CN115534925A

CN115534925A - Vehicle control method, device, equipment and computer readable medium

Info

Publication number: CN115534925A
Application number: CN202211523942.9A
Authority: CN
Inventors: 洪炽杰; 李敏; 侯凯斌; 郭祥靖; 龙文; 王倩; 陶武康; 刘智睿; 艾永军
Original assignee: GAC Aion New Energy Automobile Co Ltd
Current assignee: GAC Aion New Energy Automobile Co Ltd
Priority date: 2022-12-01
Filing date: 2022-12-01
Publication date: 2022-12-30
Anticipated expiration: 2042-12-01
Also published as: CN115534925B

Abstract

Embodiments of the present disclosure disclose vehicle control methods, apparatus, devices, and computer readable media. One embodiment of the method comprises: acquiring a vehicle speed value, a steering wheel turning angle degree, sensor road fusion information and vehicle position information of a current vehicle; the method comprises the steps that a vehicle speed value, a steering wheel turning angle degree, sensor road fusion information and vehicle position information are transmitted to a path planning server to generate an expected path track point information set; constructing a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set; generating a target front wheel rotation angle number based on a target minimum control function and an optimal control constraint condition set; and transmitting the target front wheel rotation angle number to a steering controller for controlling the steering of the vehicle. The embodiment can improve the real-time performance of vehicle steering and the tracking capability of the vehicle to the path track.

Description

Vehicle control method, device, equipment and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a vehicle control method, apparatus, device, and computer-readable medium.

Background

The vehicle transverse control has important significance for the vehicle path tracking. At present, when the vehicle lateral control is carried out, the following modes are generally adopted: firstly, control is carried out by adopting a model-based predictive control or linear quadratic regulator and other related algorithms. And then solving by adopting a quadratic programming method to obtain the corner of the front wheel of the vehicle, so as to drive the steering of the wheels to track the path.

However, the inventors have found that when the vehicle control is performed in the above manner, there are often technical problems as follows:

firstly, a quadratic programming method is adopted for solving, the calculated amount is large, and the generation speed of the front wheel steering angle of the vehicle is low, so that the steering real-time performance of the vehicle is poor, and further, the tracking capability of the vehicle on a path track is poor;

secondly, although the calculated amount of the linear quadratic regulator algorithm is relatively small, the linear quadratic regulator algorithm only solves once in a prediction time domain, and the solving process is difficult to constrain the control amount, so that the course tracking capability of the vehicle at the terminal moment is easy to be poor, and the stability of vehicle path tracking is reduced.

The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose vehicle control methods, apparatuses, devices and computer readable media to address one or more of the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide a vehicle control method, including: acquiring a vehicle speed value, a steering wheel turning angle degree, sensor road fusion information and vehicle position information of a current vehicle; transmitting the vehicle speed value, the steering wheel angle degree, the sensor road fusion information and the vehicle position information to a path planning server to generate an expected path track point information set; constructing a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set; generating a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set; and transmitting the target front wheel rotation angle data to a steering controller for controlling the steering of the vehicle.

In a second aspect, some embodiments of the present disclosure provide a vehicle control apparatus including: an acquisition unit configured to acquire a vehicle speed value, a steering wheel angle degree, sensor road fusion information, and vehicle position information of a current vehicle; a first transmission and generation unit configured to transmit the vehicle speed value, the steering wheel angle number, the sensor road fusion information, and the vehicle position information to a route planning server to generate a set of desired route trace point information; the building unit is configured to build a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set; a generating unit configured to generate a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set; and the second transmission unit is configured to transmit the target front wheel rotation angle number to the steering controller so as to control the steering of the vehicle.

In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.

In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method described in any of the implementations of the first aspect.

