CN115476881B

CN115476881B - Vehicle track tracking control method, device, equipment and medium

Info

Publication number: CN115476881B
Application number: CN202211288639.5A
Authority: CN
Inventors: 雍文亮; 丁明慧; 胡旺; 王良; 周增碧
Original assignee: Chongqing Changan Automobile Co Ltd
Current assignee: Chongqing Changan Automobile Co Ltd
Priority date: 2022-10-20
Filing date: 2022-10-20
Publication date: 2024-06-04
Anticipated expiration: 2042-10-20
Also published as: CN115476881A

Abstract

The application provides a vehicle track tracking control method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring target track parameters and state information of a vehicle; determining a pretightening distance according to the target track parameter and the vehicle speed information, and determining pretightening deviation from a pretightening point to a target track according to the pretightening distance and the target track parameter; constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting a pretightening distance and pretightening deviation into the tracking control algorithm, and determining a first steering wheel rotation angle value; constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting a pretightening distance and pretightening deviation into the optimal control algorithm, and determining a second steering wheel angle value; the method and the device fully consider the vehicle dynamics characteristic, and can adapt to the accurate tracking of the target track under all working conditions through the dual-mode control comprehensive weighting treatment.

Description

Vehicle track tracking control method, device, equipment and medium

Technical Field

The application relates to the field of vehicle control or automatic driving, in particular to a vehicle track tracking control method, device, equipment and medium.

Background

With the intelligent high-speed development of automobiles, more and more automobiles are provided with automatic driving functions, and the requirements of people on the automatic driving control quality are higher and higher, so that the automatic driving control quality of the automobiles is expected to be closer to that of real drivers, and more comfortable and reliable driving experience is obtained. In automatic driving, trajectory tracking control is one of the basic approaches to realize lateral motion control of an automatically driven vehicle of the level L3 and above.

However, the existing track tracking control method adopts a single control algorithm, so that the consideration of the vehicle dynamics characteristic and engineering application is insufficient, on one hand, the personification degree is insufficient, and the problem of full-working-condition scenes is difficult to adapt; on the other hand, the automatic driving track tracking algorithm adopts a non-mechanism modeling mode and has strong dependence on specific scenes, so that the automatic driving track tracking algorithm cannot adapt to all working conditions to realize accurate tracking of the target track.

Content of the application

In view of the above-mentioned shortcomings of the prior art, the present application provides a vehicle track tracking control method, device, equipment and medium, so as to solve the technical problem that the vehicle track tracking control cannot adapt to all working conditions to achieve accurate tracking of a target track.

In a first aspect, the present application provides a vehicle track tracking control method, including:

acquiring target track parameters and state information of a vehicle, wherein the state information comprises current speed information of the vehicle;

determining a pretightening distance according to the target track parameter and the vehicle speed information, and determining pretightening deviation from a pretightening point to a target track according to the pretightening distance and the target track parameter;

constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting the pretighting distance and the pretighting deviation into the tracking control algorithm, and determining a first steering wheel corner value;

Constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting the pretightening distance and the pretightening deviation into the optimal control algorithm, and determining a second steering wheel angle value;

The first steering wheel angle value and the second steering wheel angle value are subjected to weighted fusion to obtain a comprehensive steering wheel angle value;

and finishing tracking the vehicle track according to the control quantity corresponding to the comprehensive steering wheel angle value.

In an embodiment of the application, the constructing a tracking control algorithm based on the understeer characteristic of the vehicle further includes:

Determining an understeer characteristic coefficient of the vehicle correction track tracking according to a preset minimum vehicle speed threshold value and current vehicle speed information;

and constructing a tracking control algorithm according to the incidence relation among the understeer characteristic coefficient, the wheelbase of the vehicle, the steering angle transmission ratio, the pre-aiming distance and the pre-aiming deviation.

In an embodiment of the present application, the constructing an optimal control algorithm based on the adaptive weight control manner, inputting the pretightening distance and the pretightening deviation into the optimal control algorithm, and determining a second steering wheel angle value, further includes:

Constructing a linear stable non-homogeneous equation set expressed by a state space equation according to the lateral displacement, the lateral speed, the course angle and the yaw rate of the vehicle;

Performing time domain conversion on the linear steady non-homogeneous equation set to determine a measured value of the equation set;

Dividing the pretightening distance into a plurality of equivalent pretightening points at equal intervals based on pretightening time corresponding to the pretightening distance, wherein the equivalent pretightening points correspond to different weight coefficients according to the distance of the pretightening distance;

constructing a performance index function of an optimal control algorithm according to the pretightening deviation of the equivalent pretightening point and the measured value;

Conducting derivation processing on the performance index function, inputting the derived performance index function through the weight coefficient corresponding to the equivalent pretightening point and the measured value, and determining the optimal control input quantity;

And determining the second steering wheel angle value according to the optimal control input quantity, the pretightening deviation and the steering angle transmission ratio.

In one embodiment of the present application, the expression of the first steering wheel angle value is determined as:

wherein K _v is an insufficient steering characteristic coefficient, delta _sw1 is a first steering wheel corner value, L is an automobile wheelbase, i is a steering angle transmission ratio, v _xmin is a preset minimum threshold speed, pi is a circumference rate, d _prv is a pretightening distance, y _prv is a pretightening deviation, and v _x is vehicle speed information.

In an embodiment of the present application, the determining a pretightening distance according to the target track parameter and the vehicle speed information, determining a pretightening deviation from a pretightening point to a target track according to the pretightening distance and the target track parameter, further includes:

determining pre-aiming time of the vehicle based on the current speed information of the vehicle and the target track parameter;

calculating the current vehicle speed information and the pre-aiming time of the vehicle, and determining a pre-aiming distance;

And calculating according to the pretightening distance and the target track parameter, and determining pretightening deviation from the pretightening point to the target track.

