Collaborative planning method and device for path and speed of autonomous vehicle

Abstract

The invention belongs to the field of automatic driving automobiles, and particularly relates to an automatic driving automobile path and speed collaborative planning method and device, and the method comprises the steps: constructing a whole automobile dynamic model and a tire dynamic model, obtaining the dynamic stress limit of a tire, and taking the dynamic stress limit as the motion limit boundary of an automobile; a quasi-steady-state balance method is adopted, vehicle steady-state balance states under different accelerator opening degrees and system pressures are traversed, transverse acceleration and longitudinal acceleration at each moment are recorded, and an improved G-G graph is obtained through vector calculation; based on the obtained improved G-G graph, a forward-backward Euler method is adopted to establish an optimal speed model; establishing a trajectory optimization model under the shortest travel time by adopting a road segmentation method; and constructing an optimal path and speed collaborative planning model by adopting quadratic programming based on the road safety boundary and the vehicle dynamics constraint in combination with the optimal speed model and the trajectory optimization model, and solving to obtain an optimal path and speed plan.

Description

Technical Field

[0001] The present invention belongs to the field of autonomous driving vehicles, and specifically relates to a method and device for collaboratively planning a path and speed of an autonomous driving vehicle. Background Art

[0002] With the rapid development of autonomous driving technology, the coordinated planning of the path and speed of autonomous driving vehicles has become one of the key technologies for achieving safe, efficient and comfortable driving. Path planning aims to plan the optimal or feasible driving path for the vehicle based on constraints such as road environment and traffic rules; while speed planning is to reasonably set the vehicle's driving speed on different road sections based on path planning, combined with factors such as the vehicle's own dynamic performance and traffic conditions, to ensure vehicle safety and driving efficiency. The coordinated planning of path and speed is an important part of realizing intelligent and autonomous decision-making for autonomous driving vehicles.

[0003] However, there are many problems with the current research methods. Most existing methods simply decouple the path planning problem into two parts: path planning and speed planning, ignoring the correlation between the two, which can easily lead to problems such as under-optimization of the path and unreasonable speed planning, which in turn affects driving efficiency and safety. They also often use point mass models to evaluate the vehicle limit state, ignoring the impact of the dynamic load differences of the four tires on the wheel adhesion. The simplified model cannot accurately reflect the actual dynamic characteristics of the vehicle under different working conditions, resulting in a deviation in the evaluation of the vehicle's dynamic limit performance, which will cause the driver or the automatic driving system to overestimate the vehicle's capabilities and plan a speed that exceeds the vehicle's actual execution capabilities, thereby increasing the risk of vehicle instability and safety accidents. There is also a unified standard for evaluating the dynamic limit performance of different vehicles, ignoring the differences in the force state of vehicles with different drive forms, limiting the vehicle's movement efficiency and safety, resulting in insufficient evaluation capabilities for high-performance vehicles. Existing methods also focus on the continuity of the planned speed, ignoring the impact of sudden changes in vehicle acceleration on accessibility, which can easily lead to the vehicle being unable to accurately track the planned speed during actual driving, resulting in speed fluctuations and acceleration mutations, which in turn affects the vehicle's ride comfort and driving stability. Summary of the invention

[0004] The present invention provides a method and device for collaborative planning of the path and speed of an autonomous driving vehicle, which accurately evaluates the dynamic limit performance of the vehicle, constructs an improved GG diagram to obtain the maximum lateral acceleration and the maximum longitudinal acceleration for constraints, and then calculates the optimal speed model, and constructs a trajectory optimization model under the shortest travel time based on the optimal speed model. The two are coupled to construct an optimal path and speed collaborative planning model to obtain the optimal path and speed planning, thereby achieving the purpose of reducing vehicle travel time, improving travel efficiency and safety, and then realizing high-precision tracking control, thereby improving the posture efficiency and safety of the autonomous driving vehicle.

[0005] A method for collaboratively planning a path and speed of an autonomous driving vehicle comprises the following steps:

[0006] Construct the vehicle dynamics model and tire dynamics model to obtain the dynamic force limit of the tire and use it as the vehicle's motion limit boundary;

[0007] The quasi-steady-state equilibrium method is used to traverse the steady-state equilibrium state of the vehicle under different throttle openings and system pressures, and the lateral acceleration and longitudinal acceleration at each moment are recorded. The improved GG diagram is obtained by vector calculation;

[0008] Based on the obtained improved GG graph, the optimal velocity model is established using the forward-backward Euler method;

[0009] The road segmentation method is used to establish a trajectory optimization model with the shortest travel time;

[0010] Based on road safety boundaries and vehicle dynamics constraints, combined with the optimal speed model and trajectory optimization model, quadratic programming is used to construct the optimal path and speed collaborative planning model, and the optimal path and speed planning is solved.

