Vehicle control method and device, electronic equipment, vehicle and storage medium

Abstract

The invention discloses a vehicle control method and device, electronic equipment, a vehicle and a storage medium, and the method comprises the steps: obtaining parameter information needed by a target agent under the condition that the target agent in a plurality of first agents of the vehicle is triggered, the plurality of first intelligent agents are obtained by configuring a basic intelligent agent according to user preference; the vehicle is controlled through the target intelligent agent according to the parameter information, in the steps, a user can configure the basic intelligent agent in advance according to user preferences to obtain the intelligent agent with personalized configuration, the intelligent agent with personalized configuration is used for controlling the vehicle, and the control requirements of different users can be met.

Description

Technical Field

[0001] This application belongs to the field of vehicle control technology, and particularly relates to a vehicle control method, device, electronic equipment, vehicle, and storage medium. Background Technology

[0002] With the development of intelligent agents, they have evolved from simple rule-driven drivers into intelligent entities with autonomous decision-making and environmental adaptability, demonstrating strong application potential in fields such as autonomous driving and intelligent cockpits.

[0003] Currently, complex intelligent agents, such as in-vehicle intelligent agents, are developed and built by developers who are responsible for the entire process from the underlying architecture to the implementation of functions. Ultimately, the intelligent agents are configured in vehicles for users to use. However, intelligent agents developed uniformly by developers cannot meet the personalized needs of users in actual applications. Summary of the Invention

[0004] This application provides a vehicle control method, device, electronic device, vehicle, and storage medium. Users can pre-configure basic intelligent agents according to their preferences to obtain personalized intelligent agents. Using the personalized intelligent agents to control the vehicle can meet the control needs of different users.

[0005] In a first aspect, embodiments of this application provide a vehicle control method, the method being used in a vehicle, the method comprising:

[0006] When a target agent among the multiple first agents of the vehicle is triggered, the parameter information required by the target agent is obtained. The multiple first agents are configured with basic agents according to user preferences.

[0007] The target intelligent agent controls the vehicle based on the parameter information.

[0008] In one embodiment of this application, controlling the vehicle by the target intelligent agent based on the parameter information includes:

[0009] Obtain the first prompt word configured for the target agent, wherein the first prompt word is obtained by the user modifying the first default prompt word of the basic agent according to user preferences, or the first prompt word is the first default prompt word;

[0010] The target intelligent agent controls the vehicle based on the parameter information and the first prompt word.

[0011] In one embodiment of this application, controlling the vehicle by the target intelligent agent based on the parameter information and the first prompt word includes:

[0012] The parameter information and the first prompt word are input into the large model to obtain the control information output by the large model. The large model is obtained by the user modifying the default large model of the basic agent according to the user's preferences, or the large model is the default large model.

[0013] The vehicle is controlled according to the control information.

[0014] In one embodiment of this application, the step of inputting the parameter information and the first prompt word into a large model to obtain the control information output by the large model includes:

[0015] The parameter information, the first prompt word, and the first parameter range are input into the large model to obtain the control information. The first parameter range is obtained by the user modifying the default parameter range of the basic intelligent agent according to user preferences, or the first parameter range is the default parameter range.

[0016] In one embodiment of this application, when a target agent among a plurality of first agents in the vehicle is triggered, obtaining the parameter information required by the target agent includes:

[0017] When a target agent among the multiple first agents of the vehicle is triggered, the target device corresponding to the target agent is determined. The target device is at least one of the acquisition devices of the vehicle. The target device is obtained by the user modifying the default device corresponding to the target agent according to preferences, or the target device is the default device.

[0018] The target device collects the parameter information required by the target intelligent agent.

[0019] In one embodiment of this application, before obtaining the parameter information required by the target intelligent agent when the target intelligent agent among the plurality of first intelligent agents in the vehicle is triggered, the method further includes:

[0020] In response to a modification operation on the basic agent, the modification operation is used to modify the configuration information of the basic agent according to user preferences to obtain a first agent, the configuration information including at least one of the following: default prompt word, default large model, default parameter range, and default device.

[0021] Secondly, embodiments of this application provide a vehicle control device, the device comprising:

[0022] The acquisition module is used to acquire parameter information required by the target intelligent agent when the target intelligent agent among the multiple first intelligent agents of the vehicle is triggered. The multiple first intelligent agents are obtained by configuring basic intelligent agents according to user preferences.

[0023] The control module is used to control the vehicle through the target intelligent agent based on the parameter information.

