Vehicle control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a vehicle control method and device, electronic equipment and a computer readable storage medium, and relates to the field of automatic driving. The method comprises the steps that a control information threshold value of a vehicle at the current moment is obtained based on actual state information of the vehicle at the current moment and expected state information of the vehicle at the next moment through a model for describing the nonlinear relation between vehicle state information and vehicle control information; according to the difference between the actual state information and the expected state information of the vehicle at the current moment, initial control information of the vehicle at the current moment is determined; the initial control information is restrained according to the control information threshold value of the vehicle at the current moment, and actual control information of the vehicle at the current moment is obtained; and controlling the vehicle according to the actual control information so as to enter the next moment. The embodiment of the invention can adapt to all-terrain instruction output.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology. Specifically, the present application relates to a vehicle control method, device, electronic device, computer-readable storage medium and computer program product. Background Art

[0002] The road surface of autonomous driving in mining scenarios is complex, and the operation is carried out in a step-by-step manner according to the open-pit mine area, with one step ranging from 8 to 12 meters, and uphill and downhill scenes occur frequently. In addition, the vehicle mass of the autonomous driving vehicle in the mining area can reach hundreds of tons when fully loaded.

[0003] The related technologies for autonomous driving solutions for mine trucks basically simply use gsinθ to compensate, where g represents the acceleration of gravity and θ is the current slope of the vehicle. These solutions have disadvantages: simply judging the slope of the vehicle by the pitch angle is prone to misjudgment; and simple sensor slope feedback to eliminate the problem is prone to serious amplitude jumps in the final output result of the control calculation due to angle jumps, which is very likely to cause setbacks. These phenomena show that these control methods have defects in their robustness to terrain. Summary of the invention The embodiments of the present application provide a vehicle control method, device, electronic device, computer-readable storage medium and computer program product, which can solve the above-mentioned problems of the prior art. The technical solution is as follows: According to one aspect of an embodiment of the present application, a vehicle control method is provided, the method comprising: By describing the model of the nonlinear relationship between the vehicle state information and the vehicle control information, based on the actual state information of the vehicle at the current moment and the expected state information at the next moment, the control information threshold of the vehicle at the current moment is obtained; Determining initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information; Constraining the initial control information according to the control information threshold of the vehicle at the current moment to obtain the actual control information of the vehicle at the current moment; Controlling the vehicle according to the actual control information to enter a next moment; Among them, the model is obtained by transforming the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. The dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving the vertical load distribution of the vehicle's four wheels under slope and roll angle.

[0004] According to another aspect of an embodiment of the present application, a vehicle control device is provided, the device comprising: A control threshold module, for obtaining a control information threshold of the vehicle at a current moment based on the actual state information of the vehicle at a current moment and the expected state information at a next moment, by using a model describing a nonlinear relationship between the vehicle state information and the vehicle control information; An initial control module, used to determine initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information; A constraint module, used to constrain the initial control information according to a control information threshold of the vehicle at the current moment, so as to obtain actual control information of the vehicle at the current moment; A control module, used for controlling the vehicle according to the actual control information to enter the next moment; Among them, the model is obtained by transforming the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. The dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving the vertical load distribution of the vehicle's four wheels under slope and roll angle.

[0005] According to another aspect of an embodiment of the present application, an electronic device is provided. The electronic device includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the above-mentioned vehicle control method.

[0006] According to another aspect of the embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned vehicle control method are implemented.

[0007] According to one aspect of an embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned vehicle control method are implemented.

[0008] The beneficial effects of the technical solution provided by the embodiment of the present application are: It is obtained by converting the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. Furthermore, the dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving it in combination with the vertical load distribution of the vehicle's four wheels at different slopes and roll angles. In addition, the embodiment of the present application converts the difficult-to-solve mechanical expressions into state information that can be measured or directly acquired by sensors, and converts the above model into a state space equation through the definition of state quantities and control quantities, and performs linear discretization to reduce computational complexity. In other words, the state information in the embodiments of the present application is state information that can be measured or directly acquired, which is suitable for all-terrain control and meets the requirements for precise control of empty and fully loaded driving on complex terrains with slopes and road inclinations. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0010] Figure 1 A schematic diagram of a system architecture for implementing a vehicle control method provided in an embodiment of the present application; Figure 2 A schematic diagram of the architecture of a vehicle control system provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a longitudinal control system provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a lateral control system provided in an embodiment of the present application; Figure 5 A schematic diagram of a vehicle control method provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of a vehicle control device provided in an embodiment of the present application; Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The embodiments of the present application are described below in conjunction with the drawings in the present application. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0012] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "an" and "the" used herein may also include plural forms. It should be further understood that the terms "including" and "comprising" used in the embodiments of the present application refer to that the corresponding features can be implemented as the presented features, information, data, steps, operations, elements and / or components, but do not exclude the implementation as other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the technical field. It should be understood that when we call an element "connected" or "coupled" to another element, the one element can be directly connected or coupled to the other element, or it can refer to that the one element and the other element establish a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used here may include wireless connection or wireless coupling. The term "and / or" used here indicates at least one of the items defined by the term, for example, "A and / or B" can be implemented as "A", or as "B", or as "A and B".