[0011] By accurately evaluating the dynamic limit performance of the vehicle and constructing an improved GG diagram to obtain the maximum lateral acceleration and longitudinal maximum acceleration for constraints, the optimal speed model is calculated, and a trajectory optimization model with the shortest travel time is constructed based on the optimal speed model. The two are coupled to construct an optimal path and speed collaborative planning model to obtain the optimal path and speed planning, thereby achieving the purpose of reducing vehicle travel time, improving travel efficiency and safety, and then realizing high-precision tracking control, which improves the posture efficiency and safety of autonomous driving vehicles.

[0012] Furthermore, the whole vehicle dynamics model and the tire dynamics model are constructed to obtain the dynamic force limit of the tire and use it as the motion limit boundary of the vehicle, which specifically includes the following steps:

[0013] Construct vehicle dynamics model and tire dynamics model;

[0014] Considering the transfer of the vehicle's load in the lateral and forward directions, calculate the vertical force characteristics of the four tires;

[0015] The adhesion coefficient between the tire and the road is used to calculate the limit adhesion and obtain the dynamic force limit of the tire, which is used as the movement limit boundary of the vehicle.

[0016] Furthermore, the expression of the vehicle dynamics model is:

[0017]

[0018] In the formula, m represents the vehicle mass; v x represents the longitudinal velocity; represents the longitudinal acceleration; v y represents the lateral speed; represents lateral acceleration; γ represents yaw angular velocity; F x21 Indicates the longitudinal force of the rear left wheel; F x22 Indicates the longitudinal force of the rear right wheel; F y11 Indicates the lateral force of the front left wheel; F y12 Indicates the lateral force of the front right wheel; F y21 Indicates the lateral force of the rear left wheel; F y22 Indicates the lateral force of the rear right wheel; δ f Indicates the front wheel turning angle; I z Represents the moment of inertia around the Z axis; I x represents the moment of inertia around the X-axis; a represents the front half wheelbase; b represents the rear half wheelbase; m s Indicates the quality of the vehicle body; Indicates the body roll angle; h g represents the height of the centroid; represents lateral damping; represents the roll stiffness; represents the first-order derivative; represents the second-order derivative;

[0019] The expression of the tire dynamics model is:

[0020]

[0021] In the formula, F x Indicates the longitudinal force of the tire; F y Indicates the lateral force of the tire; C x Indicates tire cornering stiffness; C y represents the tire cornering stiffness; K represents the longitudinal slip rate; α represents the sideslip angle; F represents the comprehensive transverse and longitudinal force of the tire, and its expression is:

[0022]

[0023] Where μ represents the road adhesion coefficient; F z Indicates the vertical load of the tire, which includes the vertical load F of the front left wheel z11 , vertical load F of the front right wheel z12 , vertical load F of the rear left wheel z21 , vertical load F of rear right wheel z22 , that is, the expression of the vertical load of each tire is:

[0024]

[0025] Where, l represents the wheelbase; B f Indicates the front wheelbase; B r Indicates rear track; M roll Represents the overturning moment about the X-axis.

[0026] Furthermore, the quasi-steady-state equilibrium method is used to traverse the steady-state equilibrium state of the vehicle under different throttle openings and system pressures, and the lateral acceleration and longitudinal acceleration at each moment are recorded. The process of vector calculation to obtain the improved GG diagram specifically includes the following steps:

[0027] Assume that the vehicle moves in a straight line;

[0028] The quasi-steady-state equilibrium method is used to traverse different accelerator pedal openings, record the vehicle speed at each moment, and solve the maximum speed;

[0029] The expression for the maximum speed is:

[0030] f v =-min(v x );

[0031] X v =[v x ,K e ] T ;

[0032] In the formula, f v represents the objective function of maximum speed; X v represents the optimization variable of maximum speed; K e Indicates the accelerator pedal opening;* T represents transpose;

[0033] At the current speed, the quasi-steady-state equilibrium method is used to traverse different brake pressures, record the longitudinal acceleration at each moment, and solve the maximum longitudinal speed;

[0034] The expression of the maximum longitudinal acceleration at the current speed is:

[0035]

[0036] In the formula, represents the objective function of the maximum longitudinal acceleration; represents the optimization variable of the maximum longitudinal acceleration; T b Indicates the braking torque of the brake pedal;

[0037] Under the current speed and longitudinal acceleration, the quasi-steady-state equilibrium method is used to traverse different accelerator pedal openings and brake pressures, record the lateral acceleration at each moment, and solve for the maximum lateral acceleration;

[0038] The expression for the maximum lateral acceleration at the current speed and longitudinal acceleration is:

[0039]

[0040] In the formula, represents the objective function of the maximum longitudinal acceleration; represents the optimization variable of the maximum longitudinal acceleration;

[0041] The maximum longitudinal acceleration and the maximum lateral acceleration are vector-calculated and a GG diagram is generated using mapping software to limit the maximum lateral acceleration and the maximum longitudinal acceleration of the vehicle.

