Vehicle control method, device, equipment and medium

Abstract

Embodiments of the present application provide a vehicle control method, apparatus, device, and medium, comprising: obtaining a planned trajectory sequence, vehicle kinematic parameters, and trajectory curvature of a vehicle within a first period; processing the planned trajectory sequence, trajectory curvature, and vehicle kinematic parameters based on a preset kinematic model and a preset cost function to predict trajectory curvatures corresponding to N first moments in a nonlinear time domain; determining, based on a preset delay duration of the vehicle's steering system, a trajectory curvature for a second period from the trajectory curvatures corresponding to the N first moments in the nonlinear time domain, where the second period is later than the first period; and controlling vehicle travel based on the trajectory curvature of the second period. Embodiments of the present application improve the accuracy of controlling vehicle travel.

Description

Technical Field

[0001] The present application relates to the field of vehicle control, and in particular to a vehicle control method, device, equipment and medium. Background Art

[0002] Autonomous vehicles can navigate roads and avoid obstacles without human intervention, using their autonomous driving systems and various sensors. However, existing solutions for controlling autonomous vehicles suffer from low accuracy. Summary of the Invention

[0003] The embodiments of the present application provide a vehicle control method, device, equipment and medium, which improve the accuracy of controlling vehicle driving.

[0004] In a first aspect, an embodiment of the present application provides a vehicle control method, the method comprising:

[0005] Obtain the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first cycle;

[0006] The planned trajectory sequence, trajectory curvature, and vehicle kinematic parameters are processed based on a preset kinematic model and a preset cost function to predict the trajectory curvature corresponding to N first moments in a nonlinear time domain, where N is a positive integer greater than 0, and the N first moments are all later than a first period. The preset kinematic model is set based on vehicle parameters and historical vehicle kinematic parameters of the vehicle, and the preset cost function is set based on the nonlinear time domain of the model predictive control (MPC).

[0007] Determining, according to a preset delay time of a steering system of the vehicle, a trajectory curvature of a second period from trajectory curvatures corresponding to N first moments in a nonlinear time domain, wherein the second period is later than the first period;

[0008] The vehicle is controlled to travel according to the trajectory curvature of the second cycle.

[0009] In an optional implementation of the first aspect, processing the planned trajectory sequence, trajectory curvature, and vehicle kinematic parameters based on a preset kinematic model and a preset cost function to predict trajectory curvatures corresponding to N first moments in a nonlinear time domain includes:

[0010] Calculate the vehicle's body orientation information based on the planned trajectory sequence;

[0011] Input the planned trajectory sequence, vehicle orientation information, and trajectory curvature into a preset cost function, solve the optimal solution of the preset cost function, and obtain the trajectory curvature change rate corresponding to N second moments in the nonlinear time domain;

[0012] The trajectory curvatures corresponding to N first moments in the nonlinear time domain are calculated based on the vehicle kinematic parameters, the rates of change of the trajectory curvature corresponding to N second moments in the nonlinear time domain, and a preset kinematic model, where the Nth second moment is earlier than the Nth first moment, and the time intervals between each two first moments in the N first moments are different.

[0013] In an optional implementation of the first aspect, determining the trajectory curvature of the second period from trajectory curvatures corresponding to N first moments in the nonlinear time domain according to a preset delay time of a steering system of the vehicle includes:

[0014] When the N first moments include the third moment, determining that the trajectory curvature corresponding to the third moment is the trajectory curvature of the second period;

[0015] In a case where the N first moments do not include the third moment, determining that the trajectory curvature corresponding to the fourth moment is the trajectory curvature of the second period;

[0016] The third moment is the moment obtained by extending the preset delay time of the steering system from the moment corresponding to the first cycle as the starting point, and the fourth moment is the first moment with the smallest time interval with the third moment among the N first moments.

[0017] In an optional implementation of the first aspect, controlling the vehicle to travel according to the curvature of the trajectory of the vehicle in the second period includes:

[0018] calculating, based on vehicle kinematic parameters and a preset parameter model, a cornering stiffness of the first wheel and a cornering stiffness of the second wheel within a third period, wherein the third period is later than the first period and greater than the second period;

[0019] determining steering information of the vehicle in a fourth cycle based on the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third cycle and the trajectory curvature of the second cycle, where the fourth cycle is later than the third cycle;

[0020] The vehicle is controlled to travel according to the steering information of the vehicle in the fourth cycle.

[0021] In an optional implementation of the first aspect, calculating the cornering stiffness of the first wheel and the cornering stiffness of the second wheel within the third period according to the vehicle kinematic parameters and a preset parameter model includes:

[0022] Obtaining first turn information within a first cycle;

[0023] Calculating first vehicle kinematic parameters within a third period based on the vehicle kinematic parameters, the first steering information, and a preset parameter model;

[0024] The cornering stiffness of the first wheel and the cornering stiffness of the second wheel in a third period are calculated according to the first vehicle kinematic parameter and the first steering information.

[0025] In an optional implementation of the first aspect, the vehicle kinematic parameters include a first shaft speed and a vehicle body yaw rate; and the first vehicle kinematic parameters include a first vehicle lateral speed and a first vehicle body yaw rate.

[0026] In an optional implementation of the first aspect, calculating the cornering stiffness of the first wheel and the cornering stiffness of the second wheel within the third period according to the first vehicle kinematic parameter and the first steering information includes:

[0027] Calculating a first cornering stiffness coefficient and a second cornering stiffness coefficient according to the first vehicle body yaw angular velocity, the first vehicle lateral velocity, and the first steering information;

[0028] The product of the first cornering stiffness coefficient and the first preset cornering stiffness is determined as the cornering stiffness of the first wheel, and the product of the second cornering stiffness coefficient and the second preset cornering stiffness is determined as the cornering stiffness of the second wheel.

[0029] In a second aspect, an embodiment of the present application provides a vehicle control device, the device comprising:

[0030] An acquisition module is used to obtain the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first cycle;

[0031] a processing module, configured to process the planned trajectory sequence, the trajectory curvature, and the vehicle kinematic parameters based on a preset kinematic model and a preset cost function to predict the trajectory curvatures corresponding to N first moments in a nonlinear time domain, where N is a positive integer greater than 0, and the N first moments are all later than a first period, the preset kinematic model is set based on vehicle parameters and historical vehicle kinematic parameters of the vehicle, and the preset cost function is set based on the nonlinear time domain of the model predictive control (MPC);

[0032] a determination module, configured to determine, based on a preset delay time of a steering system of the vehicle, a trajectory curvature of a second period from trajectory curvatures corresponding to N first moments in a nonlinear time domain, wherein the second period is later than the first period;

[0033] The control module is used to control the vehicle to travel according to the trajectory curvature of the second period.

