Four-wheel steering intelligent vehicle overtaking trajectory planning method based on vehicle networking communication

Abstract

The application discloses a four-wheel steering intelligent automobile overtaking track planning method based on Internet of Vehicles communication, and comprises the following steps: a lane changing scene is constructed, and surrounding vehicle information is acquired; a four-wheel steering automobile vehicle dynamics model is established; a four-wheel steering intelligent automobile track equation is constructed, an overtaking track optimization objective function is constructed according to the comfort requirement and the lane changing efficiency of the intelligent vehicle; a rectangular vehicle model is established, a rectangle slightly larger than the actual size of the vehicle is used to replace the actual vehicle contour, and the model amplification coefficient can be dynamically adjusted according to the driving style, such as conservative, cautious, traditional, enterprising and adventurous, so as to determine the collision constraint condition of the overtaking track optimization; the constraint condition of the overtaking track optimization is determined; the optimal track is solved; and the MPC controller is used to track the obtained overtaking track.

Description

Technical Field

[0001] This invention relates to a method for overtaking trajectory planning of a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication. Background Technology

[0002] Overtaking places high demands on the reliability, robustness, and driving comfort of improving road safety and transportation efficiency. Meanwhile, four-wheel steering effectively enhances vehicle agility. Reverse rotation of the front and rear wheels reduces the turning radius and improves the servo system's agility at low speeds; it also reduces the probability of skidding and improves stability at high speeds. By using polynomial functions for lane-change trajectory planning and establishing an extended rectangular vehicle model, collision avoidance conditions are obtained. Using vehicle comfort requirements and lane-change efficiency as optimization variables in the optimization function, and employing model predictive control (MPC) to control trajectory tracking, the safety and convenience of overtaking behavior can be effectively improved. Summary of the Invention

[0003] The purpose of this invention is to provide a method for overtaking trajectory planning of a four-wheel steering intelligent vehicle based on vehicle network communication, so as to solve the problems faced in the above-mentioned background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for overtaking trajectory planning of a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication, comprising the following steps:

[0005] S101. Construct a lane change scenario and obtain information about surrounding vehicles;

[0006] S201. Establish a dynamics model for a four-wheel steering vehicle.

[0007] S301. Construct the trajectory equation of a four-wheel steering intelligent vehicle, wherein the overtaking process includes the overtaking vehicle (i.e., the vehicle itself) and the slower-moving vehicle in front (i.e., the vehicle in front); the overtaking process is divided into three stages: the first stage is lane change; the second stage is overtaking; and the third stage is returning to the original lane.

[0008] S302. Construct an objective function for overtaking trajectory optimization based on the comfort requirements and lane-changing efficiency of intelligent vehicles;

[0009] S303. Establish a rectangular vehicle model. The model uses a rectangle slightly larger than the actual size of the vehicle to replace the actual vehicle outline. The model magnification factor can be dynamically adjusted according to the driving style, such as conservative, cautious, traditional, aggressive, and adventurous, in order to determine the collision constraints for overtaking trajectory optimization.

[0010] S304. Determine the constraints for overtaking trajectory optimization;

[0011] S401, Solve for the optimal trajectory;

[0012] S501. Use the MPC controller to track the obtained overtaking trajectory.

[0013] Step S101 obtains surrounding vehicle information through vehicle-to-everything (V2X) communication. The obtained vehicle information includes the position, speed, acceleration, and lane-changing intention of the current vehicle and surrounding vehicles. The current road scene is obtained through the vehicle-mounted environmental perception system and the high-definition map of the autonomous intelligent vehicle.

[0014] The four-wheel steering vehicle dynamics model established in step S201 is as follows:

[0015]

[0016] Where Y represents the lateral position, v0 represents the lateral velocity, and u represents the longitudinal velocity. K is the vehicle's heading angle. f K represents the front axle lateral stiffness. r δ is the rear axle lateral stiffness, m is the vehicle mass, I is the moment of inertia, a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle, and δ is the lateral stiffness. f δ is the front wheel steering angle. r This is the rear wheel steering angle.

