Lane changing scene transverse and longitudinal decoupling control method based on parameter self-adaption and application

Abstract

The invention belongs to the technical field of self-driving automobile chassis control, and provides a lane changing scene transverse and longitudinal decoupling control method based on parameter self-adaption and application, and the method comprises the steps: firstly generating a reasonable lane changing track based on a lane changing track generation process of hierarchical planning in combination with a path planning structure; based on a genetic optimization and neural network self-adaptive trajectory tracking process, optimal lane changing control parameters are generated through test data optimization, and finally, a control parameter model is trained in combination with a neural network algorithm, so that the optimal control process aiming at a specific lane changing working condition is realized. According to the invention, high-real-time, high-comfort and high-stability driving tasks are completed through self-adaptive identification of the lane changing scene, and self control behaviors are adjusted by combining real-time identification of working conditions in the lane changing process, so that high-performance control effect output is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of chassis control of autonomous driving vehicles, and in particular relates to a lateral and longitudinal decoupling control method and application of lane changing scenarios based on parameter adaptation. Background Art

[0002] Autonomous driving vehicles driving in complex traffic environments need to be able to flexibly change lanes according to real-time road conditions and the dynamics of surrounding vehicles. The lane-changing process not only needs to meet the requirements of lane-changing efficiency, that is, switching from the current lane to the target lane as quickly as possible, but also needs to meet the requirements of comfort, that is, avoiding excessive acceleration, angular velocity, and lateral deviation. Therefore, the lane-changing process requires a complete lateral and longitudinal control process so that the vehicle can smoothly complete the lane change along a reasonable trajectory. How to design a control method for lane-changing scenarios is a key research content in the field of vehicle chassis control.

[0003] Due to the complexity and variability of the lane change process and the real-time requirements of the control process, the existing lane change control methods are difficult to apply to the lane change process in all scenarios. On the one hand, most of the basic PID, LQR and other lane change control methods are based on fixed control parameters, while the dynamic characteristics of the vehicle in the actual test process will change with factors such as load, speed, and road surface. The basic algorithm with fixed parameters cannot identify the current scene state, so it cannot face all lane change scenarios, resulting in a decrease in control effect. On the other hand, some existing intelligent or nonlinear solution algorithms such as reinforcement learning and new MPC can achieve control effects through long-term domain operations, but such algorithms will bring a huge amount of process operations, affecting the real-time performance of the algorithm, and thus cannot be truly applied to the field of real-vehicle control. Therefore, there is an urgent need for a lane change control method that balances algorithm efficiency and actual effects, completes high-real-time, high-comfort, and high-stability driving tasks through adaptive identification of lane change scenarios, and combines real-time identification of lane change process conditions to adjust its own control behavior, thereby achieving high-performance control effect output. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a lateral and longitudinal decoupling control method for lane changing scenarios based on parameter adaptation. First, based on the lane changing trajectory generation process of hierarchical planning, a reasonable lane changing trajectory is generated in combination with the path planning structure; then, based on the adaptive trajectory tracking process of genetic optimization and neural network, the optimal lane changing control parameters are generated by optimizing the test data; finally, the control parameter model is trained in combination with the neural network algorithm to realize the optimal control process for specific lane changing conditions.

[0005] The present invention provides a lateral and longitudinal decoupling control method for lane changing scenarios based on parameter adaptation, which specifically includes the following steps:

[0006] Step 1: Lane change scenario initialization and trajectory cluster generation: Initialize the lane change scenario, abstract the lane change process of the autonomous vehicle into a vector transition process, convert the lane change trajectory into a combination of driving scenario elements, use quintic polynomials for pre-planning, calculate multiple sets of applicable results, generate lane change trajectory clusters, and retain the lane change scenario elements for backup.

[0007] Preferably, the specific steps include:

[0008] The lane-changing process of an autonomous vehicle is abstracted as a vector transition process, that is, a trajectory of the motion vector from the starting point to the end point, which needs to satisfy the constraints of speed direction and position orientation. The speed direction of the main vehicle at the initial moment of lane change is defined as the x-axis, and the y-axis is obtained by rotating 90° counterclockwise. The lane-changing process is converted into a combination of the following scene elements: the initial speed of the main vehicle V1, the horizontal axis speed of the end point V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later ;

[0009] According to the above scenario elements, use the fifth-order polynomial to calculate the applicable results of the array;

[0010] The quintic polynomial planning process is decomposed into three calculation formulas: position, speed, and acceleration:

[0011] The position calculation formula is:

[0012] x(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5

[0013] y(t)=b0+b1t+b2t 2 +b3t 3 +b4t 4 +b5t 5

[0014] The speed calculation formula is:

[0015] x′(t)=a1+2a2t+3a3t 2 +4a4t 3 +5a5t 4

[0016] y′(t)=b1+2b2t+3b3t 2 +4b4t 3 +5b5t 4

[0017] The acceleration calculation formula is:

[0018] x″(t)=2a2+6a3t+12a4t 2 +20a5t 3

[0019] y″(t)=2b2+6b3t+12b4t 2 +20b5t 3

[0020] Wherein, a0, a1, a2, a3, a4, a5, b0, b1, b2, b3, b4, b5 are the coefficients of the polynomial; x(t) is the position of the object on the x-axis at time t; y(t) is the position of the object on the y-axis at time t; x′(t) is the first-order derivative of x(t), which is the instantaneous velocity of the object on the x-axis at time t; y′(t) is the first-order derivative of y(t), which is the instantaneous velocity of the object on the y-axis at time t; x″(t) is the second-order derivative of x(t), which is the instantaneous acceleration of the object on the x-axis at time t; y″(t) is the second-order derivative of y(t), which is the instantaneous acceleration of the object on the y-axis at time t.

[0021] The trajectory is expressed as the horizontal and vertical position, speed, and acceleration information after time t from the starting point of the lane change.

[0022] Step 2: Design of trajectory evaluation index and selection of optimal trajectory: Design comfort index and traffic efficiency index to reasonably evaluate the lane change trajectory. The comfort index is expressed as the extreme value of the acceleration change rate during the lane change process, and the traffic efficiency index is expressed as the time length to complete the lane change task; design normalized weights, perform weighted operations on each sub-indicator, and select the planned trajectory with the highest evaluation score as the optimal lane change trajectory.

