Electric commercial vehicle lane changing trajectory planning and tracking control method and related equipment

Abstract

The invention discloses an electric commercial vehicle lane changing trajectory planning and tracking control method and related equipment, and the method comprises the steps: determining a weight coefficient based on an analytic hierarchy process, optimizing a control point radian value through combining a particle swarm optimization algorithm, and generating a B-spline lane changing trajectory; a vehicle multi-dimensional state data set under different loads is collected through a driving simulator, and a K-means clustering algorithm is adopted to determine a vehicle driving state grade in real time; establishing a function relation model of the tire cornering stiffness and the vertical load, correcting the tire lateral force in real time in combination with a magic tire formula, and introducing a tire bending stiffness compensation coefficient to obtain corrected tire stiffness; a prediction time domain of a model prediction controller is dynamically adjusted by taking a vehicle state grade and a trajectory tracking transverse error as input of a fuzzy controller; on the basis of the B-spline lane changing trajectory and the corrected tire rigidity, the front wheel steering angle control quantity is output through the adjusted model prediction controller, and trajectory tracking is achieved. According to the method, key vehicle state parameters are effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lane changing of commercial vehicles, in particular to a lane changing trajectory planning and tracking control method for electric commercial vehicles and related equipment. BACKGROUND

[0002] With the continuous deepening of the research in the field of automatic driving, the automatic lane changing technology has been widely developed in the field of passenger cars. However, for commercial vehicles, the use of this technology is obviously different from that of passenger cars. Commercial vehicles have larger size, longer wheelbase, higher center of mass, and more passengers and larger load variation. When changing lanes at high speed on a road with a certain curvature and lateral slope, it is more likely to cause rollover and other instability problems, which can cause serious economic losses and safety accidents. Therefore, it is of great significance to study the lane changing path planning and tracking control in improving the driving safety of intelligent commercial vehicles on curved roads. At present, researchers have conducted in-depth research on this method for different lane changing scenarios (straight and curved roads).

[0003] At present, the basic vehicle trajectory tracking control algorithms are: PID algorithm, LQR algorithm, model predictive control algorithm (MPC). The model-based path tracking control strategy is widely used because it can handle the constraint optimization problem of nonlinear systems. Shi et al. studied the trajectory tracking control of a distributed drive six-wheel steering commercial vehicle chassis, and obtained the control amount of six-wheel steering through the model predictive control algorithm (MPC). Guan et al. proposed a variable-curvature curve trajectory tracking control method based on model predictive control. This method adjusts the lateral error weight and feedforward factor of the MPC controller dynamically to achieve more accurate path tracking effect. However, most MPC trajectory trackers, including the above method, use a fixed prediction range, which reduces the tracking performance to some extent in complex driving environments. SUMMARY

[0004] In view of the problems in the prior art, the present application provides a lane changing trajectory planning and tracking control method for electric commercial vehicles and related equipment, which aims to solve the above problems.

[0005] In order to solve the above technical problems, the present application is realized by the following technical scheme:

[0006] According to the first aspect of the present application, a lane changing trajectory planning and tracking control method for electric commercial vehicles is provided, comprising:

[0007] The weight coefficient is determined based on the analytic hierarchy process, the control point radian value is optimized by combining the particle swarm optimization algorithm, and the B-spline lane changing trajectory is generated;

[0008] The multi-dimensional state data set of the vehicle under different loads is collected by a driving simulator, and the K-means clustering algorithm is used to determine the vehicle driving state level in real time;

[0009] A function relationship model of tire cornering stiffness and vertical load is established, the tire lateral force is corrected in real time by combining the magic tire formula, and a tire cornering stiffness compensation coefficient is introduced to obtain a corrected tire stiffness;

[0010] The vehicle state level and the trajectory tracking lateral error are taken as inputs of the fuzzy controller to dynamically adjust the prediction time domain of the model predictive controller;

[0011] Based on the B-spline lane-changing trajectory and the corrected tire stiffness, the front wheel steering angle control quantity is output by the adjusted model predictive controller to realize trajectory tracking.