[0017] The trajectory equation of the four-wheel steering intelligent vehicle constructed in step S301 describes the longitudinal and lateral trajectories of the third stage of overtaking using fifth-order polynomials:

[0018]

[0019]

[0020] Where X3(t) and Y3(t) represent the longitudinal and lateral trajectories of the car when it is overtaking in the third stage, respectively, v1 is the speed of the car, W0 is the lane width, T is the lane change time, and D0 is the horizontal distance traveled by the car. T0 and D0 are the parameters to be optimized. For ease of expression, let S0 = v1T - D0, then the new parameters to be optimized are T0 and S0.

[0021] Based on the trajectory characteristics of the first, second, and third stages of the overtaking process, and unifying these three stages into a single coordinate system in chronological order, the overall overtaking trajectory for the three stages is as follows:

[0022]

[0023]

[0024] in L represents the overtaking distance.

[0025] Step S302 constructs the overtaking trajectory optimization objective function J based on the comfort requirements and lane-changing efficiency of the intelligent vehicle, as follows:

[0026]

[0027] Where T represents lane change time, the shorter the lane change time, the higher the efficiency of overtaking and lane changing.

[0028] a x ,a y These represent the lateral and longitudinal accelerations of the car, respectively. The derivatives of these accelerations have a significant impact on passenger comfort. The derivatives of the lateral and longitudinal acceleration of a car are used to comprehensively measure the entire overtaking and lane-changing process. The smaller the value, the higher the passenger comfort. δ1 and δ2 are the weighting coefficients of the lateral and longitudinal acceleration derivatives, and δ1+δ2=1.

[0029] Δε represents the difference between the vehicle displacement at each moment and the vehicle displacement at the next moment. This parameter is used to comprehensively measure the stability of a smart car during the entire overtaking and lane-changing process, and it has a significant impact on passenger comfort.

[0030] S(ζ T ),S(ζ tar ) represents the sum of the areas of the triangles at each vertex of the rectangular vehicle model, ζ T This vehicle, ζ tar Indicates the preceding vehicle, [S(ζ T )-S(ζ tar [)] is the safety factor for intelligent vehicles when overtaking or changing lanes;

[0031] β1, β2, and β3 are weighting coefficients for vehicle comfort requirements, lane change efficiency requirements, and safety factor requirements. They can be dynamically adjusted according to the vehicle's driving status and the current overtaking environment, and β1 + β2 + β3 = 1.

[0032] The rectangular vehicle model in step S303 is as follows:

[0033] When the vehicle is stationary, the coordinates of the four vertices A, B, C, and D are determined by the following formula:

[0034] A=(x(t)+L veh / 2,y(t)+W veh / 2)

[0035] B=(x(t)+L veh / 2,y(t)-W veh / 2)

[0036] C=(x(t)-L veh / 2,y(t)-W veh / 2)

[0037] D=(x(t)-L veh / 2,y(t)+W veh / 2)

[0038] The coordinate system described above is a rectangular vehicle model coordinate system, with the origin coinciding with the vehicle's center of mass O. A is the left vertex of the vehicle's front, and vertices B, C, and D are determined in a clockwise direction. L veh For the vehicle length, W veh Let (x(t), y(t)) be the width of the vehicle, and (x(t), y(t)) be the coordinates of the vehicle's center of mass.

[0039] The coordinates of the four vertices of a vehicle when it is overtaking are determined by the following formula:

[0040]

[0041]

[0042]

[0043]

[0044] Where A1, B1, C1, and D1 are the coordinates of the four vertices when the vehicle is overtaking, α is the angle between the line connecting OA and the x-axis, θ is the yaw angle of the vehicle during lane changing, and λ is the model magnification factor, which is dynamically adjusted according to the driving style, such as cautious, conservative, traditional, aggressive, and adventurous, and λ is greater than 1.

[0045] The collision constraints for overtaking trajectory optimization in step S304 are determined by the following formula:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] Among them, A k B k C k D k S represents the coordinates of the four vertices of the vehicle in front when the vehicle is overtaking. A,B,C,D These are the sum of the triangle areas at each vertex of the rectangular vehicle model in front and the rectangular vehicle model in this vehicle, respectively, and S is the area of ​​the rectangular vehicle model in this vehicle.

[0052] The specific collision constraint conditions are: SA,B,C,D If all values ​​are less than S, then a collision will not occur.