[0023] Preferably, the specific steps include:

[0024] 21) The comfort index is expressed as the extreme value of the acceleration change rate during the lane change process. The real-time acceleration change rate in the lane change trajectory data is calculated using the following calculation formula:

[0025]

[0026] In the formula, Indicates the extreme value of the acceleration change rate during the lane change in this scenario, a i+Δt is the acceleration value at time i+Δt, a i is the acceleration value at time i, Δt is the time interval;

[0027] The traffic efficiency index is expressed as the length of time to complete the lane change task. The lane change time is calculated by combining the relationship between distance and speed during the process. The calculation formula is as follows:

[0028]

[0029] Where T represents the lane changing time, s represents the total length of the lane changing trajectory, Indicates the average vehicle speed during the process.

[0030] 22) Set the weights of both indicators to 0.5, and the calculation formula is as follows:

[0031]

[0032] S=0.5×S1+0.5×S2

[0033] Where S1 represents the normalized value of the acceleration rate of change, Indicates the acceleration change rate during the current lane change process. represents the maximum permissible acceleration rate of change, S2 represents the standardized value of lane change time, Indicates the countdown of the current lane change process. represents the inverse of the maximum allowed time, S represents the final lane change trajectory score, and the best lane change trajectory with the highest evaluation score is selected as the pre-lane change trajectory.

[0034] Step 3: Design of trajectory tracking algorithm and acquisition of parameters to be adjusted: Adopt the lateral and longitudinal decoupling control algorithm to control the lateral and longitudinal trajectory tracking processes separately. The PID algorithm considering the speed and position dual loops is adopted in the longitudinal direction, and the LQR algorithm is adopted in the lateral direction. Build a lane change trajectory tracking control algorithm model to obtain the control parameters required for adjustment.

[0035] Preferably, the specific steps include:

[0036] 31) The inner loop of the longitudinal dual-loop PID algorithm is the speed loop, and the outer loop is the position loop. The parameters that need to be adjusted are: the proportion, differential, and integral coefficients of the inner and outer loops, a total of 6 parameters. The control parameters in the steps need to be adjusted for any scenario.

[0037] The output of the outer loop PID controller is:

[0038]

[0039] In the formula, u1 is the intermediate variable, e1 is the outer loop error, K p1 , K i1 , K d1 They are the proportional, integral and differential coefficients of the outer loop PID controller respectively.

[0040] The output of the inner loop PID controller is:

[0041]

[0042] Where u2 is the control signal, e2 is the inner loop error, K p2 , K i2 , K d2 They are the proportional, integral and differential coefficients of the inner loop PID controller respectively.

[0043] The calculation method of inner and outer ring errors is as follows:

[0044] e1=yd-y

[0045] e2=u1-u

[0046] In the formula, y d is the expected output of the system, y is the actual output of the system, and u is the actual input of the system.

[0047] 32) The lateral LQR algorithm obtains optimal control through linear quadratic adjustment. During control, the weight coefficient matrices Q and R are set in advance. Q and R are diagonal matrices, and the elements on the diagonal are selected for adjustment.

[0048] For the LQR algorithm, the basic steps are as follows:

[0049] First, the dynamic equations of the system are expressed as a state space model:

[0050] x(t+1)=Ax(t)+Bu(t)

[0051] Where x(t) is the state vector of the system, u(t) is the control input vector of the system, A and B are the matrices of the system, A is the state transfer matrix, and B is the control input matrix;

[0052] Secondly, the cost function J(u) is designed in quadratic form to measure the performance of the system:

[0053]

[0054] Among them, Q is the penalty weight matrix of the state error, R is the penalty weight matrix of the control input, L is the cross-term weight matrix between the state and the control input, Q and R are both diagonal matrices and positive definite matrices;

[0055] Finally, the algebraic Riccati equation is used to solve the optimal feedback gain matrix to minimize the cost function, that is:

[0056] P=A T PA-A T PB(R+B T PB)- 1 B T PA+Q

[0057] Where P is a symmetric positive definite matrix;

[0058] K=(R+B T PB)- 1 B T PA

[0059] Where K is the optimal feedback gain matrix, which is used to calculate the optimal control input;

[0060] u(n)=-Kx(n)

[0061] Among them, x(n) is the state vector at the current moment, and u(n) is the optimal control input.

[0062] Step 4: Design of control parameter range and trajectory tracking index: Design the value range of each control parameter, and design trajectory tracking effect evaluation index from two aspects: trajectory tracking effect and comfort of lane change process.

[0063] Preferably, the specific steps include:

[0064] 41) Initialize the value intervals of each control parameter determined in step 3 and set a range limit for each control parameter: K p1 and K p2 Between 0.01 and 100, K i1 and K i2 Between 0.001 and 10, K d1 and K d2 Between 0.0001 and 1, due to the different dimensions, first calculate their logarithms, and then perform subsequent genetic optimization operations; the Q matrix and R matrix in the LQR algorithm are both symmetric positive definite matrices, and only the diagonal elements need to be adjusted. The dimension of the Q matrix is ​​consistent with the dimension of the system state vector, that is, if the state vector is n-dimensional, then the Q matrix is ​​an n×n matrix, and the dimension of the R matrix is ​​the same as the dimension of the system control vector, that is, if the control vector is m-dimensional, then the R matrix is ​​an m×m matrix; the values ​​of the diagonal elements of the Q and R matrices are both between 0 and 10.

[0065] 42) Design the evaluation index of the actual tracking effect of the trajectory, as follows:

[0066] Track tracking effect: It is characterized by the maximum value of the track deviation in the actual tracking control process:

[0067] Tra=max(e ij )

[0068]

[0069] Where Tra is the calculated maximum trajectory deviation, e ij is the Euclidean distance between the theoretical trajectory and the actual trajectory calculated at time t.

[0070] Lane-changing comfort: characterized by the acceleration change during the actual lane-changing process:

[0071]

[0072] Step 5: Genetic optimization of the optimal control parameters in the lane change scenario: Through the three genetic operators of selection, crossover and mutation, encoding and decoding operations as well as individual fitness calculation, the optimal control parameters in this scenario are optimized.

[0073] Preferably, the specific steps include:

[0074] 51) Genetic operator initialization parameter design: design encoding method, decoding method, selection, crossover, mutation method, and give the genetic algorithm hyperparameter values.