In one embodiment of the present application, the expression of the performance index function is determined as,

Wherein t _prv is the pretightening time, y _prv is the pretightening deviation from the pretightening point to the target track, F (t) and g (t) are intermediate variable functions, x ₀ is the initial state at the time of t=0, u is the control input quantity, and ω (t) is the angular frequency.

In an embodiment of the present application, the expression of the second steering wheel angle value is determined as:

Wherein y _prvj is the pretightening deviation from the jth equivalent pretightening point to the target track, d _prv is the pretightening distance, j is the jth equivalent pretightening point, n is the number of equivalent pretightening points equally spaced apart from the pretightening distance, F _j、g_j is an intermediate variable function, t _prv is pretightening time, y _prv is the pretightening deviation from the pretightening point to the target track, B, C is an input matrix and an output matrix of state transition distances respectively, x ₀ is an initial state at the moment of t=0, u _k is an optimal control input quantity, ω _j is an angular frequency, i is a steering angle transmission ratio, and pi is a circumference ratio.

In an embodiment of the present application, the weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value includes:

according to the current speed information of the vehicle and the curvature of a target track corresponding to the current position of the vehicle, respectively determining a first weight coefficient corresponding to the tracking control algorithm and a second weight coefficient corresponding to the optimal control algorithm;

performing weighted calculation on the first steering wheel angle value and a first weight coefficient to determine a weighted first steering wheel angle value;

Performing weighted calculation on the second steering wheel angle value and a second weight coefficient to determine a weighted second steering wheel angle value;

And accumulating the weighted second steering wheel angle value and the weighted second steering wheel angle value to obtain a comprehensive steering wheel angle value.

In an embodiment of the present application, the curvature on the target track corresponding to the current vehicle speed information and the current vehicle position further includes at least one of the following:

Wherein v _x is the current speed information of the vehicle, P _ρ is the weight coefficient corresponding to the vehicle under the current speed and the current curvature, ρ is the curvature on the target track corresponding to the current position of the vehicle,/>The product of p _ρ determines the magnitude of the second weight coefficient.

In an embodiment of the present application, the expression corresponding to the target track in the target track parameter is determined as follows:

y＝A₀+A₁x+A₂x²+A₃x³

Wherein y is the abscissa of the target track, x is the ordinate of the target track, and A ₀、A₁、A₂、A₃ is the lateral deviation, course angle, road curvature and road curvature change rate in sequence.

In an embodiment of the application, the acquiring the target track parameter and the state information of the vehicle further includes:

The state information comprises yaw rate information, vehicle speed information, steering wheel corner positions and steering machine handshake states;

Respectively preprocessing target track parameters and state information of the vehicle to obtain preprocessed target track parameters and state information;

The target track parameters and the state information of the vehicle are converted; and/or performing a outlier removal process on the target track parameters and the state information of the vehicle; and/or filtering the target track parameters and the state information of the vehicle.

In an embodiment of the application, before the obtaining the target track parameter and the state information of the vehicle, the method further includes:

Acquiring automatic driving activation information, planning state information and steering machine state information of the vehicle;

respectively determining states of automatic driving activation information, planning state information and steering machine state information of the vehicle;

And if the states of the automatic driving activation information, the planning state information and the steering machine state information are all normal, the state verification is passed, and an enabling signal of track tracking control is output.

In an embodiment of the present application, after the vehicle track tracking is completed according to the control amount corresponding to the integrated steering wheel angle value, the method further includes:

Determining a steering wheel angle request value corresponding to the comprehensive steering wheel angle value after switching based on the steering machine handshake state;

according to the vehicle speed information and the steering wheel corner position, carrying out safety judgment on the steering wheel corner request value;

If the steering wheel angle request value is unsafe, assigning a value and limiting the speed to the steering wheel angle request value according to a preset functional safety limit until the steering wheel angle request value is safe, and obtaining a final steering wheel angle request value;

And filtering the final steering wheel rotation angle request value and the state of track tracking control, and outputting the final steering wheel rotation angle request value and the state of track tracking control.

In a second aspect, the present application provides a vehicle track following control device, including:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring target track parameters and state information of a vehicle, and the state information comprises current speed information of the vehicle;

The pretightening module is used for determining a pretightening distance according to the target track parameter and the vehicle speed information, and determining pretightening deviation from a pretightening point to a target track according to the pretightening distance and the target track parameter;

the first control module is used for constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting the pre-aiming distance and the pre-aiming deviation into the tracking control algorithm, and determining a first steering wheel rotation angle value;

the second control module constructs an optimal control algorithm based on a self-adaptive weight control mode, inputs the pretightening distance and the pretightening deviation into the optimal control algorithm, and determines a second steering wheel angle value;

The comprehensive control module is used for carrying out weighted fusion on the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value;

And the track tracking module is used for completing the track tracking of the vehicle according to the control quantity corresponding to the comprehensive steering wheel angle value.

In a third aspect, the present application provides an electronic device, including:

One or more processors;

And a storage device for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the vehicle track following control method described above.

In a fourth aspect, the present application provides a vehicle device, including the above-mentioned electronic device.

In a fifth aspect, the present application provides a computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the vehicle track following control method described above.

