Vehicle lateral control method, device, medium and equipment

Abstract

The present disclosure relates to a vehicle lateral control method, device, medium and equipment. The vehicle lateral control method comprises: obtaining road environment information and self-vehicle driving state information of a vehicle; planning a target trajectory of the vehicle based on the road environment information and the self-vehicle driving state information; determining an expected yaw rate of the vehicle when the vehicle travels along the target trajectory based on the target trajectory; and determining a target control amount of a front wheel steering angle of the vehicle by using model predictive control based on the expected yaw rate, the target trajectory, and a current position and a current yaw rate of the vehicle, so that the vehicle performs lateral control based on the target control amount. The present disclosure solves the problem of high dependence of existing control algorithms on parameter calibration by implementing vehicle lateral control based on model predictive control.

Description

Technical Field

[0001] This disclosure relates to the field of automotive control technology, and in particular to a method, device, medium and equipment for lateral control of a vehicle. Background Technology

[0002] During vehicle operation, drivers rely on visual feedback to understand road conditions and control the driving direction. However, in actual driving, drivers are subject to numerous external factors, leading to instability. The shortcomings of this traditional driving method are becoming increasingly prominent, resulting in frequent traffic accidents. In response to current traffic safety issues, the emergence of intelligent vehicles offers hope for effective solutions. Intelligent vehicles perceive the surrounding environment through onboard sensors and utilize artificial intelligence technology to simulate human driving habits and emergency response methods. This avoids the impact of psychological stress on human behavior under extreme conditions, enabling cars to drive autonomously and making driving safer and more reliable.

[0003] Lateral control of a vehicle can be achieved by using obstacle information and trajectory planning information provided by advanced driver assistance systems (ADAS) to achieve lane change control. Currently, lateral control is based on the Proportional Integral Differential (PID) algorithm. However, the PID algorithm is highly dependent on parameter calibration and has very high control requirements. It does not consider the vehicle's own dynamics and road environment information. In some extreme cases, the control effect is not ideal and often exceeds the vehicle's dynamic limits, resulting in not only lower comfort but also safety hazards. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a vehicle lateral control method, apparatus, medium, and device.

[0005] This disclosure provides a method for lateral control of a vehicle, including:

[0006] Based on road environment information and vehicle driving status information, the target trajectory of the vehicle is planned.

[0007] Based on the target trajectory, determine the desired yaw rate of the vehicle as it travels along the target trajectory;

[0008] Based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate, model predictive control is used to determine the target control value of the vehicle's front wheel steering angle, so that the vehicle can perform lateral control based on the target control value.

[0009] In some embodiments, based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate, model predictive control is used to determine the target control value of the vehicle's front wheel steering angle, including:

[0010] Based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate, multiple predictive control variables for the front wheel steering angle are determined in the prediction time domain.

[0011] The multiple predictive control variables and the environmental state information of the vehicle are input into the vehicle dynamics model to obtain the vehicle state parameters of the vehicle in the prediction time domain.

[0012] Based on the vehicle state parameters, determine the instability of the vehicle;

[0013] Based on the instability, the target control quantity is determined.

[0014] In some embodiments, determining the instability of the vehicle based on the vehicle state parameters includes:

[0015] Determine at least one target vehicle state parameter related to vehicle instability from the vehicle state parameters;

[0016] If any of the target vehicle state parameters is greater than the corresponding preset parameter threshold, the vehicle is determined to have become unstable.

[0017] If all the target vehicle state parameters are less than or equal to the corresponding preset parameter thresholds, then the vehicle is determined not to have become unstable.

[0018] In some embodiments, determining the target control quantity based on the instability includes:

[0019] If the vehicle becomes unstable, the multiple predictive control variables are optimized to obtain the target control variable;

[0020] If the vehicle does not become unstable, the plurality of predictive control quantities are determined as the target control quantities.

[0021] In some embodiments, optimizing the plurality of predictive control variables to obtain the target control variable includes:

[0022] Substitute the multiple predictive control variables into the objective function, and solve for the optimal solution sequence when the objective function takes the minimum value under preset constraints;

[0023] The first element in the optimal solution sequence is determined as the target control variable.

[0024] This disclosure provides a vehicle lateral control device, including:

[0025] The trajectory planning module is used to plan the target trajectory of the vehicle based on road environment information and vehicle driving status information;

[0026] The trajectory control module is used to determine the desired yaw rate of the vehicle when it travels along the target trajectory, based on the target trajectory.