[0053] The acceleration constraint condition for overtaking trajectory optimization in step S304 is determined by the following formula:

[0054]

[0055] The X-axis velocity constraint condition for overtaking trajectory optimization in step S304 is determined by the following formula:

[0056]

[0057] Step S501 uses the MPC method to track the obtained overtaking trajectory:

[0058] Discretize the four-wheel steering vehicle dynamics model established in step S201:

[0059]

[0060] The above formula can be simplified to the following form:

[0061] x(k+1)=A(k)x(k)+B(k)u(k)

[0062] The objective function of the MPC controller is determined by the following formula:

[0063]

[0064]

[0065] Among them, Y ref The lateral position of the desired path. For the desired vehicle heading angle, Let Δu be the vehicle's angular velocity, and Q, P, S, and R be the weighting coefficients of the path following error, angular velocity error, and control output increment, respectively. Let ε be the relaxation factor, ρ be the weighting coefficient of the relaxation factor, and T be the control output increment. p and T c These are the prediction time domain and the control time domain, respectively.

[0066] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0067] 1. This invention is a four-wheel steering intelligent vehicle overtaking trajectory planning method based on vehicle-to-everything (V2X) communication. Four-wheel steering can effectively improve vehicle agility. The counter-rotation of the front and rear wheels reduces the turning radius and improves the agility of the servo system at low speeds; the counter-rotation of the front and rear wheels reduces the probability of skidding accidents and improves stability at high speeds.

[0068] 2. This invention constructs an overtaking trajectory optimization objective function based on the comfort requirements and lane-changing efficiency of intelligent vehicles. It effectively considers the comfort of the driver and passengers while also taking into account lane-changing efficiency, thereby improving the efficiency of overtaking and reducing the impact of overtaking behavior on road traffic.

[0069] 3. The rectangular vehicle model established in this invention uses a rectangle slightly larger than the actual size of the vehicle to replace the actual vehicle outline. The model magnification factor can be dynamically adjusted according to the driving style, such as conservative, cautious, traditional, aggressive, or adventurous, to determine the collision constraints for overtaking trajectory optimization. It fully considers the impact of the driver's behavior on overtaking behavior, which can improve the safety and speed of overtaking behavior. Attached Figure Description

[0070] The present invention will be further described below with reference to the accompanying drawings:

[0071] Figure 1 This invention proposes a method for overtaking trajectory planning of a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication. Detailed Implementation

[0072] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0073] like Figure 1 As shown, the present invention provides the following technical solution: a method for overtaking trajectory planning of a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication, comprising the following steps:

[0074] S101. Construct a lane change scenario and obtain information about surrounding vehicles;

[0075] S201. Establish a dynamics model for a four-wheel steering vehicle.

[0076] S301. Construct the trajectory equation of a four-wheel steering intelligent vehicle, wherein the overtaking process includes the overtaking vehicle (i.e., the vehicle itself) and the slower-moving vehicle in front (i.e., the vehicle in front); the overtaking process is divided into three stages: the first stage is lane change; the second stage is overtaking; and the third stage is returning to the original lane.

[0077] S302. Construct an objective function for overtaking trajectory optimization based on the comfort requirements and lane-changing efficiency of intelligent vehicles;

[0078] S303. Establish a rectangular vehicle model. The model uses a rectangle slightly larger than the actual size of the vehicle to replace the actual vehicle outline. The model magnification factor can be dynamically adjusted according to the driving style, such as conservative, cautious, traditional, aggressive, and adventurous, in order to determine the collision constraints for overtaking trajectory optimization.

[0079] S304. Determine the constraints for overtaking trajectory optimization;

[0080] S401, Solve for the optimal trajectory;

[0081] S501. Use the MPC controller to track the obtained overtaking trajectory.

[0082] Step S101 obtains surrounding vehicle information through vehicle-to-everything (V2X) communication. The obtained vehicle information includes the position, speed, acceleration, and lane-changing intention of the current vehicle and surrounding vehicles. The current road scene is obtained through the vehicle-mounted environmental perception system and the high-definition map of the autonomous intelligent vehicle.

[0083] The four-wheel steering vehicle dynamics model established in step S201 is as follows:

[0084]

[0085] Where Y represents the lateral position, v0 represents the lateral velocity, and u represents the longitudinal velocity. K is the vehicle's heading angle. f K represents the front axle lateral stiffness. r δ is the rear axle lateral stiffness, m is the vehicle mass, I is the moment of inertia, a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle, and δ is the lateral stiffness. f δ is the front wheel steering angle. r This is the rear wheel steering angle.