[0075] The design encoding method is 0-1 binary encoding, which can restore the overall optimization effect of the genetic algorithm to the greatest extent; the decoding method is: converting from 0-1 encoding to the original real number value; the selection method is: roulette selection; the crossover probability is: single-point crossover, the crossover probability is 0.3; the mutation method is: single-point mutation, the mutation probability is 0.05.

[0076] 52) According to the parameter value range in step 4, a genetic algorithm is used to quickly search for the genetic code with the highest fitness. For each lane change scenario combination A, the optimal control parameter combination B in this scenario is obtained through genetic algorithm optimization. The fitness function is the actual tracking effect evaluation index C designed in the previous article. The maximum number of iterations and population size are designed to find the algorithm parameters with the best control effect in this lane change scenario.

[0077] Step 6: Automatic cycle test of batch lane change scenario: the initial vehicle speed V1 obtained in step 1 and the terminal horizontal axis speed V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later There are a total of 5 lane change scenario elements. The value range of each scenario element type is defined and discrete processing is performed to obtain all lane change scenarios that require calculation of the optimal control parameters; a simulation test platform is selected, an automated cyclic simulation test process is designed, automated trajectory generation and evaluation, trajectory tracking simulation testing, and control parameter genetic optimization process are performed; combined with the automated simulation test platform, steps one, two, three, four, five, and six are repeated, and the test scenario and control parameter data are saved.

[0078] Preferably, the specific steps include:

[0079] 61) The value ranges of each scene element type are designed as follows: V1 and V x2 The value range is 0-30m / s, V y2The value range is 0-10m / s, D long The value range is 10-100m, D later The value range is 5-20m; the design speed discrete interval is 0.5m / s, and the distance discrete interval is 0.5m. By combining the above-mentioned multiple scenario elements, all lane change scenarios that require calculation of the optimal control parameters are obtained.

[0080] 62) The simulation test platform is the basis for the successful operation of the entire system. It needs to have the ability to generate and evaluate trajectories, simulate and test the automated trajectory tracking algorithm, carry genetic algorithms, and train and apply neural networks in the subsequent steps. Considering the various task requirements, the MATLAB computing platform combined with the PreScan simulation software is preferred.

[0081] 63) Combined with the automated simulation test platform, repeat steps 1, 2, 3, 4, 5, and 6 until the optimal control parameter calculation for the batch lane change scenario is completed, and the scenario state parameters and the corresponding optimal control parameters are saved in pairs for the subsequent neural network training process.

[0082] Step 7: Preprocessing of lane change scenarios and control parameter data: Preprocess the data saved in step 6 and divide them into training set and test set.

[0083] Preferably, the specific steps include:

[0084] 71) The data content saved in step 6 is: the input parameter is the value of the scene element, and the output parameter is the optimal control parameter of the lane change trajectory tracking algorithm. The data is processed and all input and output parameters are normalized using the maximum and minimum normalization principle. The formula is as follows:

[0085]

[0086] Where x is a value of the original data, x' is the normalized value, max(X) and min(X) are the maximum and minimum values ​​of the original data, respectively.

[0087] 72) Divide the above data into training set and test set in proportion for subsequent training and testing of neural network algorithm.

[0088] Step 8: Initialize and train the BP neural network: establish the input, output, and internal hierarchy of the network; save the network after training, and it can be used for adaptive control in different lane change scenarios.

[0089] Preferably, the specific steps include:

[0090] 81) Select BP neural network as the network structure of the control parameter prediction model. Determine that the input of the BP neural network is the value of the elements of the lane change scene, the number of input layer nodes is consistent with the number of scene elements, the output is the value of the control parameter under the scene, and the number of output layer nodes is consistent with the number of control parameters; set the number of layers and nodes in the middle layer.

[0091] 82) Set the activation function, initial network weights, set network training parameters, start the network training process, and save the network model after the training is completed for adaptive control in different lane change scenarios.

[0092] Furthermore, the activation functions of the hidden layer and the output layer are set to be tansig functions; random numbers are used to initialize the network weights; and the network training parameters are set including the learning rate and the maximum number of iterations.

[0093] The neural network training process is carried out in combination with the above network architecture and training data. After the training is completed, the network is saved and can be used for adaptive control of different lane change scenarios. Subsequently, the lane change scenario elements can be directly input to output the control parameters.

[0094] The present invention also provides an application of a lateral and longitudinal decoupling control method for a lane changing scenario based on parameter adaptation. The above-mentioned lateral and longitudinal decoupling control method for a lane changing scenario based on parameter adaptation is applied in a specific lane changing scenario. First, a specific lane changing task is designed, and a lane changing task is arbitrarily selected. The lane changing trajectory is abstracted as a combination of the scene elements in step 1, that is, the initial vehicle speed V1 of the main vehicle, the terminal horizontal axis speed V x2 , terminal longitudinal axis speed V y2 , Track longitudinal spacing D long , track lateral spacing D later The five scene elements are assigned values, several trajectory paths are planned using step one, and the best lane change trajectory is selected using the evaluation index of step two. Secondly, the lane change scenario is input into the trained neural network model, the optimal control parameters under the current lane change state are output, and the optimal control parameters are applied to the optimal lane change trajectory generated in the above steps to execute the tracking control process.

[0095] Beneficial effects of the present invention:

[0096] The present invention provides a method for decoupling the horizontal and vertical lane-changing scenarios based on parameter adaptation. It is a lane-changing control method that balances algorithm efficiency and actual effect. On the one hand, it generates a reasonable lane-changing trajectory based on the hierarchical planning lane-changing trajectory generation process combined with the path planning structure. On the other hand, it generates the best lane-changing control parameters through test data optimization based on the adaptive trajectory tracking process of genetic optimization and neural network, and combines the neural network algorithm to train the control parameter model to achieve the optimal control process for specific lane-changing conditions. The present invention conducts a comprehensive evaluation from the two aspects of comfort and traffic efficiency, and designs lane-changing trajectory evaluation indicators. In order to achieve better control effects, the present invention controls the horizontal and vertical trajectory tracking processes separately, adopts the PID algorithm that considers the speed and position double loops in the longitudinal direction, and adopts the LQR algorithm in the lateral direction; it completes high-real-time, high-comfort, and high-stability driving tasks through adaptive identification of lane-changing scenarios, and combines the real-time identification of lane-changing process conditions to adjust its own control behavior, thereby achieving high-performance control effect output. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 The figure is a schematic diagram of the overall process of a lateral and longitudinal decoupling control method for lane changing scenarios based on parameter adaptation according to the present invention.