The above embodiments of the present disclosure have the following advantages: by the vehicle control method of some embodiments of the disclosure, the real-time performance of vehicle steering can be improved, and the tracking capability of the vehicle on the path track can be improved. Specifically, the reason why the vehicle steering real-time performance is poor and the vehicle has poor tracking ability on the path trajectory is that: the quadratic programming method is adopted for solving, the calculated amount is large, the generation speed of the front wheel steering angle of the vehicle is low, the steering real-time performance of the vehicle is poor, and the tracking capability of the vehicle on the target course is poor. Based on this, the vehicle control method of some embodiments of the present disclosure, first, obtains a vehicle speed value, a steering wheel angle number, sensor road fusion information, and vehicle position information of a current vehicle. Here, information of the vehicle and the surrounding road environment can be obtained, which facilitates subsequent trajectory planning and tracking control for the vehicle heading on the trajectory. And secondly, transmitting the vehicle speed value, the steering wheel angle degree, the sensor road fusion information and the vehicle position information to a path planning server to generate an expected path track point information set. Thereby, a desired vehicle trajectory can be obtained. And then, constructing a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set. Thus, the tracking control problem of the vehicle heading can be converted into the optimal control problem about the front wheel steering according to the minimum value principle. The target front wheel rotation angle number can be obtained quickly in the prediction time domain, so that the vehicle can steer and track the path track in time. And finally, generating a target front wheel corner degree based on the target minimum control function and the optimal control constraint condition set. And transmitting the target front wheel rotation angle data to a steering controller for controlling the steering of the vehicle. Therefore, the vehicle can be steered according to the target front wheel steering angle number to realize the tracking of the path track. Therefore, according to the vehicle control method, the target front wheel rotation angle number can be obtained quickly by solving the target minimum control function, so that the vehicle can turn in time, and path tracking can be performed better. And because the course on the expected track can be tracked in the prediction time domain and rolling optimization can be carried out, when the path track of the vehicle is tracked, feedforward control and feedback control can be simultaneously considered. Thus, the real-time performance of the vehicle steering can be improved. Further, the capability of the vehicle to track the path trajectory can be improved.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a flow chart of some embodiments of a vehicle control method according to the present disclosure;

FIG. 2 is a schematic structural diagram of some embodiments of a vehicle control apparatus according to the present disclosure;

FIG. 3 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates a flow 100 of some embodiments of a vehicle control method according to the present disclosure. The vehicle control method includes the steps of:

step 101, acquiring a vehicle speed value, a steering wheel angle degree, sensor road fusion information and vehicle position information of a current vehicle.

In some embodiments, an executing body (e.g., a vehicle control unit) of the vehicle control method may acquire a vehicle speed value, a steering wheel angle degree, sensor road fusion information, and vehicle position information of a current vehicle through a wired connection manner or a wireless connection manner. The vehicle speed value may be a speed value of the vehicle running at the current time acquired from a vehicle speed sensor. The steering wheel angle degree may be a turning angle of a steering wheel of the vehicle at the present time acquired from a steering wheel angle sensor. The sensor road fusion information may be information of each traffic element around the vehicle, which is output by the sensor fusion server and collected by each vehicle sensor at the current time. The sensor fusion server may be a server that collects traffic elements collected by the laser radar sensor and the camera sensor to form a model of the vehicle surroundings. The traffic elements may include, but are not limited to, at least one of the following: pedestrian, vehicle, lane. The vehicle position information may be information of a road position where the vehicle is located at the present time outputted from the in-vehicle high-precision map. The vehicle position information may include a gradient and a vehicle lateral displacement value. The above-described vehicle lateral displacement value may be a lateral displacement value of the vehicle at the present time. The lateral displacement value may be a distance that the vehicle deviates from a lane center line of the lane in which the vehicle is located. For example, the lateral displacement value may be-1 or 0.5, and the unit of the lateral displacement value may be meters, -1 may indicate that the vehicle is to the left of and 1 meter from the lane centerline, and 0.5 may indicate that the vehicle is to the right of and 0.5 meter from the lane centerline.

And 102, transmitting the vehicle speed value, the steering wheel angle degree, the sensor road fusion information and the vehicle position information to a path planning server to generate an expected path track point information set.

In some embodiments, the executing body may transmit the vehicle speed value, the steering wheel angle number, the sensor road fusion information, and the vehicle position information to a route planning server to generate a set of desired route point information. The route planning server may be a server for planning a vehicle travel route according to the vehicle speed value of the current vehicle, the steering wheel angle, the sensor road fusion information, and the vehicle position information. The expected path point information in the expected path point information set may be information of a point on the planned path trajectory. The expected path trace point information in the expected path trace point information set may include: the vehicle heading angle, the track point lateral displacement value and the track point coordinate. The vehicle heading angle may be an angle between a real moving direction of the vehicle and a longitudinal axis of a preset vehicle coordinate system. The vehicle coordinate system may be a coordinate system that is centered on a center of mass of the vehicle, has a vehicle forward direction as a vertical axis, has a left side of the vehicle forward direction as a horizontal axis, and has a vehicle vertical direction as a vertical axis. The track point lateral displacement value may be a lateral displacement value corresponding to a track point coordinate desired by the vehicle. The trajectory point coordinates can be used to characterize points on the path trajectory. The vehicle speed value of the current vehicle, the steering wheel angle degree, the sensor road fusion information and the vehicle position information CAN be transmitted to a path planning server through a Controller Area Network (CAN) bus, and the path planning server CAN generate an expected path track point information set through a preset path planning algorithm.

