate the preset steering wheel angle increment. The target steering wheel angle increment corresponding to the minimum deviation value is obtained, and the vehicle is laterally controlled based on this target steering wheel angle increment, thus improving the control accuracy of vehicle lateral control. Compared to related technologies that simply use kinematic models for vehicle lateral control, this embodiment improves vehicle control accuracy. Compared to related technologies that use dynamic models for vehicle lateral control, this embodiment reduces the amount of data processed, improving processing and control efficiency. This embodiment features high control accuracy and high processing efficiency in vehicle lateral control.

[0045] Figure 2 This illustrates another embodiment of the vehicle lateral control method, see reference. Figure 2 The method may include the following steps:

[0046] Step 201: Obtain the vehicle's operating status parameters at the first moment and multiple preset steering wheel angle increments at the second moment.

[0047] The second time point is located after the first time point.

[0048] For example, the operating state parameters include the vehicle's longitudinal velocity and the vehicle's first steering wheel angle at the first moment; the vehicle kinematic model includes a yaw angle prediction model and a lateral displacement prediction model. Correspondingly, after step 201, the following steps are also included:

[0049] Step 202: For each preset steering wheel angle increment, obtain the second steering wheel angle corresponding to the preset steering wheel angle increment based on the first steering wheel angle and the preset steering wheel angle increment.

[0050] Specifically, the first steering wheel angle and the preset steering wheel angle increment are summed, and the sum is determined as the second steering wheel angle corresponding to the preset steering wheel angle increment. The preset steering wheel angle increment is the vehicle's steering wheel angle increment at the second moment. Therefore, the second steering wheel angle corresponding to the preset steering wheel angle increment, obtained based on the first steering wheel angle and the preset steering wheel angle increment, is the vehicle's steering wheel angle at the second moment.

[0051] Step 203: Discretize the time interval between the first and second moments to obtain multiple discrete moments, and discretize the second steering wheel angle to obtain multiple discrete angle values.

[0052] Specifically, there is a one-to-one correspondence between discrete time points and discrete steering angle values. Based on multiple discrete time points, the preset steering wheel angle is discretized to obtain multiple discrete steering angle values.

[0053] For example, the first moment is the current moment, the second moment is the aiming moment, and the time interval between the second moment and the first moment is the aiming time. For the aiming time t... p Discretization is performed to obtain multiple discrete time points t. pm :

[0054] t pm =[t1,t2,t3,……,t m ] T

[0055] in,[] T Represents the transpose matrix; t1, t2, t3, ..., t m These are the first discrete time, the second discrete time, the third discrete time, and the m-th discrete time, respectively.

[0056] Discretize the second steering wheel angle to obtain multiple discrete angle values ​​δ(t):

[0057] δ(t)=[δ1,δ2,δ3,…,δ m ]

[0058] Among them, δ1, δ2, δ3,…, δ m These are the discrete values ​​of the rotation angle at the 1st, 2nd, 3rd, and mth discrete times, respectively.

[0059] Step 204: For each discrete turning angle value, input the longitudinal velocity, the discrete turning angle values ​​of the two discrete moments before the discrete moment corresponding to the discrete turning angle value, and the time difference between the discrete moment corresponding to the discrete turning angle value and the previous discrete moment into the yaw angle prediction model to obtain the predicted yaw angle value corresponding to the discrete turning angle value.

[0060] For example, the yaw angle prediction model is ψ=[ψ1,ψ2,ψ3,……,ψ m ] T .

[0061] Where ψ is the yaw angle corresponding to the preset steering wheel angle increment, ψ1, ψ2, ψ3, ..., ψ j These are the predicted yaw angles for the 1st, 2nd, 3rd, and mth discrete times, respectively.

[0062] Where, ψ j =ψ j-1 +Δψ j-1 , ψ j and ψ j-1 Let Δψ be the predicted yaw angle at the j-th discrete time and the (j-1)-th discrete time, respectively. j-1 It represents the change in yaw angle between the j-th discrete time and the (j-1)-th discrete time.

[0063] For example, step 204 may include the following sub-steps:

[0064] Sub-step 2041: Determine the vehicle's operating condition based on the vehicle's operating status parameters.

[0065] The operating condition is either high-speed or low-speed. For example, the operating status parameter may include vehicle speed. If the vehicle speed is greater than or equal to a preset vehicle speed threshold, the vehicle is determined to be in high-speed condition; if the vehicle speed is less than the preset vehicle speed threshold, the vehicle is determined to be in low-speed condition.

[0066] Sub-step 2042: Input the longitudinal velocity, the discrete values ​​of the angle two discrete moments before the discrete moment corresponding to the discrete value of the angle, and the time difference between the discrete moment corresponding to the discrete value of the angle and the previous discrete moment into the yaw angle prediction model corresponding to the vehicle working condition to obtain the yaw angle prediction value corresponding to the discrete value of the angle.

[0067] Given that the vehicle is operating at low speed, the corresponding yaw angle prediction model is as follows:

[0068] ψ j =ψ j-1 +Δψ j-1

[0069]

[0070]

[0071] Where, ψ j and ψ j-1 Δψ represents the predicted yaw angle of the vehicle at the j-th and (j-1)-th discrete times, respectively.j-1 v is the difference in yaw angle between the (j-1)th and (j-2)th discrete time points; x i represents the longitudinal velocity of the vehicle at the first moment; w For vehicle steering ratio; δ j-1 and δ j-2 Let be the discrete values ​​of the steering wheel angle of the vehicle at the (j-2)th and (j-1)th discrete times, respectively. Steering wheel rotation speed; Δt p Let L be the time difference between the j-th and (j-1)-th discrete moments, and L be the vehicle wheelbase.

[0072] Given that the vehicle is operating at high speed, the corresponding yaw angle prediction model is as follows:

[0073] ψ j =ψ j-1 +Δψ j-1

[0074]

[0075]

[0076] Where, ψ j and ψ j-1 Δψ represents the predicted yaw angle of the vehicle at the j-th and (j-1)-th discrete times, respectively. j-1 Δψ is the difference in yaw angle between the (j-1)th and (j-2)th discrete time points. j δ represents the yaw angle difference between the j-th and (j-1)-th discrete time points, where k is the understeer coefficient of the vehicle; -1 and δ j-2 Let be the discrete values ​​of the steering wheel angle of the vehicle at the (j-1)th and (j-2)th discrete times, respectively. Steering wheel rotation speed; Δt p v is the time difference between the j-th and j-th discrete moments; x Let L be the longitudinal velocity of the vehicle at the first moment, and L be the wheelbase of the vehicle.

[0077] When the vehicle is determined to be operating at high speed, the method also includes:

[0078] Sub-step 2043: Obtain the target insufficient turning coefficient information table.

[0079] Specifically, the target understeering coefficient information table records multiple preset operating state parameters, as well as preset understeering coefficients corresponding to each preset operating state parameter.

[0080] Sub-step 2043 may include the following sub-steps:

[0081] Sub-step A1: Obtain the initial understeering coefficient information table.

[0082] Specifically, the initial understeering coefficient information table includes multiple initial understeering coefficients, as well as initial motion state parameters corresponding to each initial understeering coefficient.

[0083] Among them, the initial understeer coefficient of the vehicle under different initial motion state parameters can be obtained through steady-state rotation experiments.

[0084] Sub-step A2 determines whether the vehicle is in a steady-state steering state based on the vehicle's operating state parameters at the first moment.

[0085] For example, the running status parameters include the longitudinal velocity v. x yaw rate Longitudinal acceleration a x Lateral acceleration a y The vehicle can be determined to be in a steady-state steering state when the operating parameters meet the following conditions; otherwise, the vehicle is determined not to be in a steady-state steering state:

[0086]

[0087] in, It is the variance of the vehicle's yaw rate over a preset time period, (σ 2 ) * It is the threshold value for the variance of the yaw rate. and These are the average values ​​of the absolute values ​​of the vehicle's yaw rate, longitudinal velocity, lateral acceleration, and longitudinal acceleration over a preset time period. and These are the minimum values ​​of the vehicle's yaw rate, longitudinal velocity, lateral acceleration, and longitudinal acceleration within a preset time period. and This represents the maximum values ​​of the vehicle's yaw rate, longitudinal velocity, lateral acceleration, and longitudinal acceleration within a preset time period.