The application has the beneficial effects that: the method comprises the steps of constructing a tracking control algorithm based on understeer characteristics of the vehicle, inputting the pre-aiming distance and the pre-aiming deviation into the tracking control algorithm, and determining a first steering wheel corner value; constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting the pretightening distance and the pretightening deviation into the optimal control algorithm, and determining a second steering wheel angle value; the first steering wheel angle value and the second steering wheel angle value are subjected to weighted fusion to obtain a comprehensive steering wheel angle value; on one hand, the track tracking control algorithm based on the kinematic mechanism modeling is applicable to characteristic intervals with unobvious vehicle dynamics response such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on dynamic mechanism modeling is suitable for characteristic intervals with remarkable vehicle dynamic response such as high speed, large curvature and the like, vehicle dynamic characteristics are fully considered, and the target track accurate tracking under all working conditions can be adapted through dual-mode control comprehensive weighting processing.

In addition, the understeer characteristic of the vehicle is controlled by a track tracking control algorithm based on kinematic mechanism modeling, so that the track tracking precision is improved; the optimal control algorithm based on dynamic mechanism modeling adopts a self-adaptive weight control mode, and reduces the control frequency under the condition of ensuring the control accuracy as much as possible by reducing the weight of the near-end pre-aiming point and improving the weight of the far-end pre-aiming point, so that the control output high-frequency jitter is avoided, and the psychological trust feeling of drivers and passengers is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:

FIG. 1 is a schematic illustration of an environment in which a vehicle trajectory tracking control method is implemented, as shown in an exemplary embodiment of the application;

FIG. 2 is a flow chart diagram of a vehicle trajectory tracking control method shown in an exemplary embodiment of the application;

FIG. 3 is a flow chart of step S230 in the embodiment of FIG. 2 in an exemplary embodiment;

FIG. 4 is a flow chart of step S240 in the embodiment of FIG. 2 in an exemplary embodiment;

FIG. 5 is a block diagram of a vehicle track-following control device according to an exemplary embodiment of the present application;

FIG. 6 is a schematic structural diagram of the vehicle track following control device shown in the embodiment shown in FIG. 5;

fig. 7 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.

Detailed Description

Further advantages and effects of the present application will become readily apparent to those skilled in the art from the disclosure herein, by referring to the accompanying drawings and the preferred embodiments. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.

It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present application by way of illustration, and only the components related to the present application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present application, it will be apparent, however, to one skilled in the art that embodiments of the present application may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present application.

Referring to fig. 1, an implementation environment of a vehicle track following control method according to an exemplary embodiment of the present application is shown. The vehicle includes one or more data collectors 11, track planning information 12 (i.e., a road network definition file), a computer 13, and one or more controllers 14. The vehicle is typically a land-based vehicle having three or more wheels, such as a passenger car, light truck, or the like. The vehicle has a front, a rear, a left side and a right side, wherein the terms front, rear, left and right are understood from the angle of an operator of the vehicle seated in the driver's seat in a standard operating position, i.e. facing the steering wheel.

The computer 13 typically includes a processor and memory including one or more forms of computer-readable media and storing instructions executable by the processor for performing various operations. Further, the computer 13 may include and/or be communicatively connected to one or more other computing devices included in the vehicle for monitoring and/or controlling various vehicle components. The computer 13 is typically programmed and arranged for communication over a controller area network bus or the like.

The computer 13 may also have a connection to an on-board diagnostic connector (OBD-II), a CAN (controller area network) bus, and/or other wired or wireless mechanism. Through one or more of such communication mechanisms, the computer 13 may transmit and/or receive messages to and/or from various devices in the vehicle, such as controllers, actuators, sensors, etc., including the data collector 11 and the controller 14. Alternatively or additionally, in the case where the computer 13 actually includes a plurality of devices, a CAN bus or the like may be used for communication between the devices represented as the computer 13 in the present invention. In addition, the computer 13 may be configured to communicate with other devices via various wired and/or wireless network technologies, such as cellular, bluetooth, universal Serial Bus (USB), wired and/or wireless packet-switched networks, and the like.

The memory of the computer 13 typically stores the collected data. The collected data may include various data collected in and/or derived from the vehicle by the data collector 11. Examples of data collectors 11 may include, for example, data regarding driving behavior of one or more vehicles, such as, for example, location of the vehicle over time (e.g., geographic coordinates, distance to the vehicle, etc.), speed of the vehicle over time, direction of travel, direction of the vehicle at different points in time, and number and magnitude of the change in speed, etc. The collected data may further include, for example, information such as the type of vehicle or vehicles (e.g., light trucks, buses, minivans, etc.), size, make, model, etc. The collected data may additionally include data calculated from data received from the data collector 11 in the computer 13. In general, the collected data may include any data collected by the data collector 11, received through vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication, collected or received from other sources, and/or calculated from such data.

The computer 13 may be programmed to receive data from the data collector 11 and data about the target, such as the destination, route, arrival time, etc. of the vehicle. The computer 13 may be further programmed to collect data about the object of the vehicle and other data about the vehicle, such as a map of the area in which the vehicle is running. For example, the computer 13 may receive input from the user through a user interface, the input representing the user's destination, the route the user wants to take, the driving style (conservative, sporty), and so on. The computer 13 may further comprise or receive a map of, for example, an area, for example, from a global positioning system (GPS system) or from a memory. Based on the received data, the computer 13 may execute a so-called "mission plan", i.e. plan a path to a destination depending on the driving direction on the road network map. The computer 13 may be further programmed to store this data in a memory for further use, for example, for use in determining driving strategies and/or driving the vehicle.

In general, each controller 14 may include a processor programmed to receive instructions from the computer 13, execute the instructions, and send messages to the computer 13. Further, the controllers 14 may each include sensors or otherwise operate as data collectors 11 to provide data to the computer 13 regarding vehicle speed, vehicle steering angle, suspension height, etc. For example, data corresponding to the brake pressure applied by the brake controller 14 may be transmitted to the computer 13.