[0086] The trajectory equation of the four-wheel steering intelligent vehicle constructed in step S301 describes the longitudinal and lateral trajectories of the third stage of overtaking using fifth-order polynomials:

[0087]

[0088]

[0089] Where X3(t) and Y3(t) represent the longitudinal and lateral trajectories of the car when it is overtaking in the third stage, respectively, v1 is the speed of the car, W0 is the lane width, T is the lane change time, and D0 is the horizontal distance traveled by the car. T0 and D0 are the parameters to be optimized. For ease of expression, let S0 = v1T - D0, then the new parameters to be optimized are T0 and S0.

[0090] Based on the trajectory characteristics of the first, second, and third stages of the overtaking process, and unifying these three stages into a single coordinate system in chronological order, the overall overtaking trajectory for the three stages is as follows:

[0091]

[0092]

[0093] in L represents the overtaking distance.

[0094] Step S302 constructs the overtaking trajectory optimization objective function J based on the comfort requirements and lane-changing efficiency of the intelligent vehicle, as follows:

[0095]

[0096] Where T represents lane change time, the shorter the lane change time, the higher the efficiency of overtaking and lane changing.

[0097] a x ,a y These represent the lateral and longitudinal accelerations of the car, respectively. The derivatives of these accelerations have a significant impact on passenger comfort. The derivatives of the lateral and longitudinal acceleration of a car are used to comprehensively measure the entire overtaking and lane-changing process. The smaller the value, the higher the passenger comfort. δ1 and δ2 are the weighting coefficients of the lateral and longitudinal acceleration derivatives, and δ1+δ2=1.

[0098] Δε represents the difference between the vehicle displacement at each moment and the vehicle displacement at the next moment. This parameter is used to comprehensively measure the stability of a smart car during the entire overtaking and lane-changing process, and it has a significant impact on passenger comfort.

[0099] S(ζ T ),S(ζ tar ) represents the sum of the areas of the triangles at each vertex of the rectangular vehicle model, ζ T This vehicle, ζ tar Indicates the preceding vehicle, [S(ζ T )-S(ζ tar [)] is the safety factor for intelligent vehicles when overtaking or changing lanes;

[0100] β1, β2, and β3 are weighting coefficients for vehicle comfort requirements, lane change efficiency requirements, and safety factor requirements. They can be dynamically adjusted according to the vehicle's driving status and the current overtaking environment, and β1 + β2 + β3 = 1.

[0101] The rectangular vehicle model in step S303 is as follows:

[0102] When the vehicle is stationary, the coordinates of the four vertices A, B, C, and D are determined by the following formula:

[0103] A=(x(t)+L veh / 2,y(t)+W veh / 2)

[0104] B=(x(t)+L veh / 2,y(t)-W veh / 2)

[0105] C=(x(t)-Lveh / 2,y(t)-W veh / 2)

[0106] D=(x(t)-L veh / 2,y(t)+W veh / 2)

[0107] The coordinate system described above is a rectangular vehicle model coordinate system, with the origin coinciding with the vehicle's center of mass O. A is the left vertex of the vehicle's front, and vertices B, C, and D are determined in a clockwise direction. L veh For the vehicle length, W veh Let (x(t), y(t)) be the width of the vehicle, and (x(t), y(t)) be the coordinates of the vehicle's center of mass.

[0108] The coordinates of the four vertices of a vehicle when it is overtaking are determined by the following formula:

[0109]

[0110]

[0111]

[0112]

[0113] Where A1, B1, C1, and D1 are the coordinates of the four vertices when the vehicle is overtaking, α is the angle between the line connecting OA and the x-axis, θ is the yaw angle of the vehicle during lane changing, and λ is the model magnification factor, which is dynamically adjusted according to the driving style, such as cautious, conservative, traditional, aggressive, and adventurous, and λ is greater than 1.

[0114] The collision constraints for overtaking trajectory optimization in step S304 are determined by the following formula:

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] Among them, A k B k C k D k S represents the coordinates of the four vertices of the vehicle in front when the vehicle is overtaking. A,B,C,D These are the sum of the triangle areas at each vertex of the rectangular vehicle model in front and the rectangular vehicle model in this vehicle, respectively, and S is the area of ​​the rectangular vehicle model in this vehicle.

[0121] The specific collision constraint conditions are: S A,B,C,D If all values ​​are less than S, then a collision will not occur.

