A cooperative control method and device for vehicle trajectory following, a vehicle and a medium

Abstract

The application relates to the technical field of intelligent vehicle control, and discloses a cooperative control method and device for vehicle trajectory following, a vehicle and a medium, which are applied to a vehicle provided with a distributed electric drive system; the method comprises the following steps: obtaining running state information and surrounding environment information of the vehicle, and calculating a current lateral offset and a current heading angle deviation on the basis of the information; a first yaw moment is calculated by a first controller according to the current heading angle deviation; a target yaw angular velocity is determined on the basis of the running state information and the current lateral offset, and a second yaw moment is calculated by a second controller according to the target yaw angular velocity and the running state information; a total yaw moment is determined on the basis of the first yaw moment and the second yaw moment; driving torques of all the wheels are determined according to the total yaw moment, the current heading angle deviation and the running state information, and the corresponding driving torques are controlled to be executed by all the wheels, so that the vehicle can return to the original driving trajectory while the vehicle body is controlled to be returned to the normal position when the vehicle deviates in an extreme working condition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of vehicles, in particular to a cooperative control method and device for vehicle trajectory following, a vehicle and a medium. BACKGROUND

[0002] At present, with the vigorous development of intelligent transportation systems and new energy vehicle industries, the intelligent and safe control of vehicles has become the focus of industry research. However, in the high-speed driving scenario, vehicles are still seriously threatened by external interference factors such as crosswind and road water. For example, under the action of crosswind, vehicles will be subjected to lateral aerodynamic force and yaw moment, resulting in deviation of the driving direction; road water will reduce the adhesion coefficient of the tire and the ground, making the vehicle prone to side slip, spin and other dangerous situations. Based on this, the prior art uses the direction angle to effectively control the vehicle trajectory tracking, but this type of control scheme has many problems. For example, directly controlling the steering angle during vehicle driving may bring poor experience to the user; focusing on the steer-by-wire system, this type of scheme will not work for nonlinear steering systems, which has strong limitations; and this type of control scheme is more targeted at stable vehicle conditions, and has no good control effect when the vehicle body posture deviates suddenly during driving.

[0003] Based on this, existing research attempts to introduce a distributed electric drive system in vehicle trajectory control, specifically using its ability to independently control the torque of each wheel to achieve torque vectoring. However, most of this type of research uses a single control strategy, such as adjusting only the vehicle body posture or simply pursuing vehicle trajectory tracking, which cannot simultaneously consider the rapid return of the vehicle body posture and the effective recovery of the driving trajectory when the vehicle deviates. In addition, existing control algorithms lack adaptive ability in parameter adjustment and are difficult to adapt to complex and variable conditions such as different vehicle speeds and different interference intensities, resulting in unstable control effect of the vehicle in actual application, which further cannot meet the growing demand for driving safety. Therefore, it is urgent to develop a more efficient, intelligent and stable vehicle trajectory following control technology to ensure the safe driving of vehicles in complex environments. SUMMARY

[0004] The present application provides a cooperative control method and device for vehicle trajectory following, a vehicle and a medium to solve the problem that the prior art cannot achieve intelligent control of vehicle trajectory following in complex environments, thereby seriously affecting vehicle safety control and driving stability.

[0005] In a first aspect, the present application provides a cooperative control method for vehicle trajectory following applied to a vehicle equipped with a distributed electric drive system; the method comprises:

[0006] obtaining the running state information and the surrounding environment information of the vehicle;

[0007] calculating a current lateral offset and a current yaw angle deviation based on the running state information and the surrounding environment information, the current lateral offset representing a vertical distance from a front axle center of the vehicle to a center line of a lane;

[0008] calculating a first yaw moment by using a first controller according to the current yaw angle deviation;

[0009] determining a target yaw angular velocity based on the running state information and the current lateral offset, and calculating a second yaw moment by using a second controller according to the target yaw angular velocity and the running state information;

[0010] determining a total yaw moment based on the first yaw moment and the second yaw moment;

[0011] determining driving torques of each wheel according to the total yaw moment, the current yaw angle deviation and the running state information, and controlling each wheel to execute a corresponding driving torque to realize vehicle trajectory following.

[0012] The application calculates a lateral offset and a yaw angle deviation by using running state information and surrounding environment information of a vehicle, and uses a first controller to specifically correct the yaw angle deviation to ensure that a driving direction of the vehicle is consistent with a trajectory, and uses a second controller to derive a target yaw angular velocity according to the lateral offset to accurately correct a lateral position deviation of the vehicle. Furthermore, the total yaw moment is determined according to the first yaw moment and the second yaw moment to coordinate stability control and trajectory tracking, and then the attitude is stabilized preferentially in the initial stage of vehicle deviation, and a smooth transition to trajectory recovery is realized after the attitude is stabilized, and the inherent contradiction between “quick return” and “smooth return” of the vehicle is intelligently solved. Finally, the distributed electric drive system is fully utilized by accurately distributing driving torques of each wheel based on the total yaw moment, so that the control response is faster and more accurate. Even when the vehicle is disturbed by sudden interference such as crosswind and road water, the above-mentioned cooperative control strategy not only preferentially and quickly adjusts the vehicle attitude through the yaw moment to prevent side slipping and fishtailing, but also fundamentally improves the active safety and handling stability of the vehicle, and at the same time, the vehicle can smoothly and naturally return to the original driving trajectory from the deviated position, greatly improving the riding comfort and the accuracy of trajectory following.

[0013] In an optional embodiment, the current lateral offset and the current yaw angle deviation are calculated based on the running state information and the surrounding environment information, comprising:

[0014] obtaining a distance equation in a vehicle coordinate system, wherein the vehicle coordinate system is constructed with a rear axle center of the vehicle as an origin, an x-axis along a longitudinal direction of the vehicle forward, and a y-axis along a lateral direction of the vehicle left, and the distance equation represents a relationship curve between a distance from the rear axle center of the vehicle to a left lane line and a longitudinal distance of the vehicle body;

[0015] determining the vehicle front axle center coordinate according to the vehicle wheelbase in the running state information;

[0016] substituting the vehicle front axle center coordinate into the distance equation to obtain a first distance between the vehicle front axle center and the left lane line, determining a second distance between the lane center line and the left lane line according to the lane width in the surrounding environment information, and obtaining the current lateral offset according to the difference between the first distance and the second distance;

[0017] deriving the first order derivative of the distance equation to obtain a slope equation of the lane line tangent, substituting the vehicle front axle center coordinate into the slope equation to obtain the slope, solving the inverse tangent value of the slope to obtain the tangent angle, and obtaining the current heading angle deviation according to the difference between the current vehicle body heading angle and the tangent angle in the running state information.

[0018] The application designs a lateral offset calculation method, specifically, the front axle center coordinate is accurately positioned through the wheelbase, the difference between the distance from the front axle to the left lane line and the distance from the lane center line to the left lane line is combined to directly correspond to the lateral offset, that is, the definition of the vertical distance from the front axle center to the lane center line, the calculation result meets the actual control demand, and the deviation calculation accuracy is high; the heading angle deviation calculation is that the slope of the lane line tangent is obtained through the first order derivative of the distance equation, the tangent angle is obtained through the inverse tangent, and the heading angle deviation is accurately represented according to the difference between the vehicle body heading angle and the tangent angle, that is, the deviation degree of the vehicle direction and the lane direction, which further ensures that the tangent angle can truly reflect the current trend of the lane line; the above two types of deviation calculation methods realize accurate and efficient calculation of the lateral offset and the heading angle deviation, and provide reliable data support for subsequent trajectory following control.

[0019] In an optional implementation, the first controller is a proportional-integral controller; the first yaw moment is calculated by the first controller according to the current heading angle deviation, including:

[0020] a proportional-integral controller is constructed with the heading angle deviation as the feedback quantity and the yaw moment as the output control quantity, and the proportional coefficient and the integral coefficient in the proportional-integral controller are determined according to the heading angle deviation and the change rate of the vehicle and the vehicle speed;

[0021] the current heading angle deviation is input into the proportional-integral controller, and the first yaw moment is output.

[0022] The application deeply combines the traditional proportional-integral controller with the vehicle dynamics characteristics, introduces an adaptive gain mechanism based on real-time state, upgrades the first controller from a simple correction module to an intelligent, efficient and reliable vehicle body posture stabilizer, not only realizes accurate and rapid correction of the heading angle deviation to provide reliable input for the total yaw moment, but also realizes high-quality vehicle body return, and further lays a solid foundation for key technical support for subsequent trajectory recovery.

[0023] In an optional embodiment, the target yaw rate is determined based on the running state information and the current lateral offset, comprising:

[0024] Based on the running state information, a plurality of vehicle body longitudinal distances predicted in a preset time range are obtained;

[0025] Each vehicle body longitudinal distance is substituted into the distance equation respectively to obtain a corresponding vehicle body lateral distance, and the future driving trajectory of the vehicle is determined according to all vehicle body longitudinal distances and corresponding vehicle body lateral distances;

[0026] The target lateral offset is determined based on the future driving trajectory, and the current lateral offset is divided by the target lateral offset to obtain the current offset distance completion degree;

[0027] The target yaw rate is obtained by looking up the current offset distance completion degree and the current vehicle body heading angle in the running state information from a first preset table; the first preset table is constructed by a vehicle kinematics equation and is used to store the mapping relationship between different offset distance completion degrees and vehicle body heading angles and yaw rates.

[0028] The target yaw rate is determined by using the table lookup method, specifically by obtaining a plurality of longitudinal distances from the running state information of the vehicle, and substituting the plurality of longitudinal distances into the distance equation to predict the complete future driving trajectory; the target lateral offset is determined according to the predicted trajectory, and then the current lateral offset is divided by the target lateral offset to obtain the current offset distance completion degree, and the target yaw rate is obtained by looking up the current offset distance completion degree and the vehicle body heading angle, which not only realizes the accurate, efficient and actual working condition matching determination of the target yaw rate, but also provides a scientific target input for the subsequent calculation of the second yaw moment.