[0088] Sub-step A3: If the vehicle is determined to be in a steady-state steering state, obtain the average understeer coefficient of the vehicle at multiple preset times, as well as the average value of the operating state parameters.

[0089] The average values ​​of the operating status parameters may include the average longitudinal velocity and the average lateral acceleration.

[0090] For example, sub-step A3 may include:

[0091] Sub-step A31: Obtain the understeer coefficient K of the vehicle at the m-th preset time. m :

[0092]

[0093] Where, δ m Let be the steering wheel angle of the vehicle at the m-th preset time. Let v be the angular velocity of the vehicle at the m-th preset moment. xm Let L be the longitudinal velocity of the vehicle at the m-th preset time; L is the wheelbase of the vehicle.

[0094] Sub-step A32, for the insufficient turning coefficient K at multiple preset times. m The average understeer coefficient K of the vehicle at multiple preset times is obtained by averaging. cur .

[0095] The preset time is the preset time within a preset time period when the vehicle is in a steady-state steering state.

[0096] Sub-step A4: If the average understeering coefficient satisfies the understeering coefficient constraint, obtain the target initial motion state parameters that match the average running state parameters from the initial understeering coefficient information table.

[0097] The understeering coefficient constraint is:

[0098]

[0099] Among them, K cur K represents the average value of the understeering coefficient. min and K max These are the preset minimum and maximum values ​​of the understeer coefficient; K o This refers to the initial understeering coefficient in the initial understeering coefficient information table, which corresponds to the target's initial motion state.

[0100] Where R = f(v) x R is a constant related to the lateral velocity.

[0101] The initial underperformance coefficient information table includes multiple initial underperformance coefficients and initial operating state parameters corresponding to each initial underperformance coefficient. From these multiple initial operating state parameters, the initial operating state parameter with the smallest absolute value of the worst value of the average operating state parameter is selected. This initial operating state parameter is the target initial motion state that matches the average value of the operating state parameters.

[0102] In sub-step A5, the average understeering coefficient is used to replace the initial understeering coefficient corresponding to the initial motion state of the target in the initial understeering coefficient information table, thus obtaining the target understeering coefficient information table.

[0103] If the average understeering coefficient does not meet the understeering coefficient constraint, the data in the initial understeering coefficient information table will not be updated, and the initial understeering coefficient information table will be determined as the target understeering coefficient information table.

[0104] Sub-step 2044: Obtain the understeer coefficient corresponding to the vehicle's operating state parameters at the first moment from the target understeer coefficient information table.

[0105] For example, the running state parameters at the first moment include the longitudinal velocity v. x and lateral acceleration a y Obtain the target understeering coefficient information table, including each initial longitudinal acceleration and longitudinal velocity v. x The first absolute value of the first difference, and each initial lateral acceleration versus lateral acceleration a y The second absolute value of the second difference.

[0106] From the multiple initial motion state parameters in the target understeer coefficient information table, the initial motion state parameter with the smallest first absolute value and the smallest second absolute value is obtained, and the initial motion state parameter is determined as the first operating state parameter. The initial understeer coefficient corresponding to the first operating state parameter in the target understeer coefficient information table is determined as the understeer coefficient of the vehicle at the first moment.

[0107] Step 205: Input the longitudinal velocity, yaw angle prediction values, and time difference into the lateral displacement prediction model to obtain the lateral displacement prediction values ​​corresponding to the discrete values ​​of the rotation angle.

[0108] For example, the vehicle displacement corresponding to the discrete value of the j-th turning angle satisfies:

[0109]

[0110] Furthermore, the time interval between the first and second moments is discretized to obtain multiple discrete moments, and the preset steering wheel angle increment is discretized to obtain discrete angle values. For each discrete angle value, the predicted yaw angle value ψ at the j-th discrete moment is obtained according to the following yaw angle prediction model. j Then, the predicted lateral displacement value can be obtained based on the following vehicle trajectory prediction model:

[0111]

[0112] Among them, X j and X j-1 Y represents the predicted longitudinal displacement of the vehicle at the j-th and (j-1)-th discrete times, respectively. j and Y j-1These are the predicted lateral displacement values ​​of the vehicle at the j-th and (j-1)-th discrete times, respectively. The lateral displacement prediction model corresponding to the vehicle's operating condition is as follows:

[0113] Y j =Y j-1 +v x Δt p sinψ j

[0114] Among them, Y j and Y j-1 These are the predicted lateral displacement values ​​of the vehicle at the j-th and (j-1)-th discrete times, respectively.

[0115] For example, step 205 may include the following sub-steps:

[0116] Sub-step 2051: Input the longitudinal velocity, yaw angle prediction value, and time difference value into the lateral displacement prediction model corresponding to the vehicle operating condition to obtain the lateral displacement prediction value corresponding to the discrepancy value of the turning angle.

[0117] That is, the longitudinal velocity v x yaw angle prediction value ψ j and time difference Δt p Enter Y j =Y j-1 +v x Δt p sinψ j The discrete value δ of the rotation angle is obtained. j The corresponding predicted lateral displacement value.

[0118] For example, the expected trajectory calculation model includes a yaw angle calculation model and a lateral displacement calculation model; the expected trajectory includes the expected yaw angle value and the expected lateral displacement value. Further, after step 205, the following steps are also included:

[0119] Step 206: For each discrete value of the rotation angle, input the longitudinal velocity and the discrete time corresponding to the discrete value of the rotation angle into the longitudinal displacement calculation model to obtain the expected value of the longitudinal displacement corresponding to the discrete value of the rotation angle.

[0120] The longitudinal displacement calculation model is as follows:

[0121]

[0122]

[0123] in, v is the expected value of the longitudinal velocity. x For longitudinal velocity; t1, t2, t3, t mThese represent the first discrete time point, the second discrete time point, the third discrete time point, and the m-th discrete time point, respectively. pm It is the result of discrete processing of the preview time.

[0124] Step 207: Input the expected longitudinal displacement value into the lateral displacement calculation model to obtain the expected lateral displacement value corresponding to the discrete rotation angle value; the lateral displacement calculation model is a multiple function with the expected longitudinal displacement value as the variable.

[0125] For example, the lateral displacement calculation model is as follows:

[0126] Y * =C0+C1X * +C2(X * ) 2 +C3(X * ) 3

[0127] Y * For lateral displacement; C0, C1, C2, and C3 are preset coefficients.

[0128] The lateral displacement calculation model is used to calculate the expected lateral displacement from the current time to the target time. Preset coefficients C0, C1, C2, and C3 can be set according to user needs, or they can be obtained based on the vehicle's current road path.

[0129] For example, when a vehicle is driving in Lane Centering Control (LCC) mode, the lane line information in front of the vehicle can be obtained. Based on the longitudinal and lateral displacements on the lane lines, a cubic function expression of the longitudinal displacement with respect to the lateral displacement can be fitted. This cubic function expression is determined as the vehicle's lateral displacement calculation model. The coefficients in this cubic function expression are based on the preset coefficients in the vehicle's lateral displacement calculation model.

[0130] For example, an in-vehicle camera captures images of the road ahead of the vehicle. These images are then input into a trained machine learning network model, yielding a cubic function output by the model. This cubic function expression is used as the model for calculating the vehicle's lateral displacement, with each coefficient based on preset coefficients within the model. For instance, historical road images and the cubic function expressions corresponding to the lane lines in these road image samples can be obtained. The historical road images are used as samples, and the cubic function expressions are used as labels to train the machine learning model, resulting in a pre-trained model.