[0013] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0014] First, several terms involved in this application are introduced and explained: An autonomous driving system is a system that uses advanced communication, computer, network and control technologies to enable vehicles to drive automatically without human intervention, perceive the surrounding environment and make driving decisions. According to the different levels of autonomous driving technology, it can be divided into multiple levels, such as L0 (no automation), L1 (driving assistance), L2 (partial automation), L3 (conditional automation), L4 (high automation) and L5 (full automation).

[0015] Mine cars are mainly used in specific scenarios such as mines and engineering projects, and usually operate in non-road environments, such as narrow-gauge railways inside mines or rugged mine roads. These environments often have high requirements for the durability, carrying capacity and off-road performance of vehicles. Compared with ordinary vehicles, mine cars are usually larger in size, wider in body, and more complicated to operate. The control algorithm of the mine car in the embodiment of this application is different from the control algorithm of ordinary vehicles in the following aspects: 1. Differences in usage environment Mining cars are mainly used in specific scenarios such as mines and engineering projects. These environments are usually complex and changeable, including narrow mine tunnels, rugged roads, and possible obstacles and danger sources. The mining car autonomous driving algorithm needs to adapt to this complex environment and have strong perception, decision-making and execution capabilities to ensure the safe and efficient operation of the mining car.

[0016] 2. Differences in functional requirements In addition to simulating the driving of the vehicle, the mine cart control algorithm also needs to simulate automatic loading, unloading, driving, obstacle avoidance and collaborative operation with other mine carts.

[0017] 3. Differences in Algorithm Design Characteristics The mine car control algorithm usually needs to consider factors such as the heavy load characteristics, driving speed, and operating environment of the mine car. When designing the algorithm, special attention should be paid to the stability and safety of the vehicle to ensure that the mine car can still maintain stable operation under heavy loads and complex environments. The mine car control algorithm also needs to have strong perception capabilities to deal with obstacles and dangers in the mine. This is usually achieved by fusing multiple sensor data (such as lidar, camera, millimeter wave radar, etc.).

[0018] The vehicle control method, device, electronic device, computer-readable storage medium and computer program product provided in this application are intended to solve the above technical problems in the prior art.

[0019] The embodiment of the present application combines and analyzes the vertical load component of the vehicle's roll angle and slope angle on the road surface when establishing the vehicle dynamics model, builds a control analysis model system that considers the vertical load, and inputs the results obtained by the system output into the lateral and longitudinal control system, which can improve the lateral and longitudinal control errors of the vehicle traveling on a slope to the greatest extent. And because the control system is adapted based on the angle of the road, it can achieve good robustness to changes in the slope angle.

[0020] The embodiment of the present application considers the driving terrain of the autonomous driving mining car robust control system and method. The road surface factors of the vehicle driving road are considered. Based on these complex road surface factors, the tire load distribution of the vehicle is analyzed. According to the tire load, the vehicle dynamics model is established and converted to the space state equation. Based on this, the command calculation is performed in the lateral and longitudinal control command solution system to obtain the control command output. In addition, the maximum command output value used to constrain the vehicle steering according to the above road surface information is used to make the vehicle safe. Drive within the full driving range to achieve more precise control output.

[0021] The following describes several exemplary embodiments to illustrate the technical solutions of the embodiments of the present application and the technical effects produced by the technical solutions of the present application. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0022] For example, see Figure 1 , Figure 1 It is a schematic diagram of the architecture of a system 100 for implementing a vehicle control method provided in an embodiment of the present application. In order to support the application of automatic driving of mine vehicles in complex and changeable mines, a terminal device 400 is connected to a server 200 via a network 300. The network 300 may be a wide area network or a local area network, or a combination of the two.

[0023] In some embodiments, taking the electronic device as a terminal device as an example, the vehicle control method provided in the embodiment of the present application can be implemented by a terminal device (such as a vehicle-mounted terminal) alone. For example, the terminal device obtains the control information threshold of the vehicle at the current moment based on the actual state information of the vehicle at the current moment and the expected state information at the next moment by describing the model of the nonlinear relationship between the vehicle state information and the vehicle control information; determines the initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information; constrains the initial control information according to the control information threshold of the vehicle at the current moment to obtain the actual control information of the vehicle at the current moment; controls the vehicle according to the actual control information to enter the next moment; wherein, the model is obtained by converting the dynamic equation of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution, and the dynamic equation is obtained by performing dynamic modeling on the four-wheel steering model of the vehicle and solving it in combination with the vertical load distribution of the four wheels of the vehicle under the slope and roll angle.