[0029] In an optional embodiment, the running state information at least includes the current yaw rate, and the second controller is a proportional-integral-derivative controller; the second yaw moment is calculated by using the second controller according to the target yaw rate and the running state information, comprising:

[0030] The proportional-integral-derivative controller is constructed with the yaw rate deviation as the feedback quantity and the yaw moment as the output control quantity, and the proportional coefficient, the integral coefficient and the differential coefficient in the proportional-integral-derivative controller are determined according to the yaw rate deviation and its change rate of the vehicle and the vehicle speed;

[0031] The target yaw rate is subtracted from the current yaw rate to obtain the current yaw rate deviation;

[0032] The current yaw rate deviation is input into the proportional-integral-derivative controller to output the second yaw moment.

[0033] The application deeply integrates a classic proportional-integral-derivative controller with an adaptive strategy, and makes the second controller adapt to the control requirement of yaw rate deviation through the second controller, so that the second controller can not only accurately and smoothly execute the trajectory recovery instruction, but also intelligently adapt to the changes of vehicle state and working condition, thereby realizing rapid and stable calculation of the second yaw moment, providing high-quality input for total yaw moment cooperative control, and helping to realize smooth, smooth and accurate automatic return of the original driving trajectory of the vehicle, thereby greatly improving the cooperative control effect of vehicle trajectory following.

[0034] In an optional embodiment, the running state information at least includes a current yaw rate and a current yaw rate change rate; and the total yaw moment is determined based on the first yaw moment and the second yaw moment, including:

[0035] The first weight coefficient is obtained by looking up the second preset table according to the current yaw rate and the current yaw rate change rate; the second preset table is used to store the mapping relationship between different yaw rates and yaw rate change rates and weight coefficients;

[0036] The second weight coefficient is determined based on the first weight coefficient, and the sum of the first weight coefficient and the second weight coefficient is 1;

[0037] The total yaw moment is obtained by multiplying the first yaw moment by the first weight coefficient, multiplying the second yaw moment by the second weight coefficient, and then adding them.

[0038] The application determines the total yaw moment by dynamically distributing the yaw moment, which can accurately capture the dynamic running state of the vehicle such as smooth driving, rapid correction, yaw oscillation, etc., thereby avoiding the insufficient adaptation of the fixed weight under complex working conditions, such as increasing the weight of the stability guiding moment when the yaw is severe, and focusing on accurate correction when the vehicle is driving smoothly, so that the total yaw moment always meets the real-time control requirement; the above torque distribution method realizes accurate cooperation of the first and second yaw moments, makes the total yaw moment adapt to the dynamic state of the vehicle in real time, provides stable and efficient control input for trajectory following, and greatly improves the accuracy and stability of trajectory following.

[0039] In an optional embodiment, the running state information at least includes a current vehicle speed; and the driving torque of each wheel is determined according to the total yaw moment, the current heading angle deviation and the running state information, including:

[0040] The target distribution coefficient is obtained by looking up the third preset table according to the current vehicle speed and the current heading angle deviation; the third preset table is used to store the mapping relationship between different vehicle speeds and heading angle deviations and distribution coefficients;

[0041] The driving torque of each wheel is determined according to the total yaw moment and the target distribution coefficient.

[0042] The application obtains a target distribution coefficient according to the current vehicle speed and the current heading angle deviation, and distributes the total yaw moment based on the target distribution coefficient to determine the driving torque of each wheel, so as to realize accurate and efficient distribution of the wheel driving torque of the distributed electric drive vehicle, and fully exert the advantage of differential torque distribution to provide stable execution guarantee for trajectory following.

[0043] In an optional embodiment, the driving torque of each wheel is determined according to the total yaw moment and the target distribution coefficient, comprising:

[0044] The original distribution torque of each wheel, the wheel radius and the target distance of each wheel are obtained respectively, and the target distance represents the distance from the wheel center to the vehicle center of mass;

[0045] The target distribution coefficient is distributed to obtain the torque coefficient of each wheel;

[0046] The torque coefficient of each wheel is multiplied by the total yaw moment to obtain the additional torque of each wheel;

[0047] The wheel radius of each wheel is divided by the target distance to obtain the correction coefficient of each wheel;

[0048] The additional torque of each wheel is multiplied by the corresponding correction coefficient, and then added to the original distribution torque of each wheel to obtain the driving torque of each wheel.

[0049] The application independently calculates the torque coefficient, the additional torque and the correction coefficient for each wheel, multiplies each additional torque by the corresponding correction coefficient, and then adds the original distribution torque to take into account the dual requirements of basic driving and deviation correction of the vehicle, not only realizes the fine and personalized calculation of the driving torque of each wheel, fully exerts the advantage of independent torque control of each wheel of the distributed electric drive system, but also ensures the synchronous optimization of the vehicle driving performance and the trajectory following performance, which guarantees the driving demand and accurately converts the yaw deviation intention.

[0050] In an optional embodiment, the surrounding environment information at least includes the current lane line; before calculating the current lateral deviation and the current heading angle deviation based on the running state information and the surrounding environment information, the method further comprises:

[0051] Based on the future driving trajectory and the current lane line, it is judged whether the vehicle deviates or not;

[0052] When the vehicle deviates, the step of calculating the current lateral deviation and the current heading angle deviation based on the running state information and the surrounding environment information is executed.

[0053] The application designs to start the calculation of the lateral deviation and the deviation of the heading angle only when the vehicle is determined to deviate, skips the link when the vehicle is not deviated, reduces unnecessary operation load, and further optimizes resource utilization.

[0054] In a second aspect, the application provides a cooperative control device for vehicle trajectory following, applied to a vehicle equipped with a distributed electric drive system.

[0055] An information acquisition module is configured to acquire running state information and surrounding environment information of the vehicle.

[0056] A deviation calculation module is configured to calculate a current lateral deviation and a current deviation of a heading angle based on the running state information and the surrounding environment information, the current lateral deviation representing a vertical distance from a front axle center of the vehicle to a center line of a lane.

[0057] An attitude adjustment module is configured to calculate a first yaw moment using a first controller according to the current deviation of the heading angle.

[0058] A path regression module is configured to determine a target yaw angular velocity based on the running state information and the current lateral deviation, and calculate a second yaw moment using a second controller according to the target yaw angular velocity and the running state information.

[0059] A moment coordination module is configured to determine a total yaw moment based on the first yaw moment and the second yaw moment.

[0060] A cooperative control module is configured to determine driving torques of each wheel according to the total yaw moment, the current deviation of the heading angle, and the running state information, and control each wheel to execute the corresponding driving torque, so as to realize vehicle trajectory following.

[0061] The cooperative control device for vehicle trajectory following of the application calculates the lateral deviation and the deviation of the heading angle based on the running state information and the surrounding environment information of the vehicle, uses the first controller to specifically correct the deviation of the heading angle, uses the second controller to derive the target yaw angular velocity according to the lateral deviation, further determines the total yaw moment according to the first yaw moment and the second yaw moment, and finally accurately allocates the driving torques of each wheel based on the total yaw moment. Even if the vehicle is disturbed by sudden interference such as crosswind and road water, the above cooperative control strategy can still preferentially and quickly adjust the vehicle body attitude through the yaw moment, fundamentally improves the active safety and handling stability of the vehicle, controls the vehicle to smoothly and naturally return to the original driving trajectory from the deviated position, and greatly improves the riding comfort and the accuracy of trajectory following.

[0062] In a third aspect, the present application provides a vehicle, the vehicle being equipped with a distributed electric drive system; the vehicle comprising a controller, the controller comprising a memory and a processor, the memory and the processor being communicatively connected with each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the cooperative control method for vehicle trajectory following according to the first aspect or any one of the corresponding embodiments thereof.

[0063] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium storing computer instructions, the computer instructions being used to make a computer execute the cooperative control method for vehicle trajectory following according to the first aspect or any one of the corresponding embodiments thereof.

[0064] The cooperative control method and device for vehicle trajectory following provided by the present application calculate the lateral offset and the heading angle deviation through the running state information and the surrounding environment information of the vehicle, correct the heading angle deviation by using the first controller to ensure that the driving direction of the vehicle is consistent with the trajectory, derive the target yaw rate according to the lateral offset by using the second controller, and then accurately correct the lateral position deviation of the vehicle, determine the total yaw moment according to the first yaw moment and the second yaw moment, coordinate the stability control and the trajectory tracking two targets, and then prioritize the attitude stabilization in the initial stage of the vehicle deviation, and smoothly transition to the trajectory recovery after the attitude is stabilized, intelligently solve the inherent contradiction between the "fast return" and the "smooth return" of the vehicle, and finally accurately distribute the driving torque of each wheel based on the total yaw moment, fully exert the hardware potential of the distributed electric drive system, make the control response faster and more accurate, and even when the vehicle is disturbed by sudden interference such as crosswind and road water, the vehicle body attitude is still stabilized through the above-mentioned cooperative control strategy, the vehicle body attitude is adjusted by the yaw moment to prevent the vehicle from skidding and fishtailing, the active safety and the handling stability of the vehicle are fundamentally improved, and the vehicle can also be controlled to smoothly and naturally return to the original driving trajectory from the deviated position, greatly improving the accuracy of trajectory following. BRIEF DESCRIPTION OF DRAWINGS

[0065] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0066] Figure 1 is the first flowchart of the cooperative control method for vehicle trajectory following according to the embodiments of the present application;

[0067] Figure 2Figure 2 is a second flowchart illustrating a cooperative control method for vehicle trajectory following according to an embodiment of the present application;

[0068] Figure 3 Figure 3 is a schematic diagram of a double closed-loop cooperative control vehicle trajectory following system architecture based on a distributed electric drive;

[0069] Figure 4 Figure 4 is a double closed-loop cooperative control schematic diagram;

[0070] Figure 5 Figure 5 is a structural block diagram of a cooperative control device for vehicle trajectory following according to an embodiment of the present application;

[0071] Figure 6 Figure 6 is a schematic diagram of a hardware structure of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0073] According to an embodiment of the present application, a cooperative control method for vehicle trajectory following is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0074] In the present embodiment, a cooperative control method for vehicle trajectory following is provided, which is applied to a vehicle equipped with a distributed electric drive system. Figure 1 Figure 1 is a first flowchart illustrating a cooperative control method for vehicle trajectory following according to an embodiment of the present application, as shown in Figure 1 the flowchart includes the following steps:

[0075] Step S101, obtaining the running state information and surrounding environment information of the vehicle.