The data collector 11 may comprise various means, for example, the data collector 11 may comprise means for sensing the environment, for example, a lidar for tracking a vehicle, a radar, a video camera, an ultrasonic sensor, an infrared sensor. The data collector 11 may further include means for collecting data of the dynamic vehicle, such as speed, yaw rate, steering angle, etc. Furthermore, the above examples are not intended to be limiting. Other types of data collectors 11, such as accelerometers, gyroscopes, pressure sensors, thermometers, barometers, altimeters, etc. may be used to provide data to the computer 13.

The road network definition file may include a coded topology metric map of the road network in which the vehicle may operate. The topology metric map includes latitude and longitude coordinates for road features and other objects in the environment and is encoded based on derivatives in RNFD file format. The road network definition file may supply map data, for example, to the computer 13 for implementing track planning information.

The computer 13 may be programmed to store data relating to the vehicle. As described above, the data may include data representing a history of data points, such as a pose of the vehicle over time, a speed of the vehicle over time, a direction of travel, a number and magnitude of the direction and speed changes at different points in time, and the like.

The problems noted above have general applicability in general travel scenarios. It can be seen that the existing track tracking control method adopts a single control algorithm, so that the vehicle dynamics characteristics and engineering application are not considered sufficiently, on one hand, the personification degree is not enough, and the problem of full-working-condition scenes is difficult to adapt; on the other hand, the automatic driving track tracking algorithm adopts a non-mechanism modeling mode and has strong dependence on specific scenes, so that the automatic driving track tracking algorithm cannot adapt to all working conditions to realize accurate tracking of the target track. To solve these problems, embodiments of the present application respectively propose a vehicle track following control method, a vehicle track following control apparatus, an electronic device, and a computer-readable storage medium, which will be described in detail below.

Referring to fig. 2, a flow chart of a vehicle track following control method according to an exemplary embodiment of the application is shown. The method can be applied to the implementation environment shown in fig. 1 and is specifically executed by the intelligent terminal in the implementation environment. It should be understood that the method may be adapted to other exemplary implementation environments and be specifically executed by devices in other implementation environments, and the implementation environments to which the method is adapted are not limited by the present embodiment.

In an exemplary embodiment, fig. 2 is a flowchart illustrating a vehicle track following control method according to an exemplary embodiment of the present application, which is described in detail below:

Step S210, acquiring target track parameters and state information of a vehicle, wherein the state information comprises current speed information of the vehicle;

Specifically, the state information includes yaw rate information, vehicle speed information, steering wheel corner position, and steering machine handshake state. The target track comprises a polynomial expression formed by transverse deviation, course angle, road curvature and road curvature change rate, the vehicle speed information is acquired by using a vehicle chassis sensor, for example, sensing data is acquired by a vehicle-mounted sensor on the vehicle so as to obtain current vehicle speed information, for example, the current position of the vehicle is determined by the target track and the relative position of a coordinate system where the vehicle is currently located, and for example, the electronic stability program system acquires yaw rate error by using an angle signal from a steering wheel angle sensor.

The expression corresponding to the target track in the target track parameters is determined as follows:

y＝A₀+A₁x+A₂x²+A₃x³

Herein, the vehicle includes, but is not limited to, a fuel-powered vehicle, an extended range electric vehicle, a pure electric vehicle, a hybrid electric vehicle, a hydrogen energy vehicle, and the like.

Step S220, determining a pretightening distance according to the target track parameter and the vehicle speed information, and determining pretightening deviation from a pretightening point to a target track according to the pretightening distance and the target track parameter;

specifically, determining pre-aiming time of the vehicle based on current speed information of the vehicle and target track parameters; calculating the current vehicle speed information and the pre-aiming time of the vehicle, and determining a pre-aiming distance; and calculating according to the pretightening distance and the target track parameter, and determining pretightening deviation from the pretightening point to the target track.

For example, the current pre-aiming point may be determined using the current vehicle speed information and the pre-aiming time of the vehicle. The pretightening theory can accurately reflect the control behavior of a driver, has simple structure and strong adaptability, therefore, the pretightening theory has wider application in the track tracking field, and adopts a method of fixing pretightening time to calculate the pretightening distance.

Step S230, a tracking control algorithm is constructed based on the understeer characteristic of the vehicle, the pre-aiming distance and the pre-aiming deviation are input into the tracking control algorithm, and a first steering wheel rotation angle value is determined;

Specifically, the improved pure tracking algorithm considers the understeer characteristic of the vehicle, and improves the track tracking precision.

Step S240, an optimal control algorithm is constructed based on a self-adaptive weight control mode, the pre-aiming distance and the pre-aiming deviation are input into the optimal control algorithm, and a second steering wheel angle value is determined;

Specifically, an improved optimal control algorithm is utilized to adopt a self-adaptive weight control mode, and the control frequency is reduced under the condition that the control accuracy is ensured as much as possible by reducing the weight of the near-end pre-aiming point and improving the weight of the far-end pre-aiming point, so that the control output high-frequency jitter is avoided, and the psychological trust feeling of drivers and passengers is improved.

Step S250, the first steering wheel angle value and the second steering wheel angle value are subjected to weighted fusion to obtain a comprehensive steering wheel angle value;

Specifically, the weighting and summation are carried out through the weight coefficients, so that the weighting and fusion can be realized, the comprehensive steering wheel angle value is obtained, and in the control process, the comprehensive steering wheel angle value is used, the proper pre-aiming point is selected by the weight coefficients and is reasonable, so that the vehicle can stably and accurately follow the track.