[0027] The MPC control module is used to determine the target control value of the front wheel steering angle of the vehicle based on the desired yaw rate, the target trajectory, and the current position and current yaw rate of the vehicle, so that the vehicle can perform lateral control based on the target control value.

[0028] In some embodiments, the MPC control module includes:

[0029] A control quantity prediction unit is used to determine multiple predictive control quantities of the front wheel steering angle in the prediction time domain based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate.

[0030] The vehicle state prediction unit is used to input the multiple predictive control variables and the environmental state information of the vehicle into the vehicle dynamics model to obtain the vehicle state parameters of the vehicle in the prediction time domain.

[0031] An instability determination unit is used to determine the instability of the vehicle based on the vehicle state parameters.

[0032] The target control quantity determination unit is used to determine the target control quantity based on the instability situation.

[0033] This disclosure also provides a computer-readable storage medium that stores a program or instructions that cause a computer to perform the steps of any of the above methods.

[0034] This disclosure also provides a controller, including:

[0035] Memory and one or more processors;

[0036] The memory is communicatively connected to the one or more processors, and the memory stores instructions that can be executed by the one or more processors. When the instructions are executed by the one or more processors, the controller is used to implement the steps of any of the above methods.

[0037] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0038] The technical solution provided in this disclosure uses model predictive control to determine the target control quantity of the vehicle's front wheel steering angle based on the desired yaw rate, target trajectory, and the vehicle's current position and current yaw rate. Furthermore, the predictive model in the model predictive control can achieve vehicle dynamic constraints, thereby avoiding exceeding the vehicle's dynamic limits, improving vehicle stability, and eliminating safety hazards. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0040] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart of a vehicle lateral control method provided in this disclosure embodiment;

[0042] Figure 2 A structural block diagram of the vehicle lateral control device provided in the embodiments of this disclosure;

[0043] Figure 3 This is a schematic diagram of the controller provided in an embodiment of the present disclosure. Detailed Implementation

[0044] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0045] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0046] Figure 1This is a flowchart illustrating a vehicle lateral control method provided in an embodiment of this disclosure. This method is applicable to situations involving active lane change control of vehicles (especially intelligent driving vehicles), specifically active lane change control implemented to avoid obstacles, and / or active lane change control implemented by selecting a target lane based on road information. This method can be applied to a server or an onboard controller. For example, when applied to a server, the vehicle uploads road environment information and its own driving state information to the server. The server determines the target control value for the front wheel steering angle based on the road environment information and the vehicle's driving state information and sends this target control value to the onboard controller. The onboard controller then sends the target control value to the actuator to complete the steering of the vehicle's front wheels. When applied to an onboard controller, the onboard controller determines the target control value for the front wheel steering angle based on the road environment information and the vehicle's driving state information, and then directly sends the target control value to the actuator to complete the steering of the vehicle's front wheels. Alternatively, this method can be executed by a vehicle lateral control device, which can be implemented in software and / or hardware and can be applied to a controller. Figure 1 As shown, the method includes the following steps:

[0047] S110: Based on road environment information and vehicle driving status information, plan the target trajectory of the vehicle.

[0048] The road environment information can include road information and obstacle information. Road information can include lane information, road sign information, intersection information, and traffic light information, etc. Obstacle information can include static obstacle information and dynamic obstacle information. For example, static obstacle information can include the position and size information of static obstacles, while dynamic obstacle information can include the speed and acceleration of obstacles (such as the vehicle in front), as well as the longitudinal and lateral distances between the obstacle and the vehicle (the own vehicle). The vehicle's driving status information can include the vehicle's speed, acceleration, current position, and current yaw rate, etc. Both road environment information and vehicle driving status information can be provided by the vehicle's advanced driver assistance systems (such as speed sensors, acceleration sensors, yaw rate sensors, radar, cameras, GPS, and high-precision maps, etc.).

[0049] In this embodiment of the disclosure, a target trajectory can be obtained by polynomial fitting through the vehicle's trajectory planning module based on road environment information and vehicle driving status information. The polynomial can be a fifth-order polynomial, and the target trajectory is the vehicle's driving trajectory within a preset future time period.

[0050] S120. Based on the target trajectory, determine the desired yaw rate of the vehicle when it travels along the target trajectory.