[0131] Step 208: Input the expected value of the lateral displacement into the yaw angle calculation model to obtain the expected value of the yaw angle corresponding to the discrete value of the rotation angle.

[0132] The yaw angle calculation model is an arctangent function with the first derivative of the expected lateral displacement as the variable.

[0133] The yaw angle calculation model is as follows:

[0134] ψ * =arctan(C1+2C2X) * +3C3(X * ) 2 )

[0135] The discrete representation of the expected value of the yaw angle is: ψ * =[ψ1 * ,ψ2 * ,ψ3 * ,……,ψ m * ] T .

[0136] Where, ψ * Let ψ1 be the expected value of the yaw angle. * ,ψ2 * ,ψ3 * ,ψ m * These are the expected values ​​of the yaw angle at the 1st, 2nd, 3rd, and mth discrete times, respectively.

[0137] Reference Figure 3 In the vehicle coordinate system, the origin is the center of the vehicle's rear end at the first moment (t1). The positive X-axis is the axis pointing from the rear to the front of the vehicle, and the Y-axis is the axis parallel to the rear of the vehicle. The vehicle's position at the first moment (the current moment) is (X1, Y1).

[0138] For the preset steering wheel angle increment, based on the first steering wheel angle of the vehicle at the first moment and the preset steering wheel angle increment, the second steering wheel angle of the vehicle at the second moment (preview moment) is obtained, and then discretized to obtain the discrete value of the angle.

[0139] For the discrete values ​​of the turning angle at times t1, t2, and t3, the predicted values ​​of the yaw angle ψ1, ψ2, and ψ3 are obtained (other predicted values ​​are not shown), and the yaw angle at time t can be calculated. m Longitudinal displacement X at time t m and lateral displacement Y m .

[0140] Reference Figure 3 The desired trajectory of the vehicle is a cubic curve related to the longitudinal displacement. Furthermore, the calculation model for the lateral displacement of the vehicle is a cubic function related to the longitudinal displacement.

[0141] Step 209: For each discrete value of the rotation angle, obtain the square of the first difference between the expected value of the lateral displacement and the predicted value of the lateral displacement corresponding to the discrete value of the rotation angle.

[0142] Obtain the first difference between the expected value of the lateral displacement and the predicted value of the lateral displacement corresponding to the discrete value of the turning angle, and calculate the square of the first difference. The square of the first difference can reflect the lateral error of the vehicle.

[0143] Step 210: Obtain the square of the second difference between the expected value of the yaw angle and the predicted value of the yaw angle corresponding to the discrete value of the turning angle.

[0144] Obtain the second difference between the expected yaw angle value and the predicted yaw angle value corresponding to the discrete value of the turning angle, and calculate the square of the second difference. The square of the second difference can reflect the heading error of the vehicle.

[0145] Step 211: Obtain the square of the third difference between the discrete value of the steering angle and the first steering wheel angle.

[0146] Obtain the third difference between the discrete value of the steering angle and the first steering wheel angle, and calculate the square of the third difference; the square of the third difference can reflect the comfort of the vehicle.

[0147] Step 212: The squares of the first difference, the second difference, and the third difference are weighted and summed to obtain the deviation value corresponding to the discrete value of the turning angle.

[0148] The first, second, and third squared values ​​each have their own corresponding weight values. These weight values ​​can be set according to user needs. For example, if the user has high requirements for lateral error, the weight value corresponding to the first squared value can be set larger; if the user has high requirements for heading error, the weight value corresponding to the second squared value can be set larger; and if the user has high requirements for comfort, the weight value corresponding to the third squared value can be set larger.

[0149] The deviation value J between the predicted trajectory and the expected trajectory can be calculated using the following formula:

[0150] J = w1J1 + w2J2 + w3J3

[0151] J1=(Y * -Y) 2

[0152] J2=(ψ * -ψ) 2

[0153] J3=(δ(t)-δ) 2

[0154] Where J1 is the expected value of the trajectory Y * J2 is the square of the second difference between the predicted lateral displacement Y and the expected yaw angle ψ. * J1 is the square of the first difference between the predicted yaw angle ψ and the first square of the predicted yaw angle ψ; J2 is the third square of the difference between the steering wheel angle δ(t) and the steering angle δ at the first moment; w1, w2 and w3 are the first square, the second square and the third square, respectively, and each has its own corresponding weight value.

[0155] Step 213: Sum the deviation values ​​corresponding to each discrete value of the steering angle to obtain the deviation value between the predicted trajectory and the expected trajectory corresponding to the preset steering wheel angle increment.

[0156] In one embodiment, the time interval between the first and second moments is discretized to obtain multiple discrete moments, and the second steering wheel angle is discretized to obtain multiple discrete angle values. The discrete moments and discrete angle values ​​correspond one-to-one. The multiple discrete moments are arranged in chronological order to obtain the latest target discrete moment. The target angle discrete value corresponding to the target discrete moment is then obtained. The deviation value corresponding to the target angle discrete value is determined as the deviation between the predicted trajectory and the expected trajectory corresponding to the preset steering wheel angle increment.

[0157] Step 214: Obtain the target steering wheel angle increment corresponding to the minimum deviation value from multiple preset steering wheel angle increments, so as to perform lateral control of the vehicle based on the target steering wheel angle increment.

[0158] The method for this step has been explained in step 105 above, and will not be repeated here.

[0159] In this embodiment, multiple preset steering wheel angle increments are obtained. For each preset steering wheel angle increment, a predicted trajectory of the vehicle is obtained through a vehicle kinematics model. The desired trajectory of the vehicle is then calculated using a desired trajectory calculation model. Based on the predicted trajectory and the desired trajectory, a deviation value is obtained between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment. The deviation value reflects the magnitude of the difference between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment. A larger deviation value indicates a larger difference between the predicted trajectory and the desired trajectory when the vehicle travels according to the preset steering wheel angle increment. This also indicates that the predicted trajectory is less close to the user's desired vehicle trajectory, meaning the preset steering wheel angle increment is less able to meet the user's driving needs. Conversely, a smaller deviation value indicates a smaller difference between the predicted trajectory and the desired trajectory when the vehicle travels according to the preset steering wheel angle increment. This also indicates that the predicted trajectory is closer to the user's desired vehicle trajectory, meaning the preset steering wheel angle increment better meets the user's driving needs. Therefore, the deviation value is equivalent to the evaluation function value used to evaluate the preset steering wheel angle increment. The target steering wheel angle increment corresponding to the minimum deviation value is obtained, and the vehicle is laterally controlled based on this target steering wheel angle increment, thus improving the control accuracy of vehicle lateral control. Compared to related technologies that simply use kinematic models for vehicle lateral control, this embodiment improves vehicle control accuracy. Compared to related technologies that use dynamic models for vehicle lateral control, this embodiment reduces the amount of data processed, improving processing and control efficiency. In other words, this embodiment features high control accuracy and high processing efficiency in vehicle lateral control.

[0160] Reference Figure 4 The drivable area correction method in this embodiment may include the following steps:

[0161] Step S1: Obtain vehicle operating status parameters; operating status parameters include the vehicle's real-time longitudinal speed, steering wheel angle, yaw rate, longitudinal acceleration, lateral acceleration, and understeer coefficient.

[0162] Specifically, steering wheel angle δ and longitudinal speed v x yaw rate Longitudinal acceleration a x Lateral acceleration a y .

[0163] For example, refer to Figure 5 The method for obtaining the understeering coefficient in step S1 can include the following:

[0164] Sub-step S11: Obtain the vehicle's operating status parameters at the first moment.

[0165] Specifically, the vehicle's dynamic parameters are identified using sensors that acquire operational status parameters.

[0166] Sub-step S12: Based on the vehicle operating status parameters, determine whether the vehicle is in a steady-state steering state; if yes, proceed to step S13; otherwise, proceed to step S16.

[0167] The method for determining whether a vehicle is in a steady-state steering state based on its operating parameters has been explained in step A2 above and will not be repeated here.