Step S260, tracking the vehicle track according to the control quantity corresponding to the comprehensive steering wheel angle value;

in the embodiment, two control algorithms are established based on the kinematics and the dynamics mechanism, on one hand, the trajectory tracking control algorithm based on the kinematics mechanism modeling is applicable to characteristic intervals with unobvious vehicle dynamics response such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on dynamic mechanism modeling is suitable for characteristic intervals with remarkable vehicle dynamic response such as high speed, large curvature and the like, vehicle dynamic characteristics are fully considered, and the target track accurate tracking under all working conditions can be adapted through dual-mode control comprehensive weighting processing.

In other embodiments, obtaining the target track parameters and the state information of the vehicle further comprises:

The target track parameters and the state information of the vehicle are converted; and/or performing a outlier removal process on the target track parameters and the state information of the vehicle; and/or filtering the target track parameter and the state information of the vehicle, which are not described herein.

The pretreatment may be any one of the above treatment methods, or at least one of the treatment methods may be combined.

Specifically, the conversion process, that is, converting the units of the target trajectory parameter and the state information, corresponds to unit conversion.

Specifically, the wild point removing process is to filter the target track parameters and the state information, determine the wild points, and complete the wild point removing process by eliminating the wild points.

Specifically, in a specific application scenario, a corresponding filtering rule may be configured for the filtering process, for example, the configured filtering rule may be: the total kilometer number is greater than N kilometers, where N is a natural number greater than or equal to 1, and specific numerical values of N can be limited according to different application scenarios, so that vehicle speed information is limited.

Through the preprocessing, the accuracy of the target track is improved, and meanwhile, the accuracy of the state information is also improved.

In other embodiments, before the acquiring the target track parameter and the state information of the vehicle, the method further includes:

Here, the state of the planning state information is determined by the state information in the planning state information, the state information of the steering machine state information determines the state of the steering machine state information, and similarly, the state of the automatic driving activation information is determined by the state information included in the automatic driving activation information.

When the state check is passed, an enabling signal of the track following control is output, for example, the enabling signal is an on signal for controlling the track following controller to be in an operating state, and conversely, when the state check is not passed, an enabling signal of the track following control is output, for example, the enabling signal is an off signal for controlling the track following controller to be not in an operating state.

By the method, the control track tracking controller is activated and judged according to the current state of the vehicle, and the optimal control mode of the current vehicle can be determined according to local conditions, so that the robustness of vehicle track tracking control is improved, and the stability of vehicle track tracking control is enhanced.

In other embodiments, please refer to fig. 3, which is a flowchart illustrating step S230 in the embodiment shown in fig. 2 in an exemplary embodiment; wherein the constructing a tracking control algorithm based on the understeer characteristic of the vehicle further comprises:

step S310, determining an understeer characteristic coefficient of the vehicle correction track tracking according to a preset minimum vehicle speed threshold value and current vehicle speed information;

And step S320, constructing a tracking control algorithm according to the incidence relation among the understeer characteristic coefficient, the wheelbase of the vehicle, the steering angle transmission ratio, the pre-aiming distance and the pre-aiming deviation.

The expression of the first steering wheel angle value is determined as:

By the method, the understeer characteristic of the vehicle is improved based on the trajectory tracking control algorithm modeled by the kinematic mechanism, and the trajectory tracking precision is improved.

Referring to fig. 4, a flowchart of step S240 in the embodiment shown in fig. 2 is shown in an exemplary embodiment; further comprises:

Step S410, constructing a linear stable non-homogeneous equation set expressed by a state space equation according to the lateral displacement, the lateral speed, the course angle and the yaw rate of the vehicle;

step S420, performing time domain conversion on the linear stationary non-homogeneous equation set, and determining a measured value of the equation set;

step S430, dividing the pretightening distance into a plurality of equivalent pretightening points at equal intervals based on pretightening time corresponding to the pretightening distance, wherein the equivalent pretightening points correspond to different weight coefficients according to the distance of the pretightening distance;

Step S440, constructing a performance index function of an optimal control algorithm according to the pretightening deviation of the equivalent pretightening point and the measured value;

In particular, the expression of the performance index function is determined as,

Step S450, conducting derivation processing on the performance index function, inputting the weight coefficient corresponding to the equivalent pretightening point and the measured value into the derived performance index function, and determining the optimal control input quantity;

Step S460, determining the second steering wheel angle value according to the optimal control input quantity, the pretightening deviation and the steering angle transmission ratio.

In particular, the expression of the second steering wheel angle value is determined as,

In other embodiments, the weighted fusion of the first steering wheel angle value and the second steering wheel angle value results in a composite steering wheel angle value, comprising:

It should be noted that, the curvature on the target track corresponding to the current vehicle speed information and the current vehicle position according to the current vehicle speed information of the vehicle further includes at least one of the following:

By the method, two control algorithms are established based on the kinematics and the dynamics mechanism, and on one hand, the trajectory tracking control algorithm based on the kinematics mechanism modeling is applicable to characteristic intervals with unobvious vehicle dynamics response such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on dynamic mechanism modeling is suitable for characteristic intervals with obvious dynamic response of vehicles such as high speed, large curvature and the like, the dynamic characteristics of the vehicles are fully considered, the vehicles can stably and accurately follow expected tracks under various complex working conditions through dual-mode control comprehensive weighting processing, and the control accuracy of the vehicles is improved.