[0098] Figure 2 It is a schematic diagram of deconstructing the lane changing scenario of the present invention.

[0099] Figure 3 It is a schematic diagram of the horizontal and vertical trajectory tracking process of the present invention.

[0100] Figure 4 It is a schematic diagram of the genetic optimization process of the present invention.

[0101] Figure 5 It is a schematic diagram of the deployment effect of the simulation test platform of the present invention.

[0102] Figure 6 This is a schematic diagram of the application effect of the BP neural network of the present invention. DETAILED DESCRIPTION

[0103] like Figure 1 As shown, the present invention provides a lateral and longitudinal decoupling control method for lane changing scenarios based on parameter adaptation, which specifically includes the following steps:

[0104] Step 1: Lane change scenario initialization and trajectory cluster generation: Initialize the lane change scenario, abstract the lane change process of the autonomous vehicle into a vector transition process, convert the lane change trajectory into a combination of driving scenario elements, use quintic polynomials for pre-planning, calculate multiple sets of applicable results, generate lane change trajectory clusters, and retain the lane change scenario elements for backup.

[0105] Traditional lane change control only considers the generation of a single lane change trajectory. The single lane change trajectory calculated by fitting the mathematical formula based on the initial point and the target point cannot fully meet the various requirements of comfort, safety, and traffic efficiency during the lane change process. Therefore, the present invention considers a multi-trajectory generation method, which converts the trajectory fitting process into a polynomial equation group solving process. The specific steps include:

[0106] like Figure 2 As shown in the figure, the lane-changing process of the autonomous vehicle is abstracted as a vector transition process, that is, a trajectory of the motion vector from the starting point to the motion vector at the end point, which needs to satisfy the constraints of speed direction and position orientation; the speed direction of the main vehicle at the initial moment of lane change is specified as the x-axis, and the y-axis is obtained by rotating 90° counterclockwise, and the lane-changing process is converted into a combination of the following scene elements: the initial speed of the main vehicle V1, the horizontal axis speed of the end point V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later ;

[0107] According to the above scenario elements, 10 sets of applicable results were calculated using quintic polynomials. Quintic polynomials have the advantages of third-order continuous differentiability and continuous curvature without sudden changes, which can better simulate actual lane-changing curves and decouple the horizontal and vertical directions.

[0108] The quintic polynomial planning process is decomposed into three calculation formulas: position, speed, and acceleration:

[0109] The position calculation formula is:

[0110] x(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5

[0111] y(t)=b0+b1t+b2t 2 +b3t 3 +b4t 4 +b5t 5

[0112] The speed calculation formula is:

[0113] x′(t)=a1+2a2t+3a3t 2 +4a4t 3 +5a5t 4

[0114] y′(t)=b1+2b2t+3b3t 2 +4b4t 3 +5b5t 4

[0115] The acceleration calculation formula is:

[0116] x″(t)=2a2+6a3t+12a4t 2 +20a5t 3

[0117] y″(t)=2b2+6b3t+12b4t 2 +20b5t 3

[0118] Wherein, a0, a1, a2, a3, a4, a5, b0, b1, b2, b3, b4, b5 are the coefficients of the polynomial; x(t) is the position of the object on the x-axis at time t; y(t) is the position of the object on the y-axis at time t; x′(t) is the first-order derivative of x(t), which is the instantaneous velocity of the object on the x-axis at time t; y′(t) is the first-order derivative of y(t), which is the instantaneous velocity of the object on the y-axis at time t; x″(t) is the second-order derivative of x(t), which is the instantaneous acceleration of the object on the x-axis at time t; y″(t) is the second-order derivative of y(t), which is the instantaneous acceleration of the object on the y-axis at time t.

[0119] The trajectory is expressed as the horizontal and vertical position, speed, and acceleration information after time t from the starting point of the lane change.

[0120] Step 2: Design of trajectory evaluation index and selection of optimal trajectory: Design of lane change trajectory evaluation index, comprehensive evaluation from the aspects of comfort and traffic efficiency, and thus give the optimal lane change trajectory. The specific steps include:

[0121] 21) Design comfort index and traffic efficiency index to reasonably evaluate the lane change trajectory. The comfort index is expressed as the extreme value of the acceleration change rate during the lane change process, and the traffic efficiency index is expressed as the time length to complete the lane change task.

[0122] The comfort index is expressed as the extreme value of the acceleration change rate during the lane change process. The real-time acceleration change rate in the lane change trajectory data is calculated using the following calculation formula:

[0123]

[0124] In the formula, Indicates the extreme value of the acceleration change rate during the lane change in this scenario, a i+Δt is the acceleration value at time i+Δt, a i is the acceleration value at time i, Δt is the time interval;

[0125] The traffic efficiency index is expressed as the length of time to complete the lane change task. The lane change time is calculated by combining the relationship between distance and speed during the process. The calculation formula is as follows:

[0126]

[0127] Where T represents the lane changing time, s represents the total length of the lane changing trajectory, Indicates the average vehicle speed during the process.

[0128] 22) Design normalized weights, perform weighted operations on each sub-indicator, and select the planned trajectory with the highest evaluation score as the optimal lane change trajectory.

[0129] The weights of the two indicators are set to 0.5, and the calculation formula is as follows:

[0130]

[0131] S=0.5×S1+0.5×S2

[0132] Where S1 represents the normalized value of the acceleration rate of change, Indicates the acceleration change rate during the current lane change process. represents the maximum permissible acceleration rate of change, S2 represents the standardized value of lane change time, Indicates the countdown of the current lane change process. represents the inverse of the maximum allowed time, S represents the final lane change trajectory score, and the best lane change trajectory with the highest evaluation score is selected as the pre-lane change trajectory.

[0133] Step 3: Design of trajectory tracking algorithm and acquisition of parameters to be adjusted: Adopt the lateral and longitudinal decoupling control algorithm to build a lane change trajectory tracking control algorithm model and obtain the control parameters required for adjustment.