[0042] Furthermore, based on the obtained improved GG graph, the process of establishing the optimal velocity model using the forward-backward Euler method specifically includes the following steps:

[0043] The forward-backward Euler method is used, with the road curvature as the maximum vehicle speed, the improved GG diagram as the maximum acceleration constraint, and the maximum acceleration change rate as the iterative gain. The process of establishing the optimal speed model specifically includes the following steps:

[0044] According to the constraint of tire adhesion limit, the maximum passing speed of the vehicle passing through the curve under different curvatures is calculated; the maximum passing speed includes the maximum longitudinal speed and the maximum lateral speed;

[0045] The maximum passing speed is related to:

[0046]

[0047] In the formula, v xmax Indicates the maximum longitudinal speed; represents the maximum lateral velocity; ρ represents the curvature;

[0048] The function expression of longitudinal velocity and path is:

[0049]

[0050] In the formula, v x represents the longitudinal velocity; ds represents the differential unit of the path; represents the longitudinal acceleration, Indicates the maximum longitudinal acceleration at the current vehicle speed;

[0051] Based on the idea of ​​differential calculus, combined with the GG diagram and the constraint of maximum longitudinal acceleration, the function expression of longitudinal velocity and path is constructed;

[0052] Taking the maximum acceleration change rate of the power system as the iterative gain, connecting the speed and acceleration coupling points of the bidirectional iteration, the optimal speed model is established;

[0053] The expression of the optimal speed model is:

[0054]

[0055] In the formula, represents the current feedforward longitudinal acceleration, v x_forward represents the feed-forward longitudinal velocity, represents the next feed-forward longitudinal velocity; represents the previous feedforward longitudinal acceleration; ΔS represents the differential unit of path length; jerk represents the rate of change of acceleration.

[0056] Furthermore, the process of establishing the trajectory optimization model with the shortest travel time by using the road segmentation method specifically includes:

[0057] The planned path is divided into multiple sampling points using the road segmentation method;

[0058] The expression of the sampling point is:

[0059]

[0060] In the formula, represents the sampling points on the planned path, j = 1, 2, ..., n; h j represents the horizontal coordinate of the jth sampling point; r j Represents the ordinate of the jth sampling point; represents the abscissa vector unit; represents the vertical coordinate vector unit; h u,j represents the horizontal coordinate of the jth sampling point on the upper boundary u; r u,j represents the ordinate of the jth sampling point on the upper boundary u; h d,j represents the horizontal coordinate of the jth sampling point on the lower boundary d; r d,j Indicates the ordinate of the jth sampling point on the lower boundary d; Δh j Indicates the path width in the horizontal direction of the current path point; Δr j Indicates the path width in the vertical direction of the current path point; α j represents the road constraint coefficient, α j ∈(0,1);

[0061] A quintic polynomial curve is used to smoothly connect multiple sampling points to obtain the planned path;

[0062] Combined with the optimal speed model, a trajectory optimization model with the shortest travel time is constructed;

[0063] The expression of the trajectory optimization model under the shortest travel time is:

[0064]

[0065] In the formula, min(·) represents the minimum value, T s represents the travel time; Δs represents the distance between adjacent path sampling points; v x (j) represents the longitudinal velocity at the jth sampling point.

[0066] Furthermore, the expression of the optimal path and speed collaborative planning model is:

[0067]

[0068] s.tX(t)∈X.;

[0069] U(t)∈U;

[0070] g[X(t),U(t)]≤0;

[0071] q[X(t),U(t)]=0;

[0072] In the formula, S ∑ represents the total degree of the path; ds represents the path length differential unit; dt represents the time differential unit; X represents the state quantity; U represents the control quantity; g[X(t),U(t)] represents the soft constraint related to the state quantity at time t and the control quantity at time t; q[X(t),U(t)] represents the hard constraint related to the state quantity at time t and the control quantity at time t.

[0073] A device for a method for collaboratively planning a path and speed of an autonomous driving vehicle, comprising:

[0074] A constraint determination module, which is used to determine the vehicle's motion limit boundary, lateral maximum acceleration and longitudinal maximum acceleration, road safety boundary and vehicle dynamics constraints;

[0075] The model building module is used to build the vehicle dynamics model, tire dynamics model, optimal speed model, trajectory optimization model, and optimal path and speed collaborative planning model.

[0076] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0077] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0078] The beneficial effects of the present invention are:

[0079] The present invention accurately evaluates the dynamic limit performance of the vehicle, constructs an improved GG diagram to obtain the maximum lateral acceleration and the maximum longitudinal acceleration for constraints, and then calculates the optimal speed model. Based on the optimal speed model, a trajectory optimization model under the shortest travel time is constructed. The two are coupled to construct an optimal path and speed collaborative planning model to obtain the optimal path and speed planning, thereby achieving the purpose of reducing vehicle travel time, improving travel efficiency and safety, and then realizing high-precision tracking control, thereby improving the posture efficiency and safety of the autonomous driving vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic diagram of the process of the present invention;

[0081] Figure 2 Schematic diagram of the forward-backward Euler method;

[0082] Figure 3 This is a schematic diagram of the model of the road segmentation method;

[0083] Figure 4 It is a schematic diagram of the effect of acceleration change rate on the optimal speed model;

[0084] Figure 5 A schematic diagram showing the comparison of trajectory optimization models under different methods;

[0085] Figure 6 Schematic diagram for comparing path curvatures under different methods;

[0086] Figure 7 A schematic diagram showing the comparison of vehicle speed planning results using different methods;

[0087] Figure 8 It is a schematic diagram for comparing the speed control results under different methods;

[0088] Fig. 9 It is a schematic diagram for comparing trajectory error results under different methods;

[0089] Fig.10 It is a structural schematic diagram of the device in the present invention;

[0090] Fig.11 It is a schematic diagram of the structure of the computer device in the present invention.