As an example, the preset path planning algorithm may include, but is not limited to, at least one of the following: polynomial curves, bezier curves, state lattice algorithms.

And 103, constructing a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set.

In some embodiments, the execution subject may construct the target minimum control function and the optimal control constraint condition set based on the vehicle speed value and the expected path track point information set in various ways. The target minimum control function may be a function for solving an optimal control problem. The optimal control problem is a problem of solving a functional extremum with constraint conditions. The optimal control constraint set may be a set of requirements for extreme values of the performance index included in the target minimum control function. The optimal control constraint set may include optimal control requirements and terminal requirements. The optimal control requirement may be that the target minimum control function needs to satisfy a preset collaborative equation. The terminal requirement may be a cross-section condition when the target minimum control function needs to satisfy the terminal constraint.

In some optional implementation manners of some embodiments, the executing entity may construct a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set by:

and step one, generating a target transverse speed error and a target course speed error based on the vehicle speed value and the expected path track point information set. The target lateral speed error may be a lateral speed error of the vehicle at the current time. The target heading speed error may be a heading speed error of the vehicle at the current time. The lateral velocity error may be a deviation in lateral velocity between the actual trajectory and the desired trajectory of the vehicle. The heading speed error may be a deviation in heading speed between an actual trajectory of the vehicle and a desired trajectory.

In some optional implementations of some embodiments, the executing entity may generate a target lateral speed error and a target heading speed error based on the vehicle speed value and the set of expected path track point information by:

and step one, generating a transverse acceleration error, a course acceleration error, a transverse speed error, a course speed error and a course error based on the vehicle speed value and the expected path track point information set. The lateral acceleration error may be a deviation of the lateral acceleration between the actual trajectory and the expected trajectory of the vehicle. The heading acceleration error may be a deviation of a heading acceleration between an actual trajectory and a desired trajectory of the vehicle. The heading error may be a deviation in heading angle between the actual trajectory of the vehicle and the desired trajectory. The following steps may be specifically performed:

the first substep is to acquire a pose matrix of the vehicle from inertial navigation and obtain a current coordinate and a current course angle based on the pose matrix through a preset pose calculation method. The current coordinate may be a positioning coordinate of the vehicle at the current time. The current heading angle may be a vehicle heading angle at the current time.

As an example, the preset pose solution method described above may include, but is not limited to, at least one of: a pose matrix method and an Euler angle method.

And a second substep of selecting track point coordinates matched with the current coordinates from the track point coordinates included in the expected path track point information set as target track point coordinates. The matching with the current coordinate may be that the distance value between the current coordinate and the current coordinate is the minimum value of the distance values between the coordinates of each track point and the current coordinate. And selecting track point coordinates matched with the current coordinates as target track point coordinates through a preset distance algorithm.

As an example, the preset distance algorithm may include, but is not limited to, at least one of the following: euclidean distance, chebyshev distance.

And a third substep, determining the difference between the vehicle transverse displacement value and the track point transverse displacement value corresponding to the target track point coordinate as a transverse error, and determining the difference between the current heading angle and the vehicle heading angle corresponding to the target track point coordinate as a heading error.

A fourth substep of determining the first derivative of the lateral error as the lateral velocity error and the second derivative of the lateral error as the lateral acceleration error.

A fifth substep of determining the first derivative of the heading error as a heading speed error and the second derivative of the heading error as a lateral acceleration error.

A sixth substep, wherein the lateral acceleration error, the heading acceleration error, the lateral velocity error, the heading velocity error, and the heading error are represented by the following equations according to the lateral dynamics model of the vehicle:

。

wherein the content of the first and second substances,

representing a lateral acceleration error of the vehicle;

indicating a lateral velocity error of the vehicle.

Indicating a heading error of the vehicle.

Indicating a heading speed error of the vehicle.

Indicating a preset required front wheel steering angle of the vehicle as an unknown item.