[0168] When the vehicle is in a steady state, the average understeer coefficient is calculated, and it is determined whether the initial understeer coefficient in the initial understeer coefficient information table needs to be updated according to steps S13 and S14. If yes, the average understeer coefficient is used for updating; otherwise, the initial understeer coefficient in the initial understeer coefficient information table is determined as the target understeer coefficient for subsequent vehicle trajectory prediction.

[0169] Sub-step S13: Calculate the current understeering coefficient.

[0170] For autonomous vehicles, the steady-state yaw rate gain when the vehicle is in a steady state can be obtained through a two-degree-of-freedom dynamics model.

[0171]

[0172] Where s represents stable state, L is the wheelbase, L1 is the distance from the center of mass to the front axle, L2 is the distance from the center of mass to the rear axle, M is the mass of the vehicle, and K1 and K2 are the lateral stiffness of the front and rear wheels, respectively.

[0173] In practical applications, the center of gravity, mass, and tire lateral stiffness are difficult to obtain using low-cost sensors or processors. Therefore, For the above steady-state yaw rate gain Simplify the process:

[0174]

[0175] Where K is the understeer coefficient, and further, the understeer coefficient K can be expressed as:

[0176]

[0177] in, δ represents the yaw rate of the vehicle; δ represents the steering wheel angle of the vehicle.

[0178] Sub-step S14: Determine whether the current understeering coefficient satisfies the understeering coefficient constraint. If yes, proceed to step S15; otherwise, proceed to step S16.

[0179] Obtain the average of multiple understeer coefficients, including the current understeer coefficient.

[0180] When it is determined that the vehicle has entered steady-state steering, the longitudinal velocity, longitudinal acceleration, and understeer coefficient calculated according to the formula in step S13 are selected at multiple preset moments within a preset time period during which the vehicle is in steady-state steering.

[0181] The understeer coefficients at each preset time point are low-pass filtered and then averaged to obtain the average understeer coefficient K. cur =f(v x_cur ,a y_cur ).

[0182] Among them, v x_cur a y_cur These are the average values ​​of the low-pass filtered results of longitudinal velocity and longitudinal acceleration at multiple preset moments within a preset time period during which the vehicle is in steady-state steering. Specifically, the longitudinal velocity at each preset moment is low-pass filtered and then averaged to obtain the average value of the low-pass filtered result of longitudinal velocity; similarly, the longitudinal acceleration at each preset moment is low-pass filtered and then averaged to obtain the average value of the low-pass filtered result of longitudinal acceleration.

[0183] In practical applications, vehicles are usually designed with understeer characteristics for ease of handling, therefore, K must be satisfied. cur >0. Furthermore, the understeer coefficient of the vehicle has a fixed range of variation and should not deviate significantly from the default setting value K. o Based on the aforementioned characteristics of the understeer coefficient, the understeer coefficient constraint condition is determined as follows:

[0184]

[0185] Among them, K min K max These represent the minimum and maximum ranges of the understeering coefficient, and K... min >0; R is a constant, related to vehicle speed, and represents K. cur Deviation from K o The degree of.

[0186] When the above understeer coefficient constraint conditions are met, the initial understeer coefficient information table is compared with the current motion state (v). x_cur ,a y_cur The closest (v)x ,a y The default value K for the understeering coefficient is... o Update K o =K cur .

[0187] Sub-step S15: Update the understeer coefficient in the understeer coefficient information table with the average understeer coefficient to obtain the updated understeer coefficient information table, and obtain the target understeer coefficient from the updated understeer coefficient information table.

[0188] The method for this step has been explained in step A5 above, and will not be repeated here.

[0189] Sub-step S16: Obtain the understeering coefficient from the understeering coefficient information table, and determine the obtained understeering coefficient as the target understeering coefficient.

[0190] When the vehicle is not in a steady state, the initial understeer coefficient K in the initial understeer coefficient information table will be used. o The insufficient steering coefficient was determined as the target for subsequent vehicle trajectory prediction.

[0191] Among them, the initial understeer coefficient K in the initial understeer coefficient information table o The default value of the understeering coefficient, K, is obtained through steady-state gyration experiments. o It is the longitudinal velocity v x Longitudinal acceleration a x The relevant quantity, insufficient turning coefficient, default value K o It can be represented as: K o =f(v x ,a y ).

[0192] For example, in a steady-state rotation experiment, the vehicle's longitudinal velocity v is obtained at different speeds. x and different longitudinal accelerations a x The understeering coefficient at that time is determined as a function of the longitudinal velocity v. x Longitudinal acceleration a x The corresponding understeering coefficient default value K o .

[0193] Sub-step S17 outputs the target understeering coefficient.

[0194] Step S2: Determine the vehicle kinematic model corresponding to the vehicle's operating conditions.

[0195] Constructing a lateral motion controller: Setting v bod To distinguish between high-speed and low-speed operating conditions, the speed dividing line is vbod This is a preset speed threshold used in the aforementioned embodiments to determine whether the vehicle is in a high-speed or low-speed operating condition.

[0196] Furthermore, if the vehicle's longitudinal velocity v x <v bod If the vehicle is determined to be operating at low speed, the following vehicle kinematics model is used to predict the vehicle trajectory:

[0197]

[0198] Where β is the sideslip angle, and when the vehicle is operating at low speed, sideslip characteristics can be ignored, so β ​​= 0. Where δ f and δ r These are the front wheel steering angle and the rear wheel steering angle, respectively. For vehicles with only front wheel steering, there is δ. r =0. Therefore, the vehicle kinematics model can be simplified to:

[0199]

[0200] Where δ is the steering wheel angle, i w This refers to the steering ratio. Integrating the simplified vehicle kinematics model yields a vehicle kinematics model capable of predicting yaw angles:

[0201]

[0202] If the vehicle's longitudinal speed v x ≥v bod This indicates the vehicle is operating at high speed. Based on the vehicle's steady-state steering characteristics: The vehicle kinematic model can be obtained:

[0203]

[0204] Wherein, K is the understeer coefficient of the vehicle, which can reflect the dynamic characteristics of the vehicle such as lateral deviation.

[0205] Integrating the kinematic model of the vehicle under high-speed conditions yields a vehicle kinematic model that can predict yaw angles.

[0206]

[0207] For both high-speed and low-speed operating conditions, the lateral and longitudinal displacements of the vehicle satisfy the following conditions:

[0208]

[0209] In the formula, X represents the longitudinal displacement of the vehicle, and Y represents the lateral displacement of the vehicle.

[0210] To achieve better tracking accuracy and robustness, this embodiment employs an incremental driver pre-aiming model to obtain a preset search range for the optimal steering wheel angle increment:

[0211] Δδ=[Δδ1,Δδ2,Δδ3,…,Δδ n ] T

[0212] Here, Δδ is an n-dimensional column vector, and the elements in the vector represent the time from the current time t0 to the aiming time t. p The increment of the steering wheel angle. Δδ can be a constant vector. Δδ1, Δδ2, Δδ3 and Δδ n These are the first preset steering wheel angle increment, the second preset steering wheel angle increment, the third preset steering wheel angle increment, and the nth preset steering wheel angle increment, respectively.

[0213] For the i-th preset steering wheel angle increment Δδ i Based on the preset steering wheel increment and the vehicle's first steering wheel angle at the first moment, the vehicle's second steering wheel angle δ at the second moment is obtained. i =δ+Δδ i Where i∈[1,n].

[0214] To improve prediction accuracy, the aiming time t can be adjusted. p Discretize into t pm =[t1,t2,t3,……,t m ] T For each second steering wheel angle δ i Discretization is performed to obtain multiple discrete values ​​for the turning angle, where the j-th discrete value δ j for:

[0215]

[0216] Where, δ j ≤δ i That is, the discrete value of the steering angle is less than or equal to the second steering wheel angle δ. i .