[0024] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory storing computer program instructions;

[0025] When the processor executes the computer program instructions, it implements the vehicle control method as described in the first aspect.

[0026] Fourthly, embodiments of this application provide a vehicle including the electronic equipment described in the third aspect.

[0027] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the vehicle control method as described in the first aspect.

[0028] In a sixth aspect, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the vehicle control method as described in the first aspect.

[0029] The vehicle control method, apparatus, electronic device, vehicle, and storage medium of this application embodiment, when a target intelligent agent among a plurality of first intelligent agents of the vehicle is triggered, acquires parameter information required by the target intelligent agent. The plurality of first intelligent agents are configured from basic intelligent agents according to user preferences. The target intelligent agent controls the vehicle according to the parameter information. In the above steps, the user can pre-configure the basic intelligent agents according to user preferences to obtain personalized intelligent agents. Using the personalized intelligent agents to control the vehicle can meet the control needs of different users. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic flowchart of a vehicle control method provided in an embodiment of this application;

[0032] Figure 2 This is a schematic diagram of a process for installing an intelligent agent according to an embodiment of this application;

[0033] Figure 3 This is a schematic diagram of the configuration interface of the development tool provided in the embodiments of this application;

[0034] Figure 4 This is a schematic diagram of the configuration interface of the application provided in the embodiments of this application;

[0035] Figure 5 This is a schematic diagram of the system provided in the embodiments of this application;

[0036] Figure 6 This is a schematic diagram of the vehicle control device provided in the embodiments of this application;

[0037] Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0038] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0039] 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..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0040] In all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. Additionally, when embodiments of this application require access to sensitive personal information, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments obtained.

[0041] The vehicles can be private cars, such as sedans, SUVs, MPVs, or pickup trucks. They can also be commercial vehicles, such as vans, buses, small trucks, or large semi-trailers. Vehicles can be either gasoline-powered or new energy vehicles. When a vehicle is a new energy vehicle, it can be a hybrid or a pure electric vehicle.

[0042] To address the problems of the prior art, embodiments of this application provide a vehicle control method, apparatus, electronic device, vehicle, and storage medium. The vehicle control method provided in this application embodiment will be described first below.

[0043] Figure 1 A schematic flowchart of a vehicle control method according to an embodiment of this application is shown. Figure 1 As shown, the vehicle control method provided in this application embodiment is applied to an electronic device and includes the following steps 101-102, wherein:

[0044] Step 101: When the target agent among the multiple first agents of the vehicle is triggered, obtain the parameter information required by the target agent. The multiple first agents are obtained by configuring the basic agents according to user preferences.

[0045] In this embodiment, when the target intelligent agent among the multiple first intelligent agents of the vehicle is triggered, for example, when a certain intelligent agent is configured to turn on the air conditioner according to the current interior temperature when the door is detected to be open, the target intelligent agent obtains the parameter information required by the target intelligent agent, such as the interior temperature. The parameter information can be collected by the vehicle's acquisition device, such as the interior temperature collected by a temperature sensor. The above-mentioned multiple first intelligent agents are configured according to user preferences to obtain the basic intelligent agents.

[0046] Basic agents can be pre-installed in the vehicle's infotainment system, downloaded from an agent store and installed there. These basic agents are pre-developed by developers, such as... Figure 2As shown, in the vehicle's infotainment system, the user clicks on the Agent Store, selects an agent according to their needs, and the agents in the Agent Store are basic agents. The user clicks on the agent icon to install it. The vehicle's Agent Engine parses the data and generates basic agent executable code and a desktop icon based on the Agent Framework. The Agent Framework is the foundation for the agent's operation, providing the runtime environment. The user can run the basic agent by clicking on the icon on the desktop.

[0047] Step 102: The target intelligent agent controls the vehicle based on the parameter information.

[0048] In this embodiment, the target intelligent agent controls the vehicle based on the parameter information, thereby controlling the vehicle through an intelligent agent obtained through personalized configuration.

[0049] In this embodiment, when the target intelligent agent among the multiple first intelligent agents of the vehicle is triggered, the parameter information required by the target intelligent agent is obtained. The multiple first intelligent agents are configured according to user preferences to obtain basic intelligent agents. The target intelligent agent controls the vehicle according to the parameter information. In the above steps, the user can pre-configure the basic intelligent agents according to user preferences to obtain personalized intelligent agents. Using personalized intelligent agents to control the vehicle can meet the control needs of different users.