[0091] Reference numerals:

[0092] 1. Memory; 2. Processor. DETAILED DESCRIPTION

[0093] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0094] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0095] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples, and a person of ordinary skill in the art can understand the specific meanings of the above terms in this application under specific circumstances.

[0096] Example 1

[0097] Figure 1 The method shown is a path and speed collaborative planning method for an autonomous vehicle. It accurately evaluates the dynamic limit performance of the vehicle, constructs an improved GG diagram to obtain the maximum lateral acceleration and the maximum longitudinal acceleration for constraints, and then calculates the optimal speed model. Based on the optimal speed model, a trajectory optimization model with the shortest travel time is constructed. The two are coupled to construct an optimal path and speed collaborative planning model to obtain the optimal path and speed planning, thereby achieving the purpose of reducing vehicle travel time, improving travel efficiency and safety, and then realizing high-precision tracking control, which improves the posture efficiency and safety of the autonomous vehicle. The specific steps include the following:

[0098] S1: Construct the vehicle dynamics model and tire dynamics model to obtain the dynamic force limit of the tire and use it as the vehicle's motion limit boundary;

[0099] S11: Construct vehicle dynamics model and tire dynamics model;

[0100] S12: Considering the transfer of the vehicle's load in the lateral and forward directions, calculate the vertical force characteristics of the four tires;

[0101] S13: Calculate the limit adhesion using the adhesion coefficient between the tire and the road surface to obtain the dynamic force limit of the tire.

[0102] Use this as the vehicle's limit of motion.

[0103] Among them, the expression of the vehicle dynamics model is:

[0104]

[0105] In the formula, m represents the vehicle mass; v x represents the longitudinal velocity; represents the longitudinal acceleration; v y represents the lateral speed; represents lateral acceleration; γ represents yaw angular velocity; F x21 Indicates the longitudinal force of the rear left wheel; F x22 Indicates the longitudinal force of the rear right wheel; F y11 Indicates the lateral force of the front left wheel; F y12 Indicates the lateral force of the front right wheel; F y21 Indicates the lateral force of the rear left wheel; F y22 Indicates the lateral force of the rear right wheel; δ f Indicates the front wheel turning angle; I z Represents the moment of inertia around the Z axis; I x represents the moment of inertia around the X-axis; a represents the front half wheelbase; b represents the rear half wheelbase; m s Indicates the quality of the vehicle body; Indicates the body roll angle; h g represents the height of the centroid; represents lateral damping; represents the roll stiffness; represents the first-order derivative; represents the second-order derivative;

[0106] Among them, the expression of the tire dynamics model is:

[0107]

[0108] In the formula, F x Indicates the longitudinal force of the tire; F y Indicates the lateral force of the tire; C x Indicates tire cornering stiffness; C y represents the tire cornering stiffness; K represents the longitudinal slip rate; α represents the sideslip angle; F represents the comprehensive transverse and longitudinal force of the tire, and its expression is:

[0109]

[0110] Where μ represents the road adhesion coefficient; F zIndicates the vertical load of the tire, which includes the vertical load F of the front left wheel z11 , vertical load F of the front right wheel z12 , vertical load F of the rear left wheel z21 , vertical load F of rear right wheel z22 , that is, the expression of the vertical load of each tire is:

[0111]

[0112] Where, l represents the wheelbase; B f Indicates the front wheelbase; B r Indicates rear track; M roll Represents the overturning moment about the X-axis.

[0113] S2: Using the quasi-steady-state equilibrium method, the steady-state equilibrium state of the vehicle under different throttle openings and system pressures is traversed, the lateral acceleration and longitudinal acceleration at each moment are recorded, and the vector calculation is used to obtain the improved GG diagram;

[0114] S21: Setting the vehicle to travel in a straight line;

[0115] S22: using the quasi-steady-state equilibrium method, traverse different accelerator pedal openings, record the vehicle speed at each moment, and solve for the maximum speed;

[0116] The expression of maximum speed is:

[0117] f v =-min(v x );

[0118] X v =[v x ,K e ] T ;

[0119] In the formula, f v represents the objective function of maximum speed; X v represents the optimization variable of maximum speed; K e Indicates the accelerator pedal opening;* T represents transpose;

[0120] S23: At the current speed, using the quasi-steady-state equilibrium method, traverse different braking pressures, record the longitudinal acceleration at each moment, and solve for the maximum longitudinal speed;

[0121] Among them, the expression of the maximum longitudinal acceleration at the current speed is:

[0122]

[0123] In the formula, represents the objective function of the maximum longitudinal acceleration; represents the optimization variable of the maximum longitudinal acceleration; T b Indicates the brake pedal braking torque;

[0124] S24: Under the current speed and longitudinal acceleration, a quasi-steady-state equilibrium method is used to traverse different accelerator pedal openings and brake pressures, the lateral acceleration at each moment is recorded, and the maximum lateral acceleration is solved;

[0125] Among them, the expression of the maximum lateral acceleration under the current speed and longitudinal acceleration is:

[0126]

[0127] In the formula, represents the objective function of the maximum longitudinal acceleration; represents the optimization variable of the maximum longitudinal acceleration;

[0128] S25: Perform vector calculation on the maximum longitudinal acceleration and the maximum lateral acceleration, and generate a GG diagram using mapping software to limit the maximum lateral acceleration and the maximum longitudinal acceleration of the vehicle.