[0217] Where, Δt p =t m -t m-1 j∈[1,m], This refers to the rotational speed of the vehicle's steering wheel.

[0218] Where, δ i (t)=[δ1,δ2,δ3,……,δ j ], δ i (t) represents the aiming time t pThe variation of the vehicle's steering wheel angle. Here, δ1 = δ, representing the first discrete value of the steering wheel angle as the current steering wheel angle of the vehicle, δ... j Pre-aiming time t j Steering wheel angle at time, δ j This is the discrete value of the j-th turning angle.

[0219] Furthermore, the vehicle motion model is discretized, and the aiming time t is predicted in the vehicle coordinate system. p The driving trajectory of the vehicles inside.

[0220] Specifically, based on the vehicle's longitudinal velocity at the current moment and a preset speed threshold, it is determined whether the vehicle is currently operating at high speed or low speed. When the vehicle is operating at low speed, the discrete value ψ of the j-th swing angle is... j Satisfy the following equation:

[0221]

[0222] Where j∈[1,m].

[0223] When the vehicle is operating at high speed, the discrete value ψ of the j-th swing angle j Satisfy the following equation:

[0224]

[0225] The discrete value of the j-th swing angle ψ j The calculation formula is:

[0226] ψ j =ψ j-1 +Δψ j-1

[0227] The longitudinal and lateral displacements of the vehicle satisfy the following equations:

[0228]

[0229] Then the vehicle is in the aiming time t p The longitudinal and lateral displacements at any discrete moment within the range are:

[0230]

[0231] Step S3: Based on the vehicle kinematics model and operating state parameters, predict the predicted trajectory of the vehicle within the aiming time corresponding to each preset steering wheel angle increment.

[0232] According to step S2, the aiming time t can be obtained. p Within the vehicle, the predicted trajectory is: ξ = [v x ,X,Y,ψ,t pm ],in:

[0233]

[0234] Where j∈[1,m], at the aiming time t p Inside, the vehicle can be considered to be traveling at approximately a constant speed, therefore v x It is a constant vector.

[0235] Step S4: Based on the vehicle's desired trajectory calculation model and operating state parameters, obtain the vehicle's desired trajectory within the pre-aiming time, corresponding to each preset steering wheel angle increment.

[0236] The desired trajectory is input from the planning module. To simplify the description and facilitate finding the matching points between the planned and predicted trajectories, it can be considered as... Pre-aiming time t p The expected trajectory point within is in:

[0237]

[0238] Furthermore, ψ * =[ψ1 * ,ψ2 * ,ψ3 * ,…,ψ j * ,…,ψ m * ] T

[0239] Where, ψ j * =C1+2C2X j * +3C3(X j * ) 2 X j * =v x ·t j .

[0240] Where, ψ j * Y is the expected value of the yaw angle at the j-th discrete time. j * Let X be the expected value of the lateral displacement at the j-th discrete time. j * Let t be the expected value of the longitudinal displacement at the j-th discrete time. j Let j be the j-th discrete time.

[0241] Step S5: Calculate the cost function value between the predicted trajectory and the expected trajectory based on the cost function and the expected trajectory.

[0242] A cost function is established to evaluate the quality of the predicted trajectory. Specifically, the quality of the predicted trajectory is evaluated from three aspects: lateral error, heading error, and comfort.

[0243] J1=(Y * -Y) 2

[0244] J2=(ψ * -ψ) 2

[0245] J3=(δ(t)-δ) 2

[0246] J = w1J1 + w2J2 + w3J3

[0247] Where J1 is the lateral error cost function; J2 is the heading error cost function; J3 is the comfort cost function; J is the total cost function; and w1, w2 and w3 are the corresponding weights.

[0248] Step S6: Based on the cost function value, determine the optimal trajectory from multiple predicted trajectories, obtain the target steering wheel angle increment corresponding to the optimal trajectory, and perform lateral control on the vehicle based on the target steering wheel angle increment.

[0249] For example, a bisection method can be used to find the minimum cost function value J from multiple total cost function values ​​J. min The corresponding predicted trajectory is the optimal trajectory ξ. opt , and the optimal trajectory ξ opt The corresponding steering wheel angle increment Δδ opt This is the optimal angle increment, where Δδ opt If ∈Δδ, then the optimal steering angle control quantity is:

[0250] δ * =δ+Δδ opt

[0251] When the vehicle reaches the pre-aiming time, it can be based on δ * Lateral control of the vehicle.

[0252] In one embodiment, refer to Figure 6 The method may also include the following steps:

[0253] Step M1: Obtain the trajectory planner and use the trajectory planner to obtain the desired trajectory.

[0254] The method for this step has been explained in step S4 above, and will not be repeated here.

[0255] Step M2: Obtain the trajectory predictor and obtain the predicted trajectory based on the trajectory predictor.

[0256] The method for this step has been explained in step S2 above, and will not be repeated here.

[0257] Step M3: Obtain the cost function. Calculate the cost function value based on the cost function, the expected trajectory, and the predicted trajectory.

[0258] The method for this step has been explained in step S5 above, and will not be repeated here.

[0259] Step M3 involves using a bisection method to obtain the smaller cost function value from multiple cost function values.

[0260] Step M4: Is the current cost function value the minimum cost function value? If yes, proceed to step M5; otherwise, return to step M3.

[0261] It should be noted that other methods can also be used to determine whether the current cost function value is the minimum cost function value.

[0262] Step M5: Determine the preset steering wheel angle increment corresponding to the minimum cost function value as the optimal steering wheel increment.

[0263] The method for this step has been explained in step S6 above, and will not be repeated here.

[0264] refer to Figure 7 This document illustrates a vehicle lateral control device 30 provided in an embodiment of this application. The device includes: a first acquisition module 301, used to acquire the vehicle's operating state parameters at a first moment and multiple preset steering wheel angle increments at a second moment; the second moment is located after the first moment; a second acquisition module 302, used to input the operating state parameters and each preset steering wheel angle increment into a vehicle kinematics model to obtain a predicted trajectory corresponding to each preset steering wheel angle increment; a third acquisition module 303, used to input the operating state parameters and each preset steering wheel angle increment into a desired trajectory calculation model to obtain a desired trajectory corresponding to each preset steering wheel angle increment; a fourth acquisition module 304, used to acquire the deviation value between the predicted trajectory and the desired trajectory corresponding to each preset steering wheel angle increment; the deviation value reflects the difference between the predicted trajectory and the desired trajectory; and a fifth acquisition module 305, used to acquire the target steering wheel angle increment corresponding to the minimum deviation value from the multiple preset steering wheel angle increments, so as to perform lateral control on the vehicle based on the target steering wheel angle increment.

[0265] Optionally, the operating state parameters include the vehicle's longitudinal velocity and the vehicle's first steering wheel angle at the first moment; the kinematic model includes a yaw angle prediction model and a lateral displacement prediction model; the second acquisition module may include: a first acquisition submodule, used to obtain a second steering wheel angle corresponding to each preset steering wheel angle increment based on the first steering wheel angle and the preset steering wheel angle increment; and a second acquisition submodule, used to discretize the time period between the first moment and the second moment to obtain multiple discrete moments, and to discretize the second steering wheel angle. The first submodule obtains multiple discrete angle values; each discrete time point corresponds to a discrete angle value. The second submodule, for each discrete angle value, inputs the longitudinal velocity, the discrete angle values ​​at two discrete times before the discrete time point corresponding to the discrete angle value, and the time difference between the discrete time point corresponding to the discrete angle value and the previous discrete time point into the yaw angle prediction model to obtain the yaw angle prediction value corresponding to the discrete angle value. The third submodule, for each discrete angle value, inputs the longitudinal velocity, the predicted yaw angle value, and the time difference into the lateral displacement prediction model to obtain the lateral displacement prediction value corresponding to the discrete angle value.

