Vehicle driving control method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle driving control method, device and equipment and a computer program product. The method comprises the steps of optimizing vehicle state parameters of a target vehicle based on a first target function coupled with a vehicle kinematic model; wherein the vehicle kinematic model comprises a mathematical relationship between vehicle driving control parameters and vehicle state parameters. And based on a second target function coupled with the vehicle kinematic model, optimizing the vehicle driving control parameters of the target vehicle by taking the optimized vehicle state parameters of the target vehicle as constraints. And performing driving control on the target vehicle based on the optimized vehicle driving control parameters of the target vehicle. According to the invention, the vehicle driving control parameters can be quickly optimized for vehicle driving control, so that the requirement of high real-time performance of unmanned driving is met.

Description

Technical Field

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

[0002] Vehicle driving control is the core of autonomous driving. The existing vehicle driving control method is to directly optimize the vehicle driving control parameters (such as driving speed, driving steering angle, etc.) based on the linearized vehicle kinematic model in each control cycle. The optimal solutions obtained by this optimization method in different control cycles are often far apart, making it difficult to reuse the currently determined vehicle driving control parameters in the next control cycle, that is, it is impossible to accelerate the optimization of the next control cycle.

[0003] However, unmanned driving has very high real-time requirements for vehicle driving control, and the slow optimization of vehicle driving control parameters is a fatal flaw. Summary of the invention

[0004] The purpose of this application is to provide a vehicle driving control method, device, equipment and computer program product, which can quickly optimize vehicle driving control parameters for vehicle driving control, thereby meeting the high real-time requirements of unmanned driving.

[0005] In order to achieve the above purpose, the embodiment of the present application is implemented as follows: In a first aspect, a vehicle driving control method is provided, comprising: Optimizing vehicle state parameters of the target vehicle based on a first objective function coupled to a vehicle kinematics model; wherein the vehicle kinematics model includes a mathematical relationship between vehicle travel control parameters and vehicle state parameters; Based on a second objective function coupled to the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized with the optimized vehicle state parameters of the target vehicle as constraints; Based on the optimized vehicle driving control parameters of the target vehicle, driving control is performed on the target vehicle.

[0006] In a second aspect, a vehicle driving control device is provided, comprising: A first optimization module, configured to optimize a vehicle state parameter of a target vehicle based on a first objective function coupled to a vehicle kinematics model; wherein the vehicle kinematics model includes a mathematical relationship between a vehicle driving control parameter and a vehicle state parameter; a second optimization module, configured to optimize the vehicle driving control parameters of the target vehicle based on a second objective function coupled to the vehicle kinematic model and taking the optimized vehicle state parameters of the target vehicle as constraints; A driving control module is used to control the driving of the target vehicle based on the optimized vehicle driving control parameters of the target vehicle.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor; and a memory configured to store computer-executable instructions, wherein the computer-executable instructions, when executed, cause the processor to execute the method described in the first aspect.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium is used to store computer-executable instructions, and the computer-executable instructions implement the method described in the first aspect when executed by a processor.

[0009] The embodiment of the present application first optimizes the vehicle state parameters of the target vehicle based on a first objective function coupled to the vehicle kinematic model; then, with the optimized vehicle state parameters of the target vehicle as constraints, the vehicle driving control parameters of the target vehicle are optimized based on a second objective function coupled to the vehicle kinematic model. This is equivalent to reducing the spatial distribution of the vehicle driving control parameters of subsequent control cycles through the optimized vehicle state parameters, thereby quickly determining the optimized vehicle state parameters to meet the high real-time requirements of unmanned driving for vehicle driving control. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 A flow chart of a vehicle driving control method according to an embodiment of the present application.

[0012] Figure 2 Schematic diagram of the first neural network for the vehicle dynamics model in the first objective function.

[0013] Figure 3 Schematic diagram of the neural network for the incremental penalty term of the vehicle control parameters in the first objective function.

[0014] Figure 4 Schematic diagram of a neural network for the penalty term of the vehicle driving control parameters in the first objective function.

[0015] Figure 5 Schematic diagram of the neural network for the penalty term of the vehicle state parameter in the first objective function.

[0016] Figure 6 A first neural network schematic diagram of a restriction function of a vehicle driving control parameter in a first objective function.

[0017] Figure 7 Schematic diagram of the first neural network for the first objective function.

[0018] Figure 8 Schematic diagram of the second neural network for the first objective function.

[0019] Fig. 9 Schematic representation of the multiplication operation for a neural network.

