Vehicle control method, device, equipment, storage medium and product

Abstract

The embodiment of the present invention provides a vehicle control method, device, equipment, storage medium and product, the method comprising: determining the variable weight level to which the current driving condition of the vehicle belongs based on the vehicle's driving data, the variable weight level being the driving state that the vehicle needs to achieve under the current driving condition; defining multiple targets to be optimized of the vehicle and multiple first weight factors corresponding to the multiple targets to be optimized using the variable weight level, the vehicle state and the driving scene, wherein the first weight factor is dynamically changing; determining multiple control quantities corresponding to the multiple targets to be optimized using a pre-acquired motion control prediction model; and controlling the vehicle based on the multiple control quantities and the multiple first weight factors. Through the above steps, the performance of the vehicle under variable driving conditions can be kept stable.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle technology, and in particular relates to a vehicle control method, device, equipment, storage medium and product. Background Art

[0002] With the continuous development of automobile technology, vehicle dynamics control has become an important means to improve driving safety and ride comfort. Vehicle dynamics is the science of studying the various forces and moments that a vehicle is subjected to during driving, and how these forces and moments affect the vehicle's motion state. It covers multiple performance indicators such as vehicle handling stability, braking performance, and ride comfort, and is an important foundation in the field of vehicle engineering.

[0003] In the process of vehicle design and manufacturing, it is often necessary to consider multiple performance indicators at the same time, such as handling stability, comfort, energy saving, safety, etc. There are often contradictions and constraints between these goals, so multi-objective control is needed to achieve their balance and optimization. The goal of multi-objective control is to find an optimal control strategy to achieve the best overall performance of the vehicle while meeting all performance indicators.

[0004] However, existing vehicle dynamics control systems usually select fixed parameters as weight factors for multi-objective optimization solutions, which cannot adapt to changing driving conditions, resulting in unstable vehicle performance during driving. Summary of the invention

[0005] The embodiments of the present application provide a vehicle control method, device, equipment, storage medium and product to solve the problem of unstable performance of existing vehicles during driving.

[0006] In a first aspect, an embodiment of the present application provides a vehicle control method, the method comprising:

[0007] Determine, based on the vehicle driving data, a variable weight level to which the current driving condition of the vehicle belongs, wherein the variable weight level is a driving state that the vehicle needs to achieve under the current driving condition;

[0008] Using the variable weight level, the vehicle state and the driving scenario of the vehicle, defining a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized, wherein the first weight factors are dynamically changed;

[0009] Determine a plurality of control quantities corresponding one to one to the plurality of targets to be optimized by using a pre-acquired motion control prediction model;

[0010] The vehicle is controlled according to the plurality of control amounts and the plurality of first weight factors.

[0011] In a second aspect, an embodiment of the present application provides a vehicle control device, the device comprising:

[0012] A first determination module is used to determine, based on the driving data of the vehicle, a change weight level to which the current driving condition of the vehicle belongs, wherein the change weight level is a driving state that the vehicle needs to achieve under the current driving condition;

[0013] A definition module, used to define a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized one by one by using the variable weight level, the vehicle state and the driving scenario of the vehicle, wherein the first weight factors are dynamically changed;

[0014] A second determination module is used to determine a plurality of control quantities corresponding to the plurality of targets to be optimized one by one by using a pre-acquired motion control prediction model;

[0015] The first control module is used to control the vehicle according to the multiple control quantities and the multiple first weight factors.

[0016] In a third aspect, an embodiment of the present application provides a terminal device, the device comprising: a processor and a memory storing computer program instructions;

[0017] When the processor executes the computer program instructions, the vehicle control method of the first aspect is implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the vehicle control method of the first aspect is implemented.

[0019] In a fifth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the vehicle control method of the first aspect.