[0012] In a possible implementation manner of the first aspect, the function relationship model of tire cornering stiffness and vertical load is:

[0013] C=aF z 2 +bF z +c

[0014] In the formula, C is the tire cornering stiffness; a, b and c are fitting coefficients; F z is the tire vertical load.

[0015] In a possible implementation manner of the first aspect, the particle swarm optimization algorithm is combined to optimize the control point curvature value, and the objective function is:

[0016] min J c =w c1 S+w c2 k+w c3 dk

[0017] In the formula, S is the lane-changing trajectory length, k is the maximum curvature, dk is the maximum curvature derivative, w c1 , w c2 , and w c3 are weight coefficients.

[0018] In a possible implementation manner of the first aspect, the multi-dimensional state data set includes lateral velocity V y , lateral acceleration ɑ y , yaw rate ω, roll angle ψ, roll rate V xR , vehicle sideslip angle β and vehicle sideslip angular velocity β R , and the lateral load transfer rate converted from the vertical load of each wheel:

[0019]

[0020] In the formula, R1 is a front wheel lateral load transfer rate; R2 is a rear wheel lateral load transfer rate; F z_L1 is a left front wheel vertical load; F z_R1 is a right front wheel vertical load; F z_L2i is a left rear axle inner side wheel vertical load; F z_L2o is a left rear axle outer side wheel vertical load; F z_R2i is a right rear axle inner side wheel vertical load; F z_R2o is a right rear axle outer side wheel vertical load.

[0021] In a possible implementation manner of the first aspect, the prediction time domain adjustment formula is:

[0022] N P = N P0 + Round(Fit(ΔN P ))

[0023] wherein, N P is an adjusted prediction time domain; N P0 is an initial prediction time domain; Round is a function of finding an integer; Fit is a data fitting function; ΔN P is a time domain change amount output by the fuzzy controller.

[0024] In a possible implementation manner of the first aspect, the target function of the model prediction controller is:

[0025]

[0026] wherein, η ref is a B-spline lane change trajectory reference value, and Nc is a control time domain.

[0027] In a possible implementation manner of the first aspect, the real-time correction of tire lateral force is combined with the magic tire formula, and specifically:

[0028] F y = D t sin{C t arctan[B t α-E t (B t α-arctan(B t α))]}

[0029] In the formula, F y represents a tire lateral force; α represents a tire side slip angle; B t , C t , D t , E t are matching coefficients.

[0030] According to a second aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for planning and tracking lane change trajectories of an electric commercial vehicle when executing the computer program.

[0031] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the method for planning and tracking lane change trajectories of an electric commercial vehicle.

[0032] According to a fourth aspect of the present invention, a computer program product is provided, which, when executed by a processor, implements the aforementioned method for lane change trajectory planning and tracking control of an electric commercial vehicle.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] The present invention provides a lane change trajectory planning and tracking control method for an electric commercial vehicle. Based on the traditional trajectory planning, a lane change trajectory planning method based on B-spline curves is proposed, which combines the analytic hierarchy process and the particle swarm optimization algorithm to improve the lane change trajectory. In the actual lane change process of a commercial vehicle, the lane change trajectory generated by this method is smoother than the lane change trajectory generated by the original B-spline curve method, and effectively reduces key vehicle state parameters such as tracking error and yaw angular velocity. In terms of trajectory tracking control strategy, the present invention establishes a new adaptive parameter correction model predictive controller (AKCMPC), including the acquisition of tire lateral stiffness, online identification of the driving stability state of the commercial vehicle under different loads, and adaptive control of the system prediction time domain based on fuzzy rules. Compared with other controllers, it not only improves the tracking accuracy of commercial vehicles in certain areas, but also effectively reduces the peak values ​​of key vehicle state parameters (including yaw angular velocity and sideslip angle) during the tracking process.