[0020] Fig.10 Schematic diagram of the second neural network for the vehicle dynamics model in the first objective function.

[0021] Fig.11 A schematic diagram of a second neural network for the restriction function of the vehicle driving control parameters in the first objective function.

[0022] Fig.12 Schematic diagram of the first neural network for the second objective function.

[0023] Fig.13 Schematic diagram of the second neural network for the second objective function.

[0024] Fig.14 Schematic diagram of the second neural network for the vehicle dynamics model in the second objective function.

[0025] Fig.15 This is a schematic diagram of the vehicle driving control method according to an embodiment of the present application performing vehicle driving control.

[0026] Fig.16 A schematic structural diagram of a vehicle driving control device according to an embodiment of the present application.

[0027] Fig.17 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0029] An embodiment of the present application provides a vehicle driving control method. Figure 1 Schematic diagram of the vehicle driving control method, comprising the following steps: S102, optimizing vehicle state parameters of the target vehicle based on a first objective function coupled to a vehicle kinematics model; wherein the vehicle kinematics model includes a mathematical relationship between vehicle driving control parameters and vehicle state parameters.

[0030] Specifically, the optimization goal of optimizing the vehicle state parameters of the target vehicle in this embodiment includes: making at least one of the driving speed, driving steering angle, driving trajectory and driving heading angle of the target vehicle in the first control cycle reach the corresponding expected value. In addition, in order to ensure the stability of vehicle driving, the optimization goal may further include: minimizing the increment of the vehicle driving control parameter within a preset interval.

[0031] S104, based on a second objective function coupled to the vehicle kinematics model, the vehicle driving control parameters of the target vehicle are optimized with the optimized vehicle state parameters of the target vehicle as constraints.

[0032] Specifically, the optimization objectives of optimizing the vehicle driving control parameters of the target vehicle in this embodiment include: making at least one of the driving speed, driving steering angle, driving trajectory and driving heading angle of the target vehicle in the second control cycle reach the corresponding expected value; in addition, in order to ensure the stability of vehicle driving, the optimization objectives may also include: minimizing the increment of the vehicle driving control parameters within a preset range.

[0033] It should be noted that in the unmanned driving scenario, in order to ensure that the target vehicle can drive stably, the target vehicle needs to be controlled in each control cycle, so the vehicle driving control parameters need to be optimized in each control cycle. However, the vehicle state parameters, as a constraint to reduce the spatial distribution of the vehicle driving control parameters, do not need to be optimized in each control cycle.

[0034] In this embodiment, the first control cycle represents a control cycle for optimizing the vehicle state parameters of the target vehicle, and the second control cycle is used to represent a control cycle for optimizing the vehicle driving control parameters of the target vehicle, and the second control cycle is no earlier than the first control cycle.

[0035] As an example, the first control cycle can be the same control cycle as the second control cycle. That is, when the hardware computing power is sufficient, each control cycle first optimizes the vehicle state parameters, and then optimizes the vehicle driving control parameters with the optimized vehicle state parameters as constraints.

[0036] Alternatively, the first control cycle is separated from the second control cycle by other control cycles, and the first control cycle is the most recent control cycle before the second control cycle for optimizing the vehicle state parameters of the target vehicle. That is, in the case of insufficient hardware computing power, the vehicle state parameters are optimized only in some control cycles, such as every two or every three control cycles. Correspondingly, this embodiment uses the vehicle state parameters obtained by the most recent optimization as a constraint to optimize the vehicle driving control parameters of the current control cycle.

[0037] S106, performing driving control on the target vehicle based on the optimized vehicle driving control parameters of the target vehicle.

[0038] Specifically, taking the vehicle driving control parameters including driving speed and driving steering angle as an example, this embodiment can control the target vehicle to perform unmanned driving according to the optimized driving speed and driving steering angle.

[0039] To summarize, the method of this embodiment first optimizes the vehicle state parameters of the target vehicle based on the first objective function coupled with the vehicle kinematic model; then, with the optimized vehicle state parameters of the target vehicle as constraints, based on the second objective function coupled with the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized, which is equivalent to reducing the spatial distribution of the vehicle driving control parameters of the subsequent control cycle through the optimized vehicle state parameters, thereby quickly determining the optimized vehicle state parameters to meet the high real-time requirements of unmanned driving for vehicle driving control.

[0040] The following describes in detail the application of the method of this embodiment in combination with specific implementation methods.