[0024] In other embodiments, the vehicle control method provided in the embodiment of the present application can also be performed by Figure 1 The terminal device 400 and the server 200 shown in the figure are implemented in collaboration. For example, a client 410 (such as a network version of a test application) is running on the terminal device 400. When vehicle control is required, the client 410 sends the actual state information of the vehicle at the current moment to the server 200 through the network 300, so that the server 200 obtains the control information threshold of the vehicle at the current moment based on the actual state information of the vehicle at the current moment and the expected state information at the next moment through a model describing the nonlinear relationship between the vehicle state information and the vehicle control information, and then determines the initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information, constrains the initial control information according to the control information threshold of the vehicle at the current moment, and obtains the actual control information of the vehicle at the current moment.

[0025] In some embodiments, the embodiments of the present application can be implemented with the help of cloud technology (Cloud Technology). Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and network within a wide area network or a local area network to achieve data calculation, storage, processing, and sharing.

[0026] Cloud technology is a general term for network technology, information technology, integration technology, management platform technology, and application technology based on the cloud computing business model. It can form a resource pool that can be used on demand and is flexible and convenient. Cloud computing technology will become an important support. The background services of the technical network system require a large amount of computing and storage resources.

[0027] For example, Figure 1 The server 200 shown in the figure can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms. The terminal device 400 can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal device 400 and the server 200 can be directly or indirectly connected by wired or wireless communication, which is not limited in the embodiments of the present application.

[0028] In other embodiments, the terminal device 400 or the server 200 can implement the vehicle control method provided in the embodiments of the present application by running a computer program. For example, the computer program can be a native program or software module in the operating system; it can be a native application (APP, Application), that is, a program that needs to be installed in the operating system to run, such as a game application (corresponding to the client 410 above); it can also be a small program, that is, a program that can be run only after being downloaded to a browser environment; it can also be a small program that can be embedded in any APP, such as a small program component embedded in a game application, wherein the small program component can be controlled to run or closed by the user. In short, the above-mentioned computer program can be an application, module or plug-in in any form.

[0029] Figure 2 The schematic diagram of the architecture of the vehicle control system provided for the embodiment of the present application, as shown in the figure, the sensor data acquisition system of the vehicle is responsible for collecting some sensor data related to the vehicle. The sensor data of the sensor of the present application includes but is not limited to the following: the speed of the vehicle, the front wheel angle, the current position (xyz coordinates in the world coordinate system), the attitude angle of the vehicle (pitch angle, yaw angle and roll angle), etc. The sensor acquisition system collects these data in real time at a fixed frequency, and outputs them to the vehicle model system for analysis after simple preprocessing. The vehicle model system will establish a dynamic model of the three degrees of freedom of the vehicle considering the slope angle and the roll angle, and output it to the lateral and longitudinal control system in combination with the steering constraint. The lateral and longitudinal control system will output the throttle / brake / front wheel angle command according to the above-mentioned state equation, and give these command results to the vehicle's underlying actuator for execution, so as to achieve the purpose of precise control. It should be understood that the longitudinal direction of the embodiment of the present application refers to the direction of vehicle travel, and the lateral direction refers to the direction perpendicular to the vehicle travel direction in the horizontal plane.

[0030] The vehicle model system of the embodiment of the present application is used for vehicle kinematics modeling - a three-degree-of-freedom dynamic model is established based on the vehicle's lateral dynamic characteristics, longitudinal dynamic characteristics and lateral swing angle dynamic characteristics. The three degrees of freedom mentioned in the embodiment of the present application refer to lateral velocity, longitudinal velocity and yaw angular velocity.

[0031] On this basis, the embodiment of the present application further considers the influence of road terrain factors on the vehicle trajectory tracking ability, and can expand the above dynamic model to obtain a vehicle four-wheel vehicle dynamic model that considers the slope angle and roll angle. The dynamic model can be expressed as:

[0032]

[0033] The above dynamic models respectively characterize the mechanical characteristics of the vehicle in the longitudinal, lateral and steering directions, among which: m Indicates the mass of the vehicle, I zCG represents the vehicle's yaw moment of inertia, l f Indicates the distance from the center of gravity of the vehicle to the front axle. l r Indicates the distance from the center of gravity of the vehicle to the rear axle. l w Indicates the wheel axle width, represents the tilt angle, θ Indicates the slope, ψ represents the yaw angle, δ represents the front wheel turning angle, F xfr , F xfl , F xrr , F xrl Respectively represent the forces on the right front wheel, left front wheel, right rear wheel and left rear wheel of the vehicle during longitudinal driving. F yfr , F yfl , F yrr , F yrl They respectively represent the forces on the right front wheel, left front wheel, right rear wheel and left rear wheel of the vehicle during lateral driving.