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the specific embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1A flowchart of a lane change trajectory planning and tracking control method for an electric commercial vehicle provided by one embodiment of the present invention;

[0038] Figure 2 A method step diagram of a lane change trajectory planning and tracking control method for an electric commercial vehicle provided by one embodiment of the present invention;

[0039] Figure 3 A control point location diagram of a lane change trajectory planning and tracking control method for an electric commercial vehicle provided by one embodiment of the present invention;

[0040] Figure 4 A framework diagram of a path tracking control system for a lane change trajectory planning and tracking control method for an electric commercial vehicle provided by one embodiment of the present invention;

[0041] Figure 5 A comparison diagram of tire lateral force errors for a lane change trajectory planning and tracking control method for an electric commercial vehicle provided by one embodiment of the present invention;

[0042] Figure 6 A comparison chart of tire cornering stiffness compensation coefficients for a lane change trajectory planning and tracking control method for an electric commercial vehicle provided by one embodiment of the present invention;

[0043] Figure 7 A diagram showing the path tracking simulation results of a lane change trajectory planning and tracking control method for an electric commercial vehicle provided by one embodiment of the present invention;

[0044] Figure 8 This is a verification analysis diagram of the path tracking simulation results of the electric commercial vehicle lane change trajectory planning and tracking control method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] like Figure 1 As shown, an embodiment of the present invention provides a lane change trajectory planning and tracking control method for an electric commercial vehicle, characterized by comprising the following steps:

[0047] (a) The weight coefficients are determined based on the analytic hierarchy process, and the radian values ​​of the control points are optimized using the particle swarm optimization algorithm to generate the B-spline lane change trajectory.

[0048] Specifically, the particle swarm optimization algorithm is combined to optimize the control point radian value, and the optimization objective function is:

[0049] min J c = w c1 S + w c2 k + w c3 dk

[0050] wherein S is the lane-changing trajectory length, k is the maximum curvature, dk is the maximum curvature derivative, w c1 , w c2 , w c3 are weight coefficients.

[0051] (b) Collect multi-dimensional state data sets of the vehicle under different loads through the driving simulator, and determine the vehicle driving state level in real time by using the K-means clustering algorithm.

[0052] Specifically, the multi-dimensional state data set includes lateral velocity V y , lateral acceleration ɑ y , yaw rate w, roll angle ψ, roll rate V xR , vehicle sideslip angle β and vehicle sideslip angular velocity β R , and the lateral load transfer rate converted from the vertical load of each wheel:

[0053]

[0054] In the formula, R1 is the front wheel lateral load transfer rate; R2 is the rear wheel lateral load transfer rate; F z_L1 is the left front wheel vertical load; F z_R1 is the right front wheel vertical load; F z_L2i is the left rear axle inner side wheel vertical load; F z_L2o is the left rear axle outer side wheel vertical load; F z_R2i is the right rear axle inner side wheel vertical load; F z_R2o is the right rear axle outer side wheel vertical load.

[0055] (c) Establish a functional relationship model of tire cornering stiffness and vertical load, combine the magic tire formula to correct the tire lateral force in real time, and introduce a tire cornering stiffness compensation coefficient to obtain the modified tire stiffness.

[0056] Specifically, the functional relationship model of tire cornering stiffness and vertical load is:

[0057] C = aF z 2 +bF z +c

[0058] In the formula, C is the tire cornering stiffness; a, b, and c are fitting coefficients; F z is the tire vertical load.

[0059] The compensation coefficient K is introduced to obtain the modified stiffness C C = KC = K(aF z 2 +bF z +c).

[0060] The tire lateral force is corrected in real time in combination with the magic tire formula, specifically:

[0061] F y = D t sin{C t arctan[B t α-E t (B t α-arctan(B t α))]}

[0062] In the formula, F y represents the tire lateral force; α represents the tire side slip angle; B t , C t , D t , and E t are matching coefficients.

[0063] (d) Taking the vehicle state level and the trajectory tracking lateral error as the input of the fuzzy controller, the prediction horizon of the model predictive controller is dynamically adjusted.

[0064] Specifically, the prediction horizon adjustment formula is:

[0065] N P = N P0 + Round(Fit(ΔN P ))

[0066] Wherein, N P is the adjusted prediction horizon; N P0 is the initial prediction horizon; Round is a function of finding an integer; Fit is a data fitting function; ΔN P is the time domain change amount output by the fuzzy controller.

[0067] (e) Based on the B-spline lane-changing trajectory and the modified tire stiffness, the front wheel steering angle control amount is output through the adjusted model predictive controller to realize trajectory tracking.