[0041] Here we first introduce the meaning of some mathematical symbols involved in the following text: m-dimensional vector is represented as For vector , Represents the sum of the absolute values ​​of its elements ,and The activation functions related to the construction of neural networks include: linear function (purelin), radial basis function (radbas), symmetric sigmoid function (tansig), and positive linear function (poslin). The expressions are as follows: The vehicle kinematic model of this embodiment can be described as:

[0042] in, for System status at the moment; for System input at the moment.

[0043] In order to facilitate the subsequent analysis of vehicle state parameters, the above vehicle kinematic model can be converted into the following form:

[0044] On the basis of the above, this embodiment sets a model prediction controller, which is further composed of a main controller and a sub-controller. The main controller is responsible for optimizing the vehicle state parameters; the sub-controller is responsible for optimizing the vehicle driving control parameters.

[0045] 1. Introduction of main controller For the main controller, the corresponding objective function is the first objective function mentioned above, which can be expressed as:

[0046] st

[0047]

[0048]

[0049] in, is the increment of vehicle driving control parameter; ; is the control time domain of the second objective function, ; for The expected vehicle state parameters at the time are obtained by optimizing the first objective function; for The expected vehicle driving control parameters at time; is the current vehicle status; is the current vehicle driving control parameter; is the weight of the vehicle driving control parameter; is the weight of the vehicle state parameter; The weight of the increment of the vehicle driving control parameter; is the vehicle driving control parameter The constraint set of is the vehicle status parameter Constraint set; is the increment of vehicle driving control parameters The set of constraints.

[0050] Among them, the vehicle control parameters It can be regarded as part of the vehicle state parameters. With the driving speed and steering angle as input, the main optimization goal is to accurately track the given path while maintaining a certain driving speed. Based on the geometric analysis of vehicle driving, it can be seen that when the turning curvature is The vehicle's steering angle should be approximately ,in is the turning radius, is the vehicle wheelbase. Given the expected driving path, the expected value of the steering angle can be determined. The non-equality constraints are further converted into soft constraints using penalty functions, and the first objective function can be converted into:

[0051] st

[0052]

[0053] in, and is the weight constant; ; ; and is called the limit function.

[0054] The above-mentioned first objective function is a 1-norm nonlinear problem. This embodiment can construct a neural network that represents the first objective function, and optimize the vehicle state parameters of the target vehicle in the neural network of the first objective function according to the neural network optimization algorithm.

[0055] As an example introduction, the vehicle dynamics model It can be expressed as Figure 2 The neural network shown. Figure 2 In the figure, the neural network on the right is a simplified representation of the neural network on the left; the weights of the black connections are all defaulted to 1 (the same representation is used for the following neural networks); the system model is the vehicle kinematic model. This network can be based on a discretized nonlinear model It can be obtained by training with input and output data, or it can be obtained by construction. Later, the method of constructing the corresponding neural network based on the vehicle dynamics model will be introduced.

[0056] The incremental penalty term of the vehicle control parameter as input in the first objective function can be expressed as Figure 3 The neural network shown. Figure 3 In the figure, the neural network on the right is a simplified representation of the neural network on the left. The neurons in red and green belong to the input layer and output layer respectively. The activation function of the neuron is a linear function (purelin); the weight of the blue connection is the weight of the corresponding input increment. The network takes the increment of the vehicle driving control parameter as input and then multiplies it by the weight This network corresponds to the first objective function .

[0057] The penalty term of the vehicle driving control parameter in the first objective function can be expressed as Figure 4 The neural network shown. Figure 4 In the figure, the neural network on the right is a simplified representation of the neural network on the left. The neurons in red and green belong to the input layer and output layer respectively. The activation function of the neuron is a linear function (purelin); the weights of the blue connections are marked. and expected input As input, then subtract the two, and finally multiply the difference between the two by the weight This network corresponds to the first objective function .

[0058] The penalty term of the vehicle state parameter in the first objective function can be expressed as Figure 5 The neural network shown. Figure 5 In the figure, the neural network on the right is a simplified representation of the neural network on the left. The neurons in red and green belong to the input layer and output layer respectively. The activation function of the neuron is a linear function (purelin); the weights of the blue connections are marked. The network is based on state and desired state As input, then subtract the two, and finally multiply the difference between the two by the weight This network corresponds to the first objective function .

[0059] The restriction function of the vehicle driving control parameters in the first objective function can be expressed as Figure 6 The neural network shown. Figure 6 In the figure, the neural network on the right is a simplified representation of the neural network on the left. The neurons in red and green belong to the input layer and output layer respectively. The activation function of the neurons in purple is , the activation function of other neurons is a linear function (purelin); the weights of the blue connections are marked. The network is based on the vehicle state parameters As input, then limit the function to output the corresponding value, and finally multiply it by the weight This network corresponds to the first objective function In addition, the form of the neural network expression of the limit function of the increment of the vehicle driving control parameter is the same as this, and it will not be repeated here. It should be noted that in order to make the expression clear, Figure 6 The activation function of the purple neuron is an abstract statement, which will be further specified later.