[0034] Further deducing the above dynamic method, if the slope of the road is taken into account θ , the longitudinal mechanical equilibrium equation of the vehicle is as follows:

[0035] By solving the equations simultaneously, we can get the following equations:

[0036] in, F zfro and F zrea Represent the vertical loads on the front and rear wheels respectively.

[0037] Further considering the road inclination angle, the lateral mechanical equilibrium equation of the vehicle is as follows:

[0038] The following equation can be obtained by solving the equations simultaneously:

[0039] in, F zrig and F zlef Represent the vertical load distribution of the right wheel and the left wheel respectively; Define the lateral force difference △ F lat for

[0040] Therefore, the force conditions of the four tires can be solved as follows:

[0041] in, F zfr , F zfl , F zrr , F zrl Represent the vertical loads on the right front wheel, left front wheel, right rear wheel, and left rear wheel respectively.

[0042] The above dynamic model mainly considers the lateral and longitudinal forces that are difficult to measure and calculate, and needs to be further converted to the longitudinal slip rate K and lateral slip angle of the tire that are easy to calculate. α Solve it, where the expressions of longitudinal slip rate and lateral slip angle are as follows:

[0043]

[0044] in, v x and v yRespectively represent the longitudinal and lateral wheel speeds of the vehicle; Ω is the tire speed; R is the effective rolling radius of the tire. Therefore, the longitudinal force expression and lateral force expression of the vehicle can be expressed as a nonlinear relationship between the tire longitudinal slip rate, lateral slip angle and ground adhesion coefficient μ:

[0045]

[0046] It should be noted that the specific function expressions are highly nonlinear and complex, so we will not list them here, but will use simple fx and fy to demonstrate them. Based on this, the lateral and longitudinal force expressions for the vehicle's vertical load, slip rate, sideslip angle, and ground adhesion coefficient are derived. The independent variables in the formula are all variables that can be derived and solved or directly obtained by sensors. Convert the above force expression and set the one-dimensional control vector of the vehicle to , the six-dimensional state vector of the vehicle is , then the lateral and longitudinal force expressions of the vehicle can be updated to the following state space differential equations:

[0047] Considering that the function expression is highly nonlinear, the linear discretization of the above state space differential equation can be obtained as follows:

[0048] in, represents the state matrix of the vehicle, represents the control matrix of the vehicle, Represents the coefficient matrix, the above represents the vehicle at time t State changes depending on the vehicle The state and control quantity changes of the vehicle can be reversed based on the above state quantities to infer the control output changes of the lateral and longitudinal control systems.

[0049] The lateral and longitudinal control system of the embodiment of the present application is divided into a lateral control system and a longitudinal control system. Figure 3 As shown, the longitudinal control system includes a position state feedback system, a speed state feedback system, and a load identification system: First, the position state feedback system feeds back the current position of the vehicle and the expected position to be reached, and obtains the difference in the longitudinal position of the vehicle, and converts the difference into a first position compensation amount and gives it to the speed feedback system; Then, the speed feedback system uses the difference between the current speed of the vehicle and the expected speed as the first speed difference, and combines the first position compensation amount to calculate the speed difference amount to be output as the second speed difference, and provides it to the load identification system; Next, the load identification system matches different control parameters according to the vehicle's current load conditions (full load / half full load / empty) and the input second speed difference. Finally, the matched control parameters are used as coefficients to solve the throttle / brake opening control amount under the current second speed difference.

[0050] It should be noted that the throttle / brake opening control amount cannot be directly applied to the vehicle bottom layer. It needs to be limited by the model constraints obtained by the vehicle model system, and finally the command is output to the vehicle bottom layer actuator.

[0051] See also Figure 4 , which exemplarily shows a schematic diagram of the structure of a lateral control system of an embodiment of the present application. As shown in the figure, the lateral control system has both a lateral state feedback system and a vehicle orientation feedback system. The lateral state feedback system is used to calculate the lateral error of the vehicle in the current state in real time, and the vehicle orientation feedback system is used to calculate the front orientation error in real time; The lateral control system also includes a load identification system. By inputting the lateral error and the frontal orientation error into the load identification system, the load identification system calculates the current front wheel steering angle control amount of the vehicle based on the current load condition (full load / half full load / empty load) as well as the lateral error and the frontal orientation error. It should be noted that the front wheel steering angle control amount cannot act directly on the bottom layer of the vehicle. It needs to be limited by the model constraints obtained by the vehicle model system, and finally the command is output to the bottom layer actuator of the vehicle.