[0129] S3: Based on the obtained improved GG graph, the forward-backward Euler method is used to establish the optimal speed model;

[0130] S31: Calculating the maximum passing speed of the vehicle through the curve under different curvatures according to the constraint of the tire adhesion limit; the maximum passing speed includes the maximum longitudinal speed and the maximum lateral speed;

[0131] Among them, the relationship between the maximum passing speed is:

[0132]

[0133] In the formula, v xmax Indicates the maximum longitudinal speed; represents the maximum lateral velocity; ρ represents the curvature;

[0134] The function expression of longitudinal velocity and path is:

[0135]

[0136] In the formula, v x represents the longitudinal velocity; ds represents the differential unit of the path; represents the longitudinal acceleration, Indicates the maximum longitudinal acceleration at the current vehicle speed;

[0137] S32: Based on the idea of ​​differential calculus, combined with the GG diagram and the constraint of maximum longitudinal acceleration, the function expression of longitudinal velocity and path is constructed;

[0138] S33: Using the forward-backward Euler method, taking the maximum acceleration change rate of the power system as the iterative gain, connecting the speed and acceleration coupling points of the bidirectional iteration, and establishing the optimal speed model;

[0139] Figure 2 What is shown is a schematic diagram of the forward-backward Euler method; O is the drive / regulatory dividing point, point j represents the static limiting speed extreme value of the jth sampling point, and point j+1 represents the static limiting speed extreme value of the j+1th sampling point.

[0140] Among them, the expression of the optimal speed model is:

[0141]

[0142] In the formula, represents the current feedforward longitudinal acceleration, v x_forward represents the feed-forward longitudinal velocity, represents the next feed-forward longitudinal velocity; represents the previous feedforward longitudinal acceleration; ΔS represents the differential unit of path length; jerk represents the rate of change of acceleration.

[0143] S4: Use the road segmentation method to establish a trajectory optimization model with the shortest travel time;

[0144] S41: Use the road segmentation method to divide the planned path into multiple sampling points;

[0145] The expression of the sampling point is:

[0146]

[0147] In the formula, represents the sampling points on the planned path, j = 1, 2, ..., n; h j represents the horizontal coordinate of the jth sampling point; r j Represents the ordinate of the jth sampling point; represents the abscissa vector unit; represents the vertical coordinate vector unit; h u,j represents the horizontal coordinate of the jth sampling point on the upper boundary u; r u,j represents the ordinate of the jth sampling point on the upper boundary u; h d,j represents the horizontal coordinate of the jth sampling point on the lower boundary d; r d,j Indicates the ordinate of the jth sampling point on the lower boundary d; Δh j Indicates the path width in the horizontal direction of the current path point; Δr jIndicates the path width in the vertical direction of the current path point; α j represents the road constraint coefficient, α j ∈(0,1);

[0148] Figure 3 Schematic diagram of the road segmentation method shown.

[0149] S42: using a quintic polynomial curve to smoothly connect multiple sampling points to obtain a planned path;

[0150] S43: combining the optimal speed model under the selected sampling points, constructing a trajectory optimization model under the shortest travel time;

[0151] Among them, the expression of the trajectory optimization model under the shortest travel time is:

[0152]

[0153] In the formula, min(·) represents the minimum value, T s represents the travel time; Δs represents the distance between adjacent path sampling points; v x (j) represents the longitudinal velocity at the jth sampling point.

[0154] S5: Based on the road safety boundary and vehicle dynamics constraints, combined with the optimal speed model and trajectory optimization model, quadratic programming is used to construct the optimal path and speed collaborative planning model to solve the optimal path and speed planning.

[0155] The expression of the optimal path and speed collaborative planning model is:

[0156]

[0157] s.tX(t)∈X.;

[0158] U(t)∈U;

[0159] g[X(t),U(t)]≤0;

[0160] q[X(t),U(t)]=0;

[0161] In the formula, S ∑ represents the total degree of the path; ds represents the path length differential unit; dt represents the time differential unit; X represents the state quantity; U represents the control quantity; g[X(t),U(t)] represents the soft constraint related to the state quantity and the control quantity; q[X(t),U(t)] represents the hard constraint related to the state quantity and the control quantity.

[0162] Example 2

[0163] In this embodiment, a method for collaboratively planning a path and speed of an autonomous driving vehicle is provided.