[0034] In a third aspect, a vehicle control device is provided, comprising: a memory for storing computer program instructions; and a processor for reading and running the computer program instructions stored in the memory to execute the vehicle control method provided by any optional embodiment of the first aspect and the second aspect.

[0035] In a fourth aspect, a computer storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the vehicle control method provided by any optional embodiment of the first aspect and the second aspect is implemented.

[0036] In an embodiment of the present application, by obtaining the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first period, the obtained planned trajectory sequence, vehicle kinematic parameters and trajectory curvature can be processed based on a preset kinematic model and a preset cost function to predict the trajectory curvatures corresponding to the N first moments in the nonlinear time domain. In this way, the trajectory curvature of the second period can be determined from the trajectory curvatures corresponding to the N first moments in the nonlinear time domain according to the preset delay time of the vehicle's steering system, and then the vehicle driving can be controlled according to the trajectory curvature of the second period. Therefore, in an embodiment of the present application, by considering the delay time of the vehicle's steering system and the influence of the trajectory curvature of the vehicle during driving on the vehicle driving, not only the timeliness of controlling the vehicle driving is improved, but also the accuracy of controlling the vehicle driving is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 This is a flow chart of a vehicle control method provided in an embodiment of the present application;

[0039] Figure 2 This is a real-life schematic diagram of vehicle parameters required for a preset kinematic model provided in an embodiment of the present application;

[0040] Figure 3 is a flow chart of another vehicle control method provided in an embodiment of the present application;

[0041] Figure 4 This is a flow chart of another vehicle control method provided in an embodiment of the present application;

[0042] Figure 5 is a structural diagram of a vehicle control device provided in an embodiment of the present application;

[0043] Figure 6 It is a structural diagram of a vehicle control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0045] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0046] The term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0047] In real life, autonomous vehicles can navigate roads and avoid obstacles without human intervention through their autonomous driving systems and various sensors. However, existing vehicle control solutions are mostly designed for specific scenarios where parameters are calibrated. Therefore, in practice, these solutions require a set of parameters to be calibrated for different scenarios. Furthermore, these solutions do not consider changes in vehicle load during driving or the impact of trajectory curvature on vehicle movement, resulting in low control accuracy.

[0048] In summary, in order to solve the problem of low accuracy in controlling vehicle driving in the prior art, the embodiments of the present application provide a vehicle control method, device, equipment and medium, which can be applied to scenarios in which the vehicle is driving, and can also be applied to scenarios in which the vehicle is about to drive. In addition, the vehicle control method provided in the embodiments of the present application can be executed by a vehicle control device or a control module in the vehicle control device for executing the vehicle control method. In the embodiments of the present application, the vehicle control method provided in the embodiments of the present application is described in detail by taking the vehicle control device executing the vehicle control method as an example.

[0049] The vehicle control method provided in the embodiments of the present application is described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0050] Figure 1 It is a flow chart of a vehicle control method provided in an embodiment of the present application.

[0051] like Figure 1 As shown, the execution device of the vehicle control method is a vehicle control device, which may specifically include the following steps:

[0052] S110 , obtaining a planned trajectory sequence, vehicle kinematic parameters, and trajectory curvature of the vehicle in a first cycle.

[0053] S120 , processing the planned trajectory sequence, trajectory curvature, and vehicle kinematic parameters based on a preset kinematic model and a preset cost function to predict trajectory curvatures corresponding to N first moments in a nonlinear time domain.

[0054] S130 , determining a trajectory curvature of a second period from trajectory curvatures corresponding to N first moments in a nonlinear time domain according to a preset delay time of a steering system of the vehicle.

[0055] S140: Control the vehicle to travel according to the trajectory curvature of the second cycle.

[0056] In an embodiment of the present application, by obtaining the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first period, the obtained planned trajectory sequence, vehicle kinematic parameters and trajectory curvature can be processed based on a preset kinematic model and a preset cost function to predict the trajectory curvatures corresponding to the N first moments in the nonlinear time domain. In this way, the trajectory curvature of the second period can be determined from the trajectory curvatures corresponding to the N first moments in the nonlinear time domain according to the preset delay time of the vehicle's steering system, and then the vehicle driving can be controlled according to the trajectory curvature of the second period. Therefore, in an embodiment of the present application, by considering the delay time of the vehicle's steering system and the influence of the trajectory curvature of the vehicle during driving on the vehicle driving, not only the timeliness of controlling the vehicle driving is improved, but also the accuracy of controlling the vehicle driving is improved.

[0057] The above steps are described in detail below.

[0058] First, regarding S110, the vehicle in the embodiment of the present application may be a vehicle including at least two drive axles, that is, the vehicle in the embodiment of the present application may be a two-axle vehicle, or a multi-axle vehicle, such as a three-axle vehicle. The first cycle may be a cycle preset based on actual needs or experience values. For example, the first cycle may be from 10:59 to 11:00. If the duration of the first cycle can be infinitely small, the first cycle may be determined as a moment, which is not specifically limited here. The planned trajectory sequence may be a local trajectory sequence planned based on the terminal position sequence information within the first cycle. The above-mentioned planned trajectory sequence may include the position information of multiple trajectory points. For example, the position information of multiple trajectory points can be expressed by (x i ,y i ), where i = 1, 2, ..., m, and m is a positive integer. The vehicle kinematic parameters represent the kinematic parameters of the vehicle during driving or when the vehicle is about to drive, and will not be elaborated here. The trajectory curvature can be the rotation rate of the tangent direction angle to the arc length for a certain point on the trajectory. The trajectory curvature can be characterized by the degree to which the curve deviates from a straight line. The greater the trajectory curvature, the greater the curvature of the trajectory, and vice versa. Specifically, the vehicle control device can obtain the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first cycle during driving or when the vehicle is about to drive.