[0134] The lateral and longitudinal motions are decoupled by a fifth-order polynomial. In order to achieve a better control effect, the present invention controls the lateral and longitudinal trajectory tracking processes separately. The PID algorithm considering the speed and position double loop is used in the longitudinal direction, and the LQR algorithm is used in the lateral direction. Figure 3 As shown, since the PID control operation is simple and can quickly realize the control process, and the LQR algorithm has a fast nonlinear solution capability, it can search for the optimal result under the current state. Therefore, the longitudinal control of the present invention is designed as a double-loop PID algorithm, and the lateral algorithm is an LQR algorithm, but the overall control algorithm needs to adjust many parameters, and the specific steps include:

[0135] 31) The inner loop of the longitudinal dual-loop PID algorithm is the speed loop, and the outer loop is the position loop. The parameters that need to be adjusted are: the proportion, differential, and integral coefficients of the inner and outer loops, a total of 6 parameters.

[0136] The control parameters in this step need to be adjusted for any scenario.

[0137] The output of the outer loop PID controller is:

[0138]

[0139] In the formula, u1 is the intermediate variable, e1 is the outer loop error, K p1 , K i1 , K d1 They are the proportional, integral and differential coefficients of the outer loop PID controller respectively.

[0140] The output of the inner loop PID controller is:

[0141]

[0142] Where u2 is the control signal, e2 is the inner loop error, K p2 , K i2 , K d2 They are the proportional, integral and differential coefficients of the inner loop PID controller respectively.

[0143] The calculation method of inner and outer ring errors is as follows:

[0144] e1=yd-y

[0145] e2=u1-u

[0146] In the formula, y d is the expected output of the system, y is the actual output of the system, and u is the actual input of the system.

[0147] 32) The lateral LQR algorithm obtains optimal control through linear quadratic adjustment, but the weight coefficient matrices Q and R need to be set in advance during control. Q and R are diagonal matrices, and the elements on the diagonal are selected for adjustment.

[0148] For the LQR algorithm, the basic steps are as follows:

[0149] First, the dynamic equations of the system are expressed as a state space model:

[0150] x(t+1)=Ax(t)+Bu(t)

[0151] Where x(t) is the state vector of the system, u(t) is the control input vector of the system, and A and B are the matrices of the system.

[0152] Secondly, the cost function J(u) is designed in quadratic form to measure the performance of the system:

[0153]

[0154] Among them, Q is the penalty weight matrix of the state error, R is the penalty weight matrix of the control input, L is the cross-term weight matrix between the state and the control input, Q and R are both diagonal matrices and positive definite matrices;

[0155] Finally, the algebraic Riccati equation is used to solve the optimal feedback gain matrix to minimize the cost function, that is:

[0156] P=A T PA-A T PB(R+B T PB)- 1 B T PA+Q

[0157] Where P is a symmetric positive definite matrix;

[0158] K=(R+B T PB)- 1 B T PA

[0159] Where K is the optimal feedback gain matrix, which is used to calculate the optimal control input;

[0160] u(n)=-Kx(n)

[0161] Among them, x(n) is the state vector at the current moment, and u(n) is the optimal control input.

[0162] Step 4: Design of control parameter range and trajectory tracking index: Design the value range of each control parameter and trajectory tracking effect evaluation index. The specific steps include:

[0163] 41) Initialize the value intervals of each control parameter determined in step 3 and set a range limit for each control parameter: K p1 and K p2 Between 0.01 and 100, K i1 and K i2 Between 0.001 and 10, K d1 and K d2 Between 0.0001 and 1, due to the different dimensions, first calculate their logarithms, and then perform subsequent genetic optimization operations; the Q matrix and R matrix in the LQR algorithm are both symmetric positive definite matrices, and only the diagonal elements need to be adjusted. The dimension of the Q matrix is ​​consistent with the dimension of the system state vector, that is, if the state vector is n-dimensional, then the Q matrix is ​​an n×n matrix, and the dimension of the R matrix is ​​the same as the dimension of the system control vector, that is, if the control vector is m-dimensional, then the R matrix is ​​an m×m matrix; the values ​​of the diagonal elements of the Q and R matrices are both between 0 and 10.

[0164] 42) Design trajectory tracking effect evaluation indicators from two aspects: trajectory tracking effect and lane change comfort:

[0165] The actual trajectory tracking process is that the autonomous driving vehicle tracks the above-mentioned optimal lane change trajectory according to the output of the control algorithm. However, due to the influence of algorithm parameters, control effect, simulation accuracy and other issues, the trajectory tracking control result will have certain errors and instability. Considering the possible instability of the trajectory tracking process, the present invention designs the evaluation index of the actual trajectory tracking effect, which is as follows:

[0166] Track tracking effect: It is characterized by the maximum value of the track deviation in the actual tracking control process:

[0167] Tra=max(e ij )

[0168]

[0169] Where Tra is the calculated maximum trajectory deviation, e ij is the Euclidean distance between the theoretical trajectory and the actual trajectory calculated at time t.

[0170] Lane-changing comfort: characterized by the acceleration change during the actual lane-changing process:

[0171]

[0172] The calculation method of the rate of change is consistent with that in step 2, but here it is the rate of change exhibited by the vehicle itself during the actual trajectory tracking process.

[0173] Step 5: Genetic optimization of optimal control parameters in lane change scenario: Through the three genetic operators of selection, crossover and mutation, encoding and decoding operations and individual fitness calculation, the optimal control parameters in this scenario are optimized. The specific steps include:

[0174] 51) Genetic operator initialization parameter design: design encoding method, decoding method, selection, crossover, mutation method, and give the genetic algorithm hyperparameter values.

[0175] The design encoding method is 0-1 binary encoding, which can restore the overall optimization effect of the genetic algorithm to the greatest extent; the decoding method is: converting from 0-1 encoding to the original real number value; the selection method is: roulette selection; the crossover probability is: single-point crossover, the crossover probability is 0.3; the mutation method is: single-point mutation, the mutation probability is 0.05.

[0176] 52) According to the parameter value range in step 4, a genetic algorithm is used to quickly search for the genetic code with the highest fitness, thereby finding the algorithm parameters with the best control effect in the lane change scenario.

[0177] The optimal control parameter calculation process based on genetic algorithm is as follows: Figure 4As shown in the figure, for each lane change scenario combination A, the optimal control parameter combination B in this scenario is obtained through genetic algorithm optimization, and the fitness function is the actual tracking effect evaluation index C designed in the previous article; the maximum number of iterations is designed to be 100, and the population size is 50.