[0051] To enable the vehicle to travel along the target trajectory within a preset time in the future, the vehicle's desired yaw rate can be calculated based on the target trajectory through the vehicle's trajectory control module.

[0052] S130. Based on the desired yaw rate, target trajectory, and the vehicle's current position and current yaw rate, model predictive control is used to determine the target control value of the vehicle's front wheel steering angle, so that the vehicle can perform lateral control based on the target control value.

[0053] The working mechanism of Model Predictive Control (MPC) can be described as follows: At each sampling time, based on the obtained current measurement information, a finite-time open-loop optimization problem is solved online, and the first element of the resulting control sequence is applied to the controlled object. At the next sampling time, the above process is repeated, using the new measurement value as the initial condition for predicting the future dynamics of the system, refreshing the optimization problem, and solving it again. Therefore, based on the working mechanism of MPC, and based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate, MPC can obtain the control quantity required for the vehicle's front wheel steering angle at multiple future times, i.e., the target control quantity.

[0054] Understandably, the vehicle's current position can be obtained by combining the GPS coordinate system with a high-precision map, where the vehicle is located in the map coordinate system. The target trajectory, on the other hand, is a set of multiple consecutive coordinate points generated in the vehicle coordinate system. Therefore, when using both the vehicle's current position and the target trajectory as inputs to the model's predictive control, it is necessary to transform both into the same coordinate system. For example, the target trajectory can be transformed into the map coordinate system based on the vehicle's current position in the map coordinate system and its trajectory in the vehicle coordinate system.

[0055] In some embodiments, based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate, model predictive control is used to determine the target control value of the vehicle's front wheel steering angle, including the following steps:

[0056] S131. Based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate, determine multiple predictive control variables for the front wheel steering angle in the prediction time domain.

[0057] In this embodiment of the disclosure, the yaw rate, the target trajectory, and the vehicle's current position and current yaw rate are expected to be used as inputs to the model predictive control. After internal calculations by the model predictive control, multiple predictive control quantities in the prediction time domain (i.e., a preset future time) can be obtained.

[0058] S132. Input multiple predictive control variables and the vehicle's environmental state information into the vehicle dynamics model to obtain the vehicle state parameters in the prediction time domain.

[0059] Considering that vehicle stability is also affected by environmental conditions (such as road surface adhesion coefficient and slope information), using only multiple predictive control variables cannot fully guarantee future vehicle stability. Therefore, embodiments of this disclosure further determine vehicle state parameters in the prediction time domain to optimize multiple predictive control variables to obtain the optimal control variable. In embodiments of this disclosure, the vehicle dynamics model may include a 3-DOF vehicle dynamics model, a 14-DOF vehicle dynamics model, or a 15-DOF vehicle dynamics model. In some embodiments, a 15-DOF vehicle dynamics model is selected as the vehicle dynamics model of this disclosure to more comprehensively consider the influencing factors of vehicle dynamics, thereby achieving more accurate and stable lateral control. The 15 degrees of freedom may include the longitudinal velocity of the vehicle's center of gravity, the lateral velocity of the vehicle's center of gravity, the vertical velocity of the vehicle's center of gravity, the vehicle's roll angular velocity, the vehicle's pitch angular velocity, the vehicle's yaw angular velocity, the rotational angular velocities of the four wheels about their respective central axes, the vertical motion of the four suspensions, and the steering wheel angle.

[0060] Specifically, multiple predictive control variables and vehicle environmental state information are used as inputs to a 15-DOF vehicle dynamics model, which outputs vehicle state parameters. Thus, using the 15-DOF vehicle dynamics model as the predictive model for model predictive control allows for accurate predictions of the vehicle's state under future control inputs, resulting in precise and stable lateral control variables and preventing vehicle instability. Optionally, the number of vehicle state parameters output by the 15-DOF vehicle dynamics model can be less than the number of multiple predictive control variables, thereby reducing computational effort.

[0061] S133. Based on vehicle state parameters, determine the vehicle's instability.

[0062] The vehicle instability condition includes both instability and non-instability. Vehicle state parameters can be obtained based on a 15-DOF vehicle dynamics model and typically include parameters unrelated to instability. Therefore, to avoid invalid instability assessments, at least one target vehicle state parameter related to instability can be identified from the vehicle state parameters. If any target vehicle state parameter is greater than its corresponding preset threshold, the vehicle is considered to have instable; if all target vehicle state parameters are less than or equal to their corresponding preset thresholds, the vehicle is considered not to have instability.