[0059] In some embodiments, the vehicle kinematic parameters may include a first axle speed, which may be a speed of a drive axle other than a first drive axle located at a head of the vehicle.

[0060] It should be noted that if a vehicle has two drive shafts, the first shaft speed is the speed of the first drive shaft at the rear of the vehicle, i.e., the rear axle speed. If a vehicle has multiple drive shafts, assuming the vehicle has three drive shafts, the first shaft speed is the speed of the drive shafts other than the first drive shaft at the front of the vehicle, that is, the speed of the two drive shafts other than the first drive shaft at the front of the vehicle is equivalent to a single drive shaft.

[0061] In a specific embodiment, the above-mentioned S110 may include the following steps:

[0062] Obtaining a planned trajectory sequence and vehicle dynamics parameters of the vehicle in a first cycle, and a trajectory curvature change rate in a fifth cycle, wherein the first cycle is later than the fifth cycle;

[0063] The trajectory curvature of the vehicle in the first cycle is calculated based on the vehicle dynamics parameters, the trajectory curvature change rate and the preset kinematic model.

[0064] The trajectory curvature change rate can be represented as the rate of change of the curvature of the vehicle's trajectory per unit time. The preset kinematic model can be set based on vehicle parameters and historical vehicle kinematic parameters. The vehicle parameters may include first axis speed, vehicle yaw angle, vehicle wheelbase, and other parameters, which are not detailed here.

[0065] Specifically, the vehicle control device can acquire the planned trajectory sequence and vehicle kinematic parameters of the vehicle in the first cycle through real-time acquisition. Furthermore, the vehicle's trajectory curvature change rate can be acquired during the fifth cycle. The acquired vehicle dynamics parameters during the first cycle and the obtained trajectory curvature change rate during the fifth cycle are input into a preset kinematic model to obtain the trajectory curvature change rate of the vehicle during the first cycle. Furthermore, the curvature of the vehicle's trajectory during the first cycle can be calculated based on the trajectory curvature change rate of the vehicle during the first cycle. In this way, the vehicle control device can accurately acquire the planned trajectory sequence, vehicle kinematic parameters, and trajectory curvature of the vehicle during the first cycle.

[0066] The specific construction process of the preset kinematic model involved above is as follows:

[0067] Assume that the first axis speed v r X-axis speed And the speed in the Y-axis direction It can be specifically expressed as the following formula (1):

[0068]

[0069] Among them, v r is the first axis speed, is the vehicle's body yaw angle.

[0070] Based on the above formula (1), and Figure 2 The geometric relationship between the first axis and the second axis can be used to infer the position information of the second axis, as shown in the following formula (2):

[0071]

[0072] Among them, x r is the X-axis position of the first axis, y r is the Y-axis position of the first axis, x f is the X-axis position of the second axis, y f is the Y-axis position of the second axis, and l is the wheelbase between the vehicle's drive shafts. If the vehicle has two drive shafts, l can be the distance between the first drive shaft at the front of the vehicle and the first drive shaft at the rear of the vehicle. If the vehicle has multiple drive shafts, l can be the distance between the first drive shaft at the front of the vehicle and the midpoint of the vertical distance between the first drive shaft at the rear of the vehicle and the second drive shaft.

[0073] The preset kinematic constraint between the first axis and the second axis can be expressed as formula (3):

[0074]

[0075] Where δ is the steering angle of the vehicle, is the X-axis speed of the first axis, is the speed of the first axis Y-axis, is the X-axis speed of the second axis, is the Y-axis speed of the second axis.

[0076] Combining the above formulas (1), (2), and (3) yields the vehicle body yaw rate ω, the vehicle's turning radius R, the vehicle's trajectory curvature ρ, and the vehicle's steering angle δ, as shown in the following formulas (4) to (6):

[0077]

[0078]

[0079]

[0080] Based on the above formula, by defining the vehicle state quantity as The initial value of the vehicle state is [0 0 0 0] T , control quantity u lat is the rate of change of trajectory curvature Then the preset kinematic model of the Model Predictive Control (MPC) can be obtained, as shown in the following formula (7):

[0081]

[0082] in, is the X-axis speed of the second axis, is the Y-axis speed of the second axis, is the rate of change of the vehicle's yaw angle, is the rate of change of trajectory curvature.

[0083] After obtaining the preset kinematic model, the preset kinematic model can also be trained by obtaining multiple historical vehicle kinematic parameters to obtain a more accurate preset kinematic model. Then, the vehicle dynamics parameters of the first cycle and the trajectory curvature change rate of the fifth cycle can be input into the trained preset kinematic model to obtain the trajectory curvature change rate of the vehicle in the first cycle. Then, the vehicle dynamics parameters of the first cycle and the trajectory curvature change rate of the fifth cycle can be input into the trained preset kinematic model to obtain the trajectory curvature change rate of the vehicle in the first cycle. Calculate and obtain the curvature of the vehicle's trajectory in the first cycle. Where i = 1, 2, ..., n, t i is the time series in the nonlinear domain of model predictive control MPC, It represents the differential of vehicle state quantity with respect to time, i.e. the rate of change of vehicle state quantity, and n is the number of optimization steps. i The calculation formula is shown in the following formula (8):

[0084] t i =f(i)=0.01*T max *(γ*i) 2 (8)

[0085] Where i = 1, 2, ..., n, T max is the maximum prediction time domain, and γ is the nonlinear adjustment parameter.

[0086] It should be noted that Model Predictive Control (MPC) is a model-based closed-loop optimization control method that applies vehicle kinematics and preset dynamic constraints to future inputs, outputs or state quantities, and converts the constraints into a quadratic programming problem for online solution. It can well predict the changes in system state quantities, and can optimize the control sequence for a period of time in the future (nonlinear time domain) through optimization algorithms to minimize the error between the output of the prediction model and the reference value. The next control cycle will re-predict and optimize based on the latest state to achieve feedback correction.