[0068] Specifically, the objective function of the model predictive controller is:

[0069]

[0070] Wherein, η ref is the B-spline lane-changing trajectory reference value, and Nc is the control horizon.

[0071] In an embodiment, referring to Figures 2 to 7 , the embodiment provides a lane-changing trajectory planning and tracking control method for an electric commercial vehicle, comprising:

[0072] S1: establishing the overall structure of the lane-changing trajectory planning method (AHPPSO-B spline curve), selecting appropriate control points, and obtaining a B-spline lane-changing trajectory that ensures the driving safety of the commercial vehicle through the analytic hierarchy process (AHP) and the particle swarm optimization (PSO) algorithm:

[0073] In a curved road environment, the selection of the control points mainly considers the positional relationship between the curvature of each control point and the original lane center line, the target lane center line, the tangent of the lane-changing starting point, and the tangent of the ending point. Specifically, the curvature of some control points is mainly obtained through simplified calculations of the lane-changing driving length of the vehicle and the turning radius of the road.

[0074] The actual arcs A and B are the center lines of the target lane and the original lane, respectively. The straight lines C and D are the tangents of the lane-changing starting point P1 and the ending point P6, respectively. P2, P3, P4, P5, and P6 are determined by α1-α4 and the lane center lines A and B or the tangents C and D, wherein P3 and P4 have the same curvature. The curvature values of some control points are simplified in the present application:

[0075]

[0076] In the formula, L is the longitudinal displacement of the vehicle during lane changing, and R is the turning radius of the inner lane. Different lane-changing trajectories are obtained according to the positions of different control points.

[0077] During the lane-changing process, in order to avoid the dangerous situation of the vehicle head invading the third lane due to the large volume of the commercial vehicle, the center of mass of the vehicle should be in the area between the two lane center lines, i.e., the lane-changing trajectory cannot intersect with the center lines of the original lane and the target lane. Among all the methods, only the control point selection method α-B-D meets the above safety requirements.

[0078] To ensure the driving stability of the commercial vehicle during the lane-changing process, the following optimization objective function is established:

[0079] min J c =w c1 S+w c2 k+w c3 dk

[0080] In the formula, S is the length of the lane-changing trajectory, representing the economy of the commercial vehicle during lane changing. k and dk are the maximum curvature and the maximum curvature derivative of the lane-changing trajectory, respectively, representing the driving stability of the commercial vehicle. w ci (i = 1, 2, 3) are the weight coefficients of each part, which are obtained through the analytic hierarchy process (AHP).

[0081] Furthermore, when commercial vehicles perform lane-changing maneuvers on curved roads, vehicle stability is more important than economy. Therefore, at the criterion level, the weights for stability and economy are set to b1 = 0.7 and b2 = 0.3. Among the three indicators determining vehicle stability B1, the maximum curvature C2 and the maximum curvature derivative C3 of the lane-changing trajectory are considered primary and secondary factors. A comparison matrix for vehicle stability B1 and the solution level is established, as shown in Table I.

[0082] Table I Stability Judgment Matrix

[0083]

[0084]

[0085] Among the three indicators that determine vehicle economy B2, the length of the lane change trajectory largely determines the lane change economy. Therefore, we constructed a comparison matrix as shown in Table II.

[0086] Table II Economic Judgment Matrix

[0087]

[0088] Use the maximum eigenvalue of the matrix to perform consistency check:

[0089]

[0090] where γ max represents the maximum eigenvalue of the comparison matrix. When CR < 1, the set comparison matrix is ​​considered to meet the consistency requirements. After calculation, the consistency test ratios CI / RI in Tables I and II are approximately 0.04 and 0.06, respectively. This proves that the two comparison matrices meet the consistency test requirements.

[0091] Furthermore, according to the stability and economy of the criterion layer, the weights of each solution layer are set to The weight coefficient of the optimization objective function can be obtained by the following formula.

[0092]

[0093] Where m and h are the number of factors in the criterion layer and the solution layer respectively, b i is the weight of the i-th criterion layer factor.