[0266] Optionally, the third acquisition module 303 may include: a fifth acquisition submodule, used to input the longitudinal velocity and the discrete time corresponding to the discrete angle into the longitudinal displacement calculation model for each discrete angle value, and obtain the expected longitudinal displacement value corresponding to the discrete angle value; a sixth acquisition submodule, used to input the expected longitudinal displacement value into the lateral displacement calculation model, and obtain the expected lateral displacement value corresponding to the discrete angle value; the lateral displacement calculation model is a multiple function with the expected longitudinal displacement value as the variable; a seventh acquisition submodule, used to input the expected lateral displacement value into the yaw angle calculation model, and obtain the expected yaw angle value corresponding to the discrete angle value; the yaw angle calculation model is an arctangent function with the first derivative of the expected lateral displacement value as the variable.

[0267] Optionally, the fourth acquisition module may include: an eighth acquisition submodule, used to acquire the square of the first difference between the expected value of the lateral displacement and the predicted value of the lateral displacement corresponding to each discrete angle value; a ninth acquisition submodule, used to acquire the square of the second difference between the expected value of the yaw angle and the predicted value of the yaw angle corresponding to each discrete angle value; a tenth acquisition submodule, used to acquire the square of the third difference between the discrete angle value and the first steering wheel angle; an eleventh acquisition submodule, used to perform a weighted summation of the squares of the first difference, the second difference, and the third difference to obtain the deviation value corresponding to the discrete angle value; and a twelfth acquisition submodule, used to sum the deviation values ​​corresponding to each discrete angle value to obtain the deviation value between the predicted trajectory and the expected trajectory corresponding to the preset steering wheel angle increment.

[0268] Optionally, the third acquisition submodule may include: a determination unit, used to determine the vehicle's operating condition based on the vehicle's operating state parameters; the operating condition is either a high-speed operating condition or a low-speed operating condition; a first acquisition unit, used to input the longitudinal speed and yaw angle discrete values ​​into a yaw angle prediction model corresponding to the vehicle's operating condition to obtain a yaw angle prediction value corresponding to the yaw angle discrete values; the fourth acquisition submodule may include: a second acquisition unit, used to input the longitudinal speed and yaw angle prediction values ​​into a lateral displacement prediction model corresponding to the vehicle's operating condition to obtain a lateral displacement prediction value corresponding to the yaw angle discrete values.

[0269] Optionally, when the vehicle operating condition is determined to be a low-speed condition, the yaw angle prediction model corresponding to the vehicle operating condition is as follows:

[0270] ψ j =ψ j-1 +Δψ j-1

[0271]

[0272]

[0273] Given that the vehicle is operating at low speed, the corresponding lateral displacement prediction model is as follows:

[0274] Y j =Y j-1 +v x Δt p sinψ j

[0275] Among them, Y j and Y j-1 Let Δt be the predicted lateral displacement of the vehicle at the j-th and (j-1)-th discrete times, respectively. p v is the time difference between the j-th and (j-1)-th discrete moments; x ψ is the longitudinal velocity of the vehicle at the first moment; j and ψ j-1 Δψ represents the predicted yaw angle of the vehicle at the j-th and (j-1)-th discrete times, respectively. j-1 Let i be the difference in yaw angle between the (j-1)th and (j-2)th discrete time points. w For vehicle steering ratio; δ j-1 and δ j-2 Let be the discrete values ​​of the steering wheel angle of the vehicle at the (j-1)th and (j-2)th discrete times, respectively. The value is the steering wheel speed, and L is the vehicle wheelbase.

[0276] Optionally, when the vehicle operating condition is determined to be a high-speed operating condition, the yaw angle prediction model corresponding to the vehicle operating condition is:

[0277] ψ j =ψ j-1 +Δψ j-1

[0278]

[0279]

[0280] Given that the vehicle is operating at high speed, the corresponding lateral displacement prediction model is as follows:

[0281] Y j =Y j-1 +v x Δt p sinψ j

[0282] Among them, Y j and Y j-1 Let Δt be the predicted lateral displacement of the vehicle at the j-th and (j-1)-th discrete times, respectively. p v is the time difference between the j-th and (j-1)-th discrete moments; x ψ is the longitudinal velocity of the vehicle at the first moment; j and ψ j-1 Δψ represents the predicted yaw angle of the vehicle at the j-th and (j-1)-th discrete times, respectively. j-1 δ represents the yaw angle difference between the (j-1)th and (j-2)th discrete time points; δ represents the understeer coefficient of the vehicle; j-1 and δ j-2 Let be the discrete values ​​of the steering wheel angle of the vehicle at the (j-1)th and (j-2)th discrete times, respectively. The value is the steering wheel speed, and L is the vehicle wheelbase.

[0283] Optionally, when the vehicle operating condition is determined to be a high-speed operating condition, the third acquisition submodule may include: a third acquisition unit, used to acquire a target understeer coefficient information table; the target understeer coefficient information table records multiple preset operating state parameters and preset understeer coefficients corresponding to each preset operating state parameter; and a fourth acquisition unit, used to acquire the understeer coefficient corresponding to the vehicle's operating state parameter at the first moment from the target understeer coefficient information table.

[0284] Optionally, the third acquisition unit may include: a first acquisition subunit, used to acquire an initial understeer coefficient information table; the initial understeer coefficient information table includes multiple initial understeer coefficients and initial motion state parameters corresponding to each initial understeer coefficient; a determination subunit, used to determine whether the vehicle is in a steady-state steering state based on the vehicle's operating state parameters at a first moment; a second acquisition subunit, used to acquire, when the vehicle is determined to be in a steady-state steering state, the average understeer coefficient of the vehicle at multiple preset moments and the average operating state parameters; a third acquisition subunit, used to acquire, when the average understeer coefficient satisfies the understeer coefficient constraint condition, a target initial motion state parameter matching the average operating state parameter from the initial understeer coefficient information table; and a fourth acquisition subunit, used to replace the initial understeer coefficient corresponding to the target initial motion state in the initial understeer coefficient information table with the average understeer coefficient to obtain a target understeer coefficient information table.

[0285] Optionally, the second acquisition subunit may include: a fifth acquisition subunit, used to acquire the understeer coefficient K of the vehicle at the m-th preset time. m :

[0286]

[0287] Where, δ m Let m be the steering wheel angle of the vehicle at the m-th preset time. Let v be the angular velocity of the vehicle at the m-th preset moment. xm The longitudinal velocity of the vehicle at the m-th preset moment; the sixth acquisition subunit is used to obtain the understeer coefficient K at multiple preset moments. m The average understeer coefficient K of the vehicle at multiple preset times is obtained by averaging. cur .

[0288] Optionally, the understeering coefficient constraint is:

[0289]

[0290] Among them, K cur K represents the average value of the understeering coefficient. min and K max These are the preset minimum and maximum values ​​of the understeer coefficient; K o This refers to the initial understeering coefficient in the initial understeering coefficient information table, which corresponds to the target's initial motion state.

[0291] Optionally, the longitudinal displacement calculation model is as follows:

[0292]

[0293]

[0294] The lateral displacement calculation model is as follows:

[0295] Y * =C0+C1X * +C2(X * ) 2 +C3(X * ) 3

[0296] The yaw angle calculation model is as follows:

[0297] ψ * =arctan(C1+2C2X) * +3C3(X * ) 2 )

[0298] Among them, X * For longitudinal displacement, Y * This is a lateral displacement; v is the expected value of the longitudinal velocity. x For longitudinal velocity; t1, t2, t3, t m These represent the longitudinal velocities at the first, second, third, and j-th discrete time points, respectively; C0, C1, C2, and C3 are preset coefficients; ψ * Let ψ1 be the expected value of the yaw angle. * ,ψ2 * ,ψ3 * ,ψ j * These are the expected values ​​of the yaw angle at the first, second, third, and j-th discrete moments, respectively.