Indicating the expected value.

Representing the current heading angle.

Indicating the heading angular velocity at the current time.

Indicating a desired vehicle heading angle for the vehicle.

Indicating the desired heading angular velocity of the vehicle.

Indicating a preset vehicle trim mass.

The vehicle speed value is indicated.

And represents the preset vehicle front axle equivalent cornering stiffness.

And represents the preset vehicle rear axle equivalent cornering stiffness.

Representing a preset vehicle center of mass to front axle distance.

Representing a preset vehicle center of mass to rear axle distance.

Indicating a heading acceleration error of the vehicle.

And the preset moment of inertia of the whole vehicle is represented.

Indicating the vehicle lateral speed.

And step two, discretizing the transverse acceleration error and the course acceleration error respectively to obtain a predicted transverse speed error and a predicted course speed error. The predicted lateral velocity error may be a lateral velocity error at a next time in a prediction time domain. The predicted course speed error may be a course speed error at a next time in the prediction time domain. The lateral acceleration error and the heading acceleration error can be discretized respectively through a forward Euler method to obtain a predicted lateral speed error and a predicted heading speed error, and the predicted lateral speed error and the predicted heading speed error can be expressed by the following formulas:

。

wherein the content of the first and second substances,

and

respectively representing vehicles

Lateral velocity error and heading velocity error at the moment.

And

respectively representing vehicles

Lateral error and heading error at the moment.

And

respectively representing vehicles

Lateral velocity error and heading velocity error at the moment.

Indicating vehicles

The vehicle speed value at the time.

Denotes the change of a physical quantity.

Indicating the time duration from the beginning of the sample to the end of the sample.

And thirdly, respectively carrying out conversion processing on the predicted transverse speed error and the predicted course speed error based on the transverse speed error, the course speed error and the course error to obtain a target transverse speed error and a target course speed error. The lateral velocity error, the heading velocity error, and the heading error may be substituted into the predicted lateral velocity error and the predicted heading velocity error to obtain a target lateral velocity error and a target heading velocity error, and the target lateral velocity error and the target heading velocity error may be expressed by the following equations:

。

and secondly, constructing a vehicle path tracking target function based on the expected path track point information set, the target transverse speed error and the target course speed error. The vehicle path tracking objective function may be an objective function that outputs a lateral control error in a prediction time domain. In the prediction time domain, the target transverse speed error and the target course speed error are respectively used as state variables, and meanwhile, the terminal is used

And taking the course error of the moment as an additional reference item to obtain a vehicle path tracking target function. The vehicle path tracking objective function may be expressed by the following formula:

。

wherein the content of the first and second substances,

the state quantity is represented by a quantity of state,

。

to represent

The state quantity of the vehicle at that moment, i.e.

And

。

the control amount is represented by the amount of control,

。

to represent

The control quantity of the vehicle at the moment, i.e.

。

Weight coefficients representing terminal constraints.

Presentation terminal

The heading angle of the vehicle at that moment.

Indicating the total error of lateral control.

A weight coefficient representing heading tracking.

To represent

The heading angle of the vehicle at that moment.

And thirdly, constructing a target minimum control function and an optimal control constraint condition set based on the vehicle path tracking target function. According to a minimum value principle, a target minimum control function and an optimal control constraint condition set can be constructed on the basis of the vehicle path tracking target function. Wherein, the target minimum control function can be expressed by the following formula:

。

wherein, the first and the second end of the pipe are connected with each other,

and

respectively represent

The lateral speed error and the heading speed error of the vehicle at the moment.

、

Respectively, with respect to lateral displacement

And course angle

In that

A covariate of the time of day.

The optimal control requirements included in the set of optimal control constraints may be represented by the following equations:

。

the terminal requirements included in the set of optimal control constraints may be expressed by the following formula:

。

presentation terminal

A covariate of the time of day.

And 104, generating a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set.

In some embodiments, the execution body may generate the target front wheel turning angle degree based on the target minimum control function and the optimal control constraint set in various ways. The target front wheel turning angle number may be a number of front wheel turning angles actually required by the current vehicle at the next time in the prediction time domain.