[0076] It should be noted that the running state information in this embodiment refers to the motion parameters, attitude parameters and hardware states of the vehicle itself, which can reflect the current driving state (such as speed, direction, attitude) and the control basis (such as steering intention) of the vehicle; the surrounding environment information refers to the characteristic information representing the external road environment of the vehicle, especially the lane line information (such as position, curvature, boundary, etc.) related to trajectory following, which is used to determine the driving trajectory of the vehicle, judge whether the vehicle deviates from the lane, etc.; the specific content of the running state information and the surrounding environment information, and the acquisition method can be adaptively determined according to actual needs. For example, a high-definition camera carried by the vehicle collects the road image in front of the vehicle, and identifies the visual features (such as color, shape, pixel-level position, etc.) of the lane line in the image, and outputs the curvature, direction, etc. Information of the lane line; or use millimeter wave radar to stably detect the spatial position (such as distance, angle, etc.) of the lane boundary in rain, fog, snow and other bad weather, to make up for the lack of adaptability of the camera in bad environment; it can also be measured through the multi-source sensors carried by the vehicle, such as inertial measurement unit (IMU), vehicle speed sensor, steering wheel angle sensor, acceleration sensor, etc. Real-time measurement of vehicle yaw rate, vehicle speed, acceleration, steering wheel angle, etc., which are only exemplary.

[0077] In step S102, the current lateral offset and the current heading angle deviation are calculated based on the running state information and the surrounding environment information, and the current lateral offset represents the vertical distance from the front axle center of the vehicle to the center line of the lane.

[0078] It should be noted that the current lateral offset in this embodiment represents the deviation in the position dimension (for example, if it is positive, it means that the front axle center is on the left side of the lane center line; if it is negative, it means that the front axle center is on the right side of the lane center line; if it is 0, it means that the front axle center is completely coincident with the lane center line); it can also directly reflect whether the lateral position of the vehicle in the lane is centered (for example, if the offset is too large, that is, more than half of the lane width, it means that the vehicle may be pressed or will deviate from the lane; on the contrary, a small offset within a certain distance is normal driving fluctuation). In addition, the vehicle body heading angle can reflect the actual driving direction of the vehicle, and the current heading angle deviation in this embodiment represents the deviation in the direction dimension (for example, if it is positive, it means that the driving direction of the vehicle is right relative to the lane tangent; if it is negative, it means that the driving direction of the vehicle is left; if it is 0, the driving direction of the vehicle is completely consistent with the lane tangent), which is used to reflect whether the driving direction of the vehicle matches the lane direction (for example, the vehicle is in a curve, if the heading angle deviation is positive, it means that the vehicle steering is insufficient; a small angle deviation in a straight line may be caused by road inclination or crosswind, which needs to be corrected through control).

[0079] In the embodiment, the lateral offset (indicating how much the position deviates) and the heading angle deviation (indicating how much the direction deviates) jointly constitute the monitoring index of the vehicle deviating from the target trajectory, and the two are cooperatively controlled to realize intelligent, accurate and stable following control of the vehicle under complex working conditions.

[0080] In step S103, a first yaw moment is calculated by the first controller according to the current heading angle deviation.

[0081] In the embodiment, this step is a core control link for direction deviation in the trajectory following cooperative control. The first controller converts the deviation (i.e. the heading angle deviation) between the actual driving direction of the vehicle and the trend of the lane line into a specific yaw moment, which in turn drives the vehicle to adjust the direction to fit the trend of the lane line.

[0082] In step S104, a target yaw angular velocity is determined based on the running state information and the current lateral offset, and a second yaw moment is calculated by the second controller according to the target yaw angular velocity and the running state information.

[0083] In the embodiment, this step is a core control link for position deviation in the trajectory following cooperative control. The target yaw angular velocity (i.e. the ideal yaw angular velocity required by the vehicle to correct the current lateral offset and return to the target trajectory) is determined based on the running state information and the current lateral offset, and the second controller converts the deviation (i.e. the difference between the target yaw angular velocity and the actual yaw angular velocity) between the lateral position of the vehicle and the original trajectory of the lane into a specific yaw moment to drive the vehicle to adjust the lateral position and in turn fit the original trajectory of the lane.

[0084] It should be noted that the specific types of the first controller and the second controller in the embodiment can be adaptively adjusted according to actual needs, which are not limited in detail here.

[0085] In step S105, a total yaw moment is determined based on the first yaw moment and the second yaw moment.

[0086] In the embodiment, this step is a core fusion link in the trajectory following cooperative control. The first yaw moment for direction correction and the second yaw moment for position return are integrated into a unified total yaw moment mainly through dynamic weight distribution and linear weighting superposition of the yaw moment, so that the vehicle control command not only meets the direction correction demand, but also satisfies the position return demand, while adapting to the real-time dynamic state of the vehicle.

[0087] In step S106, the driving torques of the wheels are determined according to the total yaw moment, the current heading angle deviation and the running state information, and the corresponding driving torques are controlled to be executed by the wheels to realize the trajectory following of the vehicle.

[0088] In this embodiment, this step is the final execution link in the trajectory following cooperative control, and specifically, the abstract control target of the total yaw moment is combined with the real-time state of the vehicle, i.e., the current heading angle deviation and the running state information, to be converted into the specific drive torque of each wheel, and through the independent control capability of the distributed electric drive system, the torque instruction is accurately executed by the wheel, and finally the accurate following of the vehicle trajectory is realized.

[0089] The cooperative control method for vehicle trajectory following provided in this embodiment calculates the lateral offset and the heading angle deviation through the running state information and the surrounding environment information of the vehicle, uses the first controller to specially correct the heading angle deviation to ensure that the driving direction of the vehicle is consistent with the trajectory, and uses the second controller to derive the target yaw angular velocity according to the lateral offset to accurately correct the lateral position deviation of the vehicle; the total yaw moment is determined according to the first yaw moment and the second yaw moment, which can coordinate the stability control and the trajectory tracking two targets, and then the inherent contradiction between the "fast return" and the "smooth return" of the vehicle is intelligently solved; finally, the total yaw moment is accurately distributed to the drive torque of each wheel, and the hardware potential of the distributed electric drive system is fully utilized, so that the control response is faster and more accurate; even when the vehicle is disturbed by sudden interference such as crosswind and road water, the vehicle body attitude can be preferentially and quickly adjusted through the yaw moment through the above-mentioned cooperative control strategy, to prevent side slipping and fishtailing, thereby fundamentally improving the active safety and the handling stability of the vehicle, and the vehicle can also be controlled to smoothly and naturally return to the original driving trajectory from the deviated position, thereby greatly improving the riding comfort and the accuracy of the trajectory following.

[0090] In this embodiment, a cooperative control method for vehicle trajectory following is provided, which is applied to a vehicle equipped with a distributed electric drive system. Figure 2 is a second flowchart of the cooperative control method for vehicle trajectory following according to the embodiment of the present application, as shown in the figure, the flowchart includes the following steps: Figure 2 as shown in the figure, the flowchart includes the following steps:

[0091] Step S201, obtaining the running state information and the surrounding environment information of the vehicle. For details, please refer to step S101 of the embodiment shown in Figure 1 herein.

[0092] Step S202, calculating the current lateral offset and the current heading angle deviation based on the running state information and the surrounding environment information, and the current lateral offset represents the vertical distance from the front axle center of the vehicle to the center line of the lane.

[0093] Specifically, the above-mentioned step S202 includes:

[0094] Step a1, obtaining a distance equation in a vehicle coordinate system; wherein the vehicle coordinate system is constructed with a rear axle center of the vehicle as an origin, an x-axis along a longitudinal direction of the vehicle forward, and a y-axis along a lateral direction of the vehicle left; and the distance equation represents a relationship curve between a distance between the rear axle center of the vehicle and a left lane line and a longitudinal distance of the vehicle body.

[0095] In the embodiment, a specific obtaining manner of the distance equation is not limited, for example, original information of the lane line can be collected by a multi-source sensor carried by the vehicle, the original information is preprocessed by data denoising, format conversion and the like, the data is unified into the vehicle coordinate system (i.e. with the rear axle center of the vehicle as the origin, the x-axis along the longitudinal direction of the vehicle forward, and the y-axis along the lateral direction of the vehicle left), and a plurality of groups of the distance between the rear axle center of the vehicle and the left lane line and the longitudinal distance of the vehicle body are obtained, and data fitting is performed on the plurality of groups to obtain the relationship curve; or, the relationship curve between the distance between the rear axle center of the vehicle and the left lane line and the longitudinal distance of the vehicle body can be directly obtained by using a smart driving system (referred to as smart driving) carried by the vehicle, which is only illustrative.

[0096] Step a2, determining a vehicle front axle center coordinate according to a wheelbase in the running state information.

[0097] In the embodiment, a longitudinal distance of the front axle center relative to the rear axle center is the wheelbase L, since the rear axle center is the origin, i.e. the coordinate is (0, 0), the vehicle front axle center coordinate is (0, L). It is noted that the coordinate of the front axle center on the y-axis is not considered in the embodiment.

[0098] Step a3, substituting the vehicle front axle center coordinate into the distance equation to obtain a first distance between the vehicle front axle center and the left lane line, determining a second distance between a lane center line and the left lane line according to a lane width in the surrounding environment information, and obtaining a current lateral deviation according to a difference between the first distance and the second distance.

[0099] In the embodiment, the distance between the lane center line and the left lane line is half of the lane width.

[0100] Step a4, obtaining a slope equation of a lane line tangent by taking a first derivative of the distance equation, substituting the vehicle front axle center coordinate into the slope equation to obtain a slope, obtaining an inverse tangent value of the slope to obtain a tangent angle, and obtaining a current heading angle deviation according to a difference between a current vehicle body heading angle in the running state information and the tangent angle.