[0087] Then, in step S120, the preset kinematic model is set based on the vehicle parameters and the historical vehicle kinematic parameters of the vehicle, and the preset cost function is set based on the nonlinear time domain of the model predictive control MPC. The nonlinear time domain includes N first moments, wherein the N first moments can be, for example, T1, T2, T3, ..., T N , and the time intervals between each two adjacent first moments in the N first moments are different. In addition, the above T1, T2, T3, ... T N These N first moments are all later than the first period, where N is a positive integer greater than 0. Specifically, the vehicle control device, upon obtaining the planned trajectory sequence, vehicle dynamics parameters, and trajectory curvature of the vehicle within the first period, may process the obtained planned trajectory sequence within the first period, the vehicle dynamics parameters within the first period, and the trajectory curvature within the first period using a preset kinematic model and a preset cost function to predict the trajectory curvature corresponding to each of the N first moments in the nonlinear time domain.

[0088] Specifically, based on the time series in the nonlinear time domain shown in the above formula (8), and the goal of minimizing the lateral position deviation and heading angle deviation as much as possible while the vehicle moves along the planned trajectory sequence, a preset cost function as shown in formula (9) can be constructed, as shown below:

[0089]

[0090] Among them, ξ is the vehicle state, u lat is the control quantity, q1, q2, q3 are preset weights, y f The vertical coordinate of the first drive shaft at the head of the vehicle, y ref is the trajectory function, ν r is the speed of the drive shaft other than the first drive shaft located at the head of the vehicle, is the vehicle yaw angle, is the vehicle's body orientation information, t i is the time series of the model predictive control algorithm MPC prediction domain, i = 1, 2, ... n, n is a positive integer.

[0091] Based on this, it is possible to avoid the problem in the prior art that a fixed time interval is often used when solving MPC, resulting in inaccurate final calculation results. The embodiment of the present application, through the design of the above-mentioned nonlinear time domain, can have a larger prediction time domain with the same number of moments, and will not increase the amount of calculation while improving the control accuracy.

[0092] In order to more accurately obtain the trajectory curvatures corresponding to the N first moments, so as to subsequently accurately determine the trajectory curvature of the vehicle in the next cycle, the accuracy of controlling the vehicle's driving can be improved. In one embodiment, Figure 3 As shown, the above-mentioned S120 specifically includes the following steps:

[0093] S310: Calculate the vehicle's body orientation information based on the planned trajectory sequence.

[0094] S320: Input the planned trajectory sequence, vehicle body orientation information, and trajectory curvature into a preset cost function, and obtain the trajectory curvature change rate corresponding to N first moments in the nonlinear time domain by solving the optimal solution of the preset cost function.

[0095] S330 , calculating the trajectory curvatures corresponding to the N first moments in the nonlinear time domain according to the vehicle kinematic parameters, the trajectory curvature change rates corresponding to the N second moments in the nonlinear time domain, and a preset kinematic model.

[0096] The vehicle body orientation information may be a vehicle body orientation angle, i.e., an angle controlling the vehicle's driving direction. The Nth second moment is earlier than the Nth first moment, and the time intervals between each two second moments in the N second moments are different. Accordingly, the time intervals between each two first moments in the N first moments are different.

[0097] Specifically, after obtaining the planned trajectory sequence, vehicle kinematic parameters, and trajectory curvature within the first cycle, the vehicle control device can calculate the vehicle body orientation information based on the planned trajectory sequence within the first cycle, and can input the planned trajectory sequence, vehicle orientation information, and trajectory curvature within the first cycle into a preset cost function. The vehicle control device can use optimization libraries such as Ipopt and qpOASES to use a nonlinear least squares optimization problem to online solve the optimal solution of the preset cost function to obtain the trajectory curvature change rate corresponding to N second moments in the nonlinear time domain after the first cycle. Then, based on the vehicle dynamic parameters, the trajectory curvature change rate corresponding to the N second moments in the nonlinear time domain, and the preset kinematic model, the trajectory curvature corresponding to the N first moments in the nonlinear time domain can be calculated. In this way, it is convenient to accurately determine the trajectory curvature of the second cycle from the N first moments in the nonlinear time domain, and then the accuracy of controlling the vehicle can be improved by considering the influence of the trajectory curvature on vehicle driving.

[0098] Specifically, based on the above formula (9), the constructed nonlinear least squares problem can be specifically shown in the following formula (10):

[0099]

[0100]

[0101] Therefore, the optimal solution of the preset cost function can be solved online by constructing a nonlinear least squares problem to obtain the trajectory curvature change rate corresponding to the N second moments in the nonlinear time domain after the first cycle, and then the trajectory curvature corresponding to the N first moments can be solved.

[0102] In one embodiment, in order to accurately calculate the vehicle body steering angle, the aforementioned S310 specifically includes the following steps:

[0103] Constructing a trajectory function according to a position sequence of multiple trajectory points included in the planned trajectory sequence;

[0104] According to the trajectory function, the vehicle's body orientation information is calculated.

[0105] Specifically, the vehicle control device may obtain a position sequence (x i ,yi ), where i = 1, 2, ... n, and a cubic polynomial fitting curve is defined to obtain the trajectory function y ref , and obtain the vehicle body orientation angle by taking the derivative The specific calculation formula is shown in formula (11):

[0106]

[0107] In order to accurately solve the above trajectory function y ref The coefficients a0, a1, a2 and a3 in the planned trajectory sequence (x i ,y i ) to construct the normal equation, as shown in the following formula (12):

[0108]

[0109] in,

[0110] Then, based on the above formula (12), we can solve A T The coefficients a0, a1, a2, and a3 in the pseudo-inverse trajectory function of A are specifically shown in the following formula (13):

[0111]

[0112] In this embodiment, the vehicle control device can construct a trajectory function based on the position sequence of multiple trajectory points included in the planned trajectory sequence, and obtain the vehicle body orientation information by taking the derivative of the trajectory function. In this way, the vehicle body orientation information can be obtained by solving the planned trajectory sequence obtained within the first cycle, so that the trajectory curvature corresponding to each of the N first moments in the nonlinear time domain can be accurately calculated.

[0113] Regarding S130 , the preset delay duration may be a delay duration of the vehicle's steering system, with the second period being later than the first period. Specifically, the vehicle control device may determine, based on the delay duration of the vehicle's steering system, a trajectory curvature corresponding to a certain moment from among the trajectory curvatures corresponding to N first moments in the nonlinear time domain, as the trajectory curvature for the second period.