[0020] The vehicle control method, device, equipment, storage medium and product of the embodiment of the present invention, in this embodiment, by determining the variable weight level to which the current driving condition of the vehicle belongs, and then determining the target to be optimized during the driving process of the vehicle and the dynamically changeable first weight factor when optimizing the target to be optimized through the variable weight level, solves the problem that only fixed parameters can be selected as weight factors for multi-objective optimization solutions in the prior art, and at the same time, the first weight factor and the control amount corresponding to multiple targets to be optimized are used to jointly control the vehicle, thereby achieving flexibility and accuracy in vehicle control. Through the dynamically changing first weight factor, it is not only possible to adapt to the changing driving conditions, but also possible to intelligently adjust the control strategy according to the real-time driving data and driving scenes, so as to maintain the safety and comfort of the vehicle in a complex and changeable driving environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 is a flow chart of a vehicle control method provided in an embodiment of the present application;

[0023] Figure 2 is a schematic diagram of the structure of a vehicle control device provided in an embodiment of the present application;

[0024] Figure 3 It is a schematic diagram of the structure of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0026] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0027] In order to solve the problems of the prior art, the embodiments of the present application provide a vehicle control method, device, equipment, storage medium and product. The following first describes an example of a vehicle control method that can be applied to the embodiments of the present application.

[0028] Figure 1FIG. 1 is a flow chart of a vehicle control method provided by an embodiment of the present application. Figure 1 As shown, the method includes:

[0029] Step 101, determining the variable weight level of the current driving condition of the vehicle according to the driving data of the vehicle, wherein the variable weight level is the driving state that the vehicle needs to achieve under the current driving condition;

[0030] In this embodiment, the vehicle's driving data can be collected in real time through various sensors of the vehicle, such as speed sensors, acceleration sensors, steering angle sensors, etc. Based on these driving data, the current driving conditions of the current vehicle (such as acceleration, deceleration, turning, straight driving, emergency braking, etc.) are analyzed, and the driving state level that the vehicle should achieve under the condition is determined, that is, the "variable weight level". This level reflects the emphasis of the vehicle's requirements on different aspects such as safety, comfort, and economy under the current condition. The variable weight levels include general curve driving, extreme driving, emergency obstacle avoidance, and fault limp. Each level corresponds to different control requirements and priorities to meet the vehicle's dynamic performance requirements under different conditions.

[0031] In one embodiment, determining the variable weight level to which the current driving condition of the vehicle belongs based on the driving data of the vehicle includes:

[0032] Screening out characteristic parameters reflecting the current driving condition of the vehicle from the driving data;

[0033] By utilizing the mapping relationship between the variable weight level and the typical operating condition set, the characteristic parameter is matched with the preset typical operating condition set to obtain the variable weight level to which the current driving condition belongs.

[0034] In this embodiment, real-time driving data is collected from various sensors and control systems of the vehicle, including but not limited to vehicle speed, acceleration, steering angle, throttle opening, brake pressure, vehicle position, etc.

[0035] From the collected driving data, select characteristic parameters that can reflect the current driving condition of the vehicle. These characteristic parameters should be representative and accurately reflect the driving state of the vehicle, the driver's intention, and the influence of the external environment. For example, the speed and acceleration can reflect the vehicle's motion state, the steering angle can reflect the vehicle's driving direction, and the throttle opening and brake pressure can reflect the driver's operating intention.

[0036] In vehicle control systems, a set of typical operating condition sets are usually preset, which are classified and defined according to the common driving status of the vehicle and the common operation intentions of the driver. Each operating condition set corresponds to one or more specific driving scenarios and vehicle states.

[0037] During the system design phase, it is necessary to establish a mapping relationship between variable weight levels and typical operating condition sets. This mapping relationship can be rule-based or machine learning model-based. It defines the driving state level (i.e. variable weight level) that the vehicle should achieve under different operating conditions, as well as the control objectives and weight factors that need to be focused on at each level.

[0038] Match the selected characteristic parameters with the preset typical working condition set. This process usually involves preprocessing the characteristic parameters (such as normalization, dimensionality reduction, etc.) to improve the accuracy and efficiency of matching. Then, use matching algorithms (such as nearest neighbor search, fuzzy matching, etc.) to find the typical working condition set that is most similar to the current characteristic parameters.