[0060] Correspondingly, the first objective function can be expressed as Figure 7 Alternatively, when the prediction horizon and control horizon are large ( ), the first objective function can be equivalent to Figure 8 The neural network shown. Figure 7 and Figure 8 In the example, the state target is the expected value of the vehicle state parameter, the input is the vehicle control parameter, and the input target is the expected value of the vehicle control parameter. The neurons with red and green backgrounds belong to the input layer and the output layer respectively. The input data have the states at time 1 and k respectively. , input at time k-1 , the expected state at time k+1 And the expected input at time k ; All output data are set to 0 to represent minimization of the first objective function. The activation function of all neurons is a linear function (excluding neurons in the rectangular module). The weights of red connections are undetermined, the weights of black connections are all defaulted to 1, and the weights of blue connections are -1.

[0061] This embodiment is based on the neural network optimization algorithm. Figure 7 or Figure 8 The weights corresponding to the red connections are optimized, which is equivalent to the vehicle state parameters of the target vehicle. Thanks to the expressive power of neural networks and their good optimization strategies, the above constrained 1-norm nonlinear optimization problem can be solved quickly, and the first objective function also converges.

[0062] The following introduces the discrete model based on deformation Methods for building neural networks.

[0063] The vehicle kinematic model is decomposed into:

[0064]

[0065]

[0066] Afterwards it is further converted to:

[0067]

[0068]

[0069] Furthermore, the vehicle kinematic model is discretized at small time intervals:

[0070]

[0071]

[0072] in, is the front wheel turning angle, is the longitudinal velocity at the center of mass of the vehicle, L is the vehicle wheelbase, is the heading angle of the vehicle, For the time interval.

[0073] Before building a neural network based on the above formula, let’s first introduce how to use neural networks to express multiplication operations. Fig. 9 The example neural network can express the multiplication operation. Fig. 9 is a fully connected network. The neural network on the right is a simplified representation of the neural network on the left. The neurons in red and green belong to the input layer and output layer respectively. The activation function used by the four neurons in the middle hidden layer is the radial basis function (radbas), while the activation function used by the other neurons is the linear function (purelin). The weights of the sky blue connections are marked and meet

[0074] In the above model, the function ,function and function The overall neural network is Fig.10 shown. Fig.10 In the figure, the red and green neurons belong to the input layer and output layer respectively, and the input data are the x coordinates of the vehicle , vehicle y coordinate , vehicle heading angle ; The output data are vehicle speed square , the square of the vehicle speed multiplied by the square of the sine of the turning angle , vehicle y coordinate . Fig.10 The activation function of the neurons in the figure is the tangent function (tan(x)), which has been marked on the corresponding neurons. The activation functions of other neurons are all linear functions (purelin) (excluding neurons in the rectangular module). The weights of the blue connections are marked, and the weights of the black connections are all defaulted to 1.

[0075] Regarding the specific form of the restriction function and its neural network expression, the input increment and input amount constraints are considered here:

[0076]

[0077]

[0078]

[0079] These constraints are formally the same, so we will use the velocity square input constraint as an example, which is converted to the following penalty function:

[0080] Obviously, when the variable satisfies the constraint When , the function output is 0, otherwise the function will output a positive value as a penalty, and the farther the variable is from the constraint range, the larger the value of the function output will be. The neural network expression corresponding to this function is as follows Fig.11 shown. Fig.11 In the figure, the neurons with red and green backgrounds belong to the input layer and output layer respectively. The circles represent neurons, and the hexagons express the bias values ​​of the corresponding neurons. The activation functions of neurons 1 and 3 are linear functions (purelin), and the activation function of neuron 2 is positive linear function (poslin). The weights of the blue connections are marked on the figure, M is a sufficiently large constant, and the weights of the black connections are all 1 by default.