[0114] In one embodiment, in order to accurately determine the curvature of the trajectory of the vehicle in the next cycle, the aforementioned S130 may specifically include the following steps:

[0115] When the N first moments include the third moment, determining that the trajectory curvature corresponding to the third moment is the trajectory curvature of the second period;

[0116] In a case where the N first moments do not include the third moment, the trajectory curvature corresponding to the fourth moment is determined to be the trajectory curvature of the second period.

[0117] The third moment may be a moment obtained by extending the preset delay of the steering system from the moment corresponding to the first cycle as the starting point, and the fourth moment may be the first moment with the smallest time interval between the third moment and the N first moments. The moment corresponding to the first cycle may be the start moment of the first cycle, the end moment of the first cycle, or an intermediate moment within the first cycle, without specific limitation herein.

[0118] In one example, if the first period is [t1, t2], taking the starting time t1 of the first period as an example, the time t3 is obtained after extending the preset delay time of the steering system, if the N first moments in the nonlinear time domain obtained by the vehicle control device correspond to the N first moments of the trajectory curvature as T1, T2, T3, ..., T N , assuming that there is T in the above N first moments m If it is the same time as t3, that is, the N first times include the third time, then determine T m The trajectory curvature corresponding to the moment is the trajectory curvature of the second period. If there is no moment that is the same as the moment t3 among the N first moments, that is, the N first moments do not include the third moment, then the trajectory curvature corresponding to the first moment with the smallest time interval from the moment t3 among the N first moments is determined to be the trajectory curvature of the second period.

[0119] Furthermore, when determining the trajectory curvature of the second period, a preset constraint condition may be set to more accurately determine the trajectory curvature of the second period. For example, the preset constraint condition may be an upper limit and a lower limit of the trajectory curvature. Furthermore, since each of the N vehicle state quantities actually obtained when calculating the trajectory curvatures corresponding to the N first moments in the nonlinear time domain includes both trajectory curvature and vehicle body yaw angle, the preset constraint condition may also be an upper limit or a lower limit of the vehicle body yaw angle to determine the trajectory curvature of the second period. The specific constraint condition used to determine the trajectory curvature of the second period is not further limited herein.

[0120] In this embodiment, the third moment is determined based on the steering system delay and the moment corresponding to the first cycle. Thus, if the third moment is included in the N first moments, the trajectory curvature corresponding to the third moment can be determined as the trajectory curvature of the second cycle. Alternatively, if the third moment is not included in the N first moments, the trajectory curvature corresponding to the fourth moment with the smallest time interval with the third moment is selected from the N first moments as the curvature change rate of the second cycle. Thus, by considering the steering system delay, the trajectory curvature of the next cycle during vehicle travel is determined, thereby improving the accuracy of subsequent vehicle control.

[0121] Furthermore, regarding S140 , after obtaining the curvature of the trajectory of the vehicle in the second period, the vehicle control device may control the vehicle's travel based on the curvature of the trajectory in the second period.

[0122] In order to more accurately control the vehicle's travel, in one embodiment, Figure 4 As shown, the above-mentioned S140 may specifically include the following steps:

[0123] S410 , calculating the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in a third cycle according to the vehicle kinematic parameters and a preset parameter model.

[0124] S420 , determining steering information of the vehicle in a fourth cycle based on the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third cycle and the track curvature in the second cycle.

[0125] S430: Control the vehicle to travel according to the steering information of the vehicle in the fourth cycle.

[0126] The preset parameter model may be a model obtained in advance based on deep learning network training for predicting vehicle kinematic parameters. The third cycle is later than the first cycle, and the third cycle includes the second cycle, and the fourth cycle is later than the third cycle. The first wheel may be a wheel connected to a drive shaft other than the first drive shaft at the head of the vehicle, and the second wheel may be a wheel connected to the first drive shaft at the head of the vehicle. The cornering stiffness of the first wheel may be the ratio of the cornering force of the first wheel to the cornering angle, and correspondingly, the cornering stiffness of the second wheel may be the ratio of the cornering force of the second wheel to the cornering angle. The steering information of the vehicle may be the steering angle of the vehicle, that is, the angle formed by the front wheels of the vehicle turning left or right and the center line.

[0127] Specifically, the vehicle control device can calculate the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third cycle based on the vehicle kinematic parameters and the preset parameter model in the first cycle. Since the third cycle is later than the first cycle and greater than the second cycle, the steering information of the vehicle in the fourth cycle can be determined based on the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third cycle, as well as the trajectory curvature in the second cycle. The vehicle's travel can then be controlled based on the steering information in the fourth cycle. Thus, not only is the effect of trajectory curvature on vehicle travel taken into account during vehicle travel, but by calculating the cornering stiffness of the first wheel and the cornering stiffness of the second wheel, the change in weight borne by the first wheel and the second wheel during vehicle travel can also be taken into account. This improves the accuracy of the steering information, thereby improving the accuracy of controlling vehicle travel.

[0128] The above involves the step of determining the steering information of the vehicle in the fourth period based on the cornering stiffness of the first wheel in the third period, the cornering stiffness of the second wheel, and the trajectory curvature in the second period. The steering information of the vehicle can be calculated by the following formula (14):

[0129]

[0130] Where C1 is the cornering stiffness of the first wheel, C2 is the cornering stiffness of the second wheel, χ = 0 when the second wheel is turning, α1 is the distance from the center of mass to the first wheel, α2 is the distance from the center of mass to the second wheel, l is the wheelbase of the vehicle, m is the weight of the vehicle, and v r is the first axis speed of the vehicle, ρ τ is the trajectory change rate of the second period.

[0131] In addition, it should be noted that, considering that the wheels will be affected by lateral forces when the vehicle speed increases, thereby generating a slip angle, and the preset kinematic model defined above ignores the influence of the slip angle, here, in order to meet the dynamic characteristics, a slip angle model is introduced to solve the tire angle, as shown in the following formula (15):

[0132]

[0133] Therefore, based on the above formula (15) and ω=ρv, the above formula (14) can be obtained.

[0134] In one embodiment, in order to accurately calculate the cornering stiffness of the first wheel and the cornering stiffness of the second wheel, the aforementioned S310 may specifically include the following steps:

[0135] Obtaining first turn information within a first cycle;

[0136] Calculating first vehicle kinematic parameters within a third period based on the vehicle kinematic parameters, the first steering information, and a preset parameter model;

[0137] The cornering stiffness of the first wheel and the cornering stiffness of the second wheel in a third period are calculated according to the first vehicle kinematic parameter and the first steering information.