[0122] The acceleration constraint condition for overtaking trajectory optimization in step S304 is determined by the following formula:

[0123]

[0124] The X-axis velocity constraint condition for overtaking trajectory optimization in step S304 is determined by the following formula:

[0125]

[0126] Step S501 uses the MPC method to track the obtained overtaking trajectory:

[0127] Discretize the four-wheel steering vehicle dynamics model established in step S201:

[0128]

[0129] The above formula can be simplified to the following form:

[0130] x(k+1)=A(k)x(k)+B(k)u(k)

[0131] The objective function of the MPC controller is determined by the following formula:

[0132]

[0133]

[0134] Among them, Y ref The lateral position of the desired path. For the desired vehicle heading angle, Let Δu be the vehicle's angular velocity, and Q, P, S, and R be the weighting coefficients of the path following error, angular velocity error, and control output increment, respectively. Let ε be the relaxation factor, ρ be the weighting coefficient of the relaxation factor, and T be the control output increment. p and T c These are the prediction time domain and the control time domain, respectively.

Claims

1. A method for planning overtaking trajectory of a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication, characterized in that, Includes the following steps: S101. Construct an overtaking and lane-changing scenario and obtain information about surrounding vehicles; S201. Establish a dynamics model for a four-wheel steering vehicle. S301. Construct the trajectory equation of a four-wheel steering intelligent vehicle. The overtaking process includes the overtaking vehicle, i.e., the vehicle itself; and the vehicle ahead that is moving slower, i.e., the vehicle in front. The overtaking process is divided into three stages: the first stage is lane changing. The second stage is overtaking; the third stage is returning to the original lane. S302. Construct an objective function for optimizing the overtaking trajectory based on the comfort requirements and overtaking / lane-changing efficiency of intelligent vehicles. The objective function J for optimizing the overtaking trajectory, constructed based on the comfort requirements and overtaking / lane-changing efficiency of intelligent vehicles, is determined by the following formula: Where T represents lane change time; the shorter the lane change time, the higher the efficiency of overtaking and lane changing. a x ,a y These represent the lateral and longitudinal accelerations of the car, respectively. The derivatives of these accelerations have a significant impact on passenger comfort. The derivatives of the lateral and longitudinal acceleration of a car are used to comprehensively measure the entire overtaking and lane-changing process. The smaller the value, the higher the passenger comfort. δ1 and δ2 are the weighting coefficients of the lateral and longitudinal acceleration derivatives, and δ1+δ2=1. Δε represents the difference between the vehicle displacement at each moment and the vehicle displacement at the next moment. This parameter is used to comprehensively measure the stability of a smart car during the entire overtaking and lane-changing process, and it has a significant impact on passenger comfort. S(ζ T ),S(ζ tar ) represents the sum of the areas of the triangles at each vertex of the rectangular vehicle model, ζ T This vehicle, ζ tar Indicates the preceding vehicle, [S(ζ T )-S(ζ tar [)] is the safety factor for intelligent vehicles when overtaking or changing lanes; β1, β2, and β3 are weighting coefficients for vehicle comfort requirements, lane change efficiency requirements, and safety factor requirements. They can be dynamically adjusted according to the vehicle's driving status and the current overtaking environment, and β1 + β2 + β3 = 1. S303. Establish a rectangular vehicle model. The model uses a rectangle slightly larger than the actual vehicle size to represent the actual vehicle outline. The model magnification factor can be dynamically adjusted according to the driving style, which includes conservative, cautious, traditional, aggressive, and adventurous, to determine the collision constraints for overtaking trajectory optimization. The coordinates of the four vertices A, B, C, and D when the vehicle is stationary are determined by the following formula: A=(x(t)+L veh / 2,y(t)+W veh / 2) B=(x(t)+L veh / 2,y(t)-W veh / 2) C=(x(t)-L veh / 2,y(t)-W veh / 2) D=(x(t)-L veh / 2,y(t)+W veh / 2) The coordinate system described above is a rectangular vehicle model coordinate system, with the origin coinciding with the vehicle's center of mass O. A is the left vertex of the vehicle's front, and vertices B, C, and D are determined in a clockwise direction. L veh For the commander, W veh Let (x(t), y(t)) be the width of the vehicle, and (x(t), y(t)) be the coordinates of the vehicle's center of mass. The coordinates of the four vertices of a vehicle when it is overtaking are determined by the following formula: Where A1, B1, C1, and D1 are the coordinates of the four vertices when the vehicle is overtaking, α is the angle between the line connecting OA and the x-axis, θ is the yaw angle of the vehicle during lane changing, and λ is the model magnification factor, which is dynamically adjusted according to the driving style, including cautious, conservative, traditional, aggressive, and adventurous, and λ is greater than 1. S304. Determine the constraints for overtaking trajectory optimization; S401. Under the condition of satisfying the constraints, minimize the objective function to solve for the optimal trajectory; S501. Use the MPC controller to track the obtained overtaking trajectory.