[0050] In one embodiment of this application, controlling the vehicle by the target intelligent agent based on the parameter information includes:

[0051] Obtain the first prompt word configured for the target agent, wherein the first prompt word is obtained by the user modifying the first default prompt word of the basic agent according to user preferences, or the first prompt word is the first default prompt word;

[0052] The target intelligent agent controls the vehicle based on the parameter information and the first prompt word.

[0053] In this embodiment, the first prompt word configured for the target agent is obtained. The first prompt word is obtained by the user modifying the first default prompt word of the basic agent according to the user's preferences, or the first prompt word is the first default prompt word, that is, the first default prompt word is used as the first prompt word.

[0054] Optionally, the user can modify the first default prompt word of the basic intelligent agent according to the user's preferences or needs through the application to obtain the first prompt word. The application is set in the vehicle's infotainment system or in the user terminal associated with the vehicle.

[0055] Users set prompts based on their preferences, and the large model uses these prompts to make inferences, enabling the agent to meet personalized needs.

[0056] In one embodiment of this application, controlling the vehicle by the target intelligent agent based on the parameter information and the first prompt word includes:

[0057] The parameter information and the first prompt word are input into the large model to obtain the control information output by the large model. The large model is obtained by the user modifying the default large model of the basic agent according to the user's preferences, or the large model is the default large model.

[0058] The vehicle is controlled according to the control information.

[0059] In this embodiment, the parameter information and the first prompt word are input into the large model, which then performs inference to obtain the control information output by the large model. The large model is obtained by the user modifying the default large model of the basic agent according to the user's preferences. For example, the default large model is large model a, and the user selects large model b according to their own preferences. The user modifies large model a to large model b through the application. The above-mentioned large model is an open source large model.

[0060] The control information includes control commands, control parameters, and target control object identifiers. The target control object identified by the target control object identifier is the object to be controlled.

[0061] The vehicle is controlled based on the control information. Specifically, the target control object identified by the target control object identifier is controlled based on the control commands and control parameters.

[0062] For example, the parameter information could be an image of the rear seats of the cockpit, with the first prompt being, "You are a child monitoring expert. Please identify the child's behavior based on the input image and generate control information. For example, if you detect a child sleeping, please adjust the air conditioning temperature to 26 degrees Celsius and turn off the music." Inputting the rear seat image and the first prompt into the large model yields the control information output by the large model. This control information includes two target control object identifiers: the air conditioning identifier and the audio device identifier. The control command for the air conditioning is to adjust the temperature, with a control parameter of 26°C. The control command for the audio device identifier is to turn it off, with a control parameter of empty, or the control command is to lower the volume, with a control parameter of volume level.

[0063] Users can modify the default prompts according to their preferences, redefine the task for the large model using the prompts, and the large model obtains control information based on the prompts set by the users, thus meeting the control needs of different users.

[0064] In one embodiment of this application, the step of inputting the parameter information and the first prompt word into a large model to obtain the control information output by the large model includes:

[0065] The parameter information, the first prompt word, and the first parameter range are input into the large model to obtain the control information. The first parameter range is obtained by the user modifying the default parameter range of the basic intelligent agent according to user preferences, or the first parameter range is the default parameter range.

[0066] In this embodiment, parameter information, a first prompt word, and a first parameter range are input into the large model to obtain control information. For example, the parameter information can be an image of the rear seats of the cabin, and the first prompt word is "You are a caring caregiver. Please generate control information based on the state of the child in the input image to ensure the child is in a comfortable and safe state." The first parameter range can be different for different users. The first parameter range for user A can be 25-26 degrees, and the first parameter range for user B can be 20-26 degrees. The above information is input into the large model to obtain control information. The first parameter range is obtained by the user modifying the default parameter range of the basic intelligent agent according to the user's preferences through the application, or the first parameter range is the default parameter range, that is, the default parameter range is directly used as the first parameter range when the user modifies it according to preferences.

[0067] Optionally, the user can modify the default parameter range of the basic intelligent agent according to user preferences and their own needs through the application to obtain the first parameter range. The application is set in the vehicle's infotainment system or in the user terminal associated with the vehicle.

[0068] Optionally, the first prompt is "You are a caring caregiver. Please call the corresponding function based on the child's state in the input image to ensure the child is in a comfortable and safe state." The pre-set functions include: air conditioning function, music off function, volume adjustment function, etc. For example, the large model controls the air conditioning and audio playback device by calling the air conditioning function and the music off function.