[0039] According to the matching results, the variable weight level of the current driving condition is determined. This level will serve as the basis for the subsequent control strategy formulation, affecting the calculation of the control amount and the allocation of weight factors.

[0040] Step 102, using the variable weight level, the vehicle state and the driving scenario of the vehicle, defining a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized, wherein the first weight factors are dynamically changed;

[0041] In this embodiment, based on the variable weight level, vehicle status and driving scenario, multiple objectives that need to be optimized are defined, such as keeping lane centering, reducing acceleration fluctuations, and improving fuel efficiency.

[0042] Each target to be optimized is assigned a dynamically changing first weight factor. These weight factors are adjusted in real time according to the current working conditions, vehicle status, driver intention, etc., to ensure that in a complex and changing driving environment, the system can flexibly adjust the optimization strategy and give priority to the most important control needs.

[0043] In one embodiment, the use of the variable weight level, the vehicle state and the driving scenario of the vehicle to define a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized one by one includes:

[0044] Defining a plurality of objectives to be optimized for the vehicle using the vehicle state and the driving scenario;

[0045] Determine, according to the variable weight level, a plurality of second weight factors corresponding one to one to the plurality of objectives to be optimized;

[0046] Constructing a migration mechanism for the multiple targets to be optimized, wherein the migration mechanism is used to allow the multiple second weight factors to change dynamically;

[0047] Determine a plurality of first weight factors corresponding one-to-one to the plurality of objectives to be optimized by using the migration mechanism, the vehicle state, and the driving scenario;

[0048] The second weight factor corresponding to each of the multiple targets to be optimized is updated to the first weight factor.

[0049] In this embodiment, multiple optimization targets are defined according to the vehicle state and driving scenario. These targets are usually related to vehicle performance, safety, comfort, fuel economy, etc. For example, when driving at high speed, the main optimization targets may include maintaining a stable speed, keeping a safe distance, reducing fuel consumption, etc.; while in congested urban road conditions, more attention may be paid to smoothness during frequent starts and stops, emission control, and passenger comfort.

[0050] The variable weight level reflects the importance and urgency of the current driving condition. The priority and importance of each target to be optimized will also be different at different levels. It should be noted that for each variable weight level, the importance of the fixed control targets in different driving scenarios will be evaluated in advance based on the driver's prior experience and professional knowledge. By quantifying the evaluation results, the second weight factor of each fixed control target at different variable weight levels is obtained.

[0051] Therefore, in actual application, the second weight factor corresponding to the target to be optimized can be directly determined according to the variable weight level. The target to be optimized is selected from the above-mentioned fixed control targets.

[0052] In multi-objective optimization control, there may be conflicts between different targets to be optimized. In order to resolve these conflicts, a multi-objective migration mechanism is constructed using a method based on evolutionary game theory in this embodiment. Specifically, based on multiple optimization targets, the idea of ​​evolutionary game theory is used to construct a migration mechanism between multiple targets, allowing trade-offs and conversions between the second weight factors of the targets to be optimized during the optimization process.

[0053] Based on the multi-objective migration mechanism, the first weight factor of each target to be optimized is dynamically generated according to the current driving scene and vehicle status. The driver's prior experience and real-time feedback information are considered in the generation process to ensure the rationality and effectiveness of the weight setting. When there is a conflict between different targets to be optimized, the conflict is resolved by adjusting the second weight factor of each optimization target. In this process, the realization of important targets to be optimized is prioritized, while minimizing the negative impact on other targets to be optimized.

[0054] Finally, the second weight factor corresponding to each target to be optimized is updated to the first weight factor. This update process should be performed in real time to ensure that the control strategy can quickly adapt to changes in vehicle status and driving scenarios.