[0299] In summary, multiple preset steering wheel angle increments are obtained. For each preset steering wheel angle increment, the predicted trajectory of the vehicle is obtained through a vehicle kinematics model. Based on the desired trajectory calculation model, the desired trajectory of the vehicle is obtained. Based on the predicted trajectory and the desired trajectory, the deviation value between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment is obtained. The deviation value reflects the magnitude of the difference between the predicted trajectory and the desired trajectory corresponding to the preset steering wheel angle increment. Compared to related technologies that simply use kinematic models for lateral vehicle control, this embodiment improves vehicle control accuracy. Compared to related technologies that use dynamic models for lateral vehicle control, this embodiment reduces the amount of data processed, improving processing and control efficiency. In other words, this embodiment features high control accuracy and high processing efficiency in vehicle lateral control.

[0300] This application also provides a vehicle including a vehicle lateral control device for implementing the vehicle lateral control method as described in any of the foregoing embodiments.

[0301] Figure 8 This is a block diagram illustrating an electronic device 400 according to an exemplary embodiment. For example, the electronic device 400 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc. (See also...) Figure 8 The electronic device 400 may include one or more of the following components: a processing component 402, a memory 404, a power supply component 406, a multimedia component 408, an audio component 410, an input / output (I / O) interface 412, a sensor component 414, and a communication component 416. The processing component 402 typically controls the overall operation of the electronic device 400. The processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, the processing component 402 may include one or more modules to facilitate interaction between the processing component 402 and other components.

[0302] Memory 404 is used to store various types of data to support the operation of electronic device 400. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Power supply component 406 provides power to various components of electronic device 400. Multimedia component 408 includes a screen that provides an output interface between electronic device 400 and the user. Audio component 410 is used to output and / or input audio signals. I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as a keyboard. Sensor component 414 includes one or more sensors for providing status assessments of various aspects of electronic device 400. Communication component 416 facilitates wired or wireless communication between electronic device 400 and other devices. Electronic device 400 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. Electronic device 400 can be implemented by one or more application-specific integrated circuits (ASICs), microprocessors, or other electronic components to implement a vehicle lateral control method provided in this application embodiment.

[0303] This application also provides a non-transitory computer-readable storage medium including instructions, such as a memory 404 including instructions, which can be executed by a processor 420 of an electronic device 400 to perform the above-described method. For example, the non-transitory storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0304] Figure 9 This is a block diagram illustrating an electronic device 500 according to an exemplary embodiment. For example, the electronic device 500 may be provided as a server. (Refer to...) Figure 9 The electronic device 500 includes a processing component 522, which further includes one or more processors, and memory resources represented by memory 532 for storing instructions, such as applications, that can be executed by the processing component 522. The applications stored in memory 532 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 522 is configured to execute instructions to perform a vehicle lateral control method provided in embodiments of this application. The electronic device 500 may also include a power supply component 526 configured to perform power management of the electronic device 500, a wired or wireless network interface 550 configured to connect the electronic device 500 to a network, and an input / output (I / O) interface 558. The electronic device 500 can operate on an operating system stored in memory 532, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0305] This application also provides a computer program product, including a computer program that, when executed by a processor, implements a vehicle lateral control method. Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0306] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A vehicle lateral control method characterized by, The method comprises: obtaining a running state parameter of a vehicle at a first time and a plurality of preset steering wheel angle increments of the vehicle at a second time; the second time is after the first time; inputting the running state parameter and each preset steering wheel angle increment into a vehicle kinematics model to obtain a predicted trajectory corresponding to each preset steering wheel angle increment; inputting the running state parameter and each preset steering wheel angle increment into an expected trajectory calculation model to obtain an expected trajectory corresponding to each preset steering wheel angle increment; obtaining a deviation degree value between the predicted trajectory and the expected trajectory corresponding to each preset steering wheel angle increment; the deviation degree value reflects the difference between the predicted trajectory and the expected trajectory; from the plurality of preset steering wheel angle increments, obtaining a target steering wheel angle increment corresponding to a minimum deviation degree value, so as to control the vehicle in a lateral direction according to the target steering wheel angle increment; the running state parameter comprises a longitudinal speed of the vehicle and a first steering wheel angle of the vehicle at the first time; the kinematics model comprises a yaw angle prediction model and a lateral displacement prediction model; the inputting the running state parameter and each preset steering wheel angle increment into the vehicle kinematics model to obtain a predicted trajectory corresponding to each preset steering wheel angle increment comprises: for each preset steering wheel angle increment, obtaining a second steering wheel angle corresponding to the preset steering wheel angle increment according to the first steering wheel angle and the preset steering wheel angle increment; discretely processing a time period between the first time and the second time to obtain a plurality of discrete times, and discretely processing the second steering wheel angle to obtain a plurality of steering angle discrete values; the discrete times and the steering angle discrete values correspond to each other; for each steering angle discrete value, inputting the longitudinal speed, steering angle discrete values of two discrete times before a discrete time corresponding to the steering angle discrete value, and a time difference value between the discrete time corresponding to the steering angle discrete value and a previous discrete time into the yaw angle prediction model to obtain a yaw angle prediction value corresponding to the steering angle discrete value; inputting the longitudinal speed, the yaw angle prediction value, and the time difference value into the lateral displacement prediction model to obtain a lateral displacement prediction value corresponding to the steering angle discrete value.

2. The method of claim 1, wherein, the expected trajectory calculation model comprises a yaw angle calculation model, a lateral displacement calculation model, and a longitudinal displacement calculation model; the inputting the running state parameter and each preset steering wheel angle increment into the expected trajectory calculation model to obtain an expected trajectory corresponding to each preset steering wheel angle increment comprises: for each steering angle discrete value, inputting the longitudinal speed and a discrete time corresponding to the steering angle discrete value into the longitudinal displacement calculation model to obtain a longitudinal displacement expected value corresponding to the steering angle discrete value; inputting the longitudinal displacement expected value into the lateral displacement calculation model to obtain a lateral displacement expected value corresponding to the steering angle discrete value; the lateral displacement calculation model is a multiple function with the longitudinal displacement expected value as a variable. inputting the lateral displacement expected value into a yaw angle calculation model to obtain a yaw angle expected value corresponding to the steering angle discrete value; the yaw angle calculation model is an arctangent function with a first derivative of the lateral displacement expected value as a variable.

3. The method of claim 2, wherein, The method comprises the following steps: for each steering angle discrete value, obtaining a square value of a first difference value between a lateral displacement expected value corresponding to the steering angle discrete value and a lateral displacement predicted value; obtaining a square value of a second difference value between a yaw angle expected value corresponding to the steering angle discrete value and a yaw angle predicted value; obtaining a square value of a third difference value between the steering angle discrete value and the first steering wheel angle; performing weighted summation on the square value of the first difference value, the square value of the second difference value and the square value of the third difference value to obtain a deviation degree value corresponding to the steering angle discrete value; performing summation processing on the deviation degree values corresponding to the respective steering angle discrete values to obtain the deviation degree value between the predicted trajectory and the expected trajectory corresponding to the preset steering wheel angle increment.

4. The method of claim 1, wherein, inputting the longitudinal speed, the steering angle discrete values of two discrete time points before a discrete time point corresponding to the steering angle discrete value and a time difference value between the discrete time point corresponding to the steering angle discrete value and a previous discrete time point into the yaw angle prediction model to obtain the yaw angle predicted value corresponding to the steering angle discrete value, comprising: determining a working condition of the vehicle according to the operating state parameter of the vehicle; the working condition is a high-speed working condition or a low-speed working condition; inputting the longitudinal speed, the steering angle discrete values of two discrete time points before a discrete time point corresponding to the steering angle discrete value and a time difference value between the discrete time point corresponding to the steering angle discrete value and a previous discrete time point into the yaw angle prediction model corresponding to the vehicle working condition to obtain the yaw angle predicted value corresponding to the steering angle discrete value; inputting the longitudinal speed and the yaw angle predicted value into the lateral displacement prediction model to obtain the lateral displacement predicted value corresponding to the steering angle discrete value, comprising: inputting the longitudinal speed, the yaw angle predicted value and the time difference value into the lateral displacement prediction model corresponding to the vehicle working condition to obtain the lateral displacement predicted value corresponding to the steering angle discrete value.