[0069] The aforementioned vehicle control functions can be pre-configured by R&D personnel, such as... Figure 3As shown, in the configuration interface, enter the function name (tool name) and the corresponding API address of the function to build a server that conforms to the OpenAPI specification based on the function. The functions include vehicle control functions and third-party functions. Vehicle control functions include functions for air conditioning, windows, doors, etc. The large model calls the corresponding functions to control the vehicle. Optionally, it can be configured through the Dify tool. The Dify tool is an open-source application development platform. Configure the function name (tool name) and the corresponding API address of the function through the Dify tool.

[0070] By reconfiguring the prompts and parameter ranges of the basic agent, a personalized agent can be obtained to meet the user's individual needs.

[0071] In one embodiment of this application, when a target agent among a plurality of first agents in the vehicle is triggered, obtaining the parameter information required by the target agent includes:

[0072] When a target agent among the multiple first agents of the vehicle is triggered, the target device corresponding to the target agent is determined. The target device is at least one of the acquisition devices of the vehicle. The target device is obtained by the user modifying the default device corresponding to the target agent according to preferences, or the target device is the default device.

[0073] The target device collects the parameter information required by the target intelligent agent.

[0074] In this embodiment, when the target intelligent agent of the multiple first intelligent agents of the vehicle is triggered, the target device corresponding to the target intelligent agent is determined. The vehicle is equipped with multiple acquisition devices, and the target device is at least one of the multiple acquisition devices of the vehicle. The target device acquires the parameter information required by the target intelligent agent. For example, the parameter information required by intelligent agent a is the in-cabin image. Intelligent agent a controls the vehicle based on the in-cabin image. The in-cabin image is acquired by a camera. The aforementioned target device is the camera, and the camera is the data source for intelligent agent a.

[0075] In this configuration, the user can pre-configure the device according to the user's preferred intelligent agent, or use the default device. The target device is obtained by the user modifying the default device corresponding to the target intelligent agent according to their preferences, or the target device is the default device.

[0076] The devices corresponding to each of the aforementioned first intelligent agents are obtained by the user modifying the default devices corresponding to the basic intelligent agents according to their preferences, or the devices are the default devices.

[0077] By collecting the data required by the target intelligent agent through the identified target device, the target intelligent agent can perform reasoning, thereby realizing the automated and personalized control needs through the intelligent agent.

[0078] In one embodiment of this application, before obtaining the parameter information required by the target intelligent agent when the target intelligent agent among the plurality of first intelligent agents in the vehicle is triggered, the method further includes:

[0079] In response to a modification operation on the basic agent, the modification operation is used to modify the configuration information of the basic agent according to user preferences to obtain a first agent, the configuration information including at least one of the following: default prompt word, default large model, default parameter range, and default device.

[0080] In this embodiment, the user triggers a modification operation through an application, which is either an application set in the vehicle or a user terminal associated with the vehicle. In response to the modification operation on the basic intelligent agent, the user can modify the configuration information of the basic intelligent agent according to their own preferences. The configuration information includes: default prompt words and / or default large model and / or default parameter range and / or default device. The above modification operation is used to modify the configuration information of the basic intelligent agent according to the user's preferences to obtain a first intelligent agent. The intelligent agent required by the user can be obtained in the above manner.

[0081] like Figure 4 As shown, Figure 4 This is the application's configuration interface. Users can drag and drop corresponding modules within the application to modify the basic intelligent agent and generate the first intelligent agent. This application can be an application corresponding to a low-code platform. Figure 4 As shown, the process begins with: Specifying a camera, for example, the rear-seat camera for children, and selecting the rear-seat camera as the target device and source of the child's image. Image analysis: This step involves selecting a large model and modifying the prompts, including the first prompt. The default large model is open-source model A. Users can change model A to model B according to their preferences, thus modifying the basic agent's large model. The first prompt is: "You are a child monitoring expert. Based on the child in the input image, identify the child's behavior and generate control information. If the child is sleeping, please adjust the air conditioner temperature to 26 degrees and turn off the music." This process requires the large model to reason about the input image in conjunction with the first prompt to obtain the control information. After interpretation, the tool is called: The large model directly calls the corresponding function. If a function call is involved, the first prompt includes "call the corresponding function." If control information is involved, the first prompt includes "generate control information." The default parameter range can also be modified. Figure 4It also includes "function calling," which refers to the process of executing a predefined function to complete a specific task.

[0082] Users can modify at least one of the configuration information of the basic agent according to their preferences, and obtain multiple agents with different configurations based on the same basic agent to meet the user's personalized customization needs.