[0138] The first steering information may be steering information collected by the vehicle control device within the first cycle.

[0139] In this embodiment, the vehicle control device can obtain first steering information of the vehicle during the first cycle and then calculate first vehicle kinematic parameters for a third cycle based on the vehicle kinematic parameters, the first steering information, and a preset parameter model. Furthermore, the vehicle control device can calculate the cornering stiffness of the first wheel and the cornering stiffness of the second wheel during the third cycle based on the first vehicle kinematic parameters and the first steering information. In this manner, by predicting the first vehicle kinematic parameters for the third cycle, the accuracy of the calculated cornering stiffness of the first wheel and the second wheel can be improved.

[0140] In some embodiments, the vehicle kinematic parameters may include a first shaft velocity and a body yaw rate, and the first vehicle kinematic parameters may include a first vehicle lateral velocity and a first body yaw rate.

[0141] Based on this, the vehicle control device can acquire the first steering information in the first cycle in real time through the sensor, and calculate the vehicle lateral speed in the first cycle based on the acquired first steering information and the first axis speed. In addition, (v L ,ω) state transition matrix, where v L is the vehicle's lateral velocity, ω is the vehicle's yaw angular velocity, is the rate of change of the vehicle's lateral velocity, is the rate of change of the vehicle body yaw angular velocity, and the specific state transfer matrix can be expressed as follows:

[0142]

[0143] in j is the rotational inertia, and δ1 is the first steering information.

[0144] Therefore, the vehicle lateral velocity, the vehicle body yaw angular velocity, and the first steering information within the first period can be input into the above-mentioned state transfer matrix to obtain the rate of change of the vehicle lateral velocity and the rate of change of the vehicle body yaw angular velocity. Then, the first vehicle lateral velocity of the vehicle in the third period can be determined based on the vehicle lateral velocity and the rate of change of the vehicle lateral velocity within the first period, and the first vehicle body yaw angular velocity of the vehicle in the third period can be determined based on the vehicle body yaw angular velocity and the rate of change of the vehicle body yaw angular velocity within the first period.

[0145] In this embodiment, the vehicle control device can obtain the first steering information during the first cycle so that it can calculate the first vehicle kinematic parameters for the third cycle, namely, the first vehicle lateral velocity and the first vehicle yaw rate, based on the first axle velocity and the vehicle body yaw rate, included in the vehicle kinematic parameters during the first cycle, in combination with a preset parameter model. This improves the accuracy of the calculated first vehicle kinematic parameters for the third cycle, thereby facilitating subsequent improvements in the accuracy of the cornering stiffness of the first wheel and the cornering stiffness of the second wheel.

[0146] Based on this, in one embodiment, calculating the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third cycle according to the first vehicle kinematic parameter and the first steering information includes:

[0147] Calculating a first cornering stiffness coefficient and a second cornering stiffness coefficient according to the first vehicle body yaw angular velocity, the first vehicle lateral velocity, and the first steering information;

[0148] The product of the first cornering stiffness coefficient and the first preset cornering stiffness is determined as the cornering stiffness of the first wheel, and the product of the second cornering stiffness coefficient and the second preset cornering stiffness is determined as the cornering stiffness of the second wheel.

[0149] The first cornering stiffness coefficient and the second cornering stiffness coefficient may be respectively used to calculate the cornering stiffness of the corresponding wheels, and the two may be the same or different, and are not limited in detail here.

[0150] Specifically, after the first vehicle lateral velocity and the first vehicle body yaw angular velocity are obtained by the above calculation, the Extended Kalman Filter (EKF) algorithm can be introduced to define the EKF state quantity θ = [λ1 λ2 v r v L ω δ1] T , where λ1 is the first lateral stiffness coefficient, λ2 is the second lateral stiffness coefficient, and the observation quantity Z = [v r δ1] T Based on this, the cornering stiffness of the first wheel and the cornering stiffness of the second wheel can be calculated by the following formula (17) and formula (18):

[0151]

[0152]

[0153] in, is the first preset tire cornering stiffness, The second preset tire cornering stiffness.

[0154] Then the state transfer equation can be shown as formula (19):

[0155]

[0156] The observation equation can be expressed as formula (20):

[0157]

[0158] Based on the above formulas (19) and (20), and the definition of process noise and observation noise The first cornering stiffness coefficient and the second cornering stiffness coefficient can be calculated, and then the cornering stiffness of the first wheel and the cornering stiffness of the second wheel can be calculated by combining formula (17) and formula (18) respectively.

[0159] In this embodiment, after calculating the first vehicle kinematic parameters within the third cycle, the vehicle control device may calculate a first cornering stiffness coefficient and a second cornering stiffness coefficient based on the first vehicle yaw angular velocity and the first vehicle lateral velocity included in the first vehicle kinematic parameters, as well as the first steering information obtained for the vehicle within the first cycle. The device may then determine the cornering stiffness of the first wheel based on the product of the first cornering stiffness coefficient and the first preset cornering stiffness, and the cornering stiffness of the second wheel based on the product of the second cornering stiffness coefficient and the second preset cornering stiffness. In this manner, since the cornering stiffnesses of the first and second wheels vary with changes in vehicle weight, recalibration of the parameters under different operating conditions is unnecessary, reducing labor and material resources. Furthermore, by considering that the cornering stiffnesses of the first and second wheels vary with changes in vehicle weight, accurate subsequent acquisition of vehicle steering information is facilitated.

[0160] Based on the same inventive concept, the embodiment of the present application also provides a vehicle control device. Figure 5 The vehicle control device provided in the embodiments of the present application is described in detail.

[0161] Figure 5 It is a structural schematic diagram of a vehicle control device provided in an embodiment of the present application.

[0162] like Figure 5 As shown, the vehicle control device 500 may include: an acquisition module 510 , a processing module 520 , a determination module 530 and a control module 540 .