[0101] In the embodiment, the heading angle deviation is defined as an included angle between the current vehicle body heading angle and a tangent direction of the lane center line; wherein a change rate (i.e. derivative) of the lateral distance of the lane center line with respect to the longitudinal distance reflects the tangent direction (i.e. slope), and thus the slope of the lane line tangent can be directly obtained by taking the first derivative of the distance equation. It is noted that the current vehicle body heading angle of the vehicle can be directly obtained by using the IMU in the embodiment.

[0102] In the embodiment of the present application, a lateral offset calculation method is designed. Specifically, the front axle center coordinate is accurately positioned by the wheelbase, and the difference between the distance from the front axle to the left lane line and the distance from the lane center line to the left lane line is combined to directly correspond to the lateral offset, i.e., the definition of the vertical distance from the front axle center to the lane center line. The calculation result meets the actual control requirement, thereby making the deviation calculation accurate. The heading angle deviation is calculated by obtaining the lane line tangent slope through the first derivative of the distance equation, then obtaining the tangent angle through the inverse tangent, and then obtaining the heading angle deviation by the difference between the vehicle heading angle and the tangent angle, i.e., the deviation degree of the vehicle direction from the lane direction, thereby further ensuring that the tangent angle can truly reflect the current trend of the lane line. The above two types of deviation calculation methods realize accurate and efficient calculation of the lateral offset and the heading angle deviation, and provide reliable data support for subsequent trajectory following control.

[0103] It should be noted that in the present embodiment, the calculation of the lateral offset and the heading angle deviation is only started when the vehicle is determined to deviate, and the step is skipped when the vehicle does not deviate, so as to reduce unnecessary computational load. Therefore, before calculating the current lateral offset and the current heading angle deviation based on the running state information and the surrounding environment information, the cooperative control method for vehicle trajectory following of the present embodiment further comprises:

[0104] Step b1, judging whether the vehicle deviates based on the future driving trajectory and the current lane line.

[0105] In the present embodiment, the future driving trajectory represents the vehicle driving trajectory in the future time period predicted by the distance equation, and its specific acquisition method is described below.

[0106] It should be noted that the present step is to compare the boundary range of the future driving trajectory and the current lane line, i.e., to determine whether the vehicle will deviate, such as deviating out of the lane or deviating too far from the lane center line, by quantifying the positional relationship between the trajectory and the lane. It should be noted that the specific comparison method of the future driving trajectory and the boundary range of the current lane line can be adaptively adjusted according to actual needs, such as splitting the future driving trajectory into multiple prediction points and judging whether each prediction point is within the lane boundary. If there is a prediction point that is not within the lane boundary, or the number of prediction points that are not within the lane boundary exceeds a certain proportion, it is determined that the vehicle deviates. If all prediction points are within the lane boundary, or only a few prediction points are slightly close to the boundary (without exceeding the safety threshold range, or the number of points is small), it is determined that the vehicle does not deviate, which is only an exemplary description.

[0107] Step b2, when the vehicle deviates, performing the step of calculating the current lateral offset and the current heading angle deviation based on the running state information and the surrounding environment information.

[0108] In the embodiment, by using the position of the future trajectory to offset the safety boundary of the current lane for deviation determination, and starting the calculation of the lateral deviation and the heading angle deviation when the vehicle deviates, the unnecessary computational load can be effectively reduced, and the resource utilization is further optimized.

[0109] In step S203, the first controller calculates a first yaw moment according to the current heading angle deviation.

[0110] In the embodiment, the first controller is a proportional-integral controller. It should be noted that the proportional-integral controller is a PI controller. Specifically, the step S203 includes:

[0111] In step S2031, a proportional-integral controller is constructed with the heading angle deviation as the feedback quantity and the yaw moment as the output control quantity, and the proportional coefficient and the integral coefficient in the proportional-integral controller are determined according to the heading angle deviation and its rate of change of the vehicle and the vehicle speed.

[0112] In the embodiment, the proportional coefficient of the PI controller directly outputs a control quantity proportional to the deviation according to the size of the current heading angle deviation, so as to quickly respond to the deviation (wherein the larger the deviation, the larger the proportional coefficient, and the more significant the corresponding output moment); the integral coefficient of the PI controller is used to accumulate historical deviations, so as to gradually eliminate long-term existing steady-state deviations (such as continuous direction deviation caused by road inclination and load deviation), and ensure that the driving direction of the vehicle is finally consistent with the lane line.

[0113] Note that the determination of the two types of coefficients in the embodiment can be adaptively determined according to actual needs. For example, the vehicle speed directly determines the dynamic response characteristics of the vehicle (i.e., the vehicle inertia is large at high speed, and the yaw sensitivity is high, and vice versa at low speed). Therefore, the vehicle speed can be used as a reference to divide the parameter interval, i.e., a low-speed interval (less than 30 km / h), a medium-speed interval (between 30 km / h and 60 km / h), and a high-speed interval (greater than 60 km / h). In the low-speed interval, the vehicle dynamic response is slow, and a larger control amount is allowed to quickly correct the deviation. Therefore, the two types of coefficients can be set to a larger reference value (for example, the proportional coefficient reference value is 500, and the integral coefficient reference value is 50). In the medium-speed interval, the vehicle dynamic response is moderate, and the two types of coefficients can be set to a medium reference value (for example, the proportional coefficient reference value is 300, and the integral coefficient reference value is 30). In the high-speed interval, the vehicle is more sensitive to the yaw moment, and it is necessary to suppress a large control amount to avoid oscillation. Therefore, the two types of coefficients can be set to a smaller reference value (for example, the proportional coefficient reference value is 100, and the integral coefficient reference value is 10). Then, based on the vehicle speed reference parameter, the proportional coefficient and the integral coefficient are further corrected according to the current heading angle deviation and its rate of change (for example, if the heading angle deviation is large, the reference values of the coefficients are multiplied by a larger correction coefficient; if the rate of change of the heading angle deviation is small, the reference values of the coefficients are multiplied by a smaller correction coefficient), which is only an exemplary description.

[0114] It should be noted that the PI controller in the embodiment is designed for heading angle deviation correction. When it is detected that the vehicle has a heading angle deviation (e.g., the vehicle deviates to the right, and the deviation is positive), the controller calculates a corresponding first yaw moment (e.g., a left yaw moment) according to the deviation value. The moment is converted into a torque difference between the left and right wheels (e.g., the left wheel is added with torque, and the right wheel is reduced with torque) through a subsequent torque distribution link, so that the vehicle generates a left turning trend, and gradually reduces the heading angle deviation.

[0115] In step S2032, the current heading angle deviation is input into the proportional integral controller, and a first yaw moment is output.

[0116] In the embodiment, the traditional proportional integral controller is deeply combined with the vehicle dynamics characteristics. By introducing an adaptive gain mechanism based on real-time state, the first controller is upgraded from a simple correction module to an intelligent, efficient and reliable vehicle body posture stabilizer. Not only does it achieve accurate and rapid correction of the heading angle deviation to provide reliable input for the total yaw moment, but it also achieves high-quality vehicle body return, thereby providing key technical support for subsequent trajectory recovery.

[0117] In step S204, a target yaw angular velocity is determined based on the running state information and the current lateral offset, and a second yaw moment is calculated by the second controller according to the target yaw angular velocity and the running state information.

[0118] In the embodiment, the running state information at least includes the current yaw rate, and the second controller is a proportional-integral-derivative controller. It should be noted that the current yaw rate in the embodiment can be directly obtained by the IMU; the proportional-integral-derivative controller is a PID controller. Specifically, the step S204 includes:

[0119] In step c1, a plurality of vehicle body longitudinal distances predicted in a preset time range are obtained based on the running state information.

[0120] In the embodiment, the running state information is the real-time vehicle speed of the vehicle; the preset time range represents a prediction time window (i.e., each prediction moment), and the specific value can be adaptively adjusted according to actual needs, for example, predicting a distance every 0.1 s, which is only an exemplary description. Specifically, the vehicle body longitudinal distance at the corresponding prediction moment can be obtained by multiplying the current vehicle speed of the vehicle by each prediction moment.

[0121] In step c2, each vehicle body longitudinal distance is substituted into the distance equation to obtain a corresponding vehicle body lateral distance, and the future driving trajectory of the vehicle is determined according to all vehicle body longitudinal distances and corresponding vehicle body lateral distances.

[0122] In the embodiment, all vehicle body longitudinal distances and corresponding vehicle body lateral distances can be data fitted to obtain the future driving trajectory, and the data fitting method can be adaptively adjusted according to actual needs, such as linear fitting, polynomial fitting, etc.

[0123] In step c3, the target lateral offset is determined based on the future driving trajectory, and the current lateral offset is divided by the target lateral offset to obtain the current offset distance completion degree.

[0124] In the embodiment, the current offset distance completion degree is used as a normalization parameter to quantify the offset degree, so as to avoid subsequent fuzzy judgment and lay a unified foundation for subsequent fine control in stages and under different conditions, thereby greatly enhancing the adaptability of the algorithm to different offset degrees.

[0125] In step c4, the target yaw rate is obtained by looking up the first preset table according to the current offset distance completion degree and the current vehicle body heading angle in the running state information; the first preset table is constructed by a vehicle kinematics equation and is used to store the mapping relationship between different offset distance completion degrees, vehicle body heading angles and yaw rates.

[0126] It should be noted that the first preset table in the embodiment is related to multiple variables, and is intended to simulate the decision logic of a human driver when rescuing a vehicle, that is, not only to see "how far the vehicle is from the lane line", but also to see "where the vehicle head points", so that the vehicle can intelligently judge the current most needed yaw angular velocity according to the combination of the two states. For example, even if the offset is the same, the required correction strategy (i.e. target yaw angular velocity) is completely different when the vehicle head points in different directions, which makes the control decision highly intelligent and personified. Specifically, in the embodiment, the target yaw angular velocity is determined by using a lookup table, which not only realizes the accurate, efficient and actual working condition matching determination of the target yaw angular velocity, but also provides a scientific target input for the subsequent calculation of the second yaw moment.