[0052] It should be noted that the load identification system in the lateral and longitudinal control systems can be the same system.

[0053] In an embodiment of the present application, a vehicle control method is provided, such as Figure 5 As shown, the method includes: S101. Obtaining a control information threshold of the vehicle at the current moment based on the actual state information of the vehicle at the current moment and the expected state information at the next moment by using a model describing the nonlinear relationship between the vehicle state information and the vehicle control information.

[0054] It can be seen from the above embodiments that the present application can introduce the actual state information of the vehicle at the current moment and the expected state information at the next moment into the following model to obtain the control information threshold of the vehicle:

[0055] It should be noted that the model of the embodiment of the present application is obtained by converting the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. Furthermore, the dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving the vertical load distribution of the vehicle's four wheels at different slopes and roll angles. In addition, the embodiment of the present application converts the difficult-to-solve mechanical expressions into state information that can be measured or directly acquired by sensors. By defining the state quantity and the control quantity, the above model is converted into a state space equation and linearly discretized to reduce the computational complexity. In other words, the state information in the embodiments of the present application is all state information that can be measured or directly acquired.

[0056] The embodiment of the present application performs reverse deduction based on the actual state information of the vehicle at the current moment and the expected state quantity at the next moment to obtain a control information threshold, which is output to the vehicle control system as a theoretical reference limit for correction.

[0057] S102: Determine initial control information of the vehicle at the current moment according to the difference between the actual state information and the expected state information of the vehicle at the current moment.

[0058] The embodiment of the present application further determines the initial control information of the vehicle at the current moment based on the difference between the actual state information and the expected state information of the vehicle at the current moment.

[0059] S103 . Constrain the initial control information according to the control information threshold of the vehicle at the current moment, and obtain actual control information of the vehicle at the current moment.

[0060] It should be understood that the control information range provides constraints on the initial control information. If the initial control information exceeds the control information threshold, the actual control information is the control information threshold. If the initial control information does not exceed the control information, the actual control information is the initial control information.

[0061] S104. Control the vehicle according to the actual control information to enter the next moment.

[0062] By executing the actual control information at the current moment, we can enter the next moment.

[0063] The embodiment of the present application is obtained by converting the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. Furthermore, the dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving the vertical load distribution of the vehicle's four wheels at different slopes and roll angles. In addition, the embodiment of the present application converts the difficult-to-solve mechanical expressions into state information that can be measured or directly acquired by sensors, and converts the above model into a state space equation by defining the state quantity and the control quantity, and performs linear discretization to reduce the computational complexity. In other words, the state information in the embodiment of the present application is all state information that can be measured or directly acquired, which is suitable for all-terrain control and meets the requirements for precise control of empty and fully loaded driving on complex terrains with slopes and road inclinations.

[0064] Based on the above embodiments, as an optional embodiment, the kinetic equation is obtained in the following manner: S201. Establishing a three-degree-of-freedom dynamic model of the vehicle taking into account the slope angle and the roll angle, wherein the three degrees of freedom include the lateral velocity, longitudinal velocity and yaw angular velocity of the vehicle.

[0065] It can be seen from the above embodiments that the three-degree-of-freedom dynamic model of the embodiment of the present application can be expressed as:

[0066]

[0067] S202: Establishing a longitudinal mechanical balance equation and a lateral dynamic balance equation of the vehicle according to the three-degree-of-freedom dynamics model.

[0068] It can be seen from the above embodiments that the longitudinal mechanical equilibrium equation constructed in the embodiments of the present application can be expressed as:

[0069] The lateral dynamic equilibrium equation can be expressed as:

[0070] S203: Obtain vertical load distribution of the left and right wheels of the vehicle according to the longitudinal mechanical balance equation and the lateral dynamic balance equation.

[0071] It can be seen from the above embodiments that the vertical load distribution of the right wheel and the left wheel in the embodiment of the present application can be expressed by the following formula:

[0072] S204: Obtain the dynamic equation describing the force conditions of the four wheels of the vehicle according to the vertical load distribution.