In some optional implementations of some embodiments, the executing subject may generate the target front-wheel steering angle number based on the target minimum control function and the optimal control constraint condition set by:

first, a target quadratic function is generated based on the target minimum control function. The target quadratic function may be a quadratic function with a control variable as an argument. Can be combined with

The target minimum control function is expressed as a function of the controlled variable front wheel steering angle by using the controlled variable front wheel steering angle of the vehicle at the time as an independent variable, and can be expressed by the following formula:

。

wherein the content of the first and second substances,

、

and

can be expressed by the following formula:

。

wherein the content of the first and second substances,

、

、

、

and

can be expressed by the following formula:

。

wherein the content of the first and second substances,

、

、

、

、

、

can be expressed by the following formula:

。

and secondly, constructing a target constraint condition corresponding to the target quadratic function based on the expected path track point information set. The target constraint condition may be a condition for constraining an argument of the target quadratic function. The target constraints may include a turning angle value constraint and a front wheel turning speed constraint. The above-mentioned rotation angle value constraint may be that the front wheel rotation angle of the vehicle is within a first preset value range. The first preset value interval may be a preset value interval. The aforementioned front wheel turning speed constraint may be a constraint on the front wheel turning speed. The front wheel turning angular velocity may be an angle of front wheel turning per second. First, a first derivative of the front wheel steering angle of the vehicle is determined as the front wheel steering speed. And secondly, determining an initial value interval of the front wheel turning angular speed according to the turning angle value constraint. The initial value interval may be a value interval of the front wheel turning angular velocity. And then, fitting the coordinates of each track point included in the expected path track point information set to obtain a track curve. Wherein the trajectory curves may be used to characterize a planned desired path. And then, according to the track curve, determining the curvature corresponding to the coordinates of the target track point. And then, according to the curvature and the vehicle speed value of the vehicle, selecting the maximum value and the minimum value of the front wheel steering speed from the initial value-taking interval. And finally, determining the front wheel steering angle speed constraint according to the maximum value and the minimum value of the selected front wheel steering angle speed. The above target constraint can be expressed by the following formula:

。

wherein the content of the first and second substances,

represents the minimum value.

The maximum value is indicated.

Indicating the minimum value of the front wheel turning angle.

The maximum value of the front wheel turning angle is indicated.

In practice, in order to meet the requirements of high speed and different working conditions in urban areas, the constraint of the front wheel turning angular speed can be adaptively adjusted according to the curvature and the two-dimensional interpolation of the vehicle speed, specifically, when the average curvature in the prediction time domain is greater than 0.02 and the vehicle speed is less than 40km/h, a larger curvature is selected

、

I.e. the absolute value is larger. When the average curvature in the prediction time domain is more than 0.02 and the vehicle speed is more than 40km/h, taking the average curvature as medium

、

That is, the absolute value is medium; when the path with the average curvature smaller than 0.02 in the time domain is predicted and the vehicle speed is larger than 40km/h, taking the smaller path

、

I.e. the absolute value is smaller; when the path with the average curvature smaller than 0.02 in the time domain is predicted and the vehicle speed is smaller than 40km/h, the average curvature is taken

、

I.e. the absolute value is medium.

Similarly, in practice, when adaptive adjustment is performed on the prediction time domain, when the average curvature on the planned route is less than 0.005 (a value which can be adjusted for different vehicles), a smaller prediction time domain is taken to reduce the calculation amount and obtain better path tracking performance; when the average curvature on the planned route is more than 0.005 and less than 0.02, taking a medium prediction time domain; and when the average curvature on the planned route is greater than 0.02, a larger prediction time domain is taken to improve the capability of tracking the terminal path.

And thirdly, generating a target front wheel rotation angle number based on the target quadratic function, the optimal control constraint condition set and the target constraint condition. The executing body may generate the target front wheel turning angle degree in various ways.

In some optional implementations of some embodiments, the executing body may generate the target front wheel rotation angle degree based on the target quadratic function, the optimal control constraint set, and the target constraint set by:

firstly, generating an optimal solution of the front wheel steering angle based on the target quadratic function. The optimal solution of the front wheel steering angle may be an optimal solution of the front wheel steering angle. An extreme value method can be solved through a preset quadratic function, and an optimal solution of the front wheel steering angle is generated. When the front wheel steering angle is greater than or equal to 0, the optimal solution of the front wheel steering angle can be expressed as follows:

。

when the front wheel steering angle is less than 0, the optimal solution for the front wheel steering angle can be expressed as:

. Wherein the content of the first and second substances,

representing the optimal solution.

And representing the solved optimal solution of the front wheel steering angle.