[0178] Step 6: Automatic cycle test of batch lane change scenario: Design batch lane change scenario, repeat steps 1, 2, 3, 4, 5, 6, and save the test scenario and control parameter data. The specific steps include:

[0179] 61) Define the value range of each scene element type abstracted in step 1 and perform discretization to obtain all lane change scenarios that require calculation of optimal control parameters. The steps are as follows: Step 1 obtains the initial vehicle speed V1 of the main vehicle and the terminal horizontal axis speed V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later There are a total of 5 lane change scene elements.

[0180] The design value range is as follows: V1 and V x2 The value range is 0-30m / s, V y2 The value range is 0-10m / s, D long The value range is 10-100m, D later The value range is 5-20m; the design speed discrete interval is 0.5m / s, and the distance discrete interval is 0.5m. By combining the above-mentioned multiple scenario elements, all lane change scenarios that require calculation of the optimal control parameters are obtained.

[0181] 62) Select a suitable simulation test platform, design an automated cyclic simulation test process, implement automated trajectory generation and evaluation, trajectory tracking simulation test, and control parameter genetic optimization process.

[0182] The simulation test platform is the basis for the successful operation of the entire system. It needs to have the ability to generate and evaluate trajectories, simulate and test the automated trajectory tracking algorithm, carry genetic algorithms, and train and apply neural networks in the subsequent steps. Considering the various task requirements, this embodiment uses the MATLAB computing platform combined with the PreScan simulation software. The corresponding relationship between the overall algorithm and the platform is as follows: Figure 5 shown.

[0183] 63) Combined with the automated simulation test platform, repeat steps 1, 2, 3, 4, 5, and 6 until the optimal control parameter calculation for the batch lane change scenario is completed, and the scenario state parameters and the corresponding optimal control parameters are saved in pairs for the subsequent neural network training process.

[0184] Step 7: Preprocessing of lane change scenarios and control parameter data: Preprocess the data saved in step 6 and divide them into training sets and test sets. The specific steps include:

[0185] 71) The data content saved in step six is: the input parameter is the value of the scene element, and the output parameter is the optimal control parameter of the lane change trajectory tracking algorithm.

[0186] The data is analyzed. Since different types of data have their own value ranges and discrete step lengths, the data is not uniform and it is difficult to directly input it into the neural network for training. Therefore, the data needs to be processed. All input and output parameters are normalized using the maximum and minimum normalization principle. The formula is as follows:

[0187]

[0188] Where x is a value of the original data, x' is the normalized value, max(X) and min(X) are the maximum and minimum values ​​of the original data, respectively.

[0189] 72) Divide the above data into training set and test set in proportion.

[0190] In this embodiment, all lane change scenarios and control parameter corresponding data are divided into a training set and a test set in a ratio of 8:2 for subsequent training and testing of the neural network algorithm.

[0191] Step 8: Initialize and train the BP neural network: establish the network's input, output, and internal hierarchy; save the network after training, and it can be used for adaptive control in different lane change scenarios. The specific steps include:

[0192] 81) Figure 6 As shown in the figure, BP neural network has good nonlinear prediction ability and good generalization ability, so BP neural network is selected as the network structure of the control parameter prediction model. Determine that the input of BP neural network is the value of the elements of the lane change scene, the number of input layer nodes is consistent with the number of scene elements, the output is the value of the control parameter in the scene, and the number of output layer nodes is consistent with the number of control parameters; set the number of layers and nodes in the middle layer.

[0193] In this embodiment, the number of input layer nodes is consistent with the number of scene elements, which is 5; the number of output layer nodes is consistent with the number of control parameters, which is 6+m+n, where m and n represent the dimensions of the LQR control vector and the state vector, respectively; the number of intermediate layers is set to 1, and the number of nodes is consistent with the number of output layer nodes.

[0194] 82) Set the activation function, initial network weights, learning rate, maximum number of iterations and other network training parameters, start the network training process, and save the network model after the training is completed for adaptive control in different lane change scenarios.

[0195] Since the present invention is a regression prediction problem, the activation functions of the hidden layer and the output layer are set to be tansig functions, which can provide nonlinear mapping capabilities and the gradient is relatively easy to calculate; random numbers are used to initialize the network weights; the learning rate is set to 0.05, and the maximum number of iterations is 20,000.

[0196] The neural network training process is carried out in combination with the above network architecture and training data. After the training is completed, the network is saved and can be used for adaptive control of different lane change scenarios. Subsequently, the lane change scenario elements can be directly input to output the control parameters.

[0197] Step 9: Adaptive parameter control for lane changing tasks: Design a specific lane changing task and perform a complete trajectory generation-adaptive tracking parameter control process. The specific steps include:

[0198] The above steps obtain the overall algorithm and model that can be used for trajectory generation and tracking control process. This step explains how to apply the algorithm in a specific lane change scenario.

[0199] 91) Design a specific lane-changing task, abstract the lane-changing trajectory into a combination of the scene elements in step 1, use spline curves to plan multiple trajectories, and select the best lane-changing trajectory based on the indicators.

[0200] Choose any lane change task, that is, set the initial speed of the main vehicle V1 and the end horizontal axis speed V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later The five scene elements are assigned values, 10 paths are planned using step one, and the best lane change trajectory is selected for tracking using the evaluation index of step two.

[0201] 92) The lane change scenario is input into the trained neural network model, the optimal control parameters under the current lane change state are output, and the trajectory tracking control algorithm is applied to the specific lane change scenario.

[0202] After the trajectory generation is completed, the trajectory tracking control work can be performed. The values ​​of the above scene elements are input into the neural network model, and the corresponding control parameter values ​​can be output. After adjusting the parameters of each control algorithm, the optimal trajectory generated by the above steps can be used to execute the tracking control process.