In other embodiments, after the vehicle track is tracked according to the control amount corresponding to the integrated steering wheel angle value, the method further includes:

For example, the input signals of the safety limiting module are steering wheel rotation angle, lateral acceleration, vehicle speed and steering wheel rotation angle, the output signals of the safety limiting module are state signals, the state signals are used for feeding back the safety state of the track tracking controller to the state machine information module, and the safety limiting module is used for assigning values to the steering wheel rotation angle and limiting the speed according to the lateral acceleration, the vehicle speed and the steering wheel rotation angle to obtain steering wheel rotation angle request values which accord with functional safety and transmitting the steering wheel rotation angle request values to the filtering processing module.

The input signal of the filtering processing module is a steering wheel angle request value, the output signal is a final steering wheel angle, and the filtering processing module is used for carrying out low-pass filtering on the steering wheel angle request value so as to smoothly output the final steering wheel angle.

Fig. 5 is a block diagram showing the configuration of a vehicle trajectory tracking control device according to an exemplary embodiment of the present application. The device can be applied to the implementation environment shown in fig. 1, and is particularly configured in an intelligent terminal and a vehicle. The apparatus may also be adapted to other exemplary implementation environments and may be specifically configured in other devices, and the present embodiment is not limited to the implementation environments to which the apparatus is adapted.

As shown in fig. 5, the exemplary vehicle track following control device 500 includes:

An obtaining module 501, configured to obtain target track parameters and state information of a vehicle, where the state information includes current speed information of the vehicle;

The pretightening module 502 is configured to determine a pretightening distance according to the target track parameter and the vehicle speed information, and determine a pretightening deviation from a pretightening point to a target track according to the pretightening distance and the target track parameter;

A first control module 503, configured to construct a tracking control algorithm based on the understeer characteristic of the vehicle, input the pre-aiming distance and the pre-aiming deviation into the tracking control algorithm, and determine a first steering wheel rotation angle value;

the second control module 504 constructs an optimal control algorithm based on the adaptive weight control mode, inputs the pretightening distance and the pretightening deviation into the optimal control algorithm, and determines a second steering wheel angle value;

The comprehensive control module 505 is configured to weight and fuse the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value;

And the track tracking module 506 is configured to complete tracking of the vehicle track according to the control amount corresponding to the integrated steering wheel angle value.

In the exemplary vehicle track tracking control device, a tracking control algorithm is constructed based on understeer characteristics of the vehicle, the pre-aiming distance and the pre-aiming deviation are input into the tracking control algorithm, and a first steering wheel rotation angle value is determined; constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting the pretightening distance and the pretightening deviation into the optimal control algorithm, and determining a second steering wheel angle value; the first steering wheel angle value and the second steering wheel angle value are subjected to weighted fusion to obtain a comprehensive steering wheel angle value; on one hand, the track tracking control algorithm based on the kinematic mechanism modeling is applicable to characteristic intervals with unobvious vehicle dynamics response such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on dynamic mechanism modeling is suitable for characteristic intervals with remarkable vehicle dynamic response such as high speed, large curvature and the like, vehicle dynamic characteristics are fully considered, and the target track accurate tracking under all working conditions can be adapted through dual-mode control comprehensive weighting processing.

It should be noted that, the vehicle track following control device provided in the foregoing embodiment and the vehicle track following control method provided in the foregoing embodiment belong to the same concept, and a specific manner in which each module and unit perform an operation has been described in detail in the method embodiment, which is not repeated herein. In practical application, the vehicle track following control device provided in the above embodiment may distribute the functions to be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, which is not limited herein.

Referring to fig. 6, a schematic structural diagram of the vehicle track following control device shown in the embodiment shown in fig. 5 is described in detail as follows:

The track tracking controller module 4 is externally input with the automatic driving main state machine information module 1, the track planning information module 2 and the CAN bus module 3, and outputs the automatic driving main state machine information module 1 and the CAN bus module 3, wherein the track tracking controller module 4 consists of a state checking module 5, an information preprocessing module 6, a core algorithm module 12 and a post-processing module 11.

The core algorithm module 12 is composed of a first controller module 8, a second controller module 9, and a control integration module 10.

The input signals to the status check module 5 include the autopilot activation information ADSSts from the autopilot master state machine information module 1, the planning status information PCSSts from the trajectory planning information module 2, and the steering machine status EPSSts from the CAN bus module 3. The state verification module 5 is configured to determine the enabling information LATEnb of the core algorithm module 12 according to the autopilot activation information, the planning state, and the steering gear state. The trajectory tracking controller module 4 is only allowed to be activated when the autopilot activation information ADSSts allows the trajectory tracking controller module 4 to be enabled, the planning state information PCSSts is normal, and the steering state EPSSts is normal.

The input signals to the information pre-processing module 6 include the target track parameters LanePars from the track plan information module 2 and the yaw-rate information YawRate, the vehicle speed information VehSpd, the steering wheel corner position StrAng, and the steering handshake state EPSCTRLSTS from the CAN bus module 3. The information preprocessing module 6 is used for carrying out unit conversion on the input information, carrying out outlier removal processing and first-order low-pass filtering processing by adopting a median filtering algorithm. The target track parameter LanePars includes information of four aspects of lateral deviation, course angle, road curvature and road curvature change rate, and the target track is in a cubic polynomial expression form, and the expression is as follows:

y＝A₀+A₁x+A₂x²+A₃x³ (1)

In the formula (1), y is a transverse coordinate, x is a longitudinal coordinate, and A ₀～A₃ is a coefficient of a cubic polynomial, which are respectively a transverse deviation, a course angle, a 1/2 road curvature and a road curvature change rate.