[0127] In step c5, a proportional-integral-derivative controller is constructed with the yaw angular velocity deviation as the feedback quantity and the yaw moment as the output control quantity, and the proportional coefficient, the integral coefficient and the differential coefficient in the proportional-integral-derivative controller are determined according to the yaw angular velocity deviation and its rate of change of the vehicle and the vehicle speed.

[0128] In the embodiment, the proportional coefficient of the PID controller provides a proportional response to the instantaneous value of the deviation, ensuring that the yaw angular velocity can quickly follow the target value; the integral coefficient is used to accumulate and eliminate steady-state error, ensuring that the vehicle can accurately maintain the desired yaw angular velocity, thereby realizing accurate recovery of the trajectory; and the differential coefficient is used to perceive the trend of the deviation, which can predictively suppress overshoot and oscillation, making the tracking process of the yaw angular velocity more smooth and smooth, greatly improving the ride comfort. It should be noted that the specific determination method of the three types of coefficients of the above PID controller can be understood by referring to the determination of the related coefficients in the PI controller, and will not be described in detail here.

[0129] In step c6, the target yaw angular velocity is subtracted from the current yaw angular velocity to obtain the current yaw angular velocity deviation.

[0130] In step c7, the current yaw angular velocity deviation is input into the proportional-integral-derivative controller to output the second yaw moment.

[0131] In the embodiment, the classical proportional-integral-derivative controller is deeply integrated with the adaptive strategy, and the second controller is adapted to the yaw angular velocity deviation control demand, so that the second controller can not only accurately and smoothly execute the trajectory recovery instruction, but also intelligently adapt to changes in the vehicle state and working conditions, realizing fast and stable calculation of the second yaw moment, providing high-quality input for the total yaw moment cooperative control, and also helping to realize smooth, smooth and accurate automatic return of the vehicle to the original driving trajectory, thereby greatly improving the cooperative control effect of the vehicle trajectory following.

[0132] In step S205, the total yaw moment is determined based on the first yaw moment and the second yaw moment.

[0133] In the embodiment, the running state information at least includes the current yaw rate and the current yaw rate change rate. It is noted that the current yaw rate and the current yaw rate change rate can be determined by the IMU.

[0134] Specifically, the step S205 includes:

[0135] In step S2051, the first weight coefficient is obtained by looking up the second preset table according to the current yaw rate and the current yaw rate change rate; the second preset table is used to store the mapping relationship between different yaw rates and yaw rate change rates and weight coefficients.

[0136] In step S2052, the second weight coefficient is determined based on the first weight coefficient, and the sum of the first weight coefficient and the second weight coefficient is 1.

[0137] In the embodiment, the second weight coefficient can be obtained by subtracting the first weight coefficient from 1.

[0138] In step S2053, the total yaw moment is obtained by multiplying the first yaw moment by the first weight coefficient, multiplying the second yaw moment by the second weight coefficient, and then adding the two results.

[0139] In the embodiment, the total yaw moment is determined by dynamically distributing the yaw moment, which can accurately capture the dynamic running state of the vehicle, such as smooth driving, rapid deviation correction, yaw oscillation, etc., and thus avoids the insufficient adaptation of the fixed weight under complex working conditions. For example, when the yaw is severe, the weight of the stability steering moment can be increased, and when the vehicle is driving smoothly, the accurate deviation correction is focused on, so that the total yaw moment always meets the real-time control requirements. The above-mentioned torque distribution method realizes the accurate cooperation of the first and second yaw moments, and the total yaw moment is adapted to the dynamic state of the vehicle in real time, providing stable and efficient control input for trajectory following, and greatly improving the accuracy and stability of trajectory following.

[0140] In step S206, the driving torque of each wheel is determined according to the total yaw moment, the current heading angle deviation and the running state information, and each wheel is controlled to execute the corresponding driving torque to realize vehicle trajectory following.

[0141] In the embodiment, the running state information at least includes the current vehicle speed. Specifically, in step S206, the driving torque of each wheel is determined according to the total yaw moment, the current heading angle deviation and the running state information, including:

[0142] In step d1, the target distribution coefficient is obtained by looking up the third preset table according to the current vehicle speed and the current heading angle deviation; the third preset table is used to store the mapping relationship between different vehicle speeds and heading angle deviations and distribution coefficients.

[0143] Step d2, determining the driving torque of each wheel according to the total yaw moment and the target distribution coefficient.

[0144] In the embodiment, the step d2 comprises:

[0145] Step d21, obtaining the original distribution torque, wheel radius and target distance of each wheel respectively, the target distance representing the distance from the wheel center to the vehicle center of mass.

[0146] In the embodiment, the original distribution torque is the power torque that meets the basic driving of the vehicle (such as acceleration, constant speed, climbing), which is determined based on the dynamic distribution of the driving demand of the vehicle (i.e., to ensure the driving demand of the basic driving of the vehicle). For example, based on the driver's operation (such as the opening of the accelerator pedal), the current vehicle speed, the power demand (such as the target driving force converted from the accelerator pedal signal), and the vehicle state (such as the state of charge of the battery, the efficiency range of the motor), the total driving torque demand is calculated by the vehicle controller, and according to the vehicle driving type (such as four-wheel independent drive for distributed electric drive) and the driving condition, the preset rule is distributed to each wheel; wherein the preset rule includes that when the vehicle is driving in a straight line, the torque of the left and right wheels is symmetrically distributed (such as left front = right front, left rear = right rear), and the front and rear wheel torque distribution ratio is determined according to the axle load (such as 55% of the front axle load, then the front wheels bear 55% of the total torque); or when the vehicle is slightly turning, the front and rear wheel torque ratio is slightly adjusted (such as slightly reducing the torque of the rear wheel when turning to improve flexibility). Note that the distribution result is sent by the vehicle controller to the motor controller of each wheel as the original distribution torque. In addition, the wheel radius and the target distance can be obtained according to the conventional operation in the art.

[0147] Step d22, distributing the target distribution coefficient to obtain the torque coefficient of each wheel.

[0148] As known from the foregoing, the target distribution coefficient is obtained based on the current vehicle speed and the current heading angle deviation lookup table, which can avoid the insufficient adaptation of the fixed distribution coefficient in different conditions, such as focusing on smooth distribution at high speed and small deviation, and focusing on rapid correction distribution at low speed and large deviation, so that the torque distribution accurately matches the real-time control demand. In the embodiment, the step aims to decompose the target distribution coefficient reflecting the distribution strategy of the total yaw moment into the torque coefficient of each wheel according to the wheel position (such as left front, right front, left rear, right rear) and the correction demand (heading angle deviation direction), which represents the proportion of each wheel in the total yaw moment (i.e., the specific distribution ratio of each wheel, including positive and negative, representing torque increase / decrease), to ensure that the additional torque of each wheel accurately generates the required yaw effect through the "torque difference".

[0149] It should be noted that the allocation strategy of the target allocation coefficient in the embodiment can be adaptively adjusted according to actual needs. For example, assuming that the target allocation coefficient is a set of strategy values (such as “front wheel allocation ratio 60%, rear wheel 40%; left-right wheel difference 50%”; wherein each torque coefficient is denoted as: left front wheel torque coefficient k1, right front wheel k2, left rear wheel k3, and right rear wheel k4), the allocation steps include:

[0150] 1. Determine the front-rear axle allocation ratio: according to the target allocation coefficient, the total front wheel torque coefficient ratio (k1+k2) = 60%, and the rear wheel (k3+k4) = 40%.

[0151] 2. Determine the left-right wheel difference: according to the left-right difference of 50% of the target allocation coefficient, the left front wheel k1 = 30% (i.e. 60% x 50%), the right front wheel k2 = -30% (left positive and right negative, forming a front wheel torque difference); the left rear wheel k3 = 20% (i.e. 40% x 50%), and the right rear wheel k4 = -20% (rear wheel torque difference).

[0152] 3. Check and normalize: ensure that the sum of the torque coefficients satisfies the left-right difference to produce yaw and the front-rear ratio to adapt to dynamics, and finally obtain the torque coefficients of the four wheels (such as k1 = 0.3, k2 = -0.3, k3 = 0.2, and k4 = -0.2).

[0153] Step d23, multiply the torque coefficients of each wheel by the total yaw moment to obtain the additional torque of each wheel.

[0154] Step d24, divide the wheel radius of each wheel by the target distance to obtain the correction coefficient of each wheel.

[0155] In the embodiment, the correction coefficient obtained by the wheel radius / target distance can offset the influence of the geometric position difference of different wheels on the torque effect, such as the distance from the outside wheel to the center of mass being different from that of the inside wheel, and after correction, the same additional torque can ensure consistent yaw correction effect.

[0156] Step d25, multiply the additional torque of each wheel by the corresponding correction coefficient, and then add the original allocation torque of each wheel to obtain the driving torque of each wheel.

[0157] It should be noted that in the embodiment, the original allocation torque (guaranteeing the basic driving demand of the vehicle) is superimposed with the additional torque after correction (responding to the correction demand of the total yaw moment), which can realize integrated control of driving and correction, thereby avoiding the situation that a single torque allocation focuses on one aspect and neglects the other, such as focusing only on correction and ignoring driving efficiency, or focusing only on driving and leading to insufficient correction, ensuring that the vehicle driving performance and trajectory following performance are optimized simultaneously.

[0158] In the embodiment of the present application, the target distribution coefficient is obtained according to the current vehicle speed and the current heading angle deviation table, and the moment distribution is carried out on the total yaw moment based on the target distribution coefficient, and then the driving torque of each wheel is determined, so that the precise and efficient distribution of the distributed electric drive vehicle wheel driving torque can be realized, and the differentiated torque distribution advantage is fully played to provide stable execution guarantee for trajectory following; and the torque coefficient, additional torque and correction coefficient are independently calculated for each wheel, the original distribution torque is superimposed after the additional torque and the corresponding correction coefficient are multiplied, so as to take into account the dual needs of basic driving and deviation correction of the vehicle, and the fine and personalized calculation of the driving torque of each wheel is realized, and the advantage of independent torque control of each wheel of the distributed electric drive system is fully played.