[0094] After calculation, w c1 、w c2 、w c3 The values ​​of are 0.244, 0.546, and 0.21 respectively. At the same time, the overall consistency test result is CR=0.046, which verifies the validity of the weight coefficient.

[0095] Further, particle swarm optimization algorithm (PSO) is a typical swarm intelligence optimization algorithm. Before optimization calculation, the optimization objective function must be normalized because the length of trajectory and the curvature of trajectory have different dimensions:

[0096]

[0097] where k min = dk min = 0, S min is the longitudinal displacement of the vehicle when changing lanes, which is related to the speed of the vehicle and the time of changing lanes. S max is the length of the arc line of the outer lane when the radian is α4, k max and dk max are the values of α1=0.5, α2=1 / 3, α3=0.25, and α4 obtained by the traditional b-spline curve. The minimum optimization objective J c is obtained by optimizing the values of the radian α1and α2.

[0098] Taking the curve radius of 500 meters as an example, the planning curve is compared with the traditional B-spline curve. The maximum values of the improved curvature and the curvature derivative are reduced, which indicates the effectiveness of the AHPPSO-B-spline curve.

[0099] S2: Establish a vehicle dynamics model and a tire cornering stiffness-vertical load relationship model, derive the magic tire formula, use the K-means clustering algorithm to obtain the clustering center representing the vehicle stable state data set, and realize real-time correction of the tire lateral force.

[0100] First, the vehicle is subjected to stress analysis, and a seven-degree-of-freedom vehicle dynamics model is built:

[0101]

[0102] In the formula, m is the mass of the vehicle, I z is the vehicle yaw moment of inertia; v x and v y represent the longitudinal and lateral speeds of the vehicle, respectively; Ψ represents the vehicle heading angle, β is the vehicle mass side slip angle; δ is the front wheel steering angle, ω is the yaw rate, F x and F y are the longitudinal and lateral forces of the tire, respectively. The magic tire formula of the tire is:

[0103] F y = D t sin{C t arctan[B t α-E t (B t α-arctan(Bt α))]}

[0104] where F y and a are the lateral force and side slip angle of the tire, respectively. B t , C t , D t , E t are the fitting coefficients.

[0105] Further, in the case of severe emergency, the vertical load on the tire changes dramatically, resulting in a linear tire model with constant cornering stiffness, which can lead to a decline in controller performance due to insufficient accuracy. To solve this problem, the function relationship between tire cornering stiffness and vertical load is derived by data fitting using TruckSim tire parameters (3000 kg load, 510 mm radius), realizing the initial adaptive change of tire cornering stiffness with vertical load.

[0106] C = aF z 2 +bF z +c

[0107] where a, b, c are fitting coefficients, c and F z are the cornering stiffness and vertical load of the tire, respectively.

[0108] Compare this formula with the magic tire formula. Under the same road adhesion coefficient and tire side slip angle, there is an error in the tire lateral force. In order to ensure the performance of the controller when the tire is running in the nonlinear region, we fit the tire lateral force error data under different tire side slip angles and vertical loads to obtain the tire cornering stiffness compensation coefficient. In practical application, the cornering stiffness in the model can be corrected in real time by offline table lookup.

[0109] The formula between tire cornering stiffness and vertical load is as follows:

[0110] C C = KC = K(aF z 2 +bF z +c)

[0111] In the formula, K is the tire cornering stiffness compensation coefficient, C C is the corrected tire cornering stiffness.

[0112] Further, a multi-dimensional state data set of commercial vehicles under different loads is generated by a driving simulator, and then the real-time determination of vehicle driving state is realized through the K-means clustering algorithm.

[0113] Due to the potential dangers of collecting stability data from actual vehicle tests, the present invention uses a driving simulator based on Dspace to obtain a data set representing the vehicle's stability. The data acquisition conditions are set to two-line transformation conditions and the acquisition time is 10s.