[0063] In this embodiment, vehicle instability includes longitudinal instability and / or lateral instability. Vehicle instability is determined to have occurred as long as either longitudinal or lateral instability is caused by any factor. Therefore, after obtaining at least one target vehicle state parameter related to vehicle instability, each target vehicle state parameter is compared one by one. If any target vehicle state parameter is greater than the corresponding preset parameter threshold, vehicle instability is determined; only when all target vehicle state parameters are less than or equal to the corresponding preset parameter thresholds is vehicle instability determined. Optionally, the target vehicle state parameters include at least one of yaw rate, lateral acceleration, and slip ratio. Yaw rate and lateral acceleration are related to lateral instability; for example, if the yaw rate is greater than a yaw rate threshold, lateral instability is determined; if the lateral acceleration is greater than a lateral acceleration threshold, lateral instability is determined. The slip ratio is related to longitudinal instability; for example, if the slip ratio is greater than a slip ratio threshold, longitudinal instability is determined. Based on this technical solution, for example, the target vehicle state parameters include yaw rate, lateral acceleration, and slip ratio. If the yaw rate is less than or equal to a yaw rate threshold, the lateral acceleration is less than or equal to a lateral acceleration threshold, and the slip ratio is greater than a slip ratio threshold, then the vehicle is determined to have become unstable. If the yaw rate is less than or equal to a yaw rate threshold, the lateral acceleration is less than or equal to a lateral acceleration threshold, and the slip ratio is less than or equal to a slip ratio threshold, then the vehicle is determined not to have become unstable.

[0064] S134. Based on the instability situation, determine the target control quantity.

[0065] When the vehicle becomes unstable, it indicates that the multiple predictive control variables obtained so far are still insufficient to achieve stable control of the vehicle, and the target control variable needs to be further determined; when the vehicle does not become unstable, it indicates that the multiple predictive control variables obtained so far are sufficient to achieve stable control of the vehicle.

[0066] In some embodiments, if the vehicle becomes unstable, multiple predictive control quantities are optimized to obtain a target control quantity; if the vehicle does not become unstable, the multiple predictive control quantities are determined as the target control quantity.

[0067] In some embodiments, for situations where the vehicle becomes unstable, optimizing multiple predictive control variables to obtain a target control variable includes: substituting the multiple predictive control variables into the objective function, solving for the optimal solution sequence when the objective function reaches its minimum value under preset constraints; and determining the first element in the optimal solution sequence as the target control variable (the mechanism of model predictive control).

[0068] Preset constraints may include control quantity constraints, control increment constraints, yaw rate constraints, lateral acceleration constraints, and slip ratio constraints. Since the preset constraints and objective function are pre-defined, when predicting vehicle instability, the preset constraints and objective function can be directly invoked to optimize multiple predictive control quantities. This allows the actual state of the vehicle to approximate the objective function under the preset constraints, resulting in an optimal solution sequence. The first element in the optimal solution sequence is then determined as the target control quantity.

[0069] The vehicle lateral control method provided in this disclosure, based on the desired yaw rate, target trajectory, and the vehicle's current position and current yaw rate, employs model predictive control to determine the target control quantity for the front wheel steering angle. This allows the method to predict the control quantity for the front wheel steering angle over a future period based on the vehicle's current driving state information and the predictive model, thereby avoiding reliance on parameter calibration and reducing control requirements. Furthermore, the predictive model in model predictive control can implement vehicle dynamic constraints, thus preventing the vehicle from exceeding its dynamic limits, improving vehicle stability, and eliminating safety hazards.

[0070] Corresponding to the vehicle lateral control method provided in the embodiments of this disclosure, the embodiments of this disclosure also provide a vehicle lateral control device. Figure 2 A structural block diagram of the vehicle lateral control device provided in the embodiments of this disclosure, such as... Figure 2 As shown, the vehicle lateral control device includes:

[0071] The trajectory planning module 10 is used to plan the target trajectory of the vehicle based on road environment information and vehicle driving status information.

[0072] The trajectory control module 20 is used to determine the desired yaw rate of the vehicle when it travels along the target trajectory, based on the target trajectory.

[0073] The MPC control module 30 is used to determine the target control value of the front wheel steering angle of the vehicle based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate, so that the vehicle can perform lateral control based on the target control value.