[0159] In the embodiment, in order to solve the problem that the existing control strategy of applying the distributed electric drive system to vehicle trajectory control is single and cannot realize effective vehicle trajectory following in complex working conditions, a distributed electric drive based double closed loop cooperative control vehicle trajectory following system and scheme are provided, which aims to realize the cooperative work between the two closed loops by constructing the double closed loop control systems of "vehicle body offset angle and target yaw angular velocity" and "traveling trajectory and target yaw angular velocity". Even when the vehicle is offset by interference such as crosswind and road water, the vehicle body posture is quickly adjusted to the stable state through the vehicle body offset angle closed loop control; at the same time, the vehicle is controlled to return to the traveling trajectory according to the vehicle offset distance closed loop control, so that the user can make the vehicle return to the original traveling trajectory in an unconscious state. At the same time, an adaptive adjustment mechanism is introduced, the closed loop control parameters and torque distribution strategy are dynamically adjusted according to different working conditions, the adaptability and robustness of the system in complex environment are improved, and the active safety and driving stability of the vehicle are significantly improved.

[0160] In the embodiment, Figure 3 is a schematic diagram of a distributed electric drive based double closed loop cooperative control vehicle trajectory following system architecture. As shown in the figure, the system mainly consists of three parts: perception layer, decision layer and execution layer, which cooperate with each other to realize accurate control of vehicle trajectory, and the specific functions of each layer are as follows:

[0161] 1. Perception layer: as the "senses" of the system, the perception layer is responsible for real-time collection of vehicle running state information and surrounding environment information to provide data basis for subsequent decision making, including:

[0162] 1.1 Environment perception module: This module consists of high-definition cameras and millimeter wave radars. The high-definition camera uses a high-resolution image sensor, combined with advanced deep learning algorithms (such as improved YOLOv7 algorithm), which can capture real-time visual features such as lane line color, shape, curvature, etc., and accurately identify and locate lane lines. Millimeter wave radar emits millimeter wave signals and receives reflected signals, using the propagation characteristics of electromagnetic waves, it can still stably detect lane boundaries and obtain spatial position information of the lane under adverse weather conditions such as rain, fog, and snow. The data of the two are fused after time synchronization and spatial calibration, effectively improving the accuracy and reliability of lane line information collection.

[0163] 1.2 Vehicle state perception module: This module integrates inertial measurement unit (IMU), speed sensor, steering wheel angle sensor and other sensors. The IMU can measure the vehicle's yaw rate, roll rate and acceleration along three coordinate axes in real time, accurately sensing the vehicle's attitude changes; the speed sensor is installed on the wheel or transmission system to accurately obtain the vehicle's speed; the steering wheel angle sensor is used to detect the driver's steering intention. The data collected by these sensors complement each other and comprehensively describe the vehicle's motion state.

[0164] 2. Decision layer: The decision layer is the "brain" of the entire system, which processes, analyzes and decides the data collected by the perception layer, including:

[0165] 2.1 Intelligent decision unit: Based on the vehicle coordinate system, the intelligent driving provides a curve equation of the longitudinal distance between the vehicle body and the lane line, combined with the vehicle kinematics and dynamics model, to calculate the lateral offset and heading angle deviation between the vehicle body and the lane line, and predict the future trajectory of the vehicle. By comparing the predicted trajectory with the original lane line, it can determine whether the vehicle has deviated, the degree and direction of deviation, and provide a basis for subsequent control strategies.

[0166] 2.2 Dual closed-loop control unit: This unit is the core control module of the system, which aims to generate different degrees of yaw moment for the vehicle in different scenarios through differential torque control, and generate a larger yaw moment in the vehicle stability control scenario to quickly get the vehicle out of the instability state. In the stable state of trajectory following, it is a precise control that makes the tire produce elastic deformation while not producing slip through additional yaw moment, including vehicle body stability control module, dual-parameter closed-loop based trajectory following control module and torque coordination module, the functions of each module are as follows:

[0167] 2.2.1 Vehicle body stability control module: if the driver shifts the vehicle body posture without turning the steering wheel, the module actively intervenes, combines the yaw angular velocity and the vehicle yaw angle, and quickly realizes the stability control of the vehicle body through the additional yaw moment. Meanwhile, the yaw moment distribution is based on the yaw moment distribution rule of the longitudinal driving force. The main role of the module is to quickly adjust the vehicle body posture at the moment of vehicle deviation, so as to stabilize the vehicle body posture and reduce the risk of vehicle side slip and loss of control.

[0168] 2.2.2 Trajectory following control module based on double-parameter closed loop: after the vehicle body posture is stabilized, the module calculates the yaw moment based on the closed loop control of the vehicle deviation position and the yaw angular velocity according to the vehicle target and deviation information calculated by the intelligent decision unit, makes the tire produce a small elastic deformation through the additional yaw moment, so as to have a steering effect on the vehicle and adjust the vehicle running trajectory to return to the original running track.

[0169] 2.2.3 Torque coordination module: the role of the module is to coordinate the outputs of the vehicle body stability control module and the trajectory following control module based on double-parameter closed loop, and a dynamic weight distribution strategy is adopted. Specifically, at the initial stage of vehicle deviation, the vehicle body posture is unstable, so the weight of the vehicle body stability control module is increased to make the system preferentially adjust the vehicle body posture; as the vehicle body gradually returns to normal, the weight of the vehicle body return-to-normal closed loop is gradually reduced, and the weight of the trajectory following control module based on double-parameter closed loop is increased, so as to realize the smooth transition from vehicle body return-to-normal to trajectory recovery.

[0170] 3, Execution layer: the layer mainly includes a yaw moment distribution module, which is composed of a distributed electric drive system including four independently controllable drive motors, and each drive motor is equipped with a high-precision torque controller. Specifically, after the execution layer receives the total yaw moment output by the decision layer, the yaw moment provided by the front and rear axles is determined according to the yaw moment distribution coefficient, and the total yaw moment is converted into the torque of each drive motor based on the longitudinal driving force priority yaw moment distribution rule. The torque of each drive motor is optimally distributed to achieve accurate control of the torque vector, so as to correct the running track of the vehicle and return the vehicle to the original lane.

[0171] In the embodiment, a double closed loop collaborative control scheme for vehicle trajectory following is proposed based on the above-mentioned system, Figure 4 is a double closed loop collaborative control schematic diagram. As shown in the figure, the control process includes:

[0172] 1, Start-up phase: vehicle power-on and start condition judgment.

[0173] It should be noted that the vehicle in the embodiment is powered on (i.e. the vehicle power system is started, and the control system enters the initialization state), and it is judged whether the opening condition is met (i.e. it is judged whether the vehicle meets the start requirements of the control system, such as vehicle speed range, sensor state, driving mode, etc.; if it is met, the subsequent control process is entered; if it is not met, the system remains standby).

[0174] 2. The dual-path control module works in parallel to realize body stability control.

[0175] It should be noted that in this embodiment, multi-source data acquisition is first performed, i.e. the high-definition camera and millimeter wave radar in the environment perception module work synchronously, the high-definition camera continuously acquires image information of the road in front of the vehicle, and the millimeter wave radar continuously transmits and receives millimeter wave signals to obtain visual feature information and spatial position information of the lane line. At the same time, the vehicle state perception module acquires state information such as yaw angular velocity, roll angular velocity, acceleration, driving speed, and steering wheel angle of the vehicle in real time.

[0176] Further, the collected data is subjected to data fusion and state evaluation processing, i.e. the intelligent decision unit uses an extended Kalman filter algorithm to fuse the multi-source data collected by the perception layer, eliminates noise and errors in the data, and improves the accuracy and reliability of the data.

[0177] Further, based on the vehicle coordinate system, the intelligent driving provides a curve equation of the longitudinal distance between the vehicle body and the lane line , wherein s represents the distance between the rear axle center of the vehicle and the left lane line, x represents the longitudinal distance from the rear axle center, a, b, c, and d are curve equation coefficients provided by the intelligent driving, and the sending period is 10 ms; the future driving trajectory can be obtained according to the curve equation provided by the intelligent driving, i.e. the actual lateral offset between the front axle center of the vehicle body and the lane line , and the deviation of the vehicle body heading angle ; and the future driving trajectory of the vehicle is predicted according to the curve equation. By comparing the predicted trajectory with the original lane line, it can be accurately judged whether the vehicle has deviated, the degree and direction of the deviation, and the control target of the trajectory following, i.e. the lateral offset between the front axle center of the vehicle body and the lane line .

[0178] Further, the dual-loop cooperative control is realized, i.e. the deviation of the vehicle body heading angle is taken as the feedback signal to construct a proportional-integral controller (i.e. a PI controller). The yaw moment correction value is calculated according to the formula ; wherein , are proportional constant and integral constant, and the control parameters , Based on the vehicle body yaw angle deviation and its rate of change, vehicle speed and other factors, the yaw moment response speed is set according to the size of the vehicle speed and the speed of the vehicle body swing. The main function of the vehicle body stability control module is to quickly adjust the vehicle body posture at the moment of vehicle deviation, stabilize the vehicle body posture, and reduce the risk of vehicle side slip and loss of control.

[0179] Further, the position and speed based double closed loop control in the trajectory following control module based on double parameter closed loop is used to make the vehicle accurately return to the normal trajectory, that is, after the vehicle body posture is stabilized, the control target of the distance between the vehicle body front axle center and the lane line is calculated by the intelligent decision unit and the actual lateral deviation to calculate the offset distance completion percentage , based on the offset distance completion percentage and the vehicle body yaw angle deviation to determine the target yaw angular velocity . Note that the specific content of the target yaw angular velocity can refer to Table 1 (representing the actual vehicle body deviation to the left) and Table 2 (representing the actual vehicle body deviation to the right) below. Specifically, the deviation between the actual yaw angular velocity of the vehicle and the target yaw angular velocity is taken as input, which is processed by a proportional-integral-derivative controller (i.e. PID controller), and the yaw moment correction value is calculated according to the formula , where , , are the proportional, integral and derivative coefficients respectively. The main function of this module is to adjust the yaw angular velocity of the vehicle to make the vehicle travel along the original lane trajectory, achieving accurate control and recovery of the driving trajectory.