[0164] In this embodiment, the vehicle mass m=1270kg; the wheelbase l=2.91m; the front and rear wheelbase ratio a:b=1:1; the engine power is 150kw; the front and rear braking force ratio is 5:3; and the road adhesion coefficient μ=0.8.

[0165] In this embodiment, the central path, shortest path, best curvature path, optimal path, and collaborative planning path are used to compare and evaluate the steps of the method described in this application, namely:

[0166] (1) Under the standard double lane change condition, a comparative experiment was conducted using the carsim and simulink joint simulation platform to evaluate the impact of the acceleration change rate. Figure 4 As shown, the results show that: using the optimal speed model described in this application, that is, considering the acceleration change rate, the maximum speed control error obtained is 0.18m / s, and the root mean square error is 0.08m / s; without considering the acceleration change rate, the speed switches directly from rapid acceleration to rapid deceleration, the acceleration change rate is too large and there is even a sudden acceleration change, the maximum speed control error obtained is 0.93m / s, and the root mean square error is 0.39m / s.

[0167] (2) Under the standard double-lane shifting condition, different methods were used to conduct comparative experiments, such as Figure 5 As shown, the results clearly demonstrate the difference between the collaboratively planned paths and the paths planned by other methods.

[0168] (3) Under the standard double lane shifting condition, different methods are used to conduct comparative experiments based on the path curvature, such as Figure 6 As shown, the results show that: using the method described in this application, the curvature changes smoothly, and the maximum curvature is the smallest compared to other paths, which is conducive to achieving a higher speed; while in other methods, the curvature of the central path changes greatly, the maximum curvature and the average curvature are both the largest, the average curvature of the path with the optimal curvature is the smallest, and there are individual sections with large curvature.

[0169] (4) Compare the speed planning under different methods, such as Figure 7 As shown, the results show that the average expected speed of the center path is 14.00 m / s, the average expected speed of the shortest path is 28.84 m / s, the average expected speed of the optimal curvature is 31.91 m / s, the average expected speed of the optimal path is 32.25 m / s, and the average expected speed of the optimal path and speed collaborative planning method is 33.3 m / s. That is, the method described in this application has a changed average vehicle speed and higher driving efficiency.

[0170] (5) Compare the speed control results under different methods, such as Figure 8As shown, the results show that the maximum control error of the center path is 0.31m / s, the root mean square error is 0.13m / s, and the travel time is 8.58s. The maximum control error of the shortest path is 0.27m / s, the root mean square error is 0.10m / s, and the travel time is 4.16s. The maximum control error of the optimal curvature is 0.26m / s, the root mean square error is 0.10m / s, and the travel time is 3.76s. The maximum control error of the optimal path is 0.34m / s, the root mean square error is 0.13m / s, and the travel time is 3.72s. The maximum control error of the optimal path and speed collaborative planning method is 0.01m / s, the root mean square error is 0.01m / s, and the travel time is 3.60s. That is, the method described in this application can also maintain a high speed control accuracy.

[0171] (6) Using the method described in this application, Fig. 9 As shown, the results show that the maximum trajectory tracking error of the center path is 0.08m, and the root mean square error is 0.02m, the maximum trajectory tracking error of the shortest path is 0.05m, and the root mean square error is 0.02m, the maximum trajectory tracking error of the optimal curvature is 0.06m, and the root mean square error is 0.02m, the maximum trajectory tracking error of the optimal path is 0.09m, and the root mean square error is 0.04m, and the maximum trajectory tracking error of the optimal path and speed collaborative planning method is 0.04m, and the root mean square error is 0.01m, that is, the method described in this application can also maintain a high trajectory control accuracy.

[0172] Example 2

[0173] like Fig.10 As shown, the present invention also provides a device for collaborative planning of path and speed of an autonomous driving vehicle, including a constraint determination module and a model building module.

[0174] Among them, the constraint condition determination module is used to determine the vehicle's motion limit boundary, lateral maximum acceleration and longitudinal maximum acceleration, road safety boundary and vehicle dynamics constraints;

[0175] Among them, the model building module is used to build the vehicle dynamics model, tire dynamics model, optimal speed model, trajectory optimization model, and optimal path and speed collaborative planning model.

[0176] Example 3

[0177] Based on the same technical concept, the embodiment of the present application also provides a computer device, including a memory 1 and a processor 2, such as Fig.11 As shown, the memory 1 stores a computer program, and the processor 2 implements any of the above-mentioned methods when executing the computer program.

[0178] Among them, the memory 1 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 1 can be an internal storage unit of the autonomous driving vehicle path and speed collaborative planning device, such as a hard disk. In other embodiments, the memory 1 can also be an external storage device of the autonomous driving vehicle path and speed collaborative planning device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 1 can also include both the internal storage unit of the autonomous driving vehicle path and speed collaborative planning device and an external storage device. The memory 1 can not only be used to store application software and various types of data installed in the autonomous driving vehicle path and speed collaborative planning device, such as the code of the autonomous driving vehicle path and speed collaborative planning device program, but can also be used to temporarily store data that has been output or is to be output.