[0055] In this embodiment, by defining the target to be optimized, determining the second weight factor, constructing a weight migration mechanism, determining the first weight factor, and updating the weight factor, the vehicle control system can dynamically optimize the control strategy according to the variable weight level, vehicle status, and driving scenario to achieve more intelligent and efficient driving control.

[0056] Step 103, using the pre-acquired motion control prediction model to determine a plurality of control quantities corresponding to the plurality of targets to be optimized;

[0057] In this embodiment, by considering the redundancy of the wire-controlled chassis and the system delay characteristics, an integrated motion control prediction model that can reflect the dynamic behavior of the vehicle can be constructed. The model should include key elements such as the vehicle's dynamic characteristics, tire characteristics, suspension characteristics, and consider the impact of system delay on control performance.

[0058] In the prediction model, the system state variables are expanded to a wider range through the state augmentation method to better describe the dynamic behavior of the vehicle under different working conditions, thereby improving the prediction accuracy and robustness of the model and providing more accurate information for optimal control.

[0059] Step 104: Control the vehicle according to the multiple control quantities and the multiple first weight factors.

[0060] After determining the control amount and the first weight factor, multi-objective control can be performed to ensure stable driving of the vehicle under different driving conditions.

[0061] In this embodiment, by determining the variable weight level to which the current driving condition of the vehicle belongs, and then determining the target to be optimized during the driving process of the vehicle and the dynamically changeable first weight factor when optimizing the target to be optimized through the variable weight level, the problem that only fixed parameters can be selected as weight factors for multi-objective optimization solutions in the prior art is solved. At the same time, the first weight factor and the control amount corresponding to multiple targets to be optimized are used to control the vehicle together, achieving flexibility and accuracy in vehicle control. Through the dynamically changing first weight factor, not only can it adapt to the changing driving conditions, but it can also intelligently adjust the control strategy according to the real-time driving data and driving scenes, so as to maintain the safety and comfort of the vehicle in a complex and changeable driving environment.

[0062] In one embodiment of the present application, after controlling the vehicle according to the multiple control amounts and the multiple first weight factors, the method further includes:

[0063] Optimizing the multiple control variables using a preset control variable allocation rule and the current driving condition;

[0064] The vehicle is controlled using the optimized multiple control variables.

[0065] In this embodiment, the control quantity allocation rule is a set of rules that specifies how to allocate the total control demand (such as total braking force, total driving force, total steering angle, etc.) to each specific control actuator (such as the brakes, motors, steering motors, etc. of each wheel).

[0066] The specific optimization and control process may include: First, collect vehicle status information under the current driving condition, including but not limited to vehicle speed, acceleration, yaw rate, sideslip angle, etc., as well as the current status of each control actuator. Based on the collected data, evaluate the complexity and challenge of the current driving condition, and determine the focus and priority of control quantity allocation. According to the preset control quantity allocation rules and the current driving condition, calculate the control quantity that each control actuator should receive. According to the actual response and feedback of the vehicle, fine-tune the initially allocated control quantity to ensure that the vehicle's control performance is optimal. Use the optimized control quantity to control the vehicle to achieve more stable and safer driving.

[0067] In this embodiment, the control performance and driving safety of the vehicle can be further improved by introducing control amount distribution rules and an optimization process of the current driving condition after the preliminary control.

[0068] In an embodiment of the present application, after controlling the vehicle according to the multiple control amounts and the multiple first weight factors, the method further includes:

[0069] According to the vehicle state of the vehicle and the control instruction of the driver, a first weight of the feedforward control and a second weight of the feedback control of the vehicle are adjusted.

[0070] In this embodiment, the advantages of feedforward control and feedback control are combined to adopt a stable handling feedforward + feedback variable weight integrated control strategy. Feedforward control is used to predict and compensate for the future dynamic behavior of the system to improve response speed and accuracy; feedback control is used to eliminate system errors and interference to ensure the stability and robustness of the system. Through variable weight integrated control, the weights of feedforward and feedback control are dynamically adjusted according to the real-time status of the vehicle and the driver's intention, achieving a harmonious balance between the vehicle's handling agility and stability under different working conditions.