[0114] Compared with passenger cars, commercial buses have a higher center of mass and a greater risk of rollover. Moreover, changes in the number of passengers in a commercial vehicle will affect the vehicle's driving stability. In order to fully identify changes in vehicle stability during driving, we divide the vehicle passenger capacity into five levels: 0 passengers (zero passenger capacity), 8 passengers (25% passenger capacity), 16 passengers (half passenger capacity), 24 passengers (75% passenger capacity) and 32 passengers (full capacity). The selected vehicle steady-state parameters are: lateral velocity V y 、lateral accelerationɑ y , yaw angular velocity ω, roll angle ψ, roll rate V xR , vehicle sideslip angle β, vehicle sideslip angular velocity β R , and the vertical load on each wheel. The vertical load of the wheel is converted into the lateral load transfer rate and expressed as:

[0115]

[0116] S3: According to the lateral tracking error and vehicle driving state, the prediction range of the AKCMPC controller is adaptively adjusted based on fuzzy rules to improve the trajectory tracking performance.

[0117] The present invention uses the vehicle state level (L) and the trajectory tracking lateral error (E) as input to predict the time domain change (ΔN P )) as output. A fuzzy design control algorithm is used to achieve dynamic optimization and adjustment in the prediction domain.

[0118] The fuzzy subset of the vehicle state level L is {NB, NS, PS, PB}, which is described by the linguistic variable "stable (state 1)". "stable trend (state 2)", "unstable trend (state 3)", "unstable (state 4)", and the discourse domain is [1, 4]. The fuzzy subset of the trajectory tracking error E is {NB, NM, NS, ZO, PS, PM, PB}, which is described by the seven labels of the linguistic variable "very small" to "very large". When the driving state level is high, the prediction time domain N should be appropriately expanded. P , in order to improve the vehicle driving stability. On the contrary, when the vehicle state level is low and the trajectory tracking error is large, the prediction time domain N should be appropriately reduced. P , to correct the trajectory. In addition, when the vehicle generates a large tracking error due to the steering process, a larger prediction time domain should be selected to ensure the vehicle's driving stability.

[0119] The actual prediction range of the AKCMPC controller is set to:

[0120] where N P0 is the initial predicted horizon, ΔN P is the output of the fuzzy controller, Fit is the data fitting, and Round is a function to find the integer.

[0121] Further, an AKCMPC controller is designed.

[0122] Define the state vector as The input variable is u = [δ f ], and the output vector is Combining the above equations, the nonlinear time-varying state space equation of the vehicle dynamics model can be constructed as:

[0123]

[0124] The linear time-varying state space equation of the vehicle dynamics model is:

[0125]

[0126] Discretize the above equation using the Euler method, and the discretized linear equation is:

[0127]

[0128] A k = I + TA,B k = TB,C k = C

[0129] where k is the current sampling time, and T is the discrete sampling time.

[0130] To reduce the static error, the incremental model obtained is:

[0131]

[0132] where

[0133] The system output expression in the prediction horizon is:

[0134] Y(k) = Ψx(k) + Θ k ΔU(k)

[0135] where,

[0136]

[0137] N C and N P are the control horizon and the prediction horizon, respectively.

[0138] Create the following objective function:

[0139]

[0140] where ε is the relaxation factor; R, Q, ρ are weight coefficients.

[0141] Combining the objective function with the linear constraints, it can be transformed into a standard QP problem. In each step, the optimal control increment sequence in the control layer is solved, and the first element of the sequence is taken as the input of the next controlled object. By continuously repeating this process, the tracking control of the reference trajectory can be realized.

[0142] In order to quantitatively evaluate the tracking performance under different controllers, a multi-objective evaluation system is introduced, which is expressed as

[0143]

[0144] where J is the overall index for evaluating the quality of vehicle trajectory tracking, including the lateral error evaluation index Ja, the direction error evaluation index Jb, the roll angle evaluation index Jc, and the lateral acceleration evaluation index Jd. Where t represents the duration of the driving experiment, is the constant threshold value of the objective evaluation index, and W i is the weight coefficient of each evaluation index.

[0145] Figure 7 are the double-lane tracking results of the zero-load vehicle, the half-load vehicle and the full-load vehicle. From Figure 7 (a) It can be found that under different load conditions, AKCMPC controller can improve the tracking accuracy of part of the tracking area, which verifies the tracking performance of the controller.