[0179] In some embodiments, the processor 2 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, which is used to run the program code stored in the memory 1 or process data, such as executing the program of the automatic driving vehicle path and speed collaborative planning device.

[0180] The disclosed embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the steps of the method described in the above method embodiment. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0181] The computer program product of the application page content refresh method provided in the disclosed embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the steps of the method described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0182] The disclosed embodiment of the present invention also provides a computer program, which implements any one of the methods of the aforementioned embodiments when executed by a processor. The computer program product can be implemented in hardware, software or a combination thereof. In an optional embodiment, the computer program product is specifically embodied as a computer storage medium, and in another optional embodiment, the computer program product is specifically embodied as a software product, such as a software development kit (SDK) and the like.

[0183] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0184] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0185] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0186] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0187] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0188] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0189] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0190] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0191] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for collaborative planning of path and speed of an autonomous driving vehicle, characterized in that: The steps include: Construct the vehicle dynamics model and tire dynamics model to obtain the dynamic force limit of the tire and use it as the vehicle's motion limit boundary; The quasi-steady-state equilibrium method is used to traverse the steady-state equilibrium state of the vehicle under different throttle openings and system pressures, and the lateral acceleration and longitudinal acceleration at each moment are recorded. The improved GG diagram is obtained by vector calculation; Based on the obtained improved GG graph, the optimal velocity model is established using the forward-backward Euler method; The road segmentation method is used to establish a trajectory optimization model with the shortest travel time; Based on road safety boundaries and vehicle dynamics constraints, combined with the optimal speed model and trajectory optimization model, quadratic programming is used to construct the optimal path and speed collaborative planning model, and the optimal path and speed planning is solved.

2. The method for collaboratively planning the path and speed of an autonomous driving vehicle according to claim 1, characterized in that: The process of constructing the vehicle dynamics model and the tire dynamics model, obtaining the dynamic force limit of the tire, and using it as the vehicle's motion limit boundary specifically includes the following steps: Construct vehicle dynamics model and tire dynamics model; Considering the transfer of the vehicle's load in the lateral and forward directions, calculate the vertical force characteristics of the four tires; The adhesion coefficient between the tire and the road is used to calculate the limit adhesion and obtain the dynamic force limit of the tire, which is used as the movement limit boundary of the vehicle.

3. The method for collaboratively planning the path and speed of an autonomous driving vehicle according to claim 2, characterized in that: The expression of the vehicle dynamics model is: In the formula, m represents the vehicle mass; v x represents the longitudinal velocity; represents the longitudinal acceleration; v y represents the lateral speed; represents lateral acceleration; γ represents yaw angular velocity; F x21 Indicates the longitudinal force of the rear left wheel; F x22 Indicates the longitudinal force of the rear right wheel; F y11 Indicates the lateral force of the front left wheel; F y12 Indicates the lateral force of the front right wheel; F y21 Indicates the lateral force of the rear left wheel; F y22 Indicates the lateral force of the rear right wheel; δ f Indicates the front wheel turning angle; I z Represents the moment of inertia around the Z axis; I x represents the moment of inertia around the X-axis; a represents the front half wheelbase; b represents the rear half wheelbase; m s Indicates the quality of the vehicle body; Indicates the body roll angle; h g represents the height of the centroid; represents lateral damping; represents the roll stiffness; represents the first-order derivative; represents the second-order derivative; The expression of the tire dynamics model is: In the formula, F x Indicates the longitudinal force of the tire; F y Indicates the lateral force of the tire; C x Indicates tire cornering stiffness; C y represents the tire cornering stiffness; K represents the longitudinal slip rate; α represents the sideslip angle; F represents the comprehensive transverse and longitudinal force of the tire, and its expression is: Where μ represents the road adhesion coefficient; F z Indicates the vertical load of the tire, which includes the vertical load F of the front left wheel z11 , vertical load F of the front right wheel z12 , vertical load F of the rear left wheel z21 , vertical load F of rear right wheel z22 , that is, the expression of the vertical load of each tire is: Where, l represents the wheelbase; B f Indicates the front wheelbase; B r Indicates rear track; M roll Represents the overturning moment about the X-axis.

4. The method for collaboratively planning the path and speed of an autonomous driving vehicle according to claim 3, characterized in that: The quasi-steady-state equilibrium method is used to traverse the steady-state equilibrium state of the vehicle under different throttle openings and system pressures, and the lateral acceleration and longitudinal acceleration at each moment are recorded. The process of vector calculation to obtain the improved GG diagram specifically includes the following steps: Assume that the vehicle moves in a straight line; The quasi-steady-state equilibrium method is used to traverse different accelerator pedal openings, record the vehicle speed at each moment, and solve the maximum speed; The expression for the maximum speed is: f v =-min(v x ); X v =[v x ,K e ] T ; In the formula, f v represents the objective function of maximum speed; X v represents the optimization variable of maximum speed; K e Indicates the accelerator pedal opening; * T represents transpose; At the current speed, the quasi-steady-state equilibrium method is used to traverse different brake pressures, record the longitudinal acceleration at each moment, and solve the maximum longitudinal speed; The expression of the maximum longitudinal acceleration at the current speed is: In the formula, represents the objective function of the maximum longitudinal acceleration; represents the optimization variable of the maximum longitudinal acceleration; T b Indicates the braking torque of the brake pedal; Under the current speed and longitudinal acceleration, the quasi-steady-state equilibrium method is used to traverse different accelerator pedal openings and brake pressures, record the lateral acceleration at each moment, and solve for the maximum lateral acceleration; The expression for the maximum lateral acceleration at the current speed and longitudinal acceleration is: In the formula, represents the objective function of the maximum longitudinal acceleration; represents the optimization variable of the maximum longitudinal acceleration; The maximum longitudinal acceleration and the maximum lateral acceleration are vector-calculated and a GG diagram is generated using mapping software to limit the maximum lateral acceleration and the maximum longitudinal acceleration of the vehicle.