[0071] like Figure 2 As shown, the embodiment of the present application further provides a vehicle control device 200, and the vehicle control device 200 includes:

[0072] A first determination module 201 is used to determine the variable weight level to which the current driving condition of the vehicle belongs according to the driving data of the vehicle, wherein the variable weight level is the driving state that the vehicle needs to achieve under the current driving condition;

[0073] A definition module 202, configured to define a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized by using the variable weight level, the vehicle state and the driving scenario of the vehicle, wherein the first weight factors are dynamically changed;

[0074] A second determination module 203 is used to determine a plurality of control quantities corresponding to the plurality of targets to be optimized one by one by using a pre-acquired motion control prediction model;

[0075] The first control module 204 is used to control the vehicle according to the multiple control quantities and the multiple first weight factors.

[0076] Optionally, the first determining module 201 includes:

[0077] A screening submodule, used to screen out characteristic parameters reflecting the current driving condition of the vehicle from the driving data;

[0078] The matching submodule is used to match the characteristic parameter with a preset typical operating condition set by utilizing the mapping relationship between the variable weight level and the typical operating condition set, so as to obtain the variable weight level to which the current driving condition belongs.

[0079] Optionally, the definition module 202 includes:

[0080] A definition submodule, used to define a plurality of objectives to be optimized of the vehicle using the vehicle state and the driving scenario;

[0081] A first determination submodule, configured to determine, according to the variable weight level, a plurality of second weight factors corresponding one to one to the plurality of objectives to be optimized;

[0082] A construction submodule, used to construct a migration mechanism for the multiple targets to be optimized, wherein the migration mechanism is used to allow the multiple second weight factors to change dynamically;

[0083] A second determination submodule is used to determine a plurality of first weight factors corresponding to the plurality of objectives to be optimized one by one by using the migration mechanism, the vehicle state and the driving scenario;

[0084] The updating submodule is used to update the second weight factor corresponding to each of the multiple targets to be optimized to the first weight factor.

[0085] Optionally, the vehicle control device 200 includes:

[0086] An optimization module, configured to optimize the plurality of control quantities by using a preset control quantity allocation rule and the current driving condition;

[0087] The second control module is used to control the vehicle using the optimized multiple control variables.

[0088] Optionally, the vehicle control device 200 is specifically used for:

[0089] According to the vehicle state of the vehicle and the control instruction of the driver, a first weight of the feedforward control and a second weight of the feedback control of the vehicle are adjusted.

[0090] Figure 3 It is a schematic diagram of the structure of the terminal device provided in an embodiment of the present application.

[0091] The terminal device may include a processor 301 and a memory 302 storing computer program instructions.

[0092] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0093] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 302 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0094] In certain embodiments, the memory 302 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0095] The processor 301 implements any one of the vehicle control methods in the above embodiments by reading and executing computer program instructions stored in the memory 302 .

[0096] In one example, the terminal device may further include a communication interface 303 and a bus 310. Figure 3 As shown, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0097] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0098] Bus 310 includes hardware, software or both, and couples the components of online data traffic billing equipment to each other. For example, but not limitation, the bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnect (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, first video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. Where appropriate, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0099] In addition, in combination with the vehicle control method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the vehicle control methods in the above embodiment is implemented.