[0073] It can be seen from the above embodiments that the dynamic equation describing the force conditions of the four wheels of the vehicle in the embodiment of the present application is:

[0074] Based on the above embodiments, as an optional embodiment, the state information includes the position, speed and load of the vehicle; the control information includes the current throttle or brake opening control amount. The embodiment of the present application includes determining the throttle or brake opening control amount according to the vehicle's position, vehicle and load conditions, specifically: S301, obtaining a first difference between an actual position of a vehicle at a current moment and an expected position, weighting the first difference according to a load condition of the vehicle at the current moment, and obtaining a position compensation amount of the vehicle at the current moment; S302, obtaining a second difference between the actual speed and the expected speed of the vehicle at the current moment, and performing weighted summation on the position compensation amount and the second difference according to the load condition of the vehicle at the current moment, so as to obtain a speed compensation amount of the vehicle at the current moment; S303, determining an initial throttle or brake opening control amount of the vehicle at the current moment according to a linear mapping relationship between the speed compensation amount and the throttle or brake opening, combined with the speed compensation amount of the vehicle at the current moment.

[0075] The embodiment of the present application obtains a position compensation amount based on a first difference between the actual position of the vehicle and the expected position, then obtains a speed compensation amount based on a second difference between the actual speed and the expected speed combined with the position compensation amount, and finally obtains an initial opening control amount based on the relationship between the speed compensation amount and the opening of the throttle / brake.

[0076] It should be noted that the opening control amount in the embodiment of the present application refers to an absolute value, not a relative value. For example, the initial opening control amount is 30%, which does not mean increasing the actual opening by 30% at the previous moment, but controlling the opening at 30%.

[0077] Based on the above embodiments, as an optional embodiment, obtaining a first difference between the actual position of the vehicle at the current moment and the expected position includes: S301, obtaining the actual position and driving angle of the vehicle at the current moment; S302, according to the actual position, determining from the expected driving trajectory the position with the shortest distance to the actual position as the adjusted actual position; S303, according to the time best matching method, determining a position matching the current moment from the expected driving trajectory as the expected position; S304: Obtain the first difference according to the driving angle and the distance between the adjusted actual position and the expected position.

[0078] Since the mine car will configure the expected driving trajectory during the navigation process, after obtaining the actual position of the vehicle, a position with the shortest distance to the actual position can be determined from the expected driving trajectory as the adjusted actual position. The purpose is to adjust the actual position to the expected driving trajectory for subsequent processing.

[0079] The present application determines the position that matches the current moment from the expected driving trajectory according to the optimal time matching method, as the expected position; further combines the driving angle, the adjusted actual position and the distance between the expected position to obtain the first difference, which can reflect the difference between the actual and expected position and direction of the mining car.

[0080] Based on the above embodiments, as an optional embodiment, weighting the first difference according to the load condition of the vehicle at the current moment to obtain the position compensation amount of the vehicle at the current moment includes: Integrating the first difference to obtain a position error compensation term; Determining weights of the first difference and the error compensation term respectively according to the load condition of the vehicle at the current moment; The first difference and the error compensation item are weightedly summed based on their respective weights to obtain the position compensation amount.

[0081] In the control algorithm, error compensation items are introduced to eliminate some steady-state errors. For example, if the vehicle continues to lag behind the desired position, which cannot be compensated by the first difference, an error compensation item related to time accumulation is added to compensate for it. Position error compensation will continue for a period of time. When the algorithm is updated at a specific frequency, the compensation item will continue to accumulate until the set saturation value is reached.

[0082] In the embodiment of the present application, the position compensation value u1 can be obtained by the following formula: u1=kp1*x1+ki1*∫x1*dt; Among them, kp1 and ki1 are weights determined according to the load condition of the vehicle at the current moment, x1 represents the first difference, and ∫x1*dt represents the error compensation term.

[0083] Based on the above embodiments, as an optional embodiment, weighted summing of the position compensation amount and the second difference is performed according to the load condition of the vehicle at the current moment to obtain the speed compensation amount of the vehicle at the current moment, including: Integrating the second difference to obtain a speed error compensation term; Determining weights of the position compensation amount, the second error, and the speed error supplementary term, respectively, according to the load condition of the vehicle at the current moment; The position compensation amount, the second error, and the speed error supplement term are weightedly summed based on their respective weights to obtain the speed compensation amount.

[0084] In the embodiment of the present application, the speed compensation amount u2 can be calculated by the following formula: u2=kp2*v1+ki2*∫v1*dt+kt2*u1 Among them, v1 represents the second error, ∫v1*dt represents the speed error compensation term, and kp2, ki2, and kt2 are weights determined according to the load condition of the vehicle at the current moment.

[0085] Based on the above embodiments, as an optional embodiment, the state information also includes a lateral position and a vehicle head orientation; the control information includes a steering angle control amount; The determining of the initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information includes: Obtaining a state error of the vehicle at a current moment, wherein the state error includes a lateral position error and a vehicle head direction error; Determine a motion coefficient matrix according to the load condition and operating state of the vehicle at the current moment; Obtaining a weighting matrix of the state information according to the motion coefficient matrix, the coefficient matrix of the state information and the coefficient matrix of the control information; The state error is weighted according to the weighting matrix to obtain an initial steering angle control amount of the vehicle at the current moment.