2. The overtaking trajectory planning method for a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication according to claim 1, characterized in that, Step S101 obtains information about surrounding vehicles through vehicle-to-everything (V2X) communication. The obtained vehicle information includes the position, speed, acceleration, and lane-changing intention of the current vehicle and surrounding vehicles. The current road scene is obtained through the vehicle-mounted environmental perception system and high-definition maps of autonomous intelligent vehicles.

3. The overtaking trajectory planning method for a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication according to claim 1, characterized in that, The four-wheel steering vehicle dynamics model established in step S201 is determined by the following formula: Where Y represents the lateral position, v0 represents the lateral velocity, and u represents the longitudinal velocity. K is the vehicle's heading angle. f K represents the front axle lateral stiffness. r δ is the rear axle lateral stiffness, m is the vehicle mass, I is the moment of inertia, a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle, and δ is the lateral stiffness. f δ is the front wheel steering angle. r This is the rear wheel steering angle.

4. The overtaking trajectory planning method for a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication according to claim 1, characterized in that, The trajectory equation of the four-wheel steering intelligent vehicle constructed in step S301 describes the longitudinal and lateral trajectories of the third stage of overtaking using fifth-order polynomials: Where X3(t) and Y3(t) represent the longitudinal and lateral trajectories of the car when it is overtaking in the third stage, respectively, v1 is the speed of the car, W0 is the lane width, T is the lane change time, and D0 is the horizontal distance traveled by the car. T0 and D0 are the parameters to be optimized. For ease of expression, let S0 = v1T - D0, then the new parameters to be optimized are T0 and S0. Based on the trajectory characteristics of the first, second, and third stages of the overtaking process, and unifying these three stages into a single coordinate system in chronological order, the overall overtaking trajectory for the three stages is as follows: Where X(t) and Y(t) represent the longitudinal and lateral trajectories of the car during the overtaking phase, respectively. L represents the overtaking distance.

5. The overtaking trajectory planning method for a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication according to claim 1, characterized in that, The collision constraints for overtaking trajectory optimization in step S304 are determined by the following formula: Among them, A k B k C k D k S represents the coordinates of the four vertices of the vehicle in front when the vehicle is overtaking. A,B,C,D These are the sum of the triangle areas at each vertex of the rectangular vehicle model in front and the rectangular vehicle model in this vehicle, respectively, and S is the area of ​​the rectangular vehicle model in this vehicle. The specific collision constraint conditions are: S A,B,C,D If all values ​​are less than S, then a collision will not occur.

6. The overtaking trajectory planning method for a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication according to claim 1, characterized in that, The acceleration constraint condition for overtaking trajectory optimization in step S304 is determined by the following formula:

7. The overtaking trajectory planning method for a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication according to claim 1, characterized in that, The X-axis velocity constraint condition for overtaking trajectory optimization in step S304 is determined by the following formula:

8. The overtaking trajectory planning method for a four-wheel steering intelligent vehicle based on vehicle-to-everything (V2X) communication according to claim 1, characterized in that, Step S501 uses the MPC method to track the obtained overtaking trajectory: Discretize the four-wheel steering vehicle dynamics model established in step S201: The above formula can be simplified to the following form: x(k+1)=A(k)x(k)+B(k)u(k) The objective function of the MPC controller is determined by the following formula: Among them, Y ref The lateral position of the desired path. For the desired vehicle heading angle, Let Δu be the vehicle's angular velocity, and Q, P, S, and R be the weighting coefficients of the path following error, angular velocity error, and control output increment, respectively. Let ε be the relaxation factor, ρ be the weighting coefficient of the relaxation factor, and T be the control output increment. p and T c These are the prediction time domain and the control time domain, respectively.

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