Claims

1. A lateral and longitudinal decoupling control method for lane change scenarios based on parameter adaptation, characterized in that: The following steps are involved: Step 1: Lane change scenario initialization and trajectory cluster generation: Initialize the lane change scenario, abstract the lane change process of the autonomous vehicle into a vector transition process, and convert the lane change trajectory into a combination of driving scenario elements, including the initial vehicle speed V1, the terminal horizontal axis speed V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later ; Use quintic polynomials for pre-planning, calculate array applicable results, generate lane change trajectory clusters, and retain lane change scenario elements for backup; Step 2: Design of trajectory evaluation index and selection of optimal trajectory: Design comfort index and traffic efficiency index to reasonably evaluate the lane change trajectory. The comfort index is expressed as the extreme value of the acceleration change rate during the lane change process, and the traffic efficiency index is expressed as the time length to complete the lane change task; design normalized weights, perform weighted operations on each sub-indicator, and select the planned trajectory with the highest evaluation score as the optimal lane change trajectory; Step 3: Design of trajectory tracking algorithm and acquisition of parameters to be adjusted: Adopt the lateral and longitudinal decoupling control algorithm to control the lateral and longitudinal trajectory tracking processes separately. The PID algorithm considering the speed and position double loop is adopted in the longitudinal direction, and the LQR algorithm is adopted in the lateral direction. Build the lane change trajectory tracking control algorithm model and obtain the control parameters required for adjustment. Step 4: Design of control parameter range and trajectory tracking index: Design the value range of each control parameter, and design trajectory tracking effect evaluation index from two aspects: trajectory tracking effect and lane change process comfort; Step 5: Genetic optimization of optimal control parameters in lane change scenario: Through the three genetic operators of selection, crossover and mutation, encoding and decoding operations and individual fitness calculation, the optimal control parameters in this scenario are optimized; Step 6: Automatic cycle test of batch lane change scenario: the initial vehicle speed V1 obtained in step 1 and the terminal horizontal axis speed V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later There are a total of 5 lane change scenario elements. The value range of each scenario element type is defined and discretized to obtain all lane change scenarios that require calculation of optimal control parameters. Select the simulation test platform, design the automated cycle simulation test process, perform automated trajectory generation and evaluation, trajectory tracking simulation test, and control parameter genetic optimization process; Combined with the automated simulation test platform, repeat steps 1, 2, 3, 4, 5, and 6, and save the test scenario and control parameter data; Step 7: Preprocessing of lane change scenarios and control parameter data: normalize and preprocess the data saved in step 6, and divide them into training set and test set; Step 8: Initialize and train the BP neural network: establish the input, output and internal levels of the network; After training is completed, the network is saved and can be used for adaptive control in different lane change scenarios.

2. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step one includes: The lane-changing process of an autonomous vehicle is abstracted as a vector transition process, that is, a trajectory of the motion vector from the starting point to the end point, which needs to satisfy the constraints of speed direction and position orientation. The speed direction of the main vehicle at the initial moment of lane change is defined as the x-axis, and the y-axis is obtained by rotating 90° counterclockwise. The lane-changing process is converted into a combination of the following scene elements: the initial speed of the main vehicle V1, the horizontal axis speed of the end point V x2 , the end longitudinal axis speed V y2 , track longitudinal spacing D long , track lateral spacing D later ; According to the above scenario elements, use the fifth-order polynomial to calculate the applicable results of the array; The quintic polynomial planning process is decomposed into three calculation formulas: position, speed, and acceleration: The position calculation formula is: x(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5 <h2 style=";text-align:left;direction:ltr">y(t) = b0 + b1t + b2t<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +b3t<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> +b4t<h2 style=";text-align:left;direction:ltr"> 4 <h2 style=";text-align:left;direction:ltr"> +b5t<h2 style=";text-align:left;direction:ltr"> 5 The speed calculation formula is: <h2 style=";text-align:left;direction:ltr">x′(t) = a1 + 2a2t + 3a3t<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +4a4t<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> +5a5t<h2 style=";text-align:left;direction:ltr"> 4 y′(t)=b1+2b2t+3b3t 2 +4b4t 3 +5b5t 4 The acceleration calculation formula is: x″(t)=2a2+6a3t+12a4t 2 +20a5t 3 y″(t)=2b2+6b3t+12b4t 2 +20b5t 3 Wherein, a0, a1, a2, a3, a4, a5, b0, b1, b2, b3, b4, b5 are the coefficients of the polynomial; x(t) is the position of the object on the x-axis at time t; y(t) is the position of the object on the y-axis at time t; x′(t) is the first-order derivative of x(t), which is the instantaneous velocity of the object on the x-axis at time t; y′(t) is the first-order derivative of y(t), which is the instantaneous velocity of the object on the y-axis at time t; x″(t) is the second-order derivative of x(t), which is the instantaneous acceleration of the object on the x-axis at time t; y″(t) is the second-order derivative of y(t), which is the instantaneous acceleration of the object on the y-axis at time t; The trajectory is expressed as the horizontal and vertical position, speed, and acceleration information after time t from the starting point of the lane change.

3. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step 2 includes: 21) The comfort index is expressed as the extreme value of the acceleration change rate during the lane change process. The real-time acceleration change rate in the lane change trajectory data is calculated using the following calculation formula: In the formula, Indicates the extreme value of the acceleration change rate during the lane change in this scenario, a i+Δt is the acceleration value at time i+Δt, a i is the acceleration value at time i, Δt is the time interval; The traffic efficiency index is expressed as the length of time to complete the lane change task. The lane change time is calculated by combining the relationship between distance and speed during the process. The calculation formula is as follows: Where T represents the lane changing time, s represents the total length of the lane changing trajectory, Indicates the average vehicle speed during the process; 22) Set the weights of both indicators to 0.5, and the calculation formula is as follows: S=0.5×S1+0.5×S2 Where S1 represents the normalized value of the acceleration rate of change, Indicates the acceleration change rate during the current lane change process. represents the maximum permissible acceleration rate of change, S2 represents the standardized value of lane change time, Indicates the countdown of the current lane change process. represents the inverse of the maximum allowed time, S represents the final lane change trajectory score, and the best lane change trajectory with the highest evaluation score is selected as the pre-lane change trajectory.

4. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step three includes: 31) The inner loop of the longitudinal dual-loop PID algorithm is the speed loop, and the outer loop is the position loop. The parameters that need to be adjusted are: the proportion, differential, and integral coefficients of the inner and outer loops, a total of 6 parameters; The output of the outer loop PID controller is: In the formula, u1 is the intermediate variable, e1 is the outer loop error, K p1 , K i1 , K d1 They are the proportional, integral and differential coefficients of the outer loop PID controller respectively; The output of the inner loop PID controller is: Where u2 is the control signal, e2 is the inner loop error, K p2 , K i2 , K d2 They are the proportional, integral and differential coefficients of the inner loop PID controller respectively; The calculation method of inner and outer ring errors is as follows: e1=y d -y e2=u1-u In the formula, y d is the expected output of the system, y is the actual output of the system, and u is the actual input of the system; 32) The horizontal LQR algorithm obtains optimal control through linear quadratic adjustment. The weight coefficient matrices Q and R are set in advance during control. Q and R are diagonal matrices, and the elements on the diagonal are selected for adjustment; For the LQR algorithm, the basic steps are as follows: First, the dynamic equations of the system are expressed as a state space model: x(t+1)=Ax(t)+Bu(t) Where x(t) is the state vector of the system, u(t) is the control input vector of the system, A and B are the matrices of the system, A is the state transfer matrix, and B is the control input matrix; Secondly, the cost function J(u) is designed in quadratic form to measure the performance of the system: Among them, Q is the penalty weight matrix of the state error, R is the penalty weight matrix of the control input, L is the cross-term weight matrix between the state and the control input, Q and R are both diagonal matrices and positive definite matrices; Finally, the algebraic Riccati equation is used to solve the optimal feedback gain matrix to minimize the cost function, that is: P=A T PA-A T PB(R+B T PB)- 1 B T PA+Q Where P is a symmetric positive definite matrix; K=(R+B T PB)- 1 B T PA Where K is the optimal feedback gain matrix, which is used to calculate the optimal control input; u(n)=-Kx(n) Among them, x(n) is the state vector at the current moment, and u(n) is the optimal control input.

5. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step 4 includes: 41) Initialize the value intervals of each control parameter determined in step 3 and set a range limit for each control parameter: K p1 and K p2 Between 0.01 and 100, K i1 and K i2 Between 0.001 and 10, K d1 and K d2 Between 0.0001 and 1, first calculate their logarithms, and then perform subsequent genetic optimization operations; the Q matrix and R matrix in the LQR algorithm are both symmetric positive definite matrices, only the diagonal elements are adjusted, the dimension of the Q matrix is ​​consistent with the dimension of the system state vector, that is, if the state vector is n-dimensional, then the Q matrix is ​​an n×n matrix, and the dimension of the R matrix is ​​the same as the dimension of the system control vector, that is, if the control vector is m-dimensional, then the R matrix is ​​an m×m matrix; the values ​​of the diagonal elements of the Q and R matrices are both between 0 and 10; 42) Design the evaluation index of the actual tracking effect of the trajectory, as follows: Track tracking effect: It is characterized by the maximum value of the track deviation in the actual tracking control process: Between=max(e ij ) Where Tra is the calculated maximum trajectory deviation, e ij is the Euclidean distance between the theoretical trajectory and the actual trajectory calculated at time t; Comfort during lane change: Characterized by the acceleration change during the actual lane change process:

6. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step five includes: 51) Genetic operator initialization parameter design: design encoding, decoding, selection, crossover, mutation methods, and give the values ​​of genetic algorithm hyperparameters; The design encoding method is 0-1 binary encoding; the decoding method is: converting from 0-1 encoding to the original real number value; the selection method is: roulette selection; the crossover probability is: the crossover method is: single-point crossover; the mutation method is: single-point mutation; 52) According to the parameter value range in step 4, a genetic algorithm is used to quickly search for the genetic code with the highest fitness. For each lane change scenario combination A, the optimal control parameter combination B in this scenario is obtained through genetic algorithm optimization. The fitness function is the actual tracking effect evaluation index C designed in the previous article. The maximum number of iterations and population size are designed to find the algorithm parameters with the best control effect in this lane change scenario.

7. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step six includes: 61) The value ranges of each scene element type are designed as follows: V1 and V x2 The value range is 0-30m / s, V y2 The value range is 0-10m / s, D long The value range is 10-100m, D later The value range is 5-20m; the design speed discrete interval is 0.5m / s, and the distance discrete interval is 0.5m. By combining the above multiple scenario elements, all lane change scenarios that require calculation of optimal control parameters are obtained; 62) The simulation test platform is implemented by using MATLAB computing platform combined with PreScan simulation software; 63) Combined with the automated simulation test platform, repeat steps 1, 2, 3, 4, 5, and 6 until the optimal control parameter calculation for the batch lane change scenario is completed, and the scenario state parameters and the corresponding optimal control parameters are saved in pairs for the subsequent neural network training process.

8. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step seven includes: 71) The data content saved in step 6 is: the input parameter is the value of the scene element, and the output parameter is the optimal control parameter of the lane change trajectory tracking algorithm; the data is processed and all input and output parameters are normalized using the maximum and minimum normalization principle. The formula is as follows: In the formula, x is a value of the original data, x' is the normalized value, max(X) and min(X) are the maximum and minimum values ​​of the original data respectively; 72) Divide the above data into training set and test set in proportion for subsequent training and testing of neural network algorithm.

9. The method for lateral and longitudinal decoupling control in lane change scenarios based on parameter adaptation according to claim 1, characterized in that: Step eight includes: 81) Select BP neural network as the network structure of the control parameter prediction model, determine that the input of the BP neural network is the value of the elements of the lane change scene, the number of nodes in the input layer is consistent with the number of scene elements, the output is the value of the control parameter under the scene, and the number of nodes in the output layer is consistent with the number of control parameters; set the number of layers and nodes in the middle layer; 82) Set the activation function, initial network weights, set network training parameters, start the network training process, and save the network model after the training is completed for adaptive control in different lane change scenarios.

10. An application of a lateral and longitudinal decoupling control method for a lane-changing scenario based on parameter adaptation, wherein the lateral and longitudinal decoupling control method for a lane-changing scenario based on parameter adaptation described in any one of claims 1 to 9 is applied in a specific lane-changing scenario, firstly designing a specific lane-changing task, arbitrarily selecting a lane-changing task, abstracting the lane-changing trajectory into a combination of the scene elements in step 1, that is, the initial vehicle speed V1 of the main vehicle, the terminal horizontal axis speed V x2 , terminal longitudinal axis speed V y2 , Track longitudinal spacing D long , track lateral spacing D later The five scene elements are assigned values, several trajectory paths are planned using step one, and the best lane change trajectory is selected using the evaluation index of step two. Secondly, the lane change scenario is input into the trained neural network model, the optimal control parameters under the current lane change state are output, and the optimal control parameters are applied to the optimal lane change trajectory generated in the above steps to execute the tracking control process.

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