[0180] Table 1

[0181]

[0182] Table 2

[0183]

[0184] Further, the torque coordination module adjusts the weight of the vehicle body return loop and the trajectory following loop according to the deviation state and recovery degree of the vehicle, and calculates the total yaw moment by a dynamic weight coefficient , realizing the cooperative work of the two closed loops, where is obtained based on the lookup table of yaw angular velocity and its rate of change. The specific content of the weight coefficient can be referred to Table 3 below.

[0185] Table 3

[0186]

[0187] 3. Torque distribution and vectoring.

[0188] In this embodiment, the execution layer receives the total yaw moment output by the decision layer. Then, based on the front and rear axle yaw moment distribution coefficients Determine the yaw moment provided by each wheel, i.e.: , , , , , It should be noted that, This indicates the total yaw moment that the front axle (i.e., the two front wheels) needs to provide together. express, This indicates the yaw moment that the left front wheel needs to provide. This indicates the yaw moment that the right front wheel needs to provide. This indicates the total yaw moment that the rear axle (i.e., both rear wheels) needs to provide together. Indicates the yaw moment required by the left rear wheel. This indicates the yaw moment that the right rear wheel needs to provide. Specifically, the torque of the left and right motors of the vehicle is equal to the original distributed torque. Superimposed steady-state yaw torque of each wheel (i.e., the preceding text is based on) The specific calculation formula for determining the yaw moment provided by each wheel is as follows:

[0189]

[0190] in, Indicates the radius of each wheel, This indicates the distance from the center of each wheel to the center of mass.

[0191] It should be noted that the front and rear axle yaw moment distribution coefficient in this embodiment... This is derived from a table based on vehicle speed and heading angle deviations; see Table 4 below for details. Specifically, based on the longitudinal driving force priority yaw moment distribution rule, the total yaw moment is converted into the torque of each drive motor to determine the torque direction and approximate range required for each wheel. With the goal of minimizing energy consumption and achieving the best control effect, the torque of each drive motor is optimized and distributed to achieve precise control of the torque vector, thereby changing the vehicle's yaw moment, correcting the vehicle's trajectory, and allowing the vehicle to return to its original lane.

[0192] Table 4

[0193]

[0194] To sum up, in the embodiment, the body offset angle and the target yaw rate and the driving trajectory and the target yaw rate are controlled in a double closed loop, and the weight of the body return closed loop and the trajectory following closed loop is adjusted according to the offset state and the recovery degree of the vehicle, so as to realize the body return and the return to the original driving trajectory.

[0195] In the embodiment, a vehicle trajectory following cooperative control device is also provided, which is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the device described in the following embodiments is preferably realized in software, realization in hardware or a combination of software and hardware is also possible and is contemplated.

[0196] In the embodiment, a vehicle trajectory following cooperative control device is provided, which is applied to a vehicle equipped with a distributed electric drive system. As shown in the figure, the device comprises: Figure 5

[0197] The information acquisition module 501 is configured to acquire the running state information and the surrounding environment information of the vehicle.

[0198] The offset calculation module 502 is configured to calculate the current lateral offset and the current heading angle deviation based on the running state information and the surrounding environment information. The current lateral offset represents the vertical distance from the front axle center of the vehicle to the center line of the lane.

[0199] The attitude adjustment module 503 is configured to calculate the first yaw moment using the first controller according to the current heading angle deviation.

[0200] The path regression module 504 is configured to determine the target yaw rate based on the running state information and the current lateral offset, and calculate the second yaw moment using the second controller according to the target yaw rate and the running state information.

[0201] The moment coordination module 505 is configured to determine the total yaw moment based on the first yaw moment and the second yaw moment.

[0202] The cooperative control module 506 is configured to determine the driving torque of each wheel according to the total yaw moment, the current heading angle deviation and the running state information, and control each wheel to execute the corresponding driving torque, so as to realize the vehicle trajectory following.

[0203] In some optional embodiments, the offset calculation module 502 comprises:

[0204] ​The first calculation submodule is configured to obtain a distance equation in a vehicle coordinate system, wherein the vehicle coordinate system is constructed with a rear axle center of the vehicle as an origin, an x-axis along a longitudinal direction of the vehicle forward, and a y-axis along a lateral direction of the vehicle left, and the distance equation represents a relationship curve between a distance between the rear axle center of the vehicle and a left lane line and a longitudinal distance of the vehicle body.

[0205] The second calculation submodule is configured to determine a vehicle front axle center coordinate according to a vehicle wheelbase in the running state information.

[0206] The third calculation submodule is configured to substitute the vehicle front axle center coordinate into the distance equation to obtain a first distance between the vehicle front axle center and the left lane line, determine a second distance between a lane center line and the left lane line according to a lane width in the surrounding environment information, and obtain a current lateral offset according to a difference between the first distance and the second distance.

[0207] The fourth calculation submodule is configured to obtain a slope equation of a lane line tangent by taking a first derivative of the distance equation, substitute the vehicle front axle center coordinate into the slope equation to obtain a slope, obtain an inverse tangent value of the slope to obtain a tangent angle, and obtain a current heading angle deviation according to a difference between a current vehicle body heading angle in the running state information and the tangent angle.

[0208] In some optional embodiments, the attitude adjustment module 503 includes:

[0209] The first adjustment submodule is configured to construct a proportional integral controller with the heading angle deviation as a feedback quantity and a yaw moment as an output control quantity, and determine a proportional coefficient and an integral coefficient in the proportional integral controller according to the heading angle deviation and a change rate thereof of the vehicle and a vehicle speed.

[0210] The second adjustment submodule is configured to input the current heading angle deviation into the proportional integral controller to output a first yaw moment.

[0211] In some optional embodiments, the path regression module 504 includes:

[0212] The first regression submodule is configured to obtain a plurality of vehicle body longitudinal distances predicted in a preset time range based on the running state information.

[0213] The second regression submodule is configured to substitute each vehicle body longitudinal distance into the distance equation to obtain a corresponding vehicle body lateral distance, and determine a future driving trajectory of the vehicle according to all vehicle body longitudinal distances and corresponding vehicle body lateral distances.

[0214] The third regression submodule is configured to determine a target lateral offset based on the future driving trajectory, and divide the current lateral offset by the target lateral offset to obtain a current offset distance completion degree.

[0215] The fourth regression submodule is configured to obtain a target yaw rate from the first preset table according to the current offset distance completion degree and a current vehicle body heading angle in the running state information; the first preset table is constructed by a vehicle kinematics equation and is configured to store a mapping relationship between different offset distance completion degrees and the vehicle body heading angle and the yaw rate.

[0216] The seventh regression submodule is configured to input the current yaw rate deviation into the proportional-integral-derivative controller and output a second yaw moment.

[0217] The sixth regression submodule is configured to subtract the target yaw rate from a current yaw rate to obtain a current yaw rate deviation.

[0218] The seventh regression submodule is configured to input the current yaw rate deviation into the proportional-integral-derivative controller and output a second yaw moment.

[0219] In some optional embodiments, the moment coordination module 505 includes:

[0220] The first coordination submodule is configured to obtain a first weight coefficient from a second preset table according to the current yaw rate and a current yaw rate change rate; the second preset table is configured to store a mapping relationship between different yaw rates and yaw rate change rates and weight coefficients.

[0221] The second coordination submodule is configured to determine a second weight coefficient based on the first weight coefficient, and a sum of the first weight coefficient and the second weight coefficient is 1.

[0222] The third coordination submodule is configured to multiply the first yaw moment by the first weight coefficient, multiply the second yaw moment by the second weight coefficient, and then add the two results to obtain a total yaw moment.

[0223] In some optional embodiments, the cooperative control module 506 includes:

[0224] The first control submodule is configured to obtain a target distribution coefficient from a third preset table according to a current vehicle speed and a current heading angle deviation; the third preset table is configured to store a mapping relationship between different vehicle speeds and heading angle deviations and distribution coefficients.

[0225] The second control submodule is configured to determine the driving torque of each wheel according to the total yaw moment and the target distribution coefficient.

[0226] The third control submodule is configured to control each wheel to execute a corresponding driving torque to realize vehicle trajectory following.

[0227] In some optional embodiments, the device further comprises an offset detection module configured to determine whether the vehicle has deviated based on the future driving trajectory and the current lane line; and when the vehicle has deviated, the step of calculating the current lateral offset and the current heading angle deviation based on the running state information and the surrounding environment information is performed.

[0228] The vehicle trajectory following cooperative control device provided in the embodiments of the present application can perform the vehicle trajectory following cooperative control method provided in any of the embodiments of the present application, has the function modules and beneficial effects corresponding to the execution method. The further function description of the above-mentioned various modules and units is the same as that of the corresponding embodiments, and will not be described here again.

[0229] The vehicle trajectory following cooperative control device in the embodiments of the present application calculates the lateral offset and the heading angle deviation based on the running state information and the surrounding environment information of the vehicle, uses the first controller to specially correct the heading angle deviation, uses the second controller to derive the target yaw rate based on the lateral offset, further determines the total yaw moment based on the first yaw moment and the second yaw moment, and finally accurately distributes the driving torque to each wheel based on the total yaw moment. Through the above-mentioned cooperative control strategy, even if the vehicle is disturbed by sudden interference such as crosswind and road water and the body posture is unstable, the body posture can be preferentially and quickly adjusted through the yaw moment, which fundamentally improves the active safety and handling stability of the vehicle, controls the vehicle to smoothly and naturally return to the original driving trajectory from the deviated position, and greatly improves the user driving experience and the accuracy of trajectory following.