The inputs to the driver pre-sight module 7 include vehicle speed information VehSpd from the CAN bus module 3 and target trajectory parameters LanePars from the information pre-processing module 6. The driver pre-aiming module 7 is configured to calculate a pre-aiming distance PrvDis and a pre-aiming deviation PrvYErr from a pre-aiming point to a target track according to a vehicle speed VehSpd, a pre-aiming time PrvT and a target track parameter LanePars, where the expression is as follows:

In equation (2), d _prv is the pretighted distance PrvDis, v _x is the vehicle speed VehSpd, t _prv is the pretighted time PrvT, and y _prv is the lateral deviation PrvYErr from the pretighted point to the target track.

The inputs of the first controller module 8 are all input information from the core algorithm module 12 (i.e., enabling information LATEnb, target trajectory parameters LanePars, yaw rate information YawRate, vehicle speed information VehSpd) and pre-aiming distance PrvDis, pre-aiming bias PrvYErr information from the driver pre-aiming module 7. The first controller module 8 calculates the desired steering wheel angle value ExpSW by using an improved pure tracking control algorithm based on kinematic mechanism modeling, the improved pure tracking algorithm considers the understeer characteristic of the vehicle, the track tracking precision of the first controller module 8 is improved by a correction coefficient K _v, and the expression of the improved pure tracking control algorithm is as follows:

In the formula (3), δ _sw1 is a desired steering wheel angle value ExpSW1, L is a wheelbase of the automobile, i is a steering angle transmission ratio, v _xmin is a set minimum vehicle speed threshold, K _v is a vehicle understeer characteristic coefficient, and pi is a circumference ratio.

The inputs to the second controller module 9 include all input information from the core algorithm module 12 and pre-aiming distance PrvDis, pre-aiming deviation PrvYErr information from the driver pre-aiming module 7. The second controller module 9 calculates the desired steering wheel angle value ExpSW by using an improved optimal control algorithm based on dynamic mechanism modeling, the improved optimal control algorithm adopts a control mode of self-adaptive weight, and the desired steering wheel angle control quantity is obtained by solving a linear steady non-homogeneous equation. The control mode of the self-adaptive weight reduces the control frequency under the condition of ensuring the control precision as much as possible by reducing the weight of the near-end pre-aiming point and improving the weight of the far-end pre-aiming point, thereby avoiding the control output high-frequency jitter and improving the psychological trust feeling of drivers and passengers. The improved optimal control algorithm expression and solving process are as follows:

State space equation expression:

In the formula (4), the state quantity x= [ yψv _y w_r]^T ] represents the lateral displacement, the heading angle, the lateral velocity and the yaw velocity, respectively, and y is a measurement value. The state transition matrix A, the input matrix B and the output matrix C are respectively:

C＝[1 0 0 0] (7)

In the formulas (5), (6) and (7), M is the mass of the whole automobile, k ₁、k₂ is the cornering stiffness of the front axle and the cornering stiffness of the rear axle respectively, a and b are the distances from the mass center of the automobile to the front axle and the rear axle respectively, and I _z is the moment of inertia of the automobile around the z axis at the mass center.

Equation (4) is a linear and steady system of homogeneous equations, and assuming that the initial state is x ₀ at time t=0, the time domain solution can be expressed as:

The measurement can be expressed as:

In the formula (9), F (t) and g (t) are intermediate variable functions.

Dividing the pretightening distance PrvDis into n equal parts at equal intervals according to the pretightening time set in the formula (2), wherein the n equal parts are equivalent to n pretightening points, and the weight of each equivalent pretightening point is as follows:

In the formula (10), w _j、w_j1 is the weight of the j point and the j-1 point respectively.

And establishing an optimal control quadratic performance index function J through a pretightening deviation PrvYErr corresponding to an nth pretightening point and a measurement equation shown in a formula (9), wherein the expression is as follows:

The final optimum control input amount is obtained by deflecting the input u by the formula (11) and setting dJ/du=0:

discretizing the expression (10) and the expression (9) and then carrying into the expression (12) to obtain the final optimal control input quantity:

where y _prvj is the lateral deviation from the jth pretightening point to the target track after the equivalence, and δ _sw2 is the desired steering wheel angle ExpSW.

Inputs to the control integration module 10 include a desired steering wheel angle value ExpSW from the first controller module 8, a desired steering wheel angle value ExpSW from the second controller module 9, vehicle speed information VehSpd from the information preprocessing module 6, and target track parameters LanePars. The control integration module 10 is configured to adaptively adjust output weights of the first controller module 8 and the second controller module 9 according to the vehicle speed and the track curvature information, so as to calculate an integrated steering wheel angle expected value ExpSW, where the expression is as follows:

In the formula (14), c ₁、c₂ is the output weight of the first controller module 8 and the second controller module 9, ρ is the curvature on the target track corresponding to the current position of the vehicle, δ _sw is the integrated steering wheel angle expected value ExpSW outputted by the control integrated module 10, P _ρ is a weight coefficient with respect to the vehicle speed and curvature, respectively, expressed by the following formula:

/>

Inputs to the post-processing module 11 include the integrated steering wheel angle desired value ExpSW from the control integration module 10, vehicle speed information VehSpd from the information pre-processing module 6, steering wheel angle position StrAng, and steering machine handshake state EPSCTRLSTS. The post-processing module 11 is configured to switch the steering wheel angle request value according to the handshake state of the steering wheel, output the final steering wheel angle request value ExpFinalSW to the CAN bus module 3 through low-pass filtering and functional safety restriction, and output information LATCTRLSTS about whether track tracking control is effective to the autopilot host state machine information module 1.

The embodiment of the application also provides electronic equipment, which comprises: one or more processors; and a storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the vehicle track following control method provided in the respective embodiments described above.