[0086] The lateral position error in the embodiment of the present application is the current lateral position deviation of the vehicle, and the vehicle head orientation error is also the difference between the vehicle head orientation and the orientation of the trajectory point.

[0087] Initial angle control value at time t u t It can be calculated by the following formula: u t =-K t *x t K t =( R + B T P tB ) -1 B T P t BA x is the state matrix (that is, the error matrix composed of the mentioned lateral position error and the vehicle head orientation error); K is the feedback control matrix; R is the cost weight matrix, which is a constant; P is the four-dimensional coefficient matrix, which is to match the appropriate control coefficient in the mapping table according to the mentioned operating state (load condition, speed, stage, etc.); B is the coefficient matrix of control information, which is a constant matrix; A is the coefficient matrix of state information, which is a constant matrix.

[0088] The present application embodiment provides a vehicle control device, such as Figure 6 As shown, the vehicle control device may include: a control threshold module 601, an initial control module 602, a constraint module 603 and a control module 604, wherein: A control threshold module 601 is used to obtain a control information threshold of the vehicle at the current moment based on the actual state information of the vehicle at the current moment and the expected state information at the next moment by describing a model of a nonlinear relationship between the vehicle state information and the vehicle control information; The initial control module 602 is used to determine the initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information; A constraint module 603, used to constrain the initial control information according to the control information threshold of the vehicle at the current moment, to obtain the actual control information of the vehicle at the current moment; A control module 604, configured to control the vehicle according to the actual control information to enter a next moment; Among them, the model is obtained by transforming the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. The dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving the vertical load distribution of the vehicle's four wheels under slope and roll angle.

[0089] The device of the embodiments of the present application can execute the method provided by the embodiments of the present application, and the implementation principles are similar. The actions performed by each module in the device of each embodiment of the present application correspond to the steps in the method of each embodiment of the present application. For the detailed functional description of each module of the device, please refer to the description in the corresponding method shown in the previous text, which will not be repeated here.

[0090] In an embodiment of the present application, an electronic device is provided, including a memory, a processor and a computer program stored in the memory, and the processor executes the above computer program to implement the steps of the vehicle control method. Compared with the related art, it can be achieved: the vehicle is obtained by converting the dynamic equation of the lateral and longitudinal forces with respect to the vertical load distribution. Furthermore, the dynamic equation is obtained by performing dynamic modeling on the four-wheel steering model of the vehicle and solving the vertical load distribution of the four wheels of the vehicle under different slopes and roll angles. In addition, the embodiment of the present application converts the mechanical expression that is difficult to solve into state information that can be measured or directly obtained by the sensor, and converts the above model into a state space equation by defining the state quantity and the control quantity, and performs linear discretization to reduce the computational complexity. In other words, the state information in the embodiment of the present application is all state information that can be measured or directly obtained, which is suitable for all-terrain control and meets the precise control of empty and full-load driving on complex terrain with slopes and road inclinations.

[0091] In an alternative embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present application.

[0092] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0093] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus 4002 is represented by only one thick line in the figure, but it does not mean that there is only one bus or one type of bus.

[0094] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0095] The memory 4003 is used to store the computer program for executing the embodiment of the present application, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer program stored in the memory 4003 to implement the steps shown in the above method embodiment.

[0096] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.

[0097] The embodiment of the present application also provides a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiment when executed by a processor.

[0098] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that shown or described in the drawings.

[0099] It should be understood that, although each operation step is indicated by arrows in the flowchart of the embodiment of the present application, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present application, the implementation steps in each flowchart can be performed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on actual implementation scenarios. Some or all of these sub-steps or stages may be executed at the same time, and each sub-step or stage in these sub-steps or stages may also be executed at different times respectively. In different scenarios of execution time, the execution order of these sub-steps or stages may be flexibly configured according to demand, and the embodiment of the present application does not limit this.

[0100] The above is only an optional implementation method for some implementation scenarios of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the solution of the present application, other similar implementation methods based on the technical ideas of the present application are also within the protection scope of the embodiments of the present application.

Claims

1. A vehicle control method, characterized in that: include: By describing the model of the nonlinear relationship between the vehicle state information and the vehicle control information, based on the actual state information of the vehicle at the current moment and the expected state information at the next moment, the control information threshold of the vehicle at the current moment is obtained; Determining initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information; Constraining the initial control information according to the control information threshold of the vehicle at the current moment to obtain the actual control information of the vehicle at the current moment; Controlling the vehicle according to the actual control information to enter a next moment; Among them, the model is obtained by transforming the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. The dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving the vertical load distribution of the vehicle's four wheels under slope and roll angle.