[0081] The vehicle kinematics model, input increment constraint and input quantity constraint described by the neural network are correspondingly embedded into the vehicle kinematics model model, input increment constraint and input quantity constraint modules in the objective function neural network, and the corresponding input and output data are given. After network training, the following solution of the objective function can be obtained:

[0082] st

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] in, and are the input increments of the square of the vehicle speed and the square of the vehicle speed multiplied by the square of the tangent of the steering angle, ; , To control the time domain, the optimization variables are the vehicle's x-coordinate sequence and heading angle sequence. and are the penalty weights for state, input, and input increment respectively. and They are time , Expected values ​​of coordinates and heading angles. is the transformed discretized kinematic model described above. and They are The vehicle state parameters at the time The input amount at the time. are parameters related to the input increment and input quantity constraints. The purpose of the first and second items in the objective function is to make the vehicle speed and steering angle close to the given speed and steering; the purpose of the third to fifth items is to make the vehicle's driving trajectory and heading angle track the reference trajectory and expected heading as much as possible; the sixth and seventh items hope that the vehicle's input increment is as small as possible, that is, the change in the control quantity is as small as possible, so that the control is smoother.

[0089] 2. Introduction of Sub-Controller The objective function of the sub-controller is the second objective function mentioned above. The optimized vehicle state parameters can be obtained from the optimization of the main controller in the first part. If the relevant hardware computing power is sufficient, the optimization of the main controller can be completed once in each control cycle. Then, according to the first item in the obtained state optimization sequence (i.e. ), based on the model , directly calculate the control quantity and act on the system.

[0090] However, when the hardware computing power is not enough to complete the optimization task of the main controller in each control cycle, each control cycle needs to measure the actual state of the system according to the cycle and the state optimization sequence obtained by the most recent optimization of the main controller, and optimize the control quantity of the current control cycle based on the algorithm introduced below.

[0091] The form of the second objective function used by the sub-controller is the same as the first objective function of the main controller, but the sub-controller directly optimizes the vehicle driving control parameters. The second objective function can be described as:

[0092] st

[0093]

[0094]

[0095] in, is the increment of vehicle driving control parameter; ; is the control time domain of the second objective function, ; for The expected vehicle state parameters at the time are obtained by optimizing the first objective function; for The expected vehicle driving control parameters at time; is the current vehicle status; is the current vehicle driving control parameter; is the weight of the vehicle driving control parameter; is the weight of the vehicle state parameter; The weight of the increment of the vehicle driving control parameter; is the vehicle driving control parameter The constraint set of is the vehicle status parameter Constraint set; is the increment of vehicle driving control parameters The constraint set.

[0096] The non-equality constraints are further converted into soft constraints using penalty functions, and the above second objective function is converted into:

[0097] st

[0098]

[0099] in: ; .

[0100] Similarly, the above-mentioned second objective function is a 1-norm nonlinear problem. This embodiment can construct a neural network that represents the second objective function, and optimize the vehicle state parameters of the target vehicle in the neural network of the first objective function according to the neural network optimization algorithm.

[0101] The second objective function of the neural network can be expressed as Fig.12 Alternatively, when the prediction horizon and control horizon are large ( ) The neural network of the second objective function can be expressed as Fig.13 The neural network shown. Fig.12 and Fig.13In the figure, the neurons with red and green backgrounds belong to the input layer and output layer respectively, and the input data have the states at time 1 and k respectively. , input at time k-1 , the expected state at time k+1 ; and all output data are set to 0, which means minimizing the second objective function. The activation function of all neurons in the figure is a linear function (purelin) (excluding neurons in the rectangular module). The weights of the red connections are undetermined, and the weights of the black connections are all defaulted to 1. Similarly, this embodiment only optimizes the weights corresponding to the red connections, which correspond to the vehicle form control parameters, that is, the input increment in the second objective function Therefore, the weight of the red connection obtained after training the network is the optimal solution of the objective function (when ).

[0102] The following content introduces the method of building a neural network based on the vehicle kinematic model.

[0103] The vehicle kinematic model is decomposed into:

[0104]

[0105]

[0106] Where, L is the vehicle wheelbase, is the heading angle of the vehicle.

[0107] The vehicle kinematic model can be expressed as Fig.14 The neural network shown. Fig.14 In the figure, the red and green neurons belong to the input layer and output layer respectively. The input data are the longitudinal speed of the vehicle at time k , vehicle steering angle , X coordinate of the vehicle position , the Y coordinate of the vehicle position , vehicle heading angle ; The output data includes the X coordinates of the k+1 vehicle positions , the Y coordinate of the vehicle position and the vehicle heading angle ; The activation functions of neurons in the figure are sine function (sin(x)), cosine function (cos(x)) and tangent function (tan(x)), which have been marked on the corresponding neurons. The activation functions of other neurons are all linear functions (excluding neurons in rectangular modules). The weights of blue connections are marked on the corresponding positions on the figure, and the weights of black connections are all defaulted to 1.