And secondly, generating a target course angle co-modal variable value based on the optimal control constraint condition set. The target course angle covariance variable value may be an initial value of an optimal covariance variable.

In some optional implementations of some embodiments, the set of optimal control constraints includes optimal control requirements and terminal requirements. The execution body can generate the target heading angle covariance variable value through the following steps:

the method comprises the following steps of firstly, generating an initial heading angle co-modal variable value and a terminal heading angle co-modal variable value based on preset boundary conditions and the optimal control necessary conditions. Wherein, the preset boundary condition may be an initial time storageAt two boundary values of the covariate

And

satisfy the relationship

. The initial heading angle covariance variable value may be a value of the heading angle covariance variable at an initial time. The value of the terminal heading angle covariance variable may be a value of a heading angle covariance variable at the terminal time. Firstly, according to the preset boundary condition, the method will

Or

Assigning the boundary value to the course angle covariant at the initial moment

. Then, according to the minimum value principle, solving the necessary conditions of optimal control to obtain the terminal time

Heading angle covariate of

。

And secondly, generating a target course angle co-modal variable value based on the terminal course angle co-modal variable value, the initial course angle co-modal variable value and the terminal necessary conditions. The following steps may be specifically performed:

a first substep of converting the terminal requirements into the following form:

。

wherein, the above

The function may be a function in which the initial value of the optimal covariance variable is an argument.

Indicating an iteration termination error.

A second sub-step of substituting the terminal course angle covariance variable value and the initial course angle covariance variable value into the terminal course angle covariance variable value

Function, and pair of dichotomies

And solving the function to obtain the value of the target course angle co-modal variable.

It should be noted that if the iteration termination condition is satisfied, the current value is output

And obtaining an optimal solution

(ii) a If the iteration termination condition is not satisfied, judging

And

is taken in

And

intermediate value of (1)

Find the region by dichotomyUpdating the interval to obtain a new binary interval

Or

And will be

Is assigned to

And repeating the process until the iteration termination condition is met. Wherein, the above

Can be expressed by the following formula:

。

wherein, the above

Representing the median value between the minimum and maximum values.

And thirdly, generating a target front wheel rotation angle degree based on the target constraint condition, the optimal solution of the front wheel rotation angle and the target course angle covariance variable value. Firstly, according to the target course angle covariance variable value, determining a corresponding initial optimal solution in the front wheel steering angle optimal solution. The initial optimal solution may be a solution of a target quadratic function corresponding to the target heading angle covariance variable value. Then, it is determined whether the initial optimal solution satisfies the target constraint condition. And finally, if the initial optimal solution meets the target constraint condition, determining the initial optimal solution as a target front wheel rotation angle degree. And if the initial optimal solution does not meet the target constraint condition, determining the maximum value of the front wheel steering angle corresponding to the target constraint condition as a target front wheel turning angle degree.

The step of generating the target front wheel turning angle number and the related content thereof serve as an invention point of the embodiment of the disclosure, and the technical problem that the heading tracking capability of the vehicle at the terminal moment is poor in the background art is solved. Factors that lead to poor heading tracking capability of the vehicle at the terminal moment are as follows: although the calculated amount of the linear quadratic regulator algorithm is relatively small, the linear quadratic regulator algorithm is only used for solving once in a prediction time domain, and the solving process is difficult to constrain the controlled variable, so that the heading tracking capability of the vehicle at the terminal moment is poor easily. If the factors are solved, the effect of improving the heading tracking capability of the vehicle at the terminal moment can be achieved. To achieve this, first, for the target minimal control function, an optimal set of control constraints is defined. The optimal control constraint condition set may include terminal requirements. Therefore, the initial value of the optimal covariance variable can be obtained subsequently under the terminal necessary condition, and the optimal front wheel rotation angle degree corresponding to the initial value of the optimal covariance variable is determined. Secondly, the terminal necessary conditions are converted into a function problem of solving the initial value of the optimal covariance variable. The initial value of the optimal co-modal variable meeting the terminal time state condition can be obtained conveniently in the follow-up process. The functional problem is then solved by bisection. And meeting the iteration termination condition, outputting the initial value of the optimal co-modal variable, and obtaining an initial optimal solution corresponding to the initial value of the optimal co-modal variable. And finally, constraining the initial optimal solution according to the target constraint condition to obtain the target front wheel rotation angle degree. Therefore, iterative solution can be performed for multiple times in the prediction time domain, and the control quantity can be constrained in the solution process, especially the state of the terminal time on the boundary. Therefore, the heading tracking capability of the vehicle at the terminal moment can be improved. Further, the stability of vehicle path tracking is improved.