Fig. 7 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application. It should be noted that, the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

As shown in fig. 7, the computer system 700 includes a central processing unit (Central Processing Unit, CPU) 701 that can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 702 or a program loaded from a storage portion 708 into a random access Memory (Random Access Memory, RAM) 703. In the RAM703, various programs and data required for the system operation are also stored. The CPU701, ROM702, and RAM703 are connected to each other through a bus 704. An Input/Output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker, etc.; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 710 as needed, so that a computer program read out therefrom is installed into the storage section 708 as needed.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. When executed by a Central Processing Unit (CPU) 701, performs the various functions defined in the system of the present application.

It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

Another aspect of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the vehicle trajectory tracking control method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.

The above embodiments are merely illustrative of the principles of the present application and its effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the application. It is therefore intended that all equivalent modifications and changes made by those skilled in the art without departing from the spirit and technical spirit of the present application shall be covered by the appended claims.

Claims

1. A vehicle trajectory tracking control method, characterized by comprising:

Determining an understeer characteristic coefficient of the vehicle correction track tracking according to a preset minimum vehicle speed threshold value and current vehicle speed information; constructing a tracking control algorithm according to the incidence relation among the understeer characteristic coefficient, the wheelbase of the vehicle, the steering angle transmission ratio, the pre-aiming distance and the pre-aiming deviation, inputting the pre-aiming distance and the pre-aiming deviation into the tracking control algorithm, and determining a first steering wheel corner value;

Constructing a linear stable non-homogeneous equation set expressed by a state space equation according to the lateral displacement, the lateral speed, the course angle and the yaw rate of the vehicle; performing time domain conversion on the linear steady non-homogeneous equation set to determine a measured value of the equation set; dividing the pretightening distance into a plurality of equivalent pretightening points at equal intervals based on pretightening time corresponding to the pretightening distance, wherein the equivalent pretightening points correspond to different weight coefficients according to the distance of the pretightening distance; constructing a performance index function of an optimal control algorithm according to the pretightening deviation of the equivalent pretightening point and the measured value; conducting derivation processing on the performance index function, inputting the derived performance index function through the weight coefficient corresponding to the equivalent pretightening point and the measured value, and determining the optimal control input quantity; determining a second steering wheel angle value according to the optimal control input quantity, the pretightening deviation and the steering angle transmission ratio;

2. The vehicle trajectory tracking control method according to claim 1, characterized in that the expression of the first steering wheel angle value is determined as:

3. The vehicle track following control method according to claim 1, wherein the determining a pretightening distance according to the target track parameter and the vehicle speed information, determining a pretightening deviation from a pretightening point to a target track according to the pretightening distance and the target track parameter, further comprises:

4. The vehicle trajectory tracking control method according to claim 1, characterized in that the expression of the performance index function is determined as:

5. The vehicle trajectory tracking control method according to claim 1, characterized in that the expression of the second steering wheel angle value is determined as:

6. A vehicle track following control method according to any one of claims 1 to 3, wherein said weighting and fusing said first steering wheel angle value and said second steering wheel angle value to obtain a comprehensive steering wheel angle value includes:

7. The vehicle trajectory tracking control method according to claim 6, characterized in that the curvature on the target trajectory corresponding to the current vehicle speed information and the current vehicle position further includes at least one of:

8. The vehicle track following control method according to any one of claims 1 to 3, wherein the expression corresponding to the target track in the target track parameters is determined as:

y＝A₀+A₁x+A₂x²+A₃x³

9. The vehicle track following control method according to any one of claims 1 to 3, wherein the acquiring the target track parameter and the state information of the vehicle further includes:

10. The vehicle track following control method according to any one of claims 1 to 3, characterized by further comprising, before the acquiring the target track parameter and the state information of the vehicle:

11. The vehicle track following control method according to claim 9, wherein after the vehicle track following is completed according to the control amount corresponding to the integrated steering wheel angle value, further comprising:

12. A vehicle track following control device, characterized by comprising:

The first control module is used for determining an understeer characteristic coefficient of the vehicle correction track tracking according to a preset minimum vehicle speed threshold value and current vehicle speed information; constructing a tracking control algorithm according to the incidence relation among the understeer characteristic coefficient, the wheelbase of the vehicle, the steering angle transmission ratio, the pre-aiming distance and the pre-aiming deviation, inputting the pre-aiming distance and the pre-aiming deviation into the tracking control algorithm, and determining a first steering wheel corner value;

The second control module is used for constructing a linear stable non-homogeneous equation set expressed by a state space equation according to the lateral displacement, the lateral speed, the course angle and the yaw rate of the vehicle; performing time domain conversion on the linear steady non-homogeneous equation set to determine a measured value of the equation set; dividing the pretightening distance into a plurality of equivalent pretightening points at equal intervals based on pretightening time corresponding to the pretightening distance, wherein the equivalent pretightening points correspond to different weight coefficients according to the distance of the pretightening distance; constructing a performance index function of an optimal control algorithm according to the pretightening deviation of the equivalent pretightening point and the measured value; conducting derivation processing on the performance index function, inputting the derived performance index function through the weight coefficient corresponding to the equivalent pretightening point and the measured value, and determining the optimal control input quantity; determining a second steering wheel angle value according to the optimal control input quantity, the pretightening deviation and the steering angle transmission ratio;

13. An electronic device, comprising:

One or more processors;

storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the vehicle trajectory tracking control method of any one of claims 1 to 11.

14. A vehicle device comprising the electronic device of claim 13.

15. A computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the vehicle trajectory tracking control method of any one of claims 1 to 11.