2. The method according to claim 1, characterized in that The kinetic equation is obtained by Establishing a three-degree-of-freedom dynamic model of the vehicle taking into account the slope angle and the roll angle, wherein the three degrees of freedom include the lateral velocity, longitudinal velocity and yaw angular velocity of the vehicle; According to the three-degree-of-freedom dynamics model, a longitudinal mechanical balance equation and a lateral dynamics balance equation of the vehicle are established; According to the longitudinal mechanical balance equation and the lateral dynamic balance equation, the vertical load distribution of the left and right wheels of the vehicle is obtained; According to the vertical load distribution, the dynamic equation describing the force conditions of the four wheels of the vehicle is obtained.

3. The method according to claim 1, characterized in that: The state information includes the position, speed and load of the vehicle; the control information includes the current throttle or brake opening control amount; The determining of the initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information includes: Obtaining a first difference between an actual position of the vehicle at the current moment and an expected position, weighting the first difference according to a load condition of the vehicle at the current moment, and obtaining a position compensation amount of the vehicle at the current moment; Obtaining a second difference between the actual speed and the expected speed of the vehicle at the current moment, and performing weighted summation on the position compensation amount and the second difference according to the load condition of the vehicle at the current moment, so as to obtain a speed compensation amount of the vehicle at the current moment; According to the linear mapping relationship between the speed compensation amount and the throttle or brake opening, combined with the speed compensation amount of the vehicle at the current moment, the initial throttle or brake opening control amount of the vehicle at the current moment is determined.

4. The method according to claim 3, characterized in that The obtaining of a first difference between the actual position of the vehicle at the current moment and the expected position includes: Get the actual position and driving angle of the vehicle at the current moment; According to the actual position, determining a position with the shortest distance from the actual position from the expected driving trajectory as the adjusted actual position; According to the time best matching method, determining a position matching the current moment from the expected driving trajectory as the expected position; The first difference is obtained according to the driving angle and the distance between the adjusted actual position and the expected position.

5. The method according to claim 3, characterized in that: The step of weighting the first difference according to the load condition of the vehicle at the current moment to obtain the position compensation amount of the vehicle at the current moment includes: Integrating the first difference to obtain a position error compensation term; Determining weights of the first difference and the error compensation term respectively according to the load condition of the vehicle at the current moment; The first difference and the error compensation item are weightedly summed based on their respective weights to obtain the position compensation amount.

6. The method according to claim 3, characterized in that The weighted summing of the position compensation amount and the second difference according to the load condition of the vehicle at the current moment to obtain the speed compensation amount of the vehicle at the current moment includes: Integrating the second difference to obtain a speed error compensation term; Determining weights of the position compensation amount, the second error, and the speed error supplementary term, respectively, according to the load condition of the vehicle at the current moment; The position compensation amount, the second error, and the speed error supplement term are weightedly summed based on their respective weights to obtain the speed compensation amount.

7. The method according to any one of claims 1 to 6, characterized in that: The state information also includes the lateral position and the vehicle head direction; the control information includes the steering angle control amount; The determining of the initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information includes: Obtaining a state error of the vehicle at a current moment, wherein the state error includes a lateral position error and a vehicle head direction error; Determine a motion coefficient matrix according to the load condition and operating state of the vehicle at the current moment; Obtaining a weighting matrix of the state information according to the motion coefficient matrix, the coefficient matrix of the state information and the coefficient matrix of the control information; The state error is weighted according to the weighting matrix to obtain an initial steering angle control amount of the vehicle at the current moment.

8. A vehicle control device, characterized in that: include: A control threshold module, for obtaining a control information threshold of the vehicle at a current moment based on the actual state information of the vehicle at a current moment and the expected state information at a next moment, by using a model describing a nonlinear relationship between the vehicle state information and the vehicle control information; An initial control module, used to determine initial control information of the vehicle at the current moment according to the difference between the actual state information of the vehicle at the current moment and the expected state information; A constraint module, used to constrain the initial control information according to a control information threshold of the vehicle at the current moment, so as to obtain actual control information of the vehicle at the current moment; A control module, used for controlling the vehicle according to the actual control information to enter the next moment; Among them, the model is obtained by transforming the dynamic equations of the vehicle's lateral and longitudinal forces with respect to the vertical load distribution. The dynamic equations are obtained by performing dynamic modeling on the vehicle's four-wheel steering model and solving the vertical load distribution of the vehicle's four wheels under slope and roll angle.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the vehicle control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle control method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the vehicle control method according to any one of claims 1 to 7 is implemented.

Images