And 105, transmitting the target front wheel rotation angle number to a steering controller for controlling the steering of the vehicle.

In some embodiments, the executing body may transmit the target front wheel rotation angle number to a steering controller for controlling the vehicle steering. Wherein the steering controller may be a server that obtains a compensation disturbance with respect to the longitudinal speed control by linearly expanding a state observer. The target front wheel rotation angle number CAN be transmitted to the steering controller through the CAN bus. Then, the steering controller may perform torque response control according to the target front wheel steering angle number, and drive the steering wheel to achieve vehicle steering.

The above embodiments of the present disclosure have the following beneficial effects: by the vehicle control method of some embodiments of the disclosure, the real-time performance of vehicle steering can be improved, and the tracking capability of the vehicle on the path track can be improved. Specifically, the reason why the vehicle steering real-time performance is poor and the vehicle has poor tracking ability on the path trajectory is that: the quadratic programming method is adopted for solving, the calculated amount is large, the generation speed of the front wheel steering angle of the vehicle is low, the steering real-time performance of the vehicle is poor, and the tracking capability of the vehicle on the target course is poor. Based on this, the vehicle control method of some embodiments of the present disclosure, first, obtains a vehicle speed value, a steering wheel angle number, sensor road fusion information, and vehicle position information of a current vehicle. Here, the information of the vehicle and the surrounding road environment can be obtained, which is convenient for subsequent trajectory planning and tracking control for the vehicle heading on the trajectory. And secondly, transmitting the vehicle speed value, the steering wheel angle degree, the sensor road fusion information and the vehicle position information to a path planning server to generate an expected path track point information set. Thereby, a desired vehicle trajectory can be obtained. And then, constructing a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set. Thus, the tracking control problem of the vehicle heading can be converted into the optimal control problem about the front wheel steering according to the minimum value principle. The target front wheel rotation angle number can be conveniently and quickly obtained in the prediction time domain subsequently, so that the vehicle can steer and the path track can be tracked in time. And finally, generating a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set. And transmitting the target front wheel rotation angle data to a steering controller for controlling the steering of the vehicle. Therefore, the vehicle can steer according to the target front wheel steering angle number so as to track the path track. Therefore, according to the vehicle control method, the target front wheel rotation angle number can be obtained quickly by solving the target minimum control function, so that the vehicle can turn in time, and path tracking can be performed better. And because the course on the expected track can be tracked in the prediction time domain and rolling optimization can be carried out, feedforward control and feedback control can be simultaneously considered when the path track of the vehicle is tracked. Thus, the real-time performance of the vehicle steering can be improved. Further, the capability of the vehicle to track the path trajectory can be improved.

With further reference to fig. 2, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of a vehicle control apparatus, which correspond to those method embodiments illustrated in fig. 1, and which may be particularly applicable in various electronic devices.

As shown in fig. 2, the vehicle control device 200 of some embodiments includes: an acquisition unit 201, a first transmission and generation unit 202, a construction unit 203, a generation unit 204, and a second transmission unit 205. The acquisition unit 201 is configured to acquire a vehicle speed value, a steering wheel angle degree, sensor road fusion information and vehicle position information of a current vehicle; a first transmission and generation unit 202 configured to transmit the vehicle speed value, the steering wheel angle number, the sensor road fusion information, and the vehicle position information to a path planning server to generate a set of desired path trace point information; a constructing unit 203 configured to construct a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set; a generating unit 205 configured to generate a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set; a second transmission unit 206 configured to transmit the target front wheel rotation angle number to a steering controller for controlling the vehicle to steer.

It will be understood that the units described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and advantages described above for the method are also applicable to the apparatus 200 and the units included therein, and are not described herein again.

With further reference to fig. 3, a schematic structural diagram of an electronic device 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to communicate with other devices, wireless or wired, to exchange data. While fig. 3 illustrates an electronic device 300 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may be alternatively implemented or provided. Each block shown in fig. 3 may represent one device or may represent multiple devices, as desired.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. The computer program, when executed by the processing apparatus 301, performs the above-described functions defined in the methods of some embodiments of the present disclosure.