[0108] The specific form of the above restriction function and its neural network expression are introduced below, where the input increment and input amount constraints are considered:

[0109]

[0110]

[0111]

[0112] The neural network expressions corresponding to these constraints are the same as those of the main controller, so they will not be repeated here.

[0113] The vehicle kinematics model, input increment constraint and input quantity constraint described by the neural network are correspondingly embedded into the system model, input increment restriction and input quantity restriction modules in the objective function neural network, and the corresponding input and output data are given. After network training, the following solution of the objective function can be obtained:

[0114] st

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] in, and are the input increments of the vehicle longitudinal velocity and steering angle, and are also the optimization variables. ; , is the control time domain of the sub-controller, and . and are the penalty weights for state, input, and input increment respectively. and They are time , The expected values ​​of coordinates and heading angles are optimized by the main controller. This is the kinematic model described above. and are the latest system status and input quantity respectively. These are parameters related to input increment and input quantity constraints.

[0121] 3. Introduction of Model Predictive Controller for Vehicle Driving Control As shown in reference 15, in each control cycle, the model predictive controller calculates the optimized driving steering angle and driving speed at time k based on the expected path, the expected heading sequence and the vehicle state information measured and estimated by the signal filtering and fusion processor, such as position, longitudinal speed, lateral speed, rolling angular velocity, heading angle and steering angle, as the expected values ​​of the steering controller and speed controller of the target vehicle. Then the steering controller calculates the steering output of the steering system based on the expected driving steering angle and real-time measurement, and acts on the steering system. Similarly, the speed controller calculates the speed output of the speed system based on the expected driving and real-time measurement, and acts on the speed system. The above process is then repeated at each control moment.

[0122] In addition, corresponding to Figure 1 The method shown, another embodiment of this embodiment also provides a vehicle driving control device. Fig.16 1 is a schematic diagram of the structure of the vehicle driving control device 1600, including: A first optimization module 1610, configured to optimize a vehicle state parameter of a target vehicle based on a first objective function coupled to a vehicle kinematics model; wherein the vehicle kinematics model includes a mathematical relationship between a vehicle driving control parameter and a vehicle state parameter; A second optimization module 1620, configured to optimize the vehicle driving control parameters of the target vehicle based on a second objective function coupled to the vehicle kinematic model and taking the optimized vehicle state parameters of the target vehicle as constraints; The driving control module 1630 is used to control the driving of the target vehicle based on the optimized vehicle driving control parameters of the target vehicle.

[0123] Based on the device of this embodiment, considering that the hyperlinks of illegal web pages are phishing links, they generally cannot get the hyperlink naming of regular websites. Therefore, when it is necessary to judge the target web page as an illegal web page, first determine the potential impersonating web page corresponding to the target web page, and determine the hyperlink domain name naming rules of the potential impersonating web page based on the hyperlink record of the potential impersonating web page; then, based on whether the hyperlink domain name of the target web page meets the hyperlink domain name naming rules, judge the target web page as an illegal web page, and obtain the illegal web page judgment result of the target web page. Since the entire scheme does not depend on the web page content of the target web page, it can also effectively identify illegal web pages for web pages that are configured with anti-crawling functions and / or have scrambled layouts of page content.

[0124] The device of this embodiment first optimizes the vehicle state parameters of the target vehicle based on a first objective function coupled to the vehicle kinematic model; then, taking the optimized vehicle state parameters of the target vehicle as constraints, based on a second objective function coupled to the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized, which is equivalent to reducing the spatial distribution of the vehicle driving control parameters of subsequent control cycles through the optimized vehicle state parameters, thereby quickly determining the optimized vehicle state parameters to meet the high real-time requirements of unmanned driving for vehicle driving control.

[0125] Optionally, the optimization goal for optimizing the vehicle state parameters of the target vehicle includes: making at least one of the driving speed, driving steering angle, driving trajectory and driving heading angle of the target vehicle in the first control cycle reach a corresponding expected value; the optimization goal for optimizing the vehicle driving control parameters of the target vehicle includes: making at least one of the driving speed, driving steering angle, driving trajectory and driving heading angle of the target vehicle in the second control cycle reach a corresponding expected value; wherein, the second control cycle is no earlier than the first control cycle.

[0126] Optionally, the first control cycle and the second control cycle are the same control cycle; or, the first control cycle and the second control cycle are separated by other control cycles, and the first control cycle is the most recent control cycle before the second control cycle for optimizing the vehicle state parameters of the target vehicle.