[0163] An acquisition module 510 is configured to acquire a planned trajectory sequence, vehicle kinematic parameters, and trajectory curvature of the vehicle in a first cycle;

[0164] a processing module 520 for processing the planned trajectory sequence, trajectory curvature, and vehicle motion parameters based on a preset kinematic model and a preset cost function to predict trajectory curvatures corresponding to N first moments in a nonlinear time domain, where N is a positive integer greater than 0, and each of the N first moments is later than a first period, the preset kinematic model being set based on vehicle parameters and historical vehicle kinematic parameters of the vehicle, and the preset cost function being set based on the nonlinear time domain of a model predictive control (MPC);

[0165] a determination module 530 for determining, based on a preset delay time of a steering system of the vehicle, a trajectory curvature of a second period from trajectory curvatures corresponding to N first moments in a nonlinear time domain, where the second period is later than the first period;

[0166] The control module 540 is configured to control the vehicle to travel according to the trajectory curvature of the second period.

[0167] In one embodiment, the aforementioned processing module includes a first calculation submodule, a first input submodule and a second calculation submodule.

[0168] The first calculation submodule is used to calculate the vehicle body orientation information according to the planned trajectory sequence;

[0169] The first input submodule is used to input the planned trajectory sequence, vehicle orientation information, and trajectory curvature into a preset cost function, and obtain the trajectory curvature change rate corresponding to N second moments in the nonlinear time domain by solving the optimal solution of the preset cost function;

[0170] The second calculation submodule is used to calculate the trajectory curvature corresponding to N first moments in the nonlinear time domain based on the vehicle kinematic parameters, the trajectory curvature change rate corresponding to N second moments in the nonlinear time domain, and a preset kinematic model, wherein the Nth second moment is earlier than the Nth first moment, and the time interval between each two first moments in the N first moments is different.

[0171] In one embodiment, the aforementioned determination module includes a first determination submodule and a second determination submodule.

[0172] A first determining submodule is configured to, when the N first moments include the third moment, determine that the trajectory curvature corresponding to the third moment is the trajectory curvature of the second period;

[0173] The second determining submodule is configured to determine that the trajectory curvature corresponding to the fourth moment is a trajectory curvature of a second period when the N first moments do not include the third moment.

[0174] The third moment is the moment obtained by extending the preset delay time of the steering system from the moment corresponding to the first cycle as the starting point, and the fourth moment is the first moment with the smallest time interval with the third moment among the N first moments.

[0175] In one embodiment, the control module mentioned above further includes a third calculation submodule, a third determination subunit and a control submodule.

[0176] a third calculation submodule, configured to calculate the cornering stiffness of the first wheel and the cornering stiffness of the second wheel within a third period based on the vehicle kinematic parameters and a preset parameter model, wherein the third period is later than the first period and includes the second period;

[0177] a third determining submodule, configured to determine steering information of the vehicle in a fourth cycle based on the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third cycle and the trajectory curvature of the second cycle, where the fourth cycle is later than the third cycle;

[0178] The control submodule is used to control the vehicle's driving according to the steering information of the vehicle in the fourth cycle.

[0179] In one embodiment, the third calculation submodule mentioned above includes a first acquisition unit, a first calculation unit, and a second calculation unit.

[0180] A first acquiring unit, configured to acquire first turning information within a first period;

[0181] The first calculation unit is used to calculate the first vehicle kinematic parameters in a third period according to the vehicle kinematic parameters, the first steering information and the preset parameter model.

[0182] The second calculation unit is configured to calculate the cornering stiffness of the first wheel and the cornering stiffness of the second wheel within a third period according to the first vehicle kinematic parameter and the first steering information.

[0183] In one embodiment, the vehicle kinematic parameters include a first shaft velocity and a body yaw rate; and the first vehicle kinematic parameters include a first vehicle lateral velocity and a first body yaw rate.

[0184] In one embodiment, the second calculation unit mentioned above includes a calculation subunit and a determination subunit.

[0185] The calculation subunit is configured to calculate a first cornering stiffness coefficient and a second cornering stiffness coefficient according to the first vehicle body yaw angular velocity, the first vehicle lateral velocity, and the first steering information.

[0186] The determination subunit is configured to determine the product of the first cornering stiffness coefficient and the first preset cornering stiffness as the cornering stiffness of the first wheel, and to determine the product of the second cornering stiffness coefficient and the second preset cornering stiffness as the cornering stiffness of the second wheel.

[0187] In an embodiment of the present application, by obtaining the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first period, a preset kinematic model and a preset cost function can be used to process the obtained planned trajectory sequence, vehicle kinematic parameters and trajectory curvature to predict the trajectory curvatures corresponding to N first moments in the nonlinear time domain. In this way, the trajectory curvature of the second period can be determined from the trajectory curvatures corresponding to the N first moments in the nonlinear time domain according to the preset delay time of the vehicle's steering system, and then the vehicle driving can be controlled according to the trajectory curvature of the second period. Therefore, in an embodiment of the present application, by considering the delay time of the vehicle's steering system and the influence of the trajectory curvature of the vehicle during driving on the vehicle driving, not only the timeliness of controlling the vehicle driving is improved, but also the accuracy of controlling the vehicle driving is improved.

[0188] Each module in the vehicle control device provided in the embodiment of the present application can be implemented Figure 1 、 Figure 3 and Figure 4 The method steps of the illustrated embodiment can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0189] Figure 6 It is a structural diagram of a vehicle control device provided in an embodiment of the present application.

[0190] like Figure 6 As shown, the vehicle control device 600 in this embodiment includes an input device 601, an input interface 602, a central processing unit 603, a memory 604, an output interface 605, and an output device 606. The input interface 602, the central processing unit 603, the memory 604, and the output interface 605 are interconnected via a bus 610, and the input device 601 and the output device 606 are connected to the bus 610 via the input interface 602 and the output interface 605, respectively, and are further connected to other components of the vehicle control device 600.

[0191] Specifically, the input device 601 receives input information from the outside and transmits the input information to the central processing unit 603 through the input interface 602; the central processing unit 603 processes the input information based on the computer-executable instructions stored in the memory 604 to generate output information, stores the output information temporarily or permanently in the memory 604, and then transmits the output information to the output device 606 through the output interface 605; the output device 606 outputs the output information to the outside of the vehicle control device 600 for user use.

[0192] In one embodiment, Figure 6 The vehicle control device 600 shown includes: a memory 604 for storing programs; a processor 603 for running the programs stored in the memory to execute the embodiments of the present application. Figure 1 、 Figure 3 and Figure 4 Any of the vehicle control methods shown in .