5. The method of claim 4, wherein, in a case where it is determined that the vehicle working condition is a low-speed working condition, the yaw angle prediction model corresponding to the vehicle working condition is: ψ j = ψ j-1 + Δψ j-1 in a case where it is determined that the vehicle working condition is a low-speed working condition, the lateral displacement prediction model corresponding to the vehicle working condition is: Y j = Y j-1 + v x Δt p sinψ j wherein Y j and Y j-1 are the lateral displacement prediction values of the vehicle at the jth and (j-1)th discrete time instants, Δt p is the time difference between the jth and (j-1)th discrete time instants; v x is the longitudinal speed of the vehicle at the first time instant; ψ j and ψ j-1 are the yaw angle prediction values of the vehicle at the jth and (j-1)th discrete time instants; Δψ j-1 is the yaw angle difference between the (j-1)th and jth discrete time instants, i w is the vehicle steering ratio; δ j-1 and δ j-2 are the steering angle discrete values of the vehicle at the (j-1)th and (j-2)th discrete time instants, is the steering wheel speed, and L is the wheelbase of the vehicle.

6. The method of claim 4, wherein, in a case where it is determined that the vehicle working condition is a high-speed working condition, the yaw angle prediction model corresponding to the vehicle working condition is: ψ j = ψ j-1 + Δψ j-1 in a case where it is determined that the vehicle working condition is a high-speed working condition, the lateral displacement prediction model corresponding to the vehicle working condition is: Y j = Y j-1 + v x Δt p sinψ j wherein Y j and Y j-1 are the lateral displacement prediction values of the vehicle at the jth and (j-1)th discrete time instants, Δt p is the time difference between the jth and (j-1)th discrete time instants; v x is the longitudinal speed of the vehicle at the first time instant; ψ j and ψ j-1 are the yaw angle prediction values of the vehicle at the jth and (j-1)th discrete time instants; Δψ j-1 is the yaw angle difference between the (j-1)th and jth discrete time instants; K is the understeering coefficient of the vehicle; δ j-1 and δ j-2 are the steering wheel angle discrete values of the vehicle at the (j-1)th and (j-2)th discrete time instants, is the steering wheel speed, and L is the wheelbase of the vehicle.

7. The method of claim 6, wherein, in a case where it is determined that the vehicle working condition is a high-speed working condition, the method further comprises: obtaining a target deficient steering coefficient information table; the target deficient steering coefficient information table records a plurality of preset operating state parameters and preset deficient steering coefficients corresponding to each preset operating state parameter respectively; From the target understeering coefficient information table, an understeering coefficient K corresponding to a running state parameter of the vehicle at the first time is obtained.

8. The method of claim 7, wherein, The target understeering coefficient information table is obtained, including: An initial understeering coefficient information table is obtained; the initial understeering coefficient information table includes a plurality of initial understeering coefficients, and initial running state parameters corresponding to each initial understeering coefficient, respectively; It is determined whether the vehicle is in a steady state steering state according to the running state parameter of the vehicle at the first time; In the case where it is determined that the vehicle is in a steady state steering state, an average value of understeering coefficients of the vehicle at a plurality of preset times and an average value of running state parameters are obtained; In the case where the average value of understeering coefficients meets an understeering coefficient constraint condition, a target initial running state parameter matching the average value of running state parameters is obtained from the plurality of initial understeering coefficients of the initial understeering coefficient information table; The average value of understeering coefficients is used to replace a target initial understeering coefficient in the initial understeering coefficient information table, to obtain the target understeering coefficient information table.

9. The method of claim 8, wherein, The average value of understeering coefficients of the vehicle at a plurality of preset times is obtained, including: obtaining a deficient steering coefficient K of the vehicle at the mth preset time m : wherein δ m is the steering wheel angle of the vehicle at the mth preset time, is the yaw rate of the vehicle at the mth preset time, v xm is the longitudinal speed of the vehicle at the mth preset time, and L is the wheelbase of the vehicle. a plurality of preset time instants m averaging, to obtain an average value of the understeer coefficient K of the vehicle at the plurality of preset time instants cur .

10. The method of claim 8, wherein, The understeering coefficient constraint condition is: K cur is an average value of the understeer coefficients, K min and K max are respectively a preset minimum value and a maximum value of the understeer coefficients; K o is an initial understeer coefficient corresponding to the target initial motion state in an initial understeer coefficient information table, and R is a constant.

11. The method of claim 2, wherein, The longitudinal displacement calculation model is: The lateral displacement calculation model is: Y * = C0+ C1X * + C2(X * ) 2 + C3(X * ) 3 The yaw angle calculation model is: ψ * = arctan(C1+ 2C2X * + 3C3(X * ) 2 ) wherein X * is a longitudinal displacement expectation value, Y * is a lateral displacement expectation value; is a longitudinal velocity expectation value, v x is a longitudinal velocity; t1, t2, t3, t m are longitudinal velocities at the 1st, 2nd, 3rd and mth discrete time, respectively; C0, C1, C2, C3 are preset coefficients; ψ * is a yaw angle expectation value.

12. A vehicle lateral control device characterized by comprising: including: A first obtaining module is configured to obtain a running state parameter of a vehicle at a first time and a plurality of preset steering wheel angle increments of the vehicle at a second time; The second time is after the first time; A second obtaining module is configured to input the running state parameter and each preset steering wheel angle increment into a vehicle kinematics model to obtain a predicted trajectory corresponding to each preset steering wheel angle increment, respectively; A third obtaining module is configured to input the running state parameter and each preset steering wheel angle increment into an expected trajectory calculation model to obtain an expected trajectory corresponding to each preset steering wheel angle increment, respectively; A fourth obtaining module is configured to obtain a deviation degree value between the predicted trajectory and the expected trajectory corresponding to each preset steering wheel angle increment, respectively; the deviation degree value reflects a difference between the predicted trajectory and the expected trajectory; A fifth obtaining module is configured to obtain a target steering wheel angle increment corresponding to a minimum deviation degree value from the plurality of preset steering wheel angle increments, so as to perform lateral control on the vehicle according to the target steering wheel angle increment; The running state parameter includes a longitudinal speed of the vehicle and a first steering wheel angle of the vehicle at the first time; the kinematics model includes a yaw angle prediction model and a lateral displacement prediction model; the second obtaining module includes: A first obtaining submodule is configured to obtain, for each preset steering wheel angle increment, a second steering wheel angle corresponding to the preset steering wheel angle increment according to the first steering wheel angle and the preset steering wheel angle increment; The second acquisition submodule is configured to discretize a time period between the first time and the second time to obtain a plurality of discrete times, and discretize the second steering wheel angle to obtain a plurality of discrete steering angle values; the discrete times and the discrete steering angle values correspond to each other in a one-to-one manner; The third acquisition submodule is configured to, for each discrete steering angle value, input the longitudinal speed, two discrete steering angle values before a discrete time corresponding to the discrete steering angle value, a time difference between the discrete time corresponding to the discrete steering angle value and a previous discrete time, into the yaw angle prediction model to obtain a yaw angle prediction value corresponding to the discrete steering angle value; The fourth acquisition submodule is configured to input the longitudinal speed, the yaw angle prediction value, and the time difference into the lateral displacement prediction model to obtain a lateral displacement prediction value corresponding to the discrete steering angle value.

13. A vehicle characterized by comprising: The vehicle lateral control device is configured to implement the vehicle lateral control method according to any one of claims 1 to 11.

14. An electronic device, comprising: The processor and the memory are included, the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the method according to any one of claims 1 to 11.

15. A readable storage medium, characterized by, The readable storage medium stores programs or instructions, and the programs or instructions are executed by the processor to implement the steps of the method according to any one of claims 1 to 11.

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