[0083] See Figure 5 As shown, this embodiment provides a system comprising a cloud development platform and an in-vehicle terminal. The cloud development platform is a self-built platform by the vehicle manufacturer, providing development tools for the manufacturer's R&D personnel. These tools configure function names (tool names) and corresponding API addresses, enabling the manufacturer to publish common agents to the in-vehicle Agent Store for user use. The development of intelligent agents can employ Domain-Specific Languages ​​(DSLs), specialized programming languages ​​designed to solve specific domain problems, resulting in text files, which can be in YAML format.

[0084] On the vehicle side: Users can download intelligent agents from the Agent Store in the vehicle's infotainment system. Through the application (Agent Easy Development Tool), users can modify the basic intelligent agent according to their preferences to build a private intelligent agent, which can be updated separately to the Agent Store of its own vehicle for use by other users.

[0085] The vehicle-side also includes the DSL resolution client: namely the aforementioned Agent Store. The Agent Store is used to display various agents, and users can manually click to install agents as needed.

[0086] The vehicle-side also includes a DSL parsing server: when a user clicks to install the corresponding smart agent, the DSL parsing server obtains the corresponding text file, parses the text file to install the corresponding smart agent, and can be displayed as a service running by default, or as an app-like form, displaying the corresponding icon on the Home screen, which the user can click to run.

[0087] The vehicle is equipped with an AgentFramework, which serves as the foundation for the operation of the agent and provides the operating environment.

[0088] The large model corresponding to the intelligent agent: The large model provides edge-side reasoning capabilities and is the cornerstone of the intelligent agent's operation.

[0089] Provides a cloud-based Agent development framework: First, it automatically generates vehicle control capabilities (such as vehicle control functions) and third-party tool capabilities (such as third-party functions or tools), and deploys them into the Agent development framework for developers to simulate and call; it provides users such as car owners with the ability to develop intelligent agents, truly realizing the intelligence of intelligent agents and creating personalized intelligent agents for each user.

[0090] Figure 6 A structural diagram of the vehicle control device provided in an embodiment of this application is shown. Figure 6 As shown, the vehicle control device 600 includes:

[0091] The acquisition module 601 is used to acquire parameter information required by the target intelligent agent when the target intelligent agent among the multiple first intelligent agents of the vehicle is triggered. The multiple first intelligent agents are obtained by configuring basic intelligent agents according to user preferences.

[0092] The control module 602 is used to control the vehicle through the target intelligent agent based on the parameter information.

[0093] In one embodiment of this application, the control module 602 includes a first acquisition submodule and a control submodule;

[0094] The first acquisition submodule is used to acquire the first prompt word configured for the target intelligent agent. The first prompt word is obtained by the user modifying the first default prompt word of the basic intelligent agent according to the user's preferences, or the first prompt word is the first default prompt word.

[0095] The control submodule is used to control the vehicle through the target intelligent agent based on the parameter information and the first prompt word.

[0096] In one embodiment of this application, the control submodule includes a first processing subunit and a control subunit;

[0097] The first processing subunit is used to input the parameter information and the first prompt word into the large model to obtain the control information output by the large model. The large model is obtained by the user modifying the default large model of the basic agent according to the user's preferences, or the large model is the default large model.

[0098] A control subunit is used to control the vehicle according to the control information.

[0099] In one embodiment of this application, the first processing subunit is specifically used to input the parameter information, the first prompt word, and the first parameter range into the large model to obtain the control information. The first parameter range is obtained by the user modifying the default parameter range of the basic intelligent agent according to user preferences, or the first parameter range is the default parameter range.

[0100] In one embodiment of this application, the acquisition module 601 includes a first determining submodule and a collection submodule;

[0101] The first determining submodule is used to determine the target device corresponding to the target intelligent agent when the target intelligent agent among the multiple first intelligent agents of the vehicle is triggered. The target device is at least one of the acquisition devices of the vehicle. The target device is obtained by the user modifying the default device corresponding to the target intelligent agent according to preferences, or the target device is the default device.

[0102] The acquisition submodule is used to acquire the parameter information required by the target intelligent agent through the target device.

[0103] In one embodiment of this application, the device further includes a response module;

[0104] A response module is used to respond to a modification operation on the basic agent, the modification operation being used to modify the configuration information of the basic agent according to user preferences to obtain the target agent, the configuration information including at least one of the following: default prompt word, default large model, default parameter range, and default device.