It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. 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 (EPROM or 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 some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a vehicle speed value, a steering wheel turning angle degree, sensor road fusion information and vehicle position information of a current vehicle; transmitting the vehicle speed value, the steering wheel angle degree, the sensor road fusion information and the vehicle position information to a path planning server to generate an expected path track point information set; constructing a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set; generating a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set; and transmitting the target front wheel rotation angle number to a steering controller so as to control the steering of the vehicle.

Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart 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 disclosure. In this regard, 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a first transmission and generation unit, a construction unit, a generation unit, and a second transmission unit. The names of these units do not constitute a limitation to the unit itself in some cases, and for example, the acquisition unit may also be described as a "unit that acquires the vehicle speed value, the steering wheel angle number, the sensor road fusion information, and the vehicle position information of the current vehicle".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims

1. A vehicle control method comprising:

acquiring a vehicle speed value, a steering wheel turning angle degree, sensor road fusion information and vehicle position information of a current vehicle;

transmitting the vehicle speed value, the steering wheel angle degree, the sensor road fusion information and the vehicle position information to a path planning server to generate an expected path track point information set;

constructing a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set;

generating a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set;

and transmitting the target front wheel rotation angle number to a steering controller for controlling the steering of the vehicle.

2. The method of claim 1, wherein constructing a set of target minimum control functions and optimal control constraints based on the set of vehicle speed values and the set of desired path trajectory point information comprises:

generating a target transverse speed error and a target course speed error based on the vehicle speed value and the expected path track point information set;

constructing a vehicle path tracking target function based on the expected path track point information set, the target transverse speed error and the target course speed error;

and tracking an objective function based on the vehicle path, and constructing an objective minimum control function and an optimal control constraint condition set.

3. The method of claim 2, wherein generating a target lateral velocity error and a target heading velocity error based on the vehicle speed value and the set of desired path trajectory point information comprises:

generating a transverse acceleration error, a course acceleration error, a transverse speed error, a course speed error and a course error based on the vehicle speed value and the expected path track point information set;

discretizing the transverse acceleration error and the course acceleration error respectively to obtain a predicted transverse speed error and a predicted course speed error;

and respectively carrying out conversion processing on the predicted transverse speed error and the predicted course speed error based on the transverse speed error, the course speed error and the course error to obtain a target transverse speed error and a target course speed error.

4. The method according to one of claims 1 to 3, wherein the generating a target number of front wheel corners based on the target minimal control function and the set of optimal control constraints comprises:

generating a target quadratic function based on the target minimum control function;

constructing a target constraint condition corresponding to the target quadratic function based on the expected path track point information set;

and generating a target front wheel rotation angle degree based on the target quadratic function, the optimal control constraint condition set and the target constraint condition.

5. The method of claim 4, wherein generating a target number of front wheel corners based on the target quadratic function, the set of optimal control constraints, and the target constraints comprises:

generating an optimal solution of the front wheel steering angle based on the target quadratic function;

generating a target course angle co-modal variable value based on the optimal control constraint condition set;

and generating a target front wheel rotation angle degree based on the target constraint condition, the optimal solution of the front wheel rotation angle and the target course angle covariance variable value.

6. The method of claim 5, wherein the set of optimal control constraints comprises optimal control requirements and terminal requirements; and

generating a target course angle covariance variable value based on the optimal control constraint condition set, wherein the method comprises the following steps of:

generating an initial course angle co-modal variable value and a terminal course angle co-modal variable value based on a preset boundary condition and the optimal control necessary condition;

and generating a target course angle co-modal variable value based on the terminal course angle co-modal variable value, the initial course angle co-modal variable value and the terminal necessary conditions.

7. A vehicle control apparatus comprising:

an acquisition unit configured to acquire a vehicle speed value, a steering wheel angle degree, sensor road fusion information, and vehicle position information of a current vehicle;

a first transmission and generation unit configured to transmit the vehicle speed value, the steering wheel angle number, the sensor road fusion information, and the vehicle position information to a path planning server to generate a set of desired path trace point information;

the construction unit is configured to construct a target minimum control function and an optimal control constraint condition set based on the vehicle speed value and the expected path track point information set;

a generating unit configured to generate a target front wheel rotation angle degree based on the target minimum control function and the optimal control constraint condition set;

a second transmission unit configured to transmit the target front wheel rotation angle number to a steering controller for controlling vehicle steering.

8. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.

9. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out the method according to any one of claims 1-6.