5. The method for collaboratively planning the path and speed of an autonomous driving vehicle according to claim 1, characterized in that: Based on the obtained improved GG graph, the process of establishing the optimal velocity model using the forward-backward Euler method specifically includes the following steps: The forward-backward Euler method is used, with the road curvature as the maximum vehicle speed, the improved GG diagram as the maximum acceleration constraint, and the maximum acceleration change rate as the iterative gain. The process of establishing the optimal speed model specifically includes the following steps: According to the constraint of tire adhesion limit, the maximum passing speed of the vehicle passing through the curve under different curvatures is calculated; the maximum passing speed includes the maximum longitudinal speed and the maximum lateral speed; The maximum passing speed is related to: In the formula, v xmax Indicates the maximum longitudinal speed; represents the maximum lateral velocity; ρ represents the curvature; The function expression of longitudinal velocity and path is: In the formula, v x represents the longitudinal velocity; ds represents the differential unit of the path; represents the longitudinal acceleration, Indicates the maximum longitudinal acceleration at the current vehicle speed; Based on the idea of ​​differential calculus, combined with the GG diagram and the constraint of maximum longitudinal acceleration, the function expression of longitudinal velocity and path is constructed; Taking the maximum acceleration change rate of the power system as the iterative gain, connecting the speed and acceleration coupling points of the bidirectional iteration, the optimal speed model is established; The expression of the optimal speed model is: In the formula, represents the current feedforward longitudinal acceleration, v x_forward represents the feed-forward longitudinal velocity, represents the next feed-forward longitudinal velocity; represents the previous feedforward longitudinal acceleration; ΔS represents the differential unit of path length; jerk represents the rate of change of acceleration.

6. The method for collaboratively planning the path and speed of an autonomous driving vehicle according to claim 1, characterized in that: The process of establishing the trajectory optimization model with the shortest travel time using the road segmentation method specifically includes: The planned path is divided into multiple sampling points using the road segmentation method; The expression of the sampling point is: In the formula, represents the sampling points on the planned path, j = 1, 2, ..., n; h j represents the horizontal coordinate of the jth sampling point; r j Represents the ordinate of the jth sampling point; represents the abscissa vector unit; represents the vertical coordinate vector unit; h u，j represents the horizontal coordinate of the jth sampling point on the upper boundary u; r u，j represents the ordinate of the jth sampling point on the upper boundary u; h d，j represents the horizontal coordinate of the jth sampling point on the lower boundary d; r d，j Indicates the ordinate of the jth sampling point on the lower boundary d; Δh j Indicates the path width in the horizontal direction of the current path point; Δr j Indicates the path width in the vertical direction of the current path point; α j represents the road constraint coefficient, α j ∈(0,1); A quintic polynomial curve is used to smoothly connect multiple sampling points to obtain the planned path; Combined with the optimal speed model, a trajectory optimization model with the shortest travel time is constructed; The expression of the trajectory optimization model under the shortest travel time is: In the formula, min(·) represents the minimum value, T s represents the travel time; Δs represents the distance between adjacent path sampling points; v x (j) represents the longitudinal velocity at the jth sampling point.

7. The method for collaboratively planning the path and speed of an autonomous driving vehicle according to claim 6, characterized in that: The expression of the optimal path and speed collaborative planning model is: s.tX(t)∈X.; U(t)∈U; g[X(t),U(t)]≤0; q[X(t),U(t)]=0; In the formula, S ∑ represents the total degree of the path; ds represents the path length differential unit; dt represents the time differential unit; X represents the state quantity; U represents the control quantity; g[X(t), U(t)] represents the soft constraint related to the state quantity at time t and the control quantity at time t; q[X(t), U(t)] represents the hard constraint related to the state quantity at time t and the control quantity at time t.

8. A device for the method for coordinated planning of path and speed of an autonomous driving vehicle according to claim 1, characterized in that: include: A constraint determination module, which is used to determine the vehicle's motion limit boundary, lateral maximum acceleration and longitudinal maximum acceleration, road safety boundary and vehicle dynamics constraints; The model building module is used to build the vehicle dynamics model, tire dynamics model, optimal speed model, trajectory optimization model, and optimal path and speed collaborative planning model.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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