[0146] Figure 7 (b) (c) show the handling stability results under different load conditions. Compared with CMPC and KCMPC controllers, AKCMPC controller reduces the maximum yaw rate and sideslip angle to the greatest extent. For the zero-load vehicle, AKCMPC controller reduces the maximum yaw rate and sideslip angle by 22.2% and 6.6% respectively compared with KCMPC controller, and by 11.6% and 7% respectively compared with CMPC controller. When half-loaded, it reduces by 5.1% and 7.5% compared with KCMPC controller, and by 3.6% and 7.7% compared with CMPC controller. For the full-load vehicle, AKCMPC controller reduces the yaw rate and sideslip angle by 6.5% and 9.8% respectively compared with KCMPC controller, and by 4.6% and 9.9% respectively compared with CMPC controller. The results show that AKCMPC controller significantly improves the handling stability of the vehicle during tracking.

[0147] In addition, the comprehensive evaluation index J of the AKCMPC controller is obviously lower than that of the KCMPC and CMPC controllers under different load conditions. This further verifies the superior tracking performance of the AKCMPC controller.

[0148] Please refer to Figure 8 , provides a commercial vehicle lane change trajectory planning and tracking control method, the effectiveness verification method comprises:

[0149] A joint verification platform based on a dSPACE SCALEXIO box is established.

[0150] The tracking results are as follows. It should be noted that ACMPC and B-CMPC represent the tracking results of the CMPC controller for the new (ahppso-b spline curve) and original (b spline curve) lane change trajectories, respectively. A-AKCMPC and A-KCMPC represent the tracking results of the AKCMPC and KCMPC controllers for the new trajectory, respectively.

[0151] For the lane change trajectory planning method, from Figure 7 It can be seen that the new lane change trajectory (ahppso-b spline curve) is smoother than the original lane change trajectory (b spline curve). Therefore, in the actual tracking process of the commercial vehicle, the extreme values and fluctuation ranges of vehicle state parameters such as yaw rate and roll angle are relatively small. This shows that compared with the original method, the ahppso-b spline curve proposed in the present application can reduce the risk of instability when the commercial vehicle changes lanes, and further improve the driving safety.

[0152] Further, the effectiveness of the trajectory tracking controller AKCMPC is verified. Compared with the KCMPC and CMPC controllers, the AKCMPC controller not only improves the tracking accuracy in some aspects, but also effectively reduces the maximum values of vehicle state parameters such as yaw rate and roll angle during the lane change process, and accelerates the convergence speed of the parameters. These conclusions are consistent with the results of the TruckSim / Simulink joint simulation, indicating that the AKCMPC controller proposed in the present application can effectively improve the tracking performance during the lane change process of the commercial vehicle.

[0153] Further, under the full load condition, the comprehensive evaluation index J of the CMPC controller tracking the new trajectory (ahppso-b spline curve) is less than the comprehensive evaluation index J of tracking the original trajectory (b spline curve). In addition, under the same lane change trajectory, the comprehensive evaluation index J of the AKCMPC controller is significantly lower than that of the KCMPC and CMPC controllers. These results further verify the good planning and tracking performance of the planning method based on the ahppso-b spline curve and the AKCMPC controller.

[0154] In another embodiment of the present application, a computer device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method process or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the lane trajectory planning and tracking control method of the electric commercial vehicle.

[0155] In another embodiment of the present application, the present application further provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in the computer device, and is configured to store programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the computer device, and of course can also include the expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the lane trajectory planning and tracking control method of the electric commercial vehicle in the above embodiments.

[0156] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0157] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the

[0158] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the

[0160] The application further provides a computer program product, which is used for executing any one of the electric commercial vehicle lane-changing trajectory planning and tracking control methods described above. Since the computer program product provided by the application and the electric commercial vehicle lane-changing trajectory planning and tracking control method described above belong to the same inventive concept, the computer program product provided by the application has all the advantages of the electric commercial vehicle lane-changing trajectory planning and tracking control method described above, and thus the beneficial effects of the computer program product provided by the application are not described one by one herein.