[0127] Optionally, the optimization goal of optimizing the vehicle state parameters and / or vehicle driving control parameters of the target vehicle also includes: minimizing the increment of the vehicle driving control parameters within a preset interval.

[0128] Optionally, the first optimization module 1610 optimizes the vehicle state parameters of the target vehicle based on a first objective function coupled to the vehicle kinematic model, including: constructing a neural network representing the first objective function, and optimizing the vehicle state parameters of the target vehicle in the neural network of the first objective function according to a neural network optimization algorithm; Optionally, the second optimization module 1620 optimizes the vehicle driving control parameters of the target vehicle based on a second objective function coupled to the vehicle kinematic model, taking the optimized vehicle state parameters of the target vehicle as constraints, including: constructing a neural network that characterizes the second objective function, and according to the neural network optimization algorithm, in the neural network of the second objective function, taking the optimized vehicle state parameters of the target vehicle as input parameters, to optimize the vehicle driving control parameters of the target vehicle.

[0129] Optionally, the first objective function is: ; st

[0130] ; ; ; in, is the increment of vehicle driving control parameter; To control the time domain; is the vehicle state parameter used as the optimization variable; for Expected vehicle state parameters at time; for Expect vehicle driving control parameters at all times; for Vehicle status parameters at the moment; for Vehicle driving control parameters at the moment; is the weight of the vehicle driving control parameter; is the weight of the vehicle state parameter; The weight of the increment of the vehicle driving control parameter; is the vehicle driving control parameter The constraint set of is the vehicle status parameter Constraint set; is the increment of vehicle driving control parameters The constraint set of and is the weight constant.

[0131] Optionally, the second objective function is: ; st ; ; ; ; in, is the increment of vehicle driving control parameter; ; is the control time domain of the second objective function, ; for The expected vehicle state parameters at the time are obtained by optimizing the first objective function; for The expected vehicle driving control parameters at time; is the current vehicle status; is the current vehicle driving control parameter; is the weight of the vehicle driving control parameter; is the weight of the vehicle state parameter; The weight of the increment of the vehicle driving control parameter; is the vehicle driving control parameter The constraint set of is the vehicle status parameter Constraint set; is the increment of vehicle driving control parameters The constraint set of and is the weight constant.

[0132] It should be noted that the vehicle driving control device of this embodiment can be used as Figure 1 The execution subject of the method shown can thus realize Figure 1 The steps and functions in the method shown.

[0133] Fig.17 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.17 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0134] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.17 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0135] The memory is used to store the computer program. Specifically, the computer program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides the computer program to the processor. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming the above-mentioned logic layer. Fig.16 The vehicle driving control device shown. Correspondingly, the processor executes the program stored in the memory and is specifically used to perform the following operations: The vehicle state parameters of the target vehicle are optimized based on a first objective function coupled to a vehicle kinematics model, wherein the vehicle kinematics model includes a mathematical relationship between vehicle driving control parameters and vehicle state parameters.

[0136] Based on a second objective function coupled to the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized with the optimized vehicle state parameters of the target vehicle as constraints.

[0137] Based on the optimized vehicle driving control parameters of the target vehicle, driving control is performed on the target vehicle.

[0138] The electronic device of this embodiment first optimizes the vehicle state parameters of the target vehicle based on the first objective function coupled with the vehicle kinematic model; then, taking the optimized vehicle state parameters of the target vehicle as constraints, based on the second objective function coupled with the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized, which is equivalent to reducing the spatial distribution of the vehicle driving control parameters of the subsequent control cycle through the optimized vehicle state parameters, thereby quickly determining the optimized vehicle state parameters to meet the high real-time requirements of unmanned driving for vehicle driving control.

[0139] The above is as in this manual Figure 1The method disclosed in the illustrated embodiment can be applied to a processor and implemented by the processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0140] Of course, in addition to software implementation, the electronic device of this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0141] In addition, the present application also provides a computer-readable storage medium, which stores one or more computer programs, wherein the one or more computer programs include instructions. When the instructions are executed by a portable electronic device including multiple application programs, the portable electronic device can execute Figure 1 The steps in the method shown include: Based on a first objective function coupled to a vehicle kinematics model, a vehicle state parameter of a target vehicle is optimized; wherein the vehicle kinematics model includes a mathematical relationship between vehicle driving control parameters and vehicle state parameters.

[0142] Based on a second objective function coupled to the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized with the optimized vehicle state parameters of the target vehicle as constraints.

[0143] Based on the optimized vehicle driving control parameters of the target vehicle, driving control is performed on the target vehicle.