[0074] In this embodiment, the road environment information and vehicle driving status information provided by the vehicle's advanced driver assistance system are acquired. The trajectory planning module 10 acquires the road environment information and vehicle driving status information provided by the vehicle's advanced driver assistance system. Based on the road environment information and vehicle driving status information, a fifth-order polynomial is used to fit the target trajectory. The trajectory control module 20 solves the expected yaw rate of the vehicle when it travels along the target trajectory based on the target trajectory. The expected yaw rate, the target trajectory, and the vehicle's current position and current yaw rate are used as inputs to the MPC control module 30. The MPC control module 30 outputs the target control quantity of the vehicle's front wheel steering angle.

[0075] In some embodiments, the MPC control module includes:

[0076] The control quantity prediction unit is used to determine multiple predictive control quantities of the front wheel steering angle in the prediction time domain based on the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate.

[0077] The vehicle state prediction unit is used to input multiple predictive control variables and the vehicle's environmental state information into the vehicle dynamics model to obtain the vehicle state parameters in the prediction time domain.

[0078] The instability judgment unit is used to determine the instability of the vehicle based on vehicle state parameters;

[0079] The target control quantity determination unit is used to determine the target control quantity based on the instability situation.

[0080] In some embodiments, the instability determination unit is specifically used to determine at least one target vehicle state parameter related to vehicle instability from the vehicle state parameters.

[0081] If any target vehicle state parameter is greater than the corresponding preset parameter threshold, the vehicle is determined to be unstable.

[0082] If all target vehicle state parameters are less than or equal to the corresponding preset parameter thresholds, the vehicle is determined not to have become unstable.

[0083] In some embodiments, the target control quantity determination unit is specifically used to optimize multiple predictive control quantities to obtain a target control quantity if the vehicle becomes unstable; and to determine the multiple predictive control quantities as the target control quantity if the vehicle does not become unstable.

[0084] In some embodiments, the target control quantity determination unit is specifically used to substitute multiple predictive control quantities into the objective function and solve the optimal solution sequence when the objective function takes the minimum value under preset constraints.

[0085] The first element in the optimal solution sequence is determined as the target control variable.

[0086] The vehicle lateral control device disclosed in the above embodiments can execute the vehicle lateral control method disclosed in the above embodiments and has the same or corresponding beneficial effects. To avoid repetition, it will not be described again here.

[0087] This disclosure also provides a computer-readable storage medium that stores a program or instructions that cause a computer to perform the steps of any of the above methods.

[0088] For example, a program or instructions cause a computer to perform a vehicle lateral control method, the method comprising:

[0089] Based on road environment information and vehicle driving status information, plan the vehicle's target trajectory;

[0090] Based on the target trajectory, determine the desired yaw rate of the vehicle as it travels along the target trajectory;

[0091] Based on the desired yaw rate, target trajectory, and the vehicle's current position and current yaw rate, model predictive control is used to determine the target control value for the vehicle's front wheel steering angle, so that the vehicle can perform lateral control based on the target control value.

[0092] Optionally, when executed by a computer processor, the computer-executable instructions can also be used to execute any of the vehicle lateral control methods described above in the embodiments of this disclosure, thereby achieving the corresponding beneficial effects.

[0093] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the embodiments of this disclosure can be implemented using software and necessary general-purpose hardware, and of course, they can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solutions of the embodiments of this disclosure, in essence, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0094] This disclosure also provides a controller, including: a memory and one or more processors; wherein the memory is communicatively connected to the one or more processors, the memory stores instructions that can be executed by the one or more processors, and when the instructions are executed by the one or more processors, the controller is used to implement the steps of any of the above methods to achieve the corresponding beneficial effects.

[0095] Figure 3 This is a schematic diagram of the hardware structure of the controller provided in an embodiment of this disclosure. Figure 3 As shown, the controller includes one or more processors 301 and memory 302.

[0096] The processor 301 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the controller to perform desired functions.

[0097] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may execute the program instructions to implement the vehicle lateral control method of the embodiments of this disclosure described above, and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0098] In one example, the controller may also include an input device 303 and an output device 304, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0099] In addition, the input device 303 may also include, for example, a keyboard, a mouse, etc.

[0100] The output device 304 can output various information to the outside, including determined distance information, direction information, etc. The output device 304 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0101] Of course, for the sake of simplicity, Figure 3 Only some of the components of the controller relevant to this disclosure are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the controller may include any other suitable components depending on the specific application.