[0100] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0101] The functional blocks shown in the above structural block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0102] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0103] Aspects of the present disclosure are described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable vehicle control device to produce a machine so that these instructions executed by the processor of the computer or other programmable vehicle control device enable the implementation of the functions / actions specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0104] The above are only specific implementation methods of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A vehicle control method, characterized in that: The method comprises: Determine, based on the vehicle driving data, a variable weight level to which the current driving condition of the vehicle belongs, wherein the variable weight level is a driving state that the vehicle needs to achieve under the current driving condition; Using the variable weight level, the vehicle state and the driving scenario of the vehicle, defining a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized, wherein the first weight factors are dynamically changed; Determine a plurality of control quantities corresponding one to one to the plurality of targets to be optimized by using a pre-acquired motion control prediction model; Controlling the vehicle according to the plurality of control amounts and the plurality of first weight factors; Determining the variable weight level to which the current driving condition of the vehicle belongs based on the driving data of the vehicle includes: Screening out characteristic parameters reflecting the current driving condition of the vehicle from the driving data; Using the mapping relationship between the variable weight level and the typical operating condition set, the characteristic parameter is matched with the preset typical operating condition set to obtain the variable weight level to which the current driving condition belongs; The method of defining a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized by using the variable weight level, the vehicle state and the driving scenario of the vehicle includes: Defining a plurality of objectives to be optimized for the vehicle using the vehicle state and the driving scenario; Determine, according to the variable weight level, a plurality of second weight factors corresponding one to one to the plurality of objectives to be optimized; Constructing a migration mechanism for the multiple targets to be optimized, wherein the migration mechanism is used to allow the multiple second weight factors to change dynamically; Determine a plurality of first weight factors corresponding one-to-one to the plurality of objectives to be optimized by using the migration mechanism, the vehicle state, and the driving scenario; The second weight factor corresponding to each of the multiple targets to be optimized is updated to the first weight factor.

2. The vehicle control method according to claim 1, characterized in that: After controlling the vehicle according to the plurality of control amounts and the plurality of first weight factors, the method further includes: Optimizing the multiple control variables using a preset control variable allocation rule and the current driving condition; The vehicle is controlled using the optimized multiple control variables.

3. The vehicle control method according to claim 1, characterized in that: After controlling the vehicle according to the plurality of control amounts and the plurality of first weight factors, the method further includes: According to the vehicle state of the vehicle and the control instruction of the driver, a first weight of the feedforward control and a second weight of the feedback control of the vehicle are adjusted.

4. A vehicle control device, characterized in that: The device comprises: A first determination module is used to determine, based on the driving data of the vehicle, a change weight level to which the current driving condition of the vehicle belongs, wherein the change weight level is a driving state that the vehicle needs to achieve under the current driving condition; A definition module, used to define a plurality of targets to be optimized of the vehicle and a plurality of first weight factors corresponding to the plurality of targets to be optimized one by one by using the variable weight level, the vehicle state and the driving scenario of the vehicle, wherein the first weight factors are dynamically changed; A second determination module is used to determine a plurality of control quantities corresponding to the plurality of targets to be optimized one by one by using a pre-acquired motion control prediction model; a first control module, configured to control the vehicle according to the plurality of control quantities and the plurality of first weight factors; The first determining module includes: A screening submodule, used to screen out characteristic parameters reflecting the current driving condition of the vehicle from the driving data; A matching submodule, used for matching the characteristic parameter with a preset typical operating condition set by using a mapping relationship between a variable weight level and a typical operating condition set, so as to obtain a variable weight level to which the current driving operating condition belongs; The definition module includes: A definition submodule, used to define a plurality of objectives to be optimized of the vehicle using the vehicle state and the driving scenario; A first determination submodule, configured to determine, according to the variable weight level, a plurality of second weight factors corresponding one to one to the plurality of objectives to be optimized; A construction submodule, used to construct a migration mechanism for the multiple targets to be optimized, wherein the migration mechanism is used to allow the multiple second weight factors to change dynamically; A second determination submodule is used to determine a plurality of first weight factors corresponding to the plurality of objectives to be optimized one by one by using the migration mechanism, the vehicle state and the driving scenario; The updating submodule is used to update the second weight factor corresponding to each of the multiple targets to be optimized to the first weight factor.

5. A terminal device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the vehicle control method as described in any one of claims 1-3 is implemented.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the vehicle control method according to any one of claims 1 to 3 is implemented.

7. A computer program product, characterized in that The computer product comprises a computer program, and when the computer program is executed by a processor, the vehicle control method according to any one of claims 1 to 3 is implemented.

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