[0144] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0146] The above are only embodiments of this specification and are not intended to limit this specification. For those skilled in the art, this specification may be subject to various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included in the scope of the claims of this specification. In addition, all other embodiments obtained by ordinary technicians in this field without creative work shall fall within the scope of protection of this document.

Claims

1. A vehicle driving control method, characterized in that: include: Optimizing vehicle state parameters of the target vehicle based on a first objective function coupled to a vehicle kinematics model; wherein the vehicle kinematics model includes a mathematical relationship between vehicle travel control parameters and vehicle state parameters; Based on a second objective function coupled to the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized with the optimized vehicle state parameters of the target vehicle as constraints; Based on the optimized vehicle driving control parameters of the target vehicle, driving control is performed on the target vehicle.

2. The method according to claim 1, characterized in that The optimization goal of optimizing the vehicle state parameters of the target vehicle includes: making at least one of the driving speed, driving steering angle, driving trajectory and driving heading angle of the target vehicle in the first control cycle reach a corresponding expected value; The optimization goal of optimizing the vehicle driving control parameters of the target vehicle includes: making at least one of the driving speed, driving steering angle, driving trajectory and driving heading angle of the target vehicle in the second control cycle reach a corresponding expected value; The second control period is no earlier than the first control period.

3. The method according to claim 2, characterized in that The first control period and the second control period are the same control period; or, The first control cycle is separated from the second control cycle by other control cycles, and the first control cycle is a control cycle that optimizes the vehicle state parameters of the target vehicle most recently before the second control cycle.

4. The method according to claim 3, characterized in that The optimization goal of optimizing the vehicle state parameters and / or vehicle driving control parameters of the target vehicle also includes: minimizing the increment of the vehicle driving control parameters within a preset interval.

5. The method according to claim 1, characterized in that Based on a first objective function coupled with a vehicle kinematic model, vehicle state parameters of a target vehicle are optimized, including: Constructing a neural network representing the first objective function, and optimizing the vehicle state parameters of the target vehicle in the neural network of the first objective function according to a neural network optimization algorithm; Based on a second objective function coupled to the vehicle kinematic model, the vehicle driving control parameters of the target vehicle are optimized with the optimized vehicle state parameters of the target vehicle as constraints, including: A neural network representing the second objective function is constructed, and according to a neural network optimization algorithm, in the neural network of the second objective function, the vehicle state parameters of the optimized target vehicle are used as input parameters to optimize the vehicle driving control parameters of the target vehicle.

6. The method according to claim 1, characterized in that The first objective function is: ； st ； ； ； ； in, is the increment of vehicle driving control parameter; To control the time domain; is the vehicle state parameter used as the optimization variable; for Expected vehicle state parameters at time; for Expect vehicle driving control parameters at all times; for Vehicle status parameters at the moment; for Vehicle driving control parameters at the moment; is the weight of the vehicle driving control parameter; is the weight of the vehicle state parameter; The weight of the increment of the vehicle driving control parameter; is the vehicle driving control parameter The constraint set of is the vehicle status parameter Constraint set; is the increment of vehicle driving control parameters The constraint set of and is the weight constant.

7. The method according to claim 1, characterized in that The second objective function is: ； st ； ； ； ； in, is the increment of vehicle driving control parameter; ; is the control time domain of the second objective function, ; for The expected vehicle state parameters at the time are obtained by optimizing the first objective function; for The expected vehicle driving control parameters at time; is the current vehicle status; is the current vehicle driving control parameter; is the weight of the vehicle driving control parameter; is the weight of the vehicle state parameter; The weight of the increment of the vehicle driving control parameter; is the vehicle driving control parameter The constraint set of is the vehicle status parameter Constraint set; is the increment of vehicle driving control parameters The constraint set of and is the weight constant.

8. A vehicle driving control device, characterized in that: include: A first optimization module, configured to optimize a vehicle state parameter of a target vehicle based on a first objective function coupled to a vehicle kinematics model; wherein the vehicle kinematics model includes a mathematical relationship between a vehicle driving control parameter and a vehicle state parameter; a second optimization module, configured to optimize the vehicle driving control parameters of the target vehicle based on a second objective function coupled to the vehicle kinematic model and taking the optimized vehicle state parameters of the target vehicle as constraints; A driving control module is used to control the driving of the target vehicle based on the optimized vehicle driving control parameters of the target vehicle.

9. An electronic device, comprising: processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer-readable storage medium storing a computer program, characterized in that: The computer program is operable to cause a computer to perform the method according to any one of claims 1 to 7.

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