[0230] Figure 6 A structural schematic diagram of a vehicle provided in the embodiments of the present application is shown in FIG. 1. Hereinafter, specific reference is made to Figure 6 which shows a structural schematic diagram of a vehicle suitable for being used to implement the vehicle in the embodiments of the present application, and the vehicle is equipped with a distributed electric drive system. The vehicle comprises a controller, which can comprise a processor (for example, a central processor, a graphic processor, etc.) 601, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 602 or loaded from a memory 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for vehicle operation are also stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0231] Generally, the following devices can be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; storage devices 608 including, for example, a magnetic tape, a hard disk, and the like; and communication devices 609. The communication devices 609 can allow the vehicle to communicate wirelessly or wired with other devices to exchange data. Although Figure 6 The vehicle is shown with various devices, but it is understood that not all of the shown devices are required to be implemented or present, and more or fewer devices can alternatively be implemented or present.

[0232] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, a computer program product is included in embodiments of the present application, which includes a computer program carried on a non-transitory computer readable medium, the computer program containing program code for executing the method shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication devices 609, or installed from the storage devices 608, or installed from the ROM 602. When the computer program is executed by the processor 601, the above-mentioned functions defined in the cooperative control method of vehicle trajectory following of embodiments of the present application are performed.

[0233] Figure 6 The vehicle shown is merely an example and should not impose any limitation on the functions and use range of embodiments of the present application.

[0234] A computer readable storage medium is also provided in embodiments of the present application, and the above-mentioned method according to embodiments of the present application can be implemented in hardware, firmware, or as computer code that can be recorded on a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium originally stored through network download and to be stored in a local storage medium, so that the method described herein can be processed by such software stored on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc.; further, the storage medium can also include a combination of the above-mentioned types of storage. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, processor or hardware, implements the cooperative control method of vehicle trajectory following shown in the above-mentioned embodiments.

[0235] While embodiments of the application have been described in connection with the preferred embodiments of the various figures, those of ordinary skill in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the application, and that such modifications and changes fall within the scope of the appended claims.

Claims

1. A method of cooperative control of vehicle trajectory following, applied to a vehicle equipped with a distributed electric drive system, characterized in that, The method comprises: acquiring running state information and surrounding environment information of the vehicle; calculating a current lateral offset and a current heading angle deviation based on the running state information and the surrounding environment information, the current lateral offset representing a vertical distance from a front axle center of the vehicle to a center line of a lane; calculating a first yaw moment by a first controller according to the current heading angle deviation; determining a target yaw angular velocity based on the running state information and the current lateral offset, and calculating a second yaw moment by a second controller according to the target yaw angular velocity and the running state information; determining a total yaw moment based on the first yaw moment and the second yaw moment; determining driving torques of each wheel according to the total yaw moment, the current heading angle deviation and the running state information, and controlling each wheel to execute a corresponding driving torque to realize vehicle trajectory following; wherein the running state information at least comprises a current vehicle speed; and the determination of the driving torques of each wheel according to the total yaw moment, the current heading angle deviation and the running state information comprises: obtaining a target distribution coefficient from a third preset table according to the current vehicle speed and the current heading angle deviation, the third preset table being used to store a mapping relationship between different vehicle speeds, heading angle deviations and distribution coefficients; determining the driving torques of each wheel according to the total yaw moment and the target distribution coefficient.

2. The cooperative control method for vehicle trajectory following according to claim 1, wherein The calculation of the current lateral offset and the current heading angle deviation based on the running state information and the surrounding environment information comprises: acquiring a distance equation in a vehicle coordinate system, wherein the vehicle coordinate system is constructed with a rear axle center of the vehicle as an origin, an x-axis along a longitudinal direction of the vehicle forward and a y-axis along a lateral direction of the vehicle left, and the distance equation represents a relationship curve between a distance from the rear axle center of the vehicle to a left lane line and a longitudinal distance of the vehicle body; determining a front axle center coordinate of the vehicle according to a wheelbase in the running state information; substituting the front axle center coordinate of the vehicle into the distance equation to obtain a first distance from the front axle center of the vehicle to the left lane line, determining a second distance from a center line of the lane to the left lane line according to a lane width in the surrounding environment information, and obtaining the current lateral offset according to a difference between the first distance and the second distance; deriving a slope equation of a tangent line of the lane line by taking a first derivative of the distance equation, substituting the front axle center coordinate of the vehicle into the slope equation to obtain a slope, solving an inverse tangent value of the slope to obtain a tangent angle, and obtaining the current heading angle deviation according to a difference between a current vehicle body heading angle in the running state information and the tangent angle.

3. The cooperative control method for vehicle trajectory following according to claim 1, wherein, The first controller is a proportional-integral controller; and the calculation of the first yaw moment by the first controller according to the current heading angle deviation comprises: constructing a proportional-integral controller with the heading angle deviation as a feedback quantity and the yaw moment as an output control quantity, and determining a proportional coefficient and an integral coefficient in the proportional-integral controller according to a heading angle deviation and a change rate thereof of the vehicle and a vehicle speed; inputting the current heading angle deviation into the proportional-integral controller to output the first yaw moment.

4. The cooperative control method for vehicle trajectory following according to claim 2, wherein, The target yaw rate is determined based on the running state information and the current lateral offset, and the method comprises: Based on the running state information, a plurality of vehicle body longitudinal distances predicted in a preset time range are obtained; Each vehicle body longitudinal distance is substituted into the distance equation to obtain a corresponding vehicle body lateral distance, and the future driving trajectory of the vehicle is determined according to all vehicle body longitudinal distances and corresponding vehicle body lateral distances; The target lateral offset is determined based on the future driving trajectory, and the current lateral offset is divided by the target lateral offset to obtain a current offset distance completion degree; The target yaw rate is obtained by looking up a first preset table according to the current offset distance completion degree and the current vehicle body heading angle in the running state information; the first preset table is constructed by a vehicle kinematics equation and is used to store the mapping relationship between different offset distance completion degrees, vehicle body heading angles and yaw rates.

5. The cooperative control method of vehicle trajectory following according to claim 1 or 4, characterized in that, The running state information at least comprises a current yaw rate, and the second controller is a proportional-integral-derivative controller; The second yaw moment is calculated by the second controller based on the target yaw rate and the running state information, and the method comprises: A proportional-integral-derivative controller is constructed with a yaw rate deviation as a feedback quantity and a yaw moment as an output control quantity, and the proportional coefficient, integral coefficient and differential coefficient of the proportional-integral-derivative controller are determined according to the yaw rate deviation and its change rate of the vehicle and the vehicle speed; The current yaw rate deviation is obtained by subtracting the current yaw rate from the target yaw rate; The current yaw rate deviation is input into the proportional-integral-derivative controller to output the second yaw moment.

6. The collaborative control method for vehicle trajectory following of claim 1, wherein, The running state information at least comprises a current yaw rate and a current yaw rate change rate; The total yaw moment is determined based on the first yaw moment and the second yaw moment, and the method comprises: A first weight coefficient is obtained by looking up a second preset table according to the current yaw rate and the current yaw rate change rate; the second preset table is used to store the mapping relationship between different yaw rates and yaw rate change rates and weight coefficients; A second weight coefficient is determined based on the first weight coefficient, and the sum of the first weight coefficient and the second weight coefficient is 1; The first yaw moment is multiplied by the first weight coefficient, the second yaw moment is multiplied by the second weight coefficient, and then the two results are added to obtain the total yaw moment.

7. The collaborative control method for vehicle trajectory following of claim 1, wherein, The driving torque of each wheel is determined according to the total yaw moment and the target distribution coefficient, and the method comprises: The original distribution torque of each wheel, the wheel radius and the target distance of each wheel are obtained, and the target distance represents the distance from the wheel center to the vehicle center of mass; The target distribution coefficient is distributed to obtain the torque coefficient of each wheel; The torque coefficient of each wheel is multiplied by the total yaw moment to obtain the additional torque of each wheel; The wheel radius of each wheel is divided by the target distance to obtain the correction coefficient of each wheel; The additional torque of each wheel is multiplied by the corresponding correction coefficient, and then added to the original distribution torque of each wheel to obtain the driving torque of each wheel.

8. The cooperative control method for vehicle trajectory following according to claim 4, wherein, The surrounding environment information at least includes a current lane line; before the step of calculating a current lateral offset and a current heading angle deviation based on the running state information and the surrounding environment information, the method further comprises: judging whether the vehicle is deviated based on a future driving trajectory and the current lane line; when the vehicle is deviated, performing the step of calculating the current lateral offset and the current heading angle deviation based on the running state information and the surrounding environment information. 9.A cooperative control device for vehicle trajectory following, applied to a vehicle equipped with a distributed electric drive system; characterized in that, The device comprises: an information acquisition module for acquiring running state information and surrounding environment information of a vehicle; a deviation calculation module for calculating a current lateral offset and a current heading angle deviation based on the running state information and the surrounding environment information, the current lateral offset representing a vertical distance from a front axle center of the vehicle to a lane center line; a posture adjustment module for calculating a first yaw moment according to the current heading angle deviation by using a first controller; a path regression module for determining a target yaw angular velocity based on the running state information and the current lateral offset, and calculating a second yaw moment according to the target yaw angular velocity and the running state information by using a second controller; a moment coordination module for determining a total yaw moment based on the first yaw moment and the second yaw moment; a cooperative control module for determining driving torques of each wheel according to the total yaw moment, the current heading angle deviation and the running state information, and controlling each wheel to execute a corresponding driving torque to realize vehicle trajectory following; wherein the running state information at least includes a current vehicle speed; the determination of the driving torques of each wheel according to the total yaw moment, the current heading angle deviation and the running state information comprises: obtaining a target distribution coefficient from a third preset table according to the current vehicle speed and the current heading angle deviation; the third preset table is used to store a mapping relationship between different vehicle speeds and heading angle deviations and distribution coefficients; determining the driving torques of each wheel according to the total yaw moment and the target distribution coefficient.

10. A vehicle equipped with a distributed electric drive system; characterized by, The vehicle comprises a controller, and the controller comprises: a memory and a processor, which are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the cooperative control method for vehicle trajectory following according to any one of claims 1 to 8.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make a computer execute the cooperative control method for vehicle trajectory following according to any one of claims 1 to 8.

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