[0161] In the present application, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, different embodiments or examples described in the present specification and the features of different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0162] Finally, it should be noted that: the above-described embodiments are only specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them, the protection scope of the present application is not limited thereto, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily think of changes to the technical solutions recorded in the foregoing embodiments within the technical range disclosed by the present application, or make equivalent replacement to some technical features; and these modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and all should be covered in the protection scope of the present application.

Claims

1. A lane-changing trajectory planning and tracking control method for an electric commercial vehicle, characterized in that: include: The weight coefficient is determined based on the hierarchical analysis method, and the arc value of the control point is optimized by the particle swarm optimization algorithm to generate the B-spline lane change trajectory. The vehicle multi-dimensional state data set under different loads is collected through a driving simulator, and the vehicle driving state level is determined in real time using the K-means clustering algorithm; A functional relationship model between tire cornering stiffness and vertical load is established. The Magic Tire formula is used to correct tire lateral force in real time, and a tire cornering stiffness compensation coefficient is introduced to obtain the corrected tire stiffness. The vehicle state level and trajectory tracking lateral error are used as fuzzy controller inputs to dynamically adjust the prediction horizon of the model predictive controller; Based on the B-spline lane change trajectory and the corrected tire stiffness, the front wheel angle control value is output through the adjusted model predictive controller to achieve trajectory tracking.

2. The lane change trajectory planning and tracking control method for an electric commercial vehicle according to claim 1, characterized in that: The functional relationship model between tire cornering stiffness and vertical load is: C=aF z 2 +bF z +c Where C is the tire cornering stiffness; a, b, c are fitting coefficients; F z is the vertical load on the tire.

3. The lane change trajectory planning and tracking control method for an electric commercial vehicle according to claim 1, characterized in that: Combined with the particle swarm optimization algorithm to optimize the radian value of the control point, the optimization objective function is: min J c =in c1 S+w c2 w+w c3 dk Where S is the length of the lane change trajectory, k is the maximum curvature, dk is the maximum curvature derivative, and w c1 、w c2 、w c3 is the weight coefficient.

4. The lane change trajectory planning and tracking control method for an electric commercial vehicle according to claim 1, characterized in that: The multidimensional state data set includes the lateral velocity V y 、lateral accelerationɑ y , yaw angular velocity ω, roll angle ψ, roll rate V xR , vehicle sideslip angle β and vehicle sideslip angular velocity β R , and the lateral load transfer rate of each wheel converted from vertical load: Where, R1 is the front wheel lateral load transfer rate; R2 is the rear wheel lateral load transfer rate; F z_L1 is the vertical load on the left front wheel; F z_R1 is the vertical load on the right front wheel; F z_L2i is the vertical load on the inner wheel of the left rear axle; F z_L2o F is the vertical load on the outer wheel of the left rear axle; z_R2i is the vertical load on the inner wheel of the right rear axle; F z_R2o is the vertical load on the outer wheel of the right rear axle.

5. The lane change trajectory planning and tracking control method for an electric commercial vehicle according to claim 1, characterized in that: The prediction time domain adjustment formula is: N P =N P0 +Round(Fit(ΔN P )) Among them, N P is the adjusted prediction time domain; N P0 is the initial prediction time domain; Round is the function for finding integers; Fit is the data fitting function; ΔN P It is the time domain variation of the fuzzy controller output.

6. The lane change trajectory planning and tracking control method for an electric commercial vehicle according to claim 5, characterized in that: The objective function of the model predictive controller is: Among them, η ref is the reference value of the B-spline lane change trajectory, and Nc is the control time domain.

7. The lane change trajectory planning and tracking control method for an electric commercial vehicle according to claim 1, characterized in that: The magic tire formula is used to correct tire lateral force in real time, specifically: F y =D t sin{C t arcane[B t α-E t (B t α-arctan(B t α))]} Where, F y represents the lateral force of the tire; α represents the tire slip angle; B t 、C t 、D t 、E t is the matching coefficient.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for lane change trajectory planning and tracking control of an electric commercial vehicle as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for planning and tracking lane change trajectories of an electric commercial vehicle as claimed in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that When the computer program product is executed by a processor, it implements the lane change trajectory planning and tracking control method for an electric commercial vehicle as described in any one of claims 1 to 7.

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