[0105] The vehicle control device provided in this application embodiment can realize the various processes implemented in the aforementioned vehicle control method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0106] Figure 7 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0107] The electronic device may include a processor 701 and a memory 702 storing computer program instructions.

[0108] Specifically, the processor 701 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0109] Memory 702 may include mass storage for data or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 702 may include removable or non-removable (or fixed) media. Where appropriate, memory 702 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 702 is non-volatile solid-state memory.

[0110] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) 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 methods according to the first or second aspect of this disclosure.

[0111] The processor 701 implements any of the methods described above in the above embodiments by reading and executing computer program instructions stored in the memory 702.

[0112] In one example, the electronic device may also include a communication interface 703 and a bus 710. For example, Figure 7 As shown, the processor 701, memory 702, and communication interface 703 are connected through bus 710 and complete communication with each other.

[0113] The communication interface 703 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0114] Bus 710 includes hardware, software, or both, that couples components of a method or electronic device as described above together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 710 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0115] Additionally, embodiments of this application may provide a vehicle that includes the electronic devices described in the above embodiments.

[0116] Alternatively, embodiments of this application can be implemented using a computer storage medium. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the vehicle control methods described in the above embodiments.

[0117] Alternatively, this application embodiment can provide a computer program product for implementation, wherein the instructions in the computer program product, when executed by the processor of an electronic device, cause the electronic device to implement any of the vehicle control methods in the above embodiments.

[0118] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described as examples. However, the method process of this application is not limited to the specific steps described. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0119] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0120] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. 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 embodiments, or in a different order, or several steps can be performed simultaneously.

[0121] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, 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 data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. 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 is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0122] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A vehicle control method, characterized in that, The method is used for vehicles, and the method includes: When a target agent among the multiple first agents of the vehicle is triggered, the parameter information required by the target agent is obtained. The multiple first agents are configured with basic agents according to user preferences. The target intelligent agent controls the vehicle based on the parameter information.

2. The vehicle control method according to claim 1, characterized in that, The step of controlling the vehicle by the target intelligent agent based on the parameter information includes: Obtain the first prompt word configured for the target agent, wherein the first prompt word is obtained by the user modifying the first default prompt word of the basic agent according to user preferences, or the first prompt word is the first default prompt word; The target intelligent agent controls the vehicle based on the parameter information and the first prompt word.

3. The vehicle control method according to claim 2, characterized in that, The step of controlling the vehicle by the target intelligent agent based on the parameter information and the first prompt word includes: The parameter information and the first prompt word are input into the large model to obtain the control information output by the large model. The large model is obtained by the user modifying the default large model of the basic agent according to the user's preferences, or the large model is the default large model. The vehicle is controlled according to the control information.

4. The vehicle control method according to claim 3, characterized in that, The step of inputting the parameter information and the first prompt word into the large model to obtain the control information output by the large model includes: The parameter information, the first prompt word, and the first parameter range are input into the large model to obtain the control information. The first parameter range is obtained by the user modifying the default parameter range of the basic intelligent agent according to user preferences, or the first parameter range is the default parameter range.

5. The vehicle control method according to claim 1, characterized in that, When a target agent among the multiple first agents in the vehicle is triggered, the parameter information required by the target agent is obtained, including: When a target agent among the multiple first agents of the vehicle is triggered, the target device corresponding to the target agent is determined. The target device is at least one of the acquisition devices of the vehicle. The target device is obtained by the user modifying the default device corresponding to the target agent according to preferences, or the target device is the default device. The target device collects the parameter information required by the target intelligent agent.

6. The vehicle control method according to claim 1, characterized in that, Before obtaining the parameter information required by the target agent when the target agent among the multiple first agents of the vehicle is triggered, the method further includes: In response to a modification operation on the basic agent, the modification operation is used to modify the configuration information of the basic agent according to user preferences to obtain a first agent, the configuration information including at least one of the following: default prompt word, default large model, default parameter range, and default device.

7. A vehicle control device, characterized in that, The device includes: The acquisition module is used to acquire parameter information required by the target intelligent agent when the target intelligent agent among the multiple first intelligent agents of the vehicle is triggered. The multiple first intelligent agents are obtained by configuring basic intelligent agents according to user preferences. The control module is used to control the vehicle through the target intelligent agent based on the parameter information.

8. An electronic device, characterized in that, include: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the vehicle control method as described in any one of claims 1-6.

9. A vehicle, characterized in that, Including the electronic device as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the vehicle control method as described in any one of claims 1-6.

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