[0102] This disclosure also provides a vehicle that includes the controller provided in the above embodiments. Therefore, the vehicle provided in this disclosure has the beneficial effects described in the above embodiments. Furthermore, the vehicle described in this disclosure can be an intelligent driving vehicle, and this disclosure does not specifically limit it to this type.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0104] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle lateral control method, characterized in that, include: Based on road environment information and vehicle driving status information, the target trajectory of the vehicle is planned. Based on the target trajectory, determine the desired yaw rate of the vehicle as it travels along the target trajectory; Using the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate as inputs to the model predictive control, multiple predictive control variables of the front wheel steering angle in the prediction time domain are obtained. The vehicle state parameters in the prediction time domain are determined, and the multiple predictive control quantities are optimized based on the vehicle state parameters to determine the target control quantity of the vehicle's front wheel steering angle, so that the vehicle can perform lateral control based on the target control quantity. The vehicle state parameters are used to determine the instability of the vehicle. The step of optimizing the plurality of predictive control quantities based on the vehicle state parameters to determine the target control quantity for the vehicle's front wheel steering angle includes: If the vehicle becomes unstable, the multiple predictive control variables are optimized to obtain the target control variable; If the vehicle does not become unstable, the plurality of predictive control quantities are determined as the target control quantities.

2. The method according to claim 1, characterized in that, Determine the vehicle state parameters in the prediction time domain, and optimize the multiple predictive control quantities based on the vehicle state parameters to determine the target control quantity for the vehicle's front wheel steering angle, including: The multiple predictive control variables and the environmental state information of the vehicle are input into the vehicle dynamics model to obtain the vehicle state parameters of the vehicle in the prediction time domain. Based on the vehicle state parameters, determine the instability of the vehicle; Based on the instability, the target control quantity is determined.

3. The method according to claim 2, characterized in that, Based on the vehicle state parameters, the instability of the vehicle is determined, including: Determine at least one target vehicle state parameter related to vehicle instability from the vehicle state parameters; If any of the target vehicle state parameters is greater than the corresponding preset parameter threshold, the vehicle is determined to have become unstable. If all the target vehicle state parameters are less than or equal to the corresponding preset parameter thresholds, then the vehicle is determined not to have become unstable.

4. The method according to claim 1, characterized in that, Optimizing the multiple predictive control variables to obtain the target control variable includes: Substitute the multiple predictive control variables into the objective function, and solve for the optimal solution sequence when the objective function takes the minimum value under preset constraints; The first element in the optimal solution sequence is determined as the target control variable.

5. A vehicle lateral control device, characterized in that, include: The trajectory planning module is used to plan the target trajectory of the vehicle based on road environment information and vehicle driving status information; The trajectory control module is used to determine the desired yaw rate of the vehicle when it travels along the target trajectory, based on the target trajectory. The MPC control module is used to take the desired yaw rate, the target trajectory, and the vehicle's current position and current yaw rate as inputs to the model predictive control to obtain multiple predictive control quantities of the front wheel steering angle in the prediction time domain. The vehicle state parameters in the prediction time domain are determined, and the multiple predictive control quantities are optimized based on the vehicle state parameters to determine the target control quantity of the vehicle's front wheel steering angle, so that the vehicle can perform lateral control based on the target control quantity. The vehicle state parameters are used to determine the instability of the vehicle. The MPC control module is specifically used for: If the vehicle becomes unstable, the multiple predictive control variables are optimized to obtain the target control variable; If the vehicle does not become unstable, the plurality of predictive control quantities are determined as the target control quantities.

6. The apparatus according to claim 5, characterized in that, The MPC control module includes: The vehicle state prediction unit is used to input the multiple predictive control variables and the environmental state information of the vehicle into the vehicle dynamics model to obtain the vehicle state parameters of the vehicle in the prediction time domain. An instability determination unit is used to determine the instability of the vehicle based on the vehicle state parameters. The target control quantity determination unit is used to determine the target control quantity based on the instability situation.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that cause a computer to perform the steps of the method as described in any one of claims 1 to 4.

8. A controller, characterized in that, include: Memory and one or more processors; The memory is communicatively connected to the one or more processors, and the memory stores instructions that can be executed by the one or more processors. When the instructions are executed by the one or more processors, the controller is used to implement the steps of the method as described in any one of claims 1 to 4.

9. A vehicle, characterized in that, Includes the controller as described in claim 8.

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