[0193] The present invention also provides a computer-readable storage medium having computer program instructions stored thereon; when the computer program instructions are executed by a processor, the present invention also provides a computer-readable storage medium having computer program instructions stored thereon; Figure 1 、 Figure 3 and Figure 4 Any of the vehicle control methods shown in .

[0194] It should be understood that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications and additions, or change the order of the steps after understanding the spirit of the present application.

[0195] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memories (ROMs), flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0196] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0197] The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application.

Claims

1. A vehicle control method, characterized in that: The method comprises: Obtain the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first cycle; The planned trajectory sequence, the trajectory curvature, and the vehicle kinematic parameters are processed based on a preset kinematic model and a preset cost function to predict trajectory curvatures corresponding to N first moments in a nonlinear time domain, where N is a positive integer greater than 0, and the N first moments are all later than a first period, the preset kinematic model is set based on vehicle parameters of the vehicle and historical vehicle kinematic parameters of the vehicle, and the preset cost function is set based on the nonlinear time domain of model predictive control (MPC); determining, according to a preset delay time of a steering system of the vehicle, a trajectory curvature of a second period from trajectory curvatures corresponding to N first moments in the nonlinear time domain, wherein the second period is later than the first period; controlling the vehicle to travel according to the trajectory curvature of the second period; The controlling the vehicle to travel according to the curvature of the trajectory of the vehicle in the second period includes: calculating, based on the vehicle kinematic parameters and a preset parameter model, a cornering stiffness of the first wheel and a cornering stiffness of the second wheel within a third period, wherein the third period is later than the first period and includes the second period; determining steering information of the vehicle in a fourth cycle based on the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third cycle and the trajectory curvature of the second cycle, the fourth cycle being later than the third cycle; The vehicle is controlled to travel according to the steering information of the vehicle in the fourth cycle.

2. The method according to claim 1, characterized in that The processing of the planned trajectory sequence, the trajectory curvature, and the vehicle kinematic parameters based on a preset kinematic model and a preset cost function to predict trajectory curvatures corresponding to N first moments in a nonlinear time domain includes: Calculating the vehicle's body orientation information according to the planned trajectory sequence; Inputting the planned trajectory sequence, the vehicle body orientation information, and the trajectory curvature into a preset cost function, and obtaining the trajectory curvature change rates corresponding to N second moments in a nonlinear time domain by solving the optimal solution of the preset cost function; The trajectory curvatures corresponding to N first moments in the nonlinear time domain are calculated based on the vehicle kinematic parameters, the rates of change of the trajectory curvatures corresponding to N second moments in the nonlinear time domain, and the preset kinematic model, wherein the Nth second moment is earlier than the Nth first moment, and the time intervals between each two first moments in the N first moments are different.

3. The method according to claim 1, characterized in that The determining of the trajectory curvature of the second period from the trajectory curvatures corresponding to the N first moments in the nonlinear time domain according to the preset delay time of the steering system of the vehicle includes: In a case where the N first moments include a third moment, determining that the trajectory curvature corresponding to the third moment is a trajectory curvature of a second period; In a case where the N first moments do not include the third moment, determining that the trajectory curvature corresponding to the fourth moment is the trajectory curvature of the second period; The third moment is the moment obtained by extending the preset delay time of the steering system with the moment corresponding to the first cycle as the starting point, and the fourth moment is the first moment with the smallest time interval with the third moment among the N first moments.

4. The method according to claim 1, wherein Calculating the cornering stiffness of the first wheel and the cornering stiffness of the second wheel within the third period according to the vehicle kinematic parameters and the preset parameter model includes: Obtaining first turn information within a first cycle; calculating first vehicle kinematic parameters within a third period based on the vehicle kinematic parameters, the first steering information, and a preset parameter model; The cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third period are calculated according to the first vehicle kinematic parameters and the first steering information.

5. The method according to claim 4, characterized in that The vehicle kinematic parameters include a first shaft velocity and a vehicle body yaw rate; the first vehicle kinematic parameters include a first vehicle lateral velocity and a first vehicle body yaw rate.

6. The method according to claim 5, characterized in that Calculating the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third period according to the first vehicle kinematic parameter and the first steering information includes: Calculating a first cornering stiffness coefficient and a second cornering stiffness coefficient according to the first vehicle body yaw angular velocity, the first vehicle lateral velocity, and the first steering information; The product of the first cornering stiffness coefficient and the first preset cornering stiffness is determined as the cornering stiffness of the first wheel, and the product of the second cornering stiffness coefficient and the second preset cornering stiffness is determined as the cornering stiffness of the second wheel.

7. A vehicle control device, characterized in that: The device comprises: An acquisition module is used to obtain the planned trajectory sequence, vehicle kinematic parameters and trajectory curvature of the vehicle in the first cycle; a processing module, configured to process the planned trajectory sequence, the trajectory curvature, and the vehicle kinematic parameters based on a preset kinematic model and a preset cost function to predict trajectory curvatures corresponding to N first moments in a nonlinear time domain, where N is a positive integer greater than 0, and the N first moments are all later than a first period, the preset kinematic model being set based on vehicle parameters of the vehicle and historical vehicle kinematic parameters of the vehicle, and the preset cost function being set based on the nonlinear time domain of model predictive control (MPC); a determining module, configured to determine, based on a preset delay time of a steering system of the vehicle, a trajectory curvature of a second period from trajectory curvatures corresponding to N first moments in the nonlinear time domain, wherein the second period is later than the first period; a control module, configured to control the vehicle to travel according to the trajectory curvature of the second period; The control module further includes: a third calculation submodule, configured to calculate, based on the vehicle kinematic parameters and a preset parameter model, a cornering stiffness of the first wheel and a cornering stiffness of the second wheel within a third period, wherein the third period is later than the first period and includes the second period; a third determining submodule, configured to determine steering information of the vehicle in a fourth period based on the cornering stiffness of the first wheel and the cornering stiffness of the second wheel in the third period, and the trajectory curvature of the second period, wherein the fourth period is later than the third period; The control submodule is used to control the vehicle's driving according to the steering information of the vehicle in the fourth cycle.

8. A vehicle control device, characterized in that: The device includes: a processor and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the vehicle control method according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, which, when executed by a processor, implement the vehicle control method according to any one